Ali Erfanzadeh; Mohammad Saadatseresht
Abstract
Extended AbstractIntroductionNowadays, UAV photogrammetry has become one of the most effective methods of collecting spatial data according to the factors time, cost, quality and variety of outputs among terrestrial and aerial mapping technologies. Because the quality of a UAV photogrammetry products ...
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Extended AbstractIntroductionNowadays, UAV photogrammetry has become one of the most effective methods of collecting spatial data according to the factors time, cost, quality and variety of outputs among terrestrial and aerial mapping technologies. Because the quality of a UAV photogrammetry products depends on the network design parameters setting according to the existing conditions and limitations, therefore, awareness of the behavior and impact of network design parameters on the quality of 3D reconstruction to achieve optimal quality of outputs is a very important issue. However, due to the time-consuming and the high cost of doing this study with huge real data, comprehensive research has not yet been conducted to measure the behavior of the effective parameters in network design and 3D reconstruction. There are various parameters include camera field of view, positioning error and imaging tilt in flight navigation, flight altitude and designed ground pixel dimensions, amount of sidelap and overlap images, image observation noise due to image quality, aerial triangulation error, in the process of preparing the map from aerial images, which is known as the most important parameters of UAV photogrammetric network design. In this paper, the simulation method is used to investigate the effect and behavior of the above parameters on the quality of three-dimensional reconstruction. Materials & MethodsIn the proposed method in MATLAB software environment, from a point with known 3D coordinates, using the collinearity equations and the value set for the network design parameters and their standard deviation according to the reality and experience of the expert, the imaging is done in a simulated manner. Then, by applying random and systematic errors on the visual observations and aerial triangulation parameters, the collinearity equations of the photographic observations form the desired point and using the least squares method of error in solving nonlinear equations, three-dimensional reconstruction, and quality are performed, then it has been evaluated by the Monte Carlo method. To achieve the results with high reliability, the quality of three-dimensional reconstruction is evaluated in five modes, respectively, ideal, excellent, good, average and bad, according to the expert opinion in setting the values of each parameter.Results & DiscussionThe results of this study show, most effective parameters in the quality of three-dimensional reconstruction in ideal conditions are camera instability, error of exterior orientation parameters and image quality, respectively, which gradually give way to parameters of flight altitude, imaging coverage and camera field of view in bad conditions. The results of the flight navigation error show, increased imaging platform instability has no significant effect on the average accuracy of 3D reconstruction, however, the accuracy changes in different places increase up to 20% due to the heterogeneity of the coverage and the visibility of different parts of the earth in the video network. The results also show that with increasing geometric instability of the non-metric camera, the accuracy of 3D reconstruction decreases linearly, in this regard, the imaging in bad conditions and the quality of the camera, the slower the reduction speed. It has also been shown that with increasing image observation error, which depends on image quality, the accuracy of 3D reconstruction decreases linearly. The results of the study of aerial triangulation parameters show that the three-dimensional reconstruction error increases linearly with increasing tie point matching error. In addition, as the focal length increases in the fixed flight altitude mode, the horizontal accuracy increases in proportion to the inverse magnification, and as the focal length decreases, the altitude accuracy decreases linearly, in the fixed ground sampling distance (GSD) mode, the horizontal error of 3D reconstruction is slowly reduced to 20%, while the height error increases with increasing height and decreasing the geometric resistance of the network by a factor of half magnification. The results also show that unlike traditional photogrammetry here, with increasing flight altitude, the horizontal and altitude errors of the 3D reconstruction increase linearly. The results of the study of the parameters of sidelap and overlap images show that the sidelap and overlap images can change the surface error up to 10 times and the height error and complete three-dimensional reconstruction up to 5 times. ConclusionThis study, while introducing the effective parameters in three-dimensional reconstruction by UAV photogrammetric method, has investigated the behavior and effect of these parameters on the quality of three-dimensional reconstruction in the simulation environment. This means how the quality of the reconstruction changes with minor changes to each of the parameters from half to twice the standard mode. Therefore, the closer this simulation is to reality, the more practical the results will be. Naturally, this complicates the simulation and increases the computational volume. Although this simulation is not entirely consistent with the actual situation, it can provide a kind of behavioral measurement of the parameters that serves as a complementary research to routine try and error investigations.
Roghayeh Adabi; Rahim Ali Abbaspour; Alireza Chehreghan
Abstract
Extended AbstractIntroductionIn recent years, data has become the life-giving force of developing innovations in smart cities all around the world. The up-to-date, availability, and freeness of this data are the deciding factors in their frequent use in smart city projects. Today, different sources of ...
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Extended AbstractIntroductionIn recent years, data has become the life-giving force of developing innovations in smart cities all around the world. The up-to-date, availability, and freeness of this data are the deciding factors in their frequent use in smart city projects. Today, different sources of information on city-related issues are available. They are crucial for driving towards “Smart Cities”. Among these sources is the Open Street Map (OSM) project, which is a free and open-source information repository used in many urban and non-urban-related applications. At present, OSM is used for a wide range of applications, for example, navigation, location-based services, construction of 3D city models, and traffic simulation. In the meantime, building blocks are among the OSM data that plays a key role in urban-related studies. These studies include constructing 3D building models, modeling urban energy systems, and land-use management in smart cities. Regarding the importance of completeness in the quality of spatial data, this study will assess the historical course of building blocks data completeness in OSM. Materials and methodsThe 20 districts of the Tehran metropolis have been selected as the study area. This city, with an area of 730 square kilometers and a population of around 8 million people covers the center of Tehran. The main purpose of this study is to present an analysis of the completeness of building block data in the OSM for the Tehran metropolis in 10 years (between 2011 and 2020). To reach this aim, an object-based approach based on object matching was used to assess the completeness parameter. Results and DiscussionThe findings of this study demonstrate that during the recent two years, OSM building block data in Tehran increased in terms of the number of features and the completeness of geometric information considerably. The number of data increased from 300 features in 2011 to 40.138 features in 2020, as well as the number of features edited and added to the OSM dataset increased from 38 and 194 in 2011 to 28680 and 10705 in the end of 2020, respectively. The completeness of OSM building block data in Tehran has increased from 0.18% in 2011 to 2.7% in 2020. Moreover, the evaluation of the completeness of OSM data in different regions of Tehran shows that the completeness of all regions of Tehran was less than 1% from 2011 to 2014, and in the last two years, for 12 of 20 regions of Tehran, the completeness is still less than 1%, but for the other eight regions (i.e., the regions no. 1, 2, 4, 5, 11, 15, and 20), which are mostly located in the northern part of Tehran, the completeness has increased. However, the data have many weaknesses in terms of the attribute information completeness. ConclusionThis study has provided a clear view of OSM building block status in Tehran. In addition, it has provided a better view of OSM data in different regions of Tehran. The insights gained from this study can lead toward creating the awareness required to use of these data in various fields of application. It can also assist local and national managers and related organizations to support active regions and encourage inactive regions. This paper represents a potential starting point for many possible future research directions in smart cities, especially in Tehran. Smart cities can conduct similar studies to understand the state of OSM data in their regions, make plans based on the findings, and manage their space more efficiently. To conduct future research, we evaluate the factors affecting the growth and development of OSM data and the efficiency of the OSM data in some smart city applications.
Yasser Ebrahimian Ghajari
Abstract
Introduction Natural hazards have always been a part of our surrounding environment and human life would be unimaginable without considering these hazards. With the development of social life, and particularly with urbanization and increasing expansion of cities, the dimensions of such incidents have ...
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Introduction Natural hazards have always been a part of our surrounding environment and human life would be unimaginable without considering these hazards. With the development of social life, and particularly with urbanization and increasing expansion of cities, the dimensions of such incidents have become more complicated. Earthquake is one of the most important natural hazards that takes the lives of many people every year. Although definite prediction of earthquake is not still possible, high-risk areas can be identified by zoning earthquake hazard using new technologies such as GIS, and measures can be taken to deal with the critical situation of identified regions during an earthquake. Planning of temporary accommodation with the aim of crisis management and reduction of secondary damages caused by the earthquake have always been amongmajor concerns of urban planners and managers. In the past, the policy of creating temporary accommodation centers and disaster relief sites lacked a specific program, so that locating a vacant land, with no owner was the most important principle for the creation of these centers in urban areas. It is now proved that these methods lack efficiency. However, recent advances in modern technologies such as GIS have improved planning process. This kind of planning procedure takes effective parameters and criteriainto account, many of which have spatial nature. Urban resiliency is one of the most important branches of urban crisis management, thus risk assessment and risk reduction planning, including site selection for temporary accommodation (as a principle of urban resiliency),are highly essential. Materials and methods The study area of the present research is Babol, one of the major and central cities of Mazandaran Province. Babol is located in BabolCounty, 14 km from the Caspian Sea and 10 km from the Alborz Mountains. With a total area of approximately 32 km2 and a population of250,217 (at the2016 census), it is the second most populous city in Mazandaran province.The 600 km long Caspian faults and 680 km long Alborz faults are among the effective faults of the study area. In the present study, effective measures for selectionof temporary accommodation siteswere extracted and weighted using expert opinions specialized in structural engineering, earthquake, urban planning, crisis management, passive defense, traffic and transportation. Identified criteria included distance from the river, distance from the fault, land use, distance from installations network, access to the transit network, distance from fire stations, population density, distance from tall buildings, distance from police stations and distance from health centers. Then, using GIS analytic functions, standard maps were produced and combined to identify the best areas for temporary accommodation (after a possible earthquake) in Babol. Criteria were weighted using fuzzy analytic hierarchy process and weighted overlay method was also used to combine them. Results and discussion Analyzing the results indicated that only 7% of the total study area (Babol City) is appropriate for temporary accommodation. Identified areas were examined according to other temporary accommodation standards. Finally, six sites and a total of 107 hectares (less than 4% of the study area) were identified as suitable sitesfor temporary accommodation. With a very large area (37 hectares) and full access to water, electricity and gas facilities,the first site is locatednear eastern beltway of Baboland Lotus PondRecreational Complex. The second proposed site is a 11-hectarevacant arealocated in the northeastern part of Babol City, between Ramenet and Pari Kola Villages. With a total area of 22 hectares,the third proposed site is located in the south-east of Babol City and near Babol-Qa’emShahr Road. Unlike the previous three sites, the fourth proposed site is located almost inside the city. It is a vacant 5-hectarearea in the northern side of the Motamedi Martyrs’ Cemetery. The next site, also located inside the city, is Aminian Dormitory (Noushirovani University of Technology) with a total area of 4 hectares. Although the last proposed site was ranked lower than the other five sites in the final analysis, it has the highest score among available sites inwestern side of Babol river. With a total area of 28 hectares, this site is located within a short distance of Imam Khamenei Highway. Conclusion According to the international standards, per capita area for temporary accommodation is approximately 4 m2. Therefore,with a population of about 250,217,Babol needs an average space of 100 hectares for temporary accommodation. Although, the proposed space for temporary accommodation (107 hectares) in Babol almost equals the required space (100 hectares), with the present rate of population growth inBabol, increasedconstructions, and consequently, reduction of appropriate space for temporary accommodation, Babol will definitely face a shortage of suitable space for temporary accommodation of earthquake victimsin near future. Moreover, the spatial distribution of suitable sites for temporary accommodation is not reasonable, as most of the suitable sites are located in the eastern part and within the boundaries of the city. While, these sites are expected to be scattered throughout the city with an equal access for all residents.Finally, it can be concluded that temporary accommodation of earthquake victimswas not considered in urban planning of Babol, and as a result, the city does not have a suitable status regarding temporary accommodation of earthquake victims.
Kobra Bozorgniya; Hani Rezayan; Javad Sadidi
Abstract
Introduction The accuracy of positioning depends on the quality of the technology used. Various technologies and techniques are used for positioning which are classified as absolute and dead-reckoning groups. Classified as absolute positioning technologies,GPS receiversface a variety of different errors ...
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Introduction The accuracy of positioning depends on the quality of the technology used. Various technologies and techniques are used for positioning which are classified as absolute and dead-reckoning groups. Classified as absolute positioning technologies,GPS receiversface a variety of different errors in the real-time positioning of a moving object, which reduces the accuracy and precision of the position received from these receivers. On the other hand, dead-reckoning sensors such as gyroscopes and magnetometers which measure real-time state of a moving object also have cumulative errors.Therefore, observations made by all of these sensors are not free from the noise generated during the measurement process.The amount of this noise may vary depending on various factors, including the precision of the sensor and features of the measuring environment. Thus,due to thecorrelation between observations made by these two categories of sensors and the difference between their precision and the nature of their errors,ifnoise is reduced inobservations made by them, their complementary features can be used to reduce errors made by each of them.High-quality positioning technologies are expensive and require high expertise.As a result,lower quality and cheaper global navigation satellite systems (like GPS) widelyavailable in smartphones are more commonly used. One of the most important features of these inexpensive technologies is that they are highly susceptible to factors producing noise. Methodology The present studyinvestigates the effect of gradual reduction of noise from data collected by sensors, accelerometers, magnetometers, gyroscopes, and GPS technology in smartphones on improvement of vehicle positioning. The proposed method is based on using acceleration, azimuth, latitude, longitude and roll angle parameters as an input for the Kalman algorithm and investigates the effect of reducing noise produced by these parameters using the least-squares method onimprovement of the resulting position calculated by the Kalman algorithm. To reach this aim, the roll angle parameter is extracted from the angular Velocity() in y-direction and the azimuth parameter is extracted from the magnetic field() in both x and y directions. These parameters along with the acceleration(a) parameter in x and y directions and the geographic coordinates are selected for the Kalman filtering algorithm. In the proposed method, data received from sensors share common sources of noise produceddue to drift, random movements and bias errors.To reduce this noise independently and systematically, method of averaging with the least-squares is usedfor data produced by each sensor. Thus, noise in the received data is considered as a random parameter and noise reduction is performed based on the percentage of changes in the corrected and observed data in the range of 1 to 10%. Kalman algorithm is implemented for 10 levels of noise reduction and the results areinvestigated and compared.The filter calculates and improves an estimate of position vector x, denoted by with minimum mean square error using a recursive model. The main objective is to derive an accurate estimate of for the state of the observed system at time of k. Implementing Kalman filter consists of a prediction step and an updating step. The result is compared todata received from a more accurate reference using RMSE. Results and Discussions The study area consists of lane no. 2 of the South-North (East-West) Azadegan Highway, Tehran, Iran with a total area of about 26km. Results show that compared to the reference data, using Kalman filter has decreased errorsin positioning the car from 0.8274 m to 0.6763 m with a 2%noise reduction. With a 10% noise reduction, the accuracy of this method has increases to 0.6771 m. This improved accuracy is due to noise reduction and consequently an increase in the correlation between the parameters. Accordingly, the threshold limit for noise reduction and improved positioning using Kalman filter is low and can be recognized by an investigation of a few lowlimits. According to the findings, although reducing the effect of noise can improve positioning with Kalman filter and smart phone sensors, irregular changes in the accuracy of noise reduction methods require determining an optimal percentage for noise reduction.
Amir Reza Moradi; Mohammad Amin Ghannadi
Abstract
Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such ...
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Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such as natural resource management, engineering, and infrastructure projects, crisis management and risk analysis, archaeology, security, aviation industry, forestry, energy management, surveying and topography, landslide monitoring, subsidence analysis, and spatial information system (Makineci&Karabörk, 2016).
Satellite images are one of the main sources used to produce DEM. In satellite remote sensing, optical and radar imagery are often used to generate DEM. Compared to optical satellite images, the main advantage of using radar satellite images for DEM production is that they are available in different weather conditions and even at nights. Two strategies used to produce DEM from radar satellite images include radar interferometry and radargrammetry(Saadatseresht&Ghannadi, 2018).
Phase information of the images is used in radar interferometry, whereas domain information of the images is used in radargrammetry (Ghannadi, Saadatseresht, &Eftekhary, 2014). Moreover, short baseline image pairs are used in radar interferometry, while long baseline image pairs are useful in radargrammetry. These technologies both have their own advantages and disadvantages,which were investigated in previous studies (Capaldo et al., 2015).
With radar interferometry, it is possible to produce DEM forlarge areas. Sentinel is one of the recent projects in satellite remote sensing. Sentinel constellation collects multi-spectral imagery, radar imagery and thermal imagery from the earth. Sentinel-1 is the radar satellite of the constellation.
Recent studies have investigated the precision of radar interferometry using Sentinel-1 imagery (Yagüe-Martínez et al., 2016) and the precision of DEM produced using these images(Letsios, Faraslis, &Stathakis; Nikolakopoulos &Kyriou, 2015). Generally, DEMs generated through radar interferometry needs to be improved, mainly due tothe phase errors which in many cases turn into outlier points (Zhang, Wang, Huang, Zhou, & Wu, 2012). Various methods have been used to improve DEM generated from SAR imagery, one of which use the information obtained from SRTM DEM. For instance, a previous study used SRTM DEM to improve DEM generated from ESRI/2.Using the information obtained from SRTM, the interferometric phase of areas with lower coherency were improved (Zhang et al., 2012).
The present study proposed a method to improve the accuracy of DEMs generated by Sentinel-1 imagery. In this method, using ascending and descending Sentinel-1 image pairs from the study area, DEM is generated using radar interferometry process. Then, precision is improved using SRTM DEM and a method based on 2D wavelet transform.
Wavelet transform and 2D wavelet transform methods
As a spectral analysis tool, wavelet transform is based on expanding any function like f(t)
(1)
inwhichaiis the expansion coefficient and 𝜓iis the expansion function.
One of the interesting characteristics of discrete wavelet transform is that it can be used as a multi-resolution analysis tool. To do so, a series of scaling functions or are used along with the wavelets to determine coarse data of the signals. Signal detailsare also covered by different wavelets with different scales.
Separatingcoarsedataand details of the signal isthe actual basis of discrete wavelet transform algorithm which wasintroduced by Mallat (Mallat, 1989) and improved by Beylkin et al. (Beylkin, Coifman, &Rokhlin, 1991). As a fast and simple method for discrete wavelet transform,the process is performed based on the followingrecursive relationships betweenahighpass and a lowpass filters with the impulse responses h(n) and g(n), respectively (Primer et al., 1998):
(2)
and
(3)
Where the expansion coefficients h and g are scaling filter and wavelet respectively.
(4)
and
(5)
These formulas can be expanded to calculate 2D discrete wavelet transform.
Proposed Method
This section proposes a method of enhancing DEM generated from Sentinel-1 imagery using SRTM DEM and 2D wavelet transform. Considering the capability of wavelet transform as a multi-resolution analysis tool which can separate coarse data from details, figure 2 shows the proposed process of improving DEM. First, using discrete 2DWT, coarse information and details of each DEMs are separated using the ascending and descending conditions of Sentinel-1 images. Then, two stages are considered based on the nature of these models. First, filtering coefficients usingthresholding and considering the average as the detail or high frequency part of the enhanced model. Second, coarse information derived from wavelet transform method have a resolution of40m and thus data derived from SRTM (30m) has a higher quality. Therefore, inverse 2DWT will improve the results and reach a resolution of 20 m.
Experiments and Results
The study area is located in Northern areas of Tehran (Iran) at the UTM coordinates of (542450 ,3964590) northeast to (539010, 3962350) southwest. Two Sentinel-1 satellite image pairs (one ascending and one descending) are used in this study.
In addition, a SRTM DEM with a spatial resolution of30m is used to improve DEM generated from Sentinel-1 images. Sentinel-1 derived DEM is evaluated using the 1m resolutionreference DEM. RMSE values shows the effectiveness of the proposed method in enhancing the Sentinel-1 derived DEM, which means that using information obtained from the SRTM and 2D wavelet transform was reasonable. RMSE values are reduced from 24.2097m to 11.1749m which shows 54% improvement. The proposed method can enhance results to 30 - 82 percentapproximately.
Conclusion
The present study investigates methods used for generating DEM from satellite images especially Sentinel-1 radar imagery. DEM derived from Sentinel-1 data has a high spatial resolution.Yet, it has some outliers or errors in elevation points whichneeds to be modified. Therefore, the present study proposes a method based on 2D wavelet transformfor deriving elevation model witha spatial resolution (20m) equal to that of Sentinel-1 DEM and improved precision and accuracy. In this method, filtering the details of the model using discrete 2D wavelet transform and modifying coarse information using SRTM DEM results in an enhanced DEM with higher spatial resolution.
Masoud Taefi Feijani; Saeed Azadnejad
Abstract
Extended Abstract
1- Introduction
The present study primarily sought to present a new FCD model to eliminate two limitations of the initial FCD model.These limitations included the fact thatimplementing the initial FCD model for sensors without a thermal bandwas not possible, sincethe model ...
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Extended Abstract
1- Introduction
The present study primarily sought to present a new FCD model to eliminate two limitations of the initial FCD model.These limitations included the fact thatimplementing the initial FCD model for sensors without a thermal bandwas not possible, sincethe model took advantage of a combination of shadow index and thermal index to detect black soil and calculate advanced shadow index.To overcome this limitation, we replaced thermal index with NLI and GNDVI indices, and combined shadow, BI, NLI and GNDVI indices to detect black soil and calculate advanced shadow index.Defining a global threshold for thermalindex used fordetectingblack soil was the next limitation of initial FCD model.Due to variations in regional climate and temperature, selecting a global threshold for the whole scene does not seem logical.Thus, a local thresholding process was used to define a threshold level for BI, NLI and GNDVI indices.In this regard, the study sitewas divided into 14 sections and an appropriate threshold wasselected for each section.A digital elevation model was also used to define a specific threshold level for forests in flat areasand elevated areas.
2- Materials & Methods
2.1 Study area and dataset description
The present study was performed within the basin of the Caspian Sea.Drainage basin is considered to be a standard unit ofstudy in environmental studies and thus due to the applied nature of the present study, the Caspian Basin was selected as our study site. In this study, a new FCD model was implemented for data collected from Landsat 5(1366) and Landsat 8 (1396).
2.2 Proposed approach
In the present study, an improved FCD model was obtained by adding two steps to the initial FCD model. In the following paragraph, these two steps will be explained.
2.2.1 Removing thermal index
The first limitation of the initial FCD model lies in the fact that implementing this model for data collected bysensors without thermal band is impossible, because advanced shadow index in the initial FCD model is calculated by combining shadow and thermal indices. Thermal index is only used to separate the shadow of vegetation cover from black soil.In order to overcome this limitationin the improved FCD model, thermalindex is replaced with NLI and GNDVI indices. In this way, black soil and vegetation shadows are separatedusingacombinationofshadow, BI, NLI and GNDVI indices.
The NLI index can be calculated using(1):
(1)
The GNDVI index is also calculatedusing(2):
(2)
2.2.2 Local thresholding
In the initial FCD model,black soil identification and shadow index improvement (advanced shadow index calculation) wereperformedusingthresholdingand based on the combination of shadow and thermal indices.In this model, a number is selected as the threshold of the heat index, and shadow index pixels with values less than this threshold are considered as black soil.Obviously, it is practically impossible to define a threshold and calculate advanced shadow index for large scale areas.
Localthresholding is a much more accurate method of thresholding, which is also used in the improved FCD model.In this method, image received from the study site was divided into 14 sections and a suitable threshold value was selected for BI, NLI and GNDVI indices in each section to calculate advanced shadow index.
Moreover, different thresholds were selected forforests in flat areasand elevated areas.In this regard, digital elevation model of the region was used to separate low-altitude and high-altitude areas.
3. Discussion& Conclusion
Results indicated that the proposed improved FCD model has provided a more accurate estimate of forest canopy density as compared to the initial FCD model.
According to the results, the overall accuracy and kappa coefficient of the initial FCD model were 86.24% and 68.43%, respectively.However, the improved FCD model had an overall accuracy of 96.98% and a kappa coefficient of 92.31% which confirms improved performance of the model.
Moreover, the statistical analysis of changes in the canopy densityindicated that the total area of Hyrcanian forests increased by about 161,963 hectares from 1366 to 1396. This includes an increase ofabout 79, 50 and 33 hectares in Mazandaran, Gilan and Golestan provinces, respectively.
Saeed Farzaneh; Mohammad Ali Sharifi; Amir Abdolmaleki; Masood Dehvari
Abstract
Extended AbstractIntroductionSatellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’s potential field. ...
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Extended AbstractIntroductionSatellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’s potential field. Many parameters have an impact on the precision and accuracy of their information. Atmospheric friction is one of the most principal forces on satellites, which may cause deviation and falling of satellite on a short period. From the beginning of aerospace missions, many efforts have been done to determine atmospheric friction by geodesists, e.g., empirical models of atmosphere neutral density. Because of the complex nature of atmosphere behavior and also data limitations, these models may have low accuracy. So, there is a need for methods to improve the accuracy of empirical models by means of combining observations of atmospheric density to predict its future state. Materials & MethodsAlong with the extension of computer science, new reliable algorithms have been introduced which are able to predict a time series; Artificial Intelligent (AI) and Neural Networks (NN) are the best of these methods. These simple algorithms are inspirations of the human brain and its ability to learn and have been used in many different scientific fields. In these techniques without any requirement for constructing complex modeling, the relation between input and output will be provided only using weight and bias vectors during the training procedure. Simple Neural Networks are memoryless meaning that the value of time-series in previous can’t be used for predicting the future value of time series and therefore some important dependency of signal values with time will be lost. A Recurrent Neural Network (RNN) has been implemented to overcome this issue. RNN’s can store some important information of the values of the time series in the previous steps in a chain-like structure and using this information for predicting the next value of time series that will improve the accuracy of prediction. In this study, the Long Short-Term Memory (LSTM) Neural Network which is a kind of Recurrent Neural Network’s has been implemented to predict the scale for correcting atmospheric density of numerical models. The data of Grace Accelerometer observation in the 6 first month of the year 2014 have been used for training the LSTM for univariate training. Also, the LSTM has been trained in multi-variants mode once with using the coefficient of atmospheric correction expansion up to degree 2 and once with using sun geomagnetic information along with information of k_p index. Results & DiscussionAfter training the LSTM network, by using the estimated parameters of the model, the zero degrees coefficient of harmonic expansion for a scale factor of correcting atmospheric density has been predicted in periods of 7, 14, 30, 60, and 90 days. The results of the univariate model show that the lower RMSE (Root Mean Square Error) is obtained about 0.054 in the period of prediction of about 14 days. Also, the results show that the multi-variants model with input data of sun geomagnetic information and k_p index has lower RMSE values in considered prediction periods compared to the other modes and the lowest RMSE is about 0.03 and belongs to the prediction of about 7 days. For evaluation of LSTM parameters in the obtained results, the predictions have been implemented with various Window sizes. The results show that by increasing windows size, the RMSE of the prediction will be reduced and the lowest RMSE was for prediction of 7 days with a window size of about 90 days. For the purpose of more evaluation, with the predicted atmospheric densities correction coefficient, the orbit of GRACE satellites has been propagated and the calculated position and velocity of satellites have been compared with the real orbit data. The results show that the lower RMSE will be provided with the prediction of 7 days with an RMSE for position and velocity of about 50 meters and 0.15 m/s respectively. ConclusionIn this study, due to the complex nature of the atmosphere, the LSTM Neural Network has been used for modeling and predict the zero-order scale for correcting atmospheric densities harmonic expansion. For training the network, the data of Grace Satellites Accelerometer in the 180 days of the year 2014 have been used. The LSTM has been in univariate and multi-variant models. In the multi-variants model, once with using the coefficient of atmospheric correction expansion up to degree two and once with using sun geomagnetic information along with information of k_p index the network have been trained. The period of prediction was considered of about 7, 14, 30, 60, and 90 days.The results show that the LSTM is capable to predict the correction coefficient in considered periods with a mean RMSE of about 0.05 for zero-order degree. Also, the results show that the lowest RMSE was for the 7 and 14 days of prediction and by increasing the window size of LSTM the RMSE will be decreased. The results of calculating the position of GRACE satellites position and velocity using predicted correction coefficients with real data show that the lowest RMSE was for prediction of 7 days for implemented method.
Qhasem Keikhosravi; Shahriar Khaledi; Ameneh Yahyavi
Abstract
Introduction Foehn is thedecending of hot and dry air that occurs under certain conditions in the lee of a mountain range.In an adiabatic process, the humid air rises toward mountain peaks on the windward hillside. With sufficient humidity, it is saturated and thus, forms clouds or precipitation. ...
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Introduction Foehn is thedecending of hot and dry air that occurs under certain conditions in the lee of a mountain range.In an adiabatic process, the humid air rises toward mountain peaks on the windward hillside. With sufficient humidity, it is saturated and thus, forms clouds or precipitation. In this way, it loses moisture, and passing over the lee of maintain, descends and heat upin an adiabatic process. Thus, the air in the lee side gets warmer and drier than the air in the windward hillside. Moving upward toward the mountain peak, the air loses temperature. At the mountain peak, the saturated air hasreached dew point temperature, and begins to rain to discharge its moisture. This dry air descends, and cross the leeward hillside with increasing velocity, and at the base of the mountain, its temperature is higher than the initial air temperature (Haji Mohammadi, 1396). Data& Methods In order to extract the frequency of days with foehn windsin the present study, daily temperature, relative and hourly humidity and wind speed were prepared for a 10-year statistical period (2015-2006) and then heat wave index was used to extract the number of days with foehn winds. To investigate the effect of foehn on thermal stress of plants using Landsat 8 OLI images, factors affecting thermal stress inplants,such as albedo, short wavelength radiations reaching the Earth surface, long wavelengthradiations emitted from the Earth surface, long wavelength radiations entering the earth surface, net radiation flux and soil heat flux were analyzed. ENVI 5.3 and Arc GIS 10.1 wereused to perform calculations and produce the aforementioned maps. Results&Discussion The present study was conducted to investigate thefoehn phenomenon in the west Alborz Mountains and its effect on the amount of thermal stress in the vegetation cover.First, the frequency of foehn wind occurrence in the statistical period of 2006 to 2015, in stations under study was extracted using wind direction, baldiindex (heat wave index) and increasing temperature and decreasing relative humidity compared to the previous day. In other words, days with temperature higher than 0 degree Celsius were considered as a heat wave. Based on wind direction, temperature increase and relative humidity decrease compared to the previous day (which in some cases is twice or even more), days are associated with foehn wind. In order to investigate the effect of foehn on thermal stressin plants, a sample of images with better atmospheric conditions (lacking clouds) collected by Landsat 8 OLI sensor on September 24, 2015 –in which foehn phenomenon had taken place-was received from the website of US Geological Survey (Earth Explorer)in the present study.The study area (West Alborz Mountains) was selected and cut out ofthese images and radiometric corrections were performed on the resulting images using ENVI 5.3 software. Afterwards, parameters like atmospheric thickness (atmospheric conductivity), Top of AtmosphereAlbedo, Earth’s surface albedo, Earthdistancefrom the Sun, solar altitude, Normalized difference vegetation index (NDVI), leaf area index (LAI), Fracture value, brightness temperature, ground surface temperature were determined and net radiation flux reaching vegetation cover and soil heat fluxwere calculated using these parameters. The output maps were produced in ARCGIS 10.1 environment. Conclusion According to the study sample (September 4, 2015), results indicated that areas with dense forest cover (eastern hillsides of the Alborz Range) receives the highest values of net radiation.The effect of foehn infiltration on these hillsides has increased the amount of radiation received up to 600 or 700 W / m 2. In contrast, the net radiation received on the downstream of thewindwardhillsides (western hillsides) is about 75 and at higher altitudes 150 W / m 2less than areas under the influence offoehn.Due to lower vegetation densityand lower heat transfer,soil heat flux in the western hillsides is much higher than the eastern hillsides.Most of windward hillsides has a heat flux of between 80 and 120 W / m2, while in leeward hillsides,sunlight is absorbed by the canopy and the soil heat flux is between 20 and 40 W / m2.Thus, most of solar radiation is used to raise the temperature around the vegetation crown, provide the necessary conditions for higher evaporation from the vegetation and create thermal stressin the vegetation organs. Therefore, descending of air mass on trees and plants causes severe evapotranspiration.This will lead to rapid drying of the leaves, which will cause thermal stress in the plant’s organs and intensify the likelihood of forest fires.
Remote Sensing (RS)
Fateme Amjadipour; Hamid Dehghani; Mojtaba Behzad Fallahpour
Abstract
Extended AbstractIntroductionThe complexity of interpreting SAR radar images makes target recognition difficult despite many studies performed in this regard. Various factors including material and dimensions of the target, radar frequency, polarization, target shape, and vision geometry affect the response ...
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Extended AbstractIntroductionThe complexity of interpreting SAR radar images makes target recognition difficult despite many studies performed in this regard. Various factors including material and dimensions of the target, radar frequency, polarization, target shape, and vision geometry affect the response received from SAR radar. Investigating these characteristics facilitate target recognition.Synthetic Aperture Radar sensors are widely used in both airborne and space-borne systems. Space-borne systems equipped with Synthetic Aperture Radar sensors are side-looking and because of their nature as a radar, many parameters such as vision geometry will affect their ability (or disability) in seeing the target and change the resulting images. Therefore, it is very important to study the effect of this parameter to identify the target and interpret these images. The visibility geometry includes incidence angle, look angle, and the direction of the imaging. Materials & MethodsThe present study investigates visibility geometry in revision images and ascending and descending scenes. To reach this aim, a single scene captured by Sentinel-1 from a residential area is examined in different images with different directions, incidence angles, and imaging time. Results indicate that incidence angle changed slightly (4 degrees) and thus, left a negligible effect on the image. Moreover, there was a 5-day time interval between the captured images and therefore, this factor had the least effect on Synthetic Aperture Radar images. Unlike optical images, the direction of imaging had the greatest effect on SAR images. For an instance, a single ramp behaves differently in two images captured from different directions. Therefore, direction of imaging and its effects on seeing (or not seeing) the target are analyzed in ascending and descending images. Results & DiscussionThe effect of vision geometry on radar images has been rarely investigated in similar studies, and the present paper has taken a step forward in this regard. Fallahpour et al., (2016) have simulated the effect of incidence angle, which is a parameter of visibility geometry and the shape of the targets in SAR images. Shapes such as cones, cylinders, and cubes were used in this simulation representing real buildings, niches, tree trunks, etc. which are very common in SAR images. Moreover, behavioral pattern of the aforementioned geometric shapes were simulated at different landing angles (30, 40, 45, 50, and 60 degrees) from the viewpoint of SAR imaging systems to reach a more comprehensive result.Then, various studies investigating the effects of incidence angle and direction on radar images have been reviewed. Some of these studies have dealt with the effect of these parameters on the classification of radar images. Dumitru et al. have examined the effects of resolution, pixel spacing, patch size, path direction, and incidence angle on the classification of TerraSAR-X images. To reach this aim, they have selected an optimal TerraSAR-X product and then specified the number of classes. They have finally investigated the effects of incidence angle and path direction on the classification results. Results indicated that images captured in ascending direction were 80% better than the descending images. Moreover, images captured from an incidence angle near the upper wing showed better results. ConclusionThe present study has investigated the effect of usually neglected parameter of visibility geometry on SAR images. Images were captured by Sentinel-1 in both ascending and descending directions. Following speckle noise reduction and geometric correction, incidence angle and its effects on the detected changes were investigated. The slight 4-degree changes of this parameter have not caused the resulting changes. Moreover, there was a 5 day time interval between these two images and thus, time could not be an effective parameter too. Results indicate that detected changes in the residential area were due to a change in the direction of imaging. Changes of this parameter can result in seeing (or not seeing) the target, and therefore, it is very important to investigate the effects of this parameter and correct it.
Hadi Farhadi; Tayebe Managhebi; Hamid Ebadi
Abstract
Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active ...
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Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active and passive sensors provide useful information in various applications such as building extraction, natural resource management, agricultural monitoring, etc. The extraction of accurate information about the location, density and distribution of buildings in the urban areas is one of the major challenges in the urban study which is used in various applications. In this framework, the monitoring of the urban parameters, such as urban green space, public health, and environmental justice, urban density and so on has been accomplished by radar and optical image processing, in the last three decades. So far, various methods, including Artificial Intelligence (AI), Deep Learning (DL), object-based methods, etc. have been proposed to extract information in the urban areas. However, an important issue is access to the powerful computer hardware to process the time-series images. In such a situation, the use of the Google Earth Engine (GEE) as a web-based RS platform and its ability to perform spatial and temporal aggregations on a set of satellite images has been considered by many researchers. In this research, a semi-automatic method was developed building extraction in Tabriz, northwest of Iran, based on the satellite images using the GEE cloud computing platform. Since accessible data is one of the most important challenges in the use of space RS, in this study, the free Sentinel-1 and sentinel-2 data, which belongs to the European Space Agency (ESA), has been utilized. 2- Materials & Methods2-1- Study AreaThe study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 2-2- DataVarious data sources have been used in this study, including Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In addition, 400 training samples were created using High-Resolution Google Earth Imagery (GEI) in two classes: urban-residential (buildings) and non-residential areas (vegetation, soil, road, water and etc.).2-3- MethodologyThe goal of this research is to develop a method for identifying the buildings in an urban area. For this purpose, after importing images and pre-processing them in the GEE Platform, a map of the Primary Urban Areas (PUA) and High-Potential Buildings (HPB) was produced from Sentinel-1 images according to the sensitivity of the radar images to the target physical parameters. Then, in order to remove the annoying features and extract the Secondary Urban Areas (SUA), spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Water Index (NDWI), Soil Extraction Index (SOEI), Normalized Difference Built-up Index (NDBI), and Build-up Extraction Index (BUEI) were extracted from Sentinel-2 images. Also, the high slope of the area and the mountainous areas was extracted from the SRTM DEM data and used as a mask in the final results. Afterwards, the unimodal histogram thresholding method was used in order to determine the threshold value for each index. Finally, by merging the map of HPB and the map of SUA, the final map was produced and evaluated by other methods. In this research, the proposed method used images from GEI with a very high spatial resolution to validate the generated map. As a result, sampling was carried out using a visual interpretation of GEI in two classes: residential areas (buildings) and non-residential areas. The samples were selected randomly and 400 points were collected for each residential and non-residential class. In the study area, a total of 800 test points were used to evaluate the results of the proposed method. To evaluate the accuracy of the results, the criteria of overall accuracy (OA), kappa coefficient (KC), user accuracy (UA) and producer accuracy (PA) were used. 3- Results & DiscussionAccording to the visual interpretation, all buildings in urban areas with a length and width greater than 10 meters (spatial resolution of the four major bands of Sentinel2) can be extracted using the proposed method in this study, and the results are acceptable in various features. According to the proposed method, annoying features such as vegetation and water body areas were removed from the building identification process with high accuracy, and the accuracy in the study area was improved. The results showed that the OA and KC were 90.11 % and 0.803, respectively. Based on the quantitative and qualitative comparisons, the proposed method had a very satisfying performance. 4- ConclusionDue to the spectral diversity and the presence of various features in urban environments, preparing a map related to it in a large area is extremely difficult. In this regard, the current study presented a very fast semi-automatic method for preparing the urban area map and extracting buildings in Tabriz using Sentinel-1 and Sentinel-2 satellite images as a time series in the GEE platform. One of the most significant benefits of the proposed method is that the data and processing system used in our study is free. Thus, in addition to not having to download large amounts of data, the method presented in the current study has the ability to eliminate many of the limitations of traditional methods, such as classification methods and their requirement for large training samples. The proposed method did not extract the map of buildings using heavy and complex algorithms, which was an important consideration in the discussion of computational cost. Therefore, it can be concluded that the simultaneous use of Radar and optical RS data in the GEE Web-Based platform has a very high potential in distinguishing features and building mapping.
Mohamad Hosain Saraei; Shahabadin Hajforoush
Abstract
Extended Abstract
Introduction
Today, the increasing growth of urbanization and urban population and consequently, heavier traffic and larger number of motor vehicles in urban and suburban areas have created many problems for the transportation system. On the other hand, the unresolved problem ...
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Extended Abstract
Introduction
Today, the increasing growth of urbanization and urban population and consequently, heavier traffic and larger number of motor vehicles in urban and suburban areas have created many problems for the transportation system. On the other hand, the unresolved problem of traffic congestion in cities and the related air pollution have had seriously damaged health and life quality of many citizens and resulted in the death of many patients diagnosed with lung and heart diseases. Therefore, the present study seeks to investigate the desirability of urban routes' design for cycling and its relationship with the indices of a bike-friendly city in Yazd. The present study addresses these questions: how desirable is the design of urban routes for cycling in Yazd? Do present rules and standards increase the satisfaction of cyclists? What is the relationship between the desirability of urban cycling route designs and developing a bike-friendly city?
Materials & Methods
The present descriptive study is considered to be a survey in terms of nature and methodology and applied-developmental in terms of purpose. Data collection was performed using library, documentary and survey methods. Citizens of Yazd were selected as the statistical population. Personal estimation method was used to ensure a homogeneous and standard sample is selected with an appropriate size. The sample included 20 experts and urban designers and 100 cyclists who have cycled on urban routes in Yazd. Purposeful methods of sampling such as snowball and theoretical sequence have been used in the present study.
Results & Discussion
Results obtained from the UTA technique and the Fuller hierarchical method used to weigh relevant indicators show that the security criterion has ranked first (with a weight of 0/344) while the continuity criterion has ranked last (with a weight of 0/181). Pearson correlation analysis did not find any significant relationship between income, gender, age and education with cyclists' satisfaction level, but a significant relationship was found between the observance of standards in urban routes and the level of satisfaction. Considering the linear regression diagram and r2 = 0/52, a desirable design for urban cycling routes can provide up to 52 percent of the conditions required for turning Yazd into a bike-friendly city.
In general, findings of the present study are closely related with Bicalho et al. (2019), Yang et al. (2019) and Nazarpour and Saedi (2020) concluding that developing cycling infrastructure in accordance with appropriate rules and standards, holding workshops to create a positive attitude and a greater understanding in urban planners toward cycling, improving street connections and the desirability of the cycling routes' designs for cyclists will enhance the creation of a bike-friendly city. The present study indicates that compliance with national standards and regulations in urban routes is mandatory for cyclists. Findings are also closely related with Podgórniak-Krzykacza and Trippner-Hrabi (2021), Babiano et al. (2017), Shabanpour and Zareh (2019), Manafi Azar et al. (2018) and Soleimani et al. (2017) which indicate that cycling increases access to transportation network, prevents congestion and inefficiency of public transportation, reduces traffic jams, increases safety and security, prevents environmental pollution and results in sustainable urban transportation. Thus, the present study has concluded that a desirable design for urban cycling routes can turn Yazd into a bike-friendly city.
Conclusion
Results of UTA technique indicated that rules, regulations, and bylaws assigned for cycling paths in Iran such as longitudinal slope, cross slope, open sight distance and stopping sight distance, minimum radius of curvature of the bike lanes, horizontal signs, and special traffic lights shall be reviewed and practically used to create a more comfortable space for cyclists. The analysis indicates that urban routes in Iran must be designed in accordance with the standards of cycling routes, and the respondents have also emphasized on this necessity. Moreover, it was indicated that there is a positive correlation between compliance with standards and the level of comfort in cyclists. In other words, compliance with standards in urban routes' designs increases the level of comfort in cyclists. Finally, it can be concluded that there is a positive and meaningful relationship between the desirability of urban routes' designs for cycling and the chance of turning Yazd into a bike-friendly city.
Mohsen Abedi; Mohammad SaadatSeresht; Reza Shahhoseini
Abstract
Extended Abstract
Introduction
Nowadays, updating information collected from urban areas is of great importance, since it provides the basis for many fields of study such as land cover changes and environmental studies. Remote sensing provides an opportunity to obtain information from urban areas ...
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Extended Abstract
Introduction
Nowadays, updating information collected from urban areas is of great importance, since it provides the basis for many fields of study such as land cover changes and environmental studies. Remote sensing provides an opportunity to obtain information from urban areas at different levels of accuracy while widely used in various change detection applications. Detecting changes in buildings as one of the most important features in urban areas is of particular importance. Powerful and expensive processing systems are the only way to process large volume of remote sensing and photogrammetry data generated by the ever increasing number of sources to which laymen do not have access. The present study has applied deep learning methods and high computational volume of data processing in free clouds to make this possible for the public.
Materials & Methods
Two case studies have been selected in the present study. The first includes DSM and Orthophoto images captured by drones from Mashhad in 2011 and 2016. DSM and Orthophoto images in the second case study has been collected by drones from Aqda in Yazd province in 2015 and 2018. In accordance with the type of data used and high computational volume used for processing, the present study has applied fuzzy clustering method to detect buildings with a high computational speed and deep learning method to detect their changes. Object-based method and fuzzy logic theory have been used in the first step to classify features and detect buildings. In the second step, deep learning method and DSM differentiation method were also used to detect changes in buildings and evaluate results obtained from deep learning method. In the first step, buildings were detected using descriptors extracted from terrestrial and non-terrestrial features, and related decisions were made using fuzzy logic. In the second step, DSM differentiation method has applied the masks extracted from buildings in both epochs on the related DSMs to find their difference and detects changes using an elevation threshold. In deep learning method, a convolutional neural network model was trained to detect changes in buildings during both epochs. Using the DSM of buildings in both epochs and a part of their interface, the network input layers were generated for training. Changes detected in the buildings by the differentiation method were also introduced as the output layer. Following the training and introducing the entire interface in both epochs as the input layer, the trained neural network has detect changes in the buildings. The same process was performed once more using the difference between two DSMs. In other words, a single input layer was used in the network and the rest of the process was the same as before. Finally, changes detected by the neural network was compared with changes detected in the DSM differentiation method
Results & Discussion
In the first step, buildings were detected and images were classified in accordance with the fuzzy logic. The overall accuracy of the first epoch classification in Mashhad equaled 94.6% indicating higher acuracy of object-based methods as compared to pixel-based methods. The overall accuracy of first epoch in Aqda equaled 95.5%. Neural network method detected changes in buildings with an overall accuracy of 90%. In accordance with the ground truth used in network training (both using DSMs as the input layer and the difference between the epochs as the input layer), results indicated that deep learning method is highly accurate in one-dimensional convolution mode. Moreover, the second step has applied the difference between DSMs in the two epochs and thus, many areas lacking a change in height were removed in both epochs and the network was trained more appropriately and accurately.
Conclusion
Necessity of extracting features, especially urban features such as buildings and identifying their changes over time have been investigated in the present study. Due to the high computational volume of modern remote sensing and photogrammetry data and highly expensive systems required for their processing, a new method was presented in the present study to solve this problem. Considering the type of data used and the complexity of features, object-based methods were selected instead of pixel-based methods to identify features and buildings. Deep learning method was used to detect changes in buildings. The method was also compared with DSM differentiation method. A one-dimensional convolutional neural network was used in the deep learning method. Two different modes were used in the network to train and predict changes. In the first, DSMs extracted from the buildings in each epoch were used as the input layer, while in the second one, the difference between DSMs were introduced as a single input layer to the network and the network was trained in accordance with the ground truth collected from areas with and without change obtained from the DSM differentiation method. Following the training process, changes were predicted using the trained network. Much better results were obtained from the second mode in which the difference between DSMs were used.
Sara Haghbayan; Behnam Tashayo
Abstract
Extended Abstract Introduction Air pollution has become a life-threatening hazard with severe consequences. Previous studies have indicated that long-term exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by ground-based ...
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Extended Abstract Introduction Air pollution has become a life-threatening hazard with severe consequences. Previous studies have indicated that long-term exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by ground-based stations that can collect data regarding temperature, humidity, pressure, and several pollutants such as Ozone (O3), Carbon Monoxide (CO), Carbon Dioxide (CO2), Sulfur dioxide (SO2), Nitrogen dioxide (NO2), and nanoparticles (e.g. PM1, PM2.5, and PM10). However, ground-based stations are costly, scattered, and often cannot cover large areas. These stations collect the concentration ofparticulate matter with a diameter of less than 2.5 µm (PM2.5) over a year.Collected data may be lost due to an unexpected shutdown of the device. Datacollected in ground-based stations are not sufficient by their own and as a result they are modeled. The resulting models also have flaws, so new resources are needed to solve this problem. One of these resources is the use of mobile sensors to produce high-resolution temporal and spatial air quality data. As opposed to traditional air quality monitoring stations, the use of dynamic and mobile sensors is quickly developing. These mobile sensors measure the concentration of the same air pollutants as those measured by ground stations. Land-use regression (LUR) models are increasingly used to estimate the level of PM2.5exposure in urban areas. Land-use regression models often use data received fromground-based stations. Therefore, modeling the concentrations of particulate matter in a city leads to a significant increase in modeling error. Data from mobile sensors can increase the accuracy of this contaminant modeling process. The present study aims to improve modeling accuracy by integrating ground-based stations with mobile sensors. Therefore, using the proposed framework, we can accurately estimate air quality at any time and place and provide higher resolution estimations for heterogeneous urban environments. Materials & Methods The study area covers Isfahan city. With a population of more than two million and an area of 200 square kilometers, Isfahan is located in central Iran. 13% of the total pollutants entering Isfahan belong to urban industries, 11% to domestic sources, and 76% of all pollutants belong to traffic related sources in Isfahan. Therefore, most of the PM2.5concentrations are generated by the transportation system in Isfahan. The effective solution to the air pollution problem needs to have a comprehensive understanding of the air pollution process. Such an understanding primarily depends on reliable records that can depict the temporal and spatial variations in air pollution which is not possible due to the limited number of ground-based stations. The proposed method of the present study is to combine ground-based stations with mobile sensors to increase the accuracy of PM2.5concentration estimation and modeling. One of the existing methods used to estimate PM2.5levels is land use regression. Previous studies used only ground-based stations to create this model, which was not sufficiently accurate. The present study sought to increase the accuracy of PM2.5concentration modelling in contamination values of near or beyond the threshold. Using the LUR model, a prediction map was generated usinga combination of ground-based stations and mobile sensor which helps us to reach a more accurateestimation and prediction of PM2.5concentrations in a heterogeneous region such as this city. Results & Discussion Reliable and accurate estimate of temporal/spatial distribution of air pollutant concentration cannot be achieved using a limited number of ground-based stations. The present study took advantage of 14 mobile sensors along with 7 ground-based stations. Results indicated that the root mean square error of the seven ground-based stationsequaled 1.80 while the RMSE of the combination of these stations equaled 0.59. The skewness index shows asymmetry of data as compared to the standard normal distribution.This index is used to determine whether the data distribution is normal or not. Skewnessvalue of standard normal curvesequals zero. In the histogram obtained from a combination of all stations, this value is 0.11, while in the histogram obtained from the ground-based stations, skewness value equals 0.8803. In general, the results indicated that integrating ground-based stations with mobile sensors results in a PM2.5concentration distribution which looks more like a normal distribution. The normality of data distribution implies that the histogram of data frequency is approximately a normal curve, and thus T-test is used to examine whether or not the results were significant. Conclusion In this study, a new framework was proposed to integrateground-basedstations and mobile sensors with the aim of improving the accuracy of PM2.5 pollutant concentration estimation. The results of the t-test show that with only ground-based stations, the actual pattern and its distribution over the city will fail. In fact, data received from mobilesensors provide additional data necessary for air pollution profiling.
Mostafa Mahdavifard; Khalil Valizadeh Kamran; Ehsan Atazadeh; Nasrin Moradi
Abstract
Extended Abstract
Introduction
The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems.Ingenerally,global waters are classified into two types of water.In waters of the first type, such as the waters of the open ocean, phytoplankton ...
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Extended Abstract
Introduction
The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems.Ingenerally,global waters are classified into two types of water.In waters of the first type, such as the waters of the open ocean, phytoplankton dominate the inherent optical properties of water. However second type waters, like coastal waters, are complex waters that are affected by a variety of active light compounds such as phytoplankton, colored dissolved organic matter andtotalsuspended matter.Coastal wetlands are considered as the Case-2 water. These types of areas are dynamic environments that are threatened by the entry of pollutants and because the wetlands have a calm environment and away from open sea waves, they are exposed to the accumulation of natural and human pollution. As a result, the identification and monitoring of coastal and marine pollution is essential to minimize their destructive effects on human health and the environment and economic damage to coastal communities.Phytoplankton are floating or scattered single-celled algae that travel primarily through water waves.Chlorophyll-A considered as an indicator of the abundance of phytoplankton and biomass in oceanic, coastal and lake waters. Field and laboratory methods are difficult and time consuming and weak for spatial and temporal observations. In contrast to the weakness of field methods, remote sensing methods can provide the spatial perspective needed to gather information on ocean and coastal water surface on a regional and global scale.The purpose of this study was to compare and evaluate atmospheric correction methods (high atmospheric radiation and high atmospheric reflectance) on the algorithm for estimating the concentration of chlorophyll-A based on blue and green bands (OC2) in Landsat-8 and Sentinel-2 data, evaluating the results using Field data and finally the time series mapping of chlorophyll-A concentration.
Materials & Methods
In this study, Landsat-8, Sentinel-2 satellite time series data and field data collected from the study area,were used.First, the satellite images used in ENVI 5.3.1 softwarewereconverted to Surface Reflectance and Top of Atmosphere Reflectance.Then, MATLAB 2018a software was used for image processing and coding.To estimate the chlorophyll-A concentration, the bio-optical algorithm OC2 was used, which in fact uses a nonlinear relationship to link between field data and satellite data. In order to evaluate the results,two statistical parameters R2 and RMSE were used.
Results & Discussion
Based on the analysis of field data, the concentration of chlorophyll-A in all sampled stations was less than 1 mg/m3. Water in the Surface Reflectance and Top of Atmosphere ReflectanceSentinel-2 and Landsat-8 data had a relatively similar spectral signature at wavelengths, due to the similarity in the spectral signature of water on the satellites used, covering the same spectral range in the Landsat-8 and Sentinel-2 satellites systems. The OC2 algorithm had amounts R2 (0.91 and 0.64) and RMSE (0.13 and 0.33) in Landsat-8 and Sentinel-2 Surface Reflectance data, respectively, while Landsat-8 and Sentinel-2 Top of Atmosphere Reflectance data had amounts R2 (0.12 and 0.53) and RMSE (0.45 and 0.51), respectively. The time series of chlorophyll-A concentration estimated using surface reflectance data (Landsat-8) corresponds to the natural conditions of the region, However, the time series of chlorophyll-A concentrations using the surface reflectance data (Sentinel-2) during the seasons estimated the chlorophyll-A concentration to be uniformly and downward.The reason for this poor performance in the Sentinel-2 is the lack of sufficient field data for calibration.
Conclusion
In this study, we tried to evaluate and compare the reflectancealgorithms (Landsat-8 and Sentinel-2) in the OC2 algorithm.Preliminary results indicate that the type of satellite data used (Surface ReflectanceandTop Atmospherereflectance) is of great importance for entering the OC2 bio-optical algorithm because the satellite image to enter the OC2 algorithm must be surface reflectance data and atmospheric correction that In fact, these algorithms are sensitive to high-atmosphere reflectance data.In general, the results showed that 10 field data is enough to calibrate with Landsat-8 data, but for Sentinel-2 data, more than 10 numbers field data must be calibrated to obtain a good result.
Sepide Imeni; Hassan Sadough; Shahram Bahrami; Ahmad Reza Mehrabian; Kazem Nosrati
Abstract
Extended Abstract
Introduction
Geomorphologists have always considered geomorphological processes such as weathering, erosion, and sedimentation, and tectonic processes as the main factor creatingdifferent landforms in the ecosystem. Moreover, a large part of the earth’s surface is affected ...
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Extended Abstract
Introduction
Geomorphologists have always considered geomorphological processes such as weathering, erosion, and sedimentation, and tectonic processes as the main factor creatingdifferent landforms in the ecosystem. Moreover, a large part of the earth’s surface is affected by the presence and existence of organisms, thus these biological species play a major role in environmental changes and consequently in the creation of landforms. In fact, geomorphology is one of the important factors affecting vegetation heterogeneity in the scope of landscape. Alluvial fans are among the important and majorgeomorphologicalforms in which two natural parameters of landform and vegetation coexist. Various methods are used to study vegetation density. Vegetation variables are commonly estimated using land surveying, but satellite images have made more accurate methods of rangeland management and alsoestimationof plant quantities in inaccessible areas possible. However, usingdata obtained from satellite imageries for partial measurements has some limitations due to unavailabilityof high spatial resolution images such as QuickBird satellite images or high expenses of retrieving such imagery.In the present study, plant variables were investigated using large-scale aerial imagery and field sampling. Plant density and percent canopy cover were also determined in the study area using the same methods.
Materials & Methods
Study area
The area under study is located in the northeastern regions of Semnan province, northernShahroud city. The study area includes three alluvial fans including Saran, Moghatelan and Hot-Sokhteh.
Methods
Based on field observations, Google Earth images, and drainage pattern, alluvial fans were divided into active (young surfaces) and inactive (old surfaces) parts. Six sites (P1 to P6) including upstream, downstream, active and inactive parts of the alluvial fans under study were selected in order to determine the density and percent of canopy cover in channels, interfluves (in old surfaces), bars and swales (in young surfaces). The aerial image was acquired using a Dji Phantom 4 Pro Drone with a relative flying height of 100 m, and a 20 megapixel, FC6210 digital camerain December 2018 (Table 1 and Fig. 3). The canopy covers in alluvial fan landforms (including channels, interfluves, bars and swales) were measured using large-scale images (1: 500) acquiredby drone. In the next stage, 50 rectangle and squareshaped plots were selected to determine the density and percent canopy cover of the aforementioned landforms in the upstream and downstream of the three alluvial fans; 5 squareshaped plots with a dimension of 10*10 m were selected from the interfluves, 45 rectangularshaped plots with a dimension of 3*10 m were selected from the channels, swales and bars. Then, percent canopy cover was calculated in each plot and the average percent canopy cover was finally calculated for the 50 plots of each site.
Experimental studies
In order to investigate physical and chemical characteristics of soil and its effects on the density and vegetation type across alluvial fans, 48 soil samples were collected from a depth of 0-20 cm in the three alluvial fanseach including active and inactive parts, bars, swales, channels, and interfluves. PH, EC, phosphorus (P), absorbable potassium (K) and sodium (Na), calcium carbonate (CaCO3), Saturation percentage (Sp), water retention capacity of soil (WHC), soil texture, and total organic carbon (OCT) were also measured in the samples.
Sampling vegetation and identifying plant species
In order to identify plant species, field work was carried out in June 2019. Plant species of the study area were identified and a sample was collected, dried and pressed. Systematic random sampling was used in the specified types. In fact, a 200-meter transect was selected in each site, and 8 plots with a dimension of 8 * 8 m were identified along each transect including channels, interfluves, swales, and bars of the upstream and downstream alluvial fans. Therefore, 43 vegetation sampling plots were selected along the 200-meter transect.
Results & Discussion
In the active surfaces of both upstream and downstream alluvial fans, density and percent canopy cover of bars arehigher than those of swales, because of the higher amount of silt and clay in bars. Larger plant species such as shrubs and sub-shrubs requiringfine-textured soil grow in these bars. On the other hand, swales have a higher amount of organic materials and calcium carbonate. EC and PH are lower in the bars as compared to the swales. Water-holding capacity (WHC) and Saturation percentage (Sp) of the soil are higher in the swales as compared to the bars. There are more absorbable potassium and phosphorus in the bars. However, vegetation density and percent canopy cover in swales are lower than those of bars despite their high soil fertility and moisture. This is probably due to the lower stability of the swales whichresults in their higher exposure to unstable currents during occasional storms and floods.
Overall, plant species adapted to the specific environmental conditions are settled in each landform. PerovskiaAbrotanoides is the dominant plant species in active surfaces ofbars. The vegetation type is more limited in the swales of active surfaces including species likePoabulbosa and Bromusdanthoniae.
In inactive surfaces of alluvial fans, elementsrequired for soil fertility (organic materials, calcium carbonate, absorbable potassium and sodium, phosphorus, pH, saturated moisture of the soil, and soil retention) are higher in the interfluves as compared to channels. The relative higher fertility of interfluves can be attributed to their gentle slopes, higher stability and hence higher possibility of soil formation. Long-term exposure of sediments or alluviums to weathering elements on relatively flat surfaces of interfluves has resulted in the formation of more clay and silt, and thereby denser vegetation in interfluves compared to channels. Herbaceous and shrub species, which require fine-textured soils, settle in interfluves. On the other hand, vegetation density of channels with higher amounts of sand and pebbles is lower likely due to their steep slopes as well as their higher level of erosion. However, percent of canopy cover is higher in channels as compared to interfluves. Channels have a relatively higher level of moisturesince they are in the shade and in vicinity of groundwater. Hence, shrubsare settled in these landforms. These species havea denser canopy cover, and deeper roots and require coarser soil texture.
Artemisia sieberi is the dominant plant species in inactive surfacesofinterfluves.This species is a sun-loving plant requiring lots ofsunshine to grow.Apart from Artemisia sieberi, other plants such as Astragalus sp., Acanthophyllum sp., Peganumharmala, AmygdalusScoparia and convolvulus acanthocladus have also settled in the interfluves.
Conclusion
Analyzing vegetation density and percent canopy cover of alluvial fans and their related landforms indicated that bushes are more frequent in the interfluves of old surfaces as compared to other parts of these fans. Despitelower vegetation densityin bars of young fans and channels of old fans, they have a larger type of vegetation (mainly shrubs) and thus, a higherpercent canopy cover. Generally, this study has revealed that bushes are more frequent in the old alluvial fans, especially upstream parts of the fans as compared to other areas. Overall, the results indicate that geomorphological processes such as aggradation and degradation affect the texture and fertility of soil as well as type and density of vegetation.
Mohammad Kazemi Garaje; Khalil Valizadeh Kamran
Abstract
1- Introduction Direct measurement of physical parameters of water, such as sea surface temperature and water depth through traditional methods is very time-consuming and costly. Thus, new cost-effective methods, such as remote sensing technology, have always been of interest to experts, managers and ...
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1- Introduction Direct measurement of physical parameters of water, such as sea surface temperature and water depth through traditional methods is very time-consuming and costly. Thus, new cost-effective methods, such as remote sensing technology, have always been of interest to experts, managers and decision-makers. Satellite imagery is used to estimate sea surface temperature and water depth. Therefore, the present study seeks to calculate sea surface temperature and water depth and investigate their relation using satellite imagery. 2- Materials and Methods In the present study, Landsat 8 satellite images of Urmia and Van Lake were retrieved from USGS website for August 16th, and August 23rd, 2018. Information about water temperature and water depth of 3 meteorological stations in the study area were also obtained from the Artemia Research Center and the Meteorological Organization of West Azerbaijan Province for a period of three months. In the next step, geometric and atmospheric corrections were performed on the images using ENVI5.3 software. In thermal remote sensing, thermal bandwidth of satellite imagery cannot reflect black-body radiation. Moreover, electromagnetic spectrum of radiation used in the Boltzmann relationship covers a range of 3 to 300 micrometers. This is while the thermal spectrum range of thermal sensors is generally between 10.5 to 12.5 micrometers.Thus, the split-window algorithm was used to calculate the land surface temperature. Water emission coefficient equals 0.98. Multiplying the amount of water emission by the amount of land surface temperature (LST) and subtracting the results from zero Kelvins (-273), we can obtain sea surface temperature in Celsius degrees. 2-1- Calculating relative depth of water As one of the dynamic characteristics of water, water depth has an important role in the management and optimal use of marine resources. Water depth measurement refers to the underwater study of oceans, lakes and rivers. Therefore, Stump Method was used to calculate water depthin the present study. 2-2- Accuracy assessment In order to estimate the accuracy, information about water surface temperature and relative water depth in three stations in Lake Urmia, namely Qalqachi, MalekAshtar and Ashk stations, were collected from the Artemia Research Center and the Meteorological Organization of West Azerbaijan Province. 3- Results Results indicate high accuracy of remote sensing methods in sea surface temperature and water depth measurements. The lowest RMSE of sea surface temperature measurement is related to MalekAshtar station (1/1). This station also has the lowest amount of RMSE (1/5) obtained in water depthmeasurement. According to the results, a negative correlation coefficient is observed between the values of sea surface temperature and water depthvariables. The correlation between sea surface temperature and water depth in Lake Van equals -0.52, while this correlation equals -0.24in Lake Urmia. 4- Discussion Despite their relatively high accuracy, usinginformation collected from meteorological stations to calculate physical parameters of water,such as water surface temperature and water depth, has some limitations. However, new technologies such as remote sensing can overcome the limitations of traditional methods. Remote sensing technology has made estimating the physical parameters of water on a regional to a global scale possible. Results of the present study indicate high accuracy of remote sensing technology in measuring physical parameters of water such as surface temperature and depth. In this regard, shallow water bodies have the highest surface temperature and deeper water show lower temperatures. The results also indicate that fluctuations in the water surface temperature and water depth can increase or decrease the correlation coefficient between these two variables. Thus, higher correlation coefficient between water surface temperature and water depth in Lake Van compared to Lake Urmia is due to its greater depth of water. 5- Conclusion Results indicate that the upstream of Lake Urmia is deeper than itsdownstream and also has a higher level of salinity which reduce evapotranspiration in the upstream of the lake. Thus, theupstreamof Lake Urmia has not been as severely affected by the drought. The correlation coefficient between water surface temperature and water depth of Lake Van also shows that this lake has a relatively lower water surface temperature compared to Lake Urmia due to its greater depth. Therefore, the rate of evapotranspiration in this lake is less than Lake Urmia and the drying process is negligible. Due to the fact that Lake Urmia and Van are in the same climate, the high temperature of the water level of Lake Urmia due to its shallower depth can be one of the causes of Lake Urmiadrying. The amount of water in the lake can be increased by increasing the volume of water entering the lake.This can be achieved by destroying a number of dams built on the rivers flowing into the lake or by water transfer from adjacent water bodies. Therefore, increasingwater depth and reducingwater surface temperature can be considered as one of the main solutions to prevent the drying of Lake Urmia.
Parisa Golshani; Yasser Maghsoudi; Hormoz Sohrabi
Abstract
Extended Abstract Introduction Estimation of forest Carbon stocks plays an important role in assessing the quantity of carbon exchange between the forest ecosystem and the atmosphere. Direct methods of measuring carbon stock are not economically efficient. Optical remote sensing methodsalso have limited ...
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Extended Abstract Introduction Estimation of forest Carbon stocks plays an important role in assessing the quantity of carbon exchange between the forest ecosystem and the atmosphere. Direct methods of measuring carbon stock are not economically efficient. Optical remote sensing methodsalso have limited capability in predicting forest biomass, because the spectral response of optical images is related to the interaction between solar radiation and canopy, especially in mature forests. These obstacles limit the efficiency of optical sensors for forest biomass estimation. Recently, airborne data has received a great deal of scientific and operational attention for estimation of forest features. LiDAR data also faces challengessuch as limited efficiency in large areas, high costs and large data volumes. In contrast to the optical and LiDAR systems, SAR systems have some advantages, such as the possibility of data collection in any weather condition, penetration through clouds and canopy, and easy access. The potential of SAR images with quad polarization for the estimation of Iranian Hyrcanian forests biomass will be investigated. The main purpose of this study was to investigate the efficiency of ALOS-2 /PALSAR-2 backscattering coefficients andpolarimetric features in leaf-on and Leaf-off crown conditions, evaluate the linear regression model and select the most appropriate variables for biomass estimation. Material and methods The study area is located in a part of forests of Mazandaran province. The region forms a part of the deciduous broadleaf temperate plain forests. The forestsunder study was classified into 4 major types: (1) Forest reserve, (2) Natural forest, (3) Degraded forest and (4) Mixed species forest plantations. 115 circular sample plots (each including 0.1 hectares)were collected from the 4 different sites with various forest structures and biomasses. In each sample, tree species and diameter at breast height (DBH) of all trees with DBH > 7.5 cm were recorded. Allometric equations were used to convert tree diameter to biomass. The present study is based on polarimetric L-band PALSAR-2 data collected in spring and winter. Backscattering matrix was generated using the PALSAR data which consists of amplitude and phase information. Speckle noise filtering was performed using the Refined Lee adaptive filter. Following the filtering, all polarimetric features were extracted. After converting the SAR products to NRCS, geometric correction and georeferencingwere performed and the average backscattering coefficient (sigma naught value)was extracted for each sample plot by overlaying the AOI layers on corresponding SAR images. Finally, the relationship between forest biomass and backscattering intensity was investigated. Results and discussion The resultsvary regarding to the forest type, the range of biomass and forest canopy cover percentage.Forest type and biomass range as well as canopy cover percentage affect the scattering mechanism and correlations between biomass and SAR backscattering coefficient. Canopy cover percentageofthe 1stand 4thsites were over 90% and consequently, the sensitivity of HV backscatter value to biomass was higher than HH and VV. In the 2nd and especially 3rd sites, the correlation between HH backscattervalueand AGB was better than its correlation with HV backscatter. This is mainly because of the canopy structure in these sites which is not complete and the fact that the sensitivity of HH backscatter value to biomass is higher than HV. Results indicate in the 1st and 4th sites, the correlation between volume scatter component of decomposition methods with AGB was better than its correlation with double-bounce scatter component. In contrast, the double-bounce decomposition componentsexhibited the best results in the 2nd and 3rd sites. These findings are in agreement with the results obtained from the T3 matrix components. The least correlation value was observed between Freeman decomposition components and AGB. The volume scatter component of Cloude and also double-bounce component of Freeman did not provide suitable results. Results also indicate higher efficiency of images collected in spring as compared to those collected in winter.Linear regression results show that in the best possible situation, RMSE of the first forest habitat was 34.68 t/ha, and 30.09, 27.07 and 23.69 t/ha were estimated for the 2nd, 3rd, and 4th forest habitats, respectively. Therefore, it seems that classification of forests is necessary before biomass estimation. Conclusion The potential of PALSAR-2 data for Hyrcanian forest biomass estimation was assessed in this study. We demonstrated that L-band data are sensitive to the above-ground biomass (AGB) of Hyrcanianforestsand can be used to provide accurate estimates of biomass. Findings confirmed that decomposition methods are more efficient than backscattering coefficients for biomass mapping.
Sara Khanbani; Reza Shahhoseini
Abstract
Extended AbstractIntroductionChange detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can ...
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Extended AbstractIntroductionChange detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can face a different challenge; most of the CD method from a high-resolution image requires training procedure to overcome this challenge. In this paper, an unsupervised (without needing training process) CD algorithm proposed from the high-resolution image. In this method spatial and spectral features extracted from bi-temporal images of the studied area. Difference images generated from high information content features. Then generated different images mapped into spherical space. The Primary change map created using implemented multi-thresholding method on created spherical space and the second change map created using hierarchical clustering regularized by Markov random field method. The final change map created by integrating the result of primary and secondary change maps. The final change map shows an overall accuracy of 92.56% in the studied area. Data and methodsThe data used in this paper is a subset of the main data with dimensions of 2000 * 2000 from an urban area in the city of Mashhad. These images corresponded to the two periods of 1390 and 1395 and were taken with UAV. The orthoimage is related to the first time with a spatial resolution of 6 cm and the second image is taken with a pixel size of 10 cm. In this paper, in order to detect of change of high-resolution images, first, the input images are registered in terms of spectral and spatial, and then feature images are extracted from each input image separately. In the next step, the differences images corresponding to high information content feature images are calculated. . The optimal difference images are mapped to the spherical space using selected statistical methods and in order to better analysis of the results. Otsu multi-thresholding method implemented on r component of sphere space. In the next step, the optimal difference image mapped to a spherical space is divided into non-overlapping blocks with the same dimensions; a cumulative hierarchical clustering method is applied for each block separately. In this case, the computational volume and space proposed in the hierarchical clustering method are reduced. The results of the cumulative clustering of the blocks are merged together and then the Markov random field method is used in order to regularize the results of the cluster in order to reduce noise.In final clustering, the class values below the lowest Otsu threshold are known as unchanged pixels with high reliability and the values above the maximum threshold are determined as changed pixels. The class of middle interval is unknown. For determining, the class of middle interval the corresponded output of hierarchy clustering regularized with a random Markov field is used. In the last step, a vegetation and shadow mask is used for final post-processing. Results and discussionIn order to an accurate assessment of the proposed method on the mentioned study area, a ground truth image with 11073 pixels has been used as a ground test image. The proposed method has shown an overall accuracy of 92.56 in the study area. The accuracy of detecting changed pixels shows 81.61% and the accuracy of detection unchanged pixels shows 92.77%. The false alarm percentage is 0.21 percent and the missed alarm accuracy is 0.0723 percent. For comparative evaluation, the proposed method is compared with the change vector analysis algorithm. In this section, the selected features in the feature extraction section are entered in the change analysis algorithm, and then the multi thresholding algorithm and shadow analysis used to create the final change map. This method has shown increasing the alarm in comparison with the proposed method. The accuracy of changed and un-changed pixels in the change vector analysis method is equal to 52.98 and 89.24%, respectively. Comparing these results with the results of the proposed method shows the efficiency of the proposed method. ConclusionIn this paper, the new unsupervised change detection method presented based on the combination of multi thresholding and the hierarchical clustering algorithm. Compared to supervised methods that require training data, this method does not require training data. In this method, textural and spatial-spectral features are extracted from images with high spatial resolution, which covers the discussion of the importance of neighborhoods in images with high spatial resolution. In the next step, the extracted features that have a high information content are selected, which helps to reduce the redundancy of the information. The contrast images of the features with high information content are created to differentiate the location of the changes. Spherical computing space is considered as the basic computing space. In order to create a binary change map, two analyzes have been performed on the spherical computational space. First, the Otsu multi-thresholding method has been applied. The values of the smaller and larger thresholds have definite classes. But the value of the middle interval needs to be further analyzed using the hierarchical clustering method. In this section, the middle pixel class is examined, and then a final adjustment is performed using Markov field and shadow and vegetation analysis in order to post-process and prevent false changes. In this paper, the parameters of changed accuracy – unchanged accuracy - overall accuracy - false and missed alarms have been used to evaluate the accuracy of the proposed method with a ground accuracy map. In order to make a comparative study, the proposed method is compared with the change vector analysis method of the created feature space. The results show the efficiency of the proposed method.
Nikrouz Mostofi; Hossein Aghamohammadi Zanjiirabad; Alireza Vafaeinezhad; Mahdi Ramezani; Amir Houman Hemmasi
Abstract
Introduction Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature ...
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Introduction Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries. Materials and Methods Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed: a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters. Result and Discussion The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).
Moslem Darvishi; Abouzar Ramezani
Abstract
Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption ...
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Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption and overcome this crisis. Due to the rising global temperatures and reportsof the World Wildlife Fund, water scarcitycrisis will dominate most countries of the world, especially in Europe and Asia in the next ten years (Sengupta, 2018). Therefore, advanced water management principles shall be applied to decrease water consumption in the agricultural sector and maintain water security. Iran is among the top five countries of the world in terms of having vast irrigated land (Bruinsma, 2017), which shows that in many parts of the country agricultural lands are irrigated. Thus, the country’s water resources reach a critical stage, and because of limited resources, no more water can be provided for agriculture. The present study primarily seeks to optimize crop cultivation using two approaches: first, reduce water consumption and increase farmers’ income and second, reduce water consumption and meet domestic demand. In order to achieve this goal, first, the type of crops and area under cultivation were determined using remote sensing and satellite imagery. Then,spatial information system was used for data analysisand optimization of crop cultivation. Materials & Methods Remotely sensed images were used to collect data about the area under cultivationin agricultural patches and crop type. Those images were then analyzed using remote sensing techniques.According to pixel-based classification ofmultitemporal satellite images using training data, a croplabel is assigned to each pixelin this method. Moreover, borders of each agricultural land are extracted from pan-chromatic images of the region with higher spatial resolution. Finally, fitting the results of pixel-based classification with the extracted bordersof each agricultural land,a final croplabel is determinedfor the total area of the agricultural landbased on the majority labels. In order to optimize the problem, two objective functions (relationships 1 and 2) are defined in which income maximization and water consumption minimization are considered. Typically, location and allocation problems include objective and constraints functionswhich are maximized or minimized based on the goal of the problem. Linear programming is used to solve the problem. Linear programming is a classical optimization method whichdevelop a deterministic algorithm tosolve the problem. This method can only be used when the relationships between variables are linear. In other words, the relationship between variables shall be perfectly proportional and directin this method. (1) (1) (2) Result &Discussion The study area consists of 198 hectares of agricultural land in vicinity of GolangTapeh village of Asadabad city. The city covers an area of 1195 km2 and constitutes 6.1% of Hamadan province. It is located between 34° 37› to34°50 ‹northern latitude and 47°9› to 47°51›eastern latitude. Its average height is 1607 meters above sea level. The city is bounded in northwest with the province of Kordestan,in west with the province of Kermanshah, in southeast with Tuyserkancity and in the northeast withBaharcity. Assad Abad consists of three plains and a mountainside, but since it mostly consists of fertile plains, it can be considered as a flat area (Fig. 1). Fig1: Case study area Figure 2 shows the results of pixel-basedclassificationusing neural network method. In this method, network is trained using ground data. After training the network on the basis of ground truth estimator data, the estimation accuracy is about 88%. Fig. 2: The results ofclassification using neural network Following the calculation of the area under cultivation in agricultural lands and the type of crops, optimization is investigated using two scenarios (Figure 3). In the first scenario, reduction of water consumption and increased farmers’ income and in the second scenario,meeting domestic demandsto prevent capital outflow is considered. Fig3: Crop type and boundaries of agricultural lands In the first scenario, our priority is to reduce water consumption and increase farmers’ income. In this scenario, the goal is to select the type of crops according to the modeling constraints so that the crop type and water consumption are optimized. Figure 4 shows the proposed crop type. Fig4: The results of thefirst scenario Conclusion The present study used a combination of remote sensing and spatial information system to find a solution for optimization ofcultivation pattern through two different scenarios. First, land boundaries and types of crops were determinedusing pan-chromatic images and artificial intelligence. Then, two objective functions were developed to minimize water consumption and maximize income. Also, constraints such as crop type, periodicity constraints and domestic demand were modeled. Considering two objective functions, an algorithm was presented to optimize the cultivation pattern and the results were implemented in this algorithm. Results indicated that the difference between the first scenario which seeks to minimize water consumption and maximize farmers’ income and the second scenario which seeks tominimize water consumption and maximizedomestically demanded crops is relatively small. In both scenarios, the water use rate inAsadabad plain have decreased by about 1000 m3. In other words, in both scenarios there was a 50% reduction in water consumption. Moreover, if priority is given to meeting domestic demand, water consumption increase by about 3% and income decrease by about 3%. In future studies, owners of each agricultural land can be determined and each farmer’s incomecan be considered to further optimize crop cultivation.
Geographic Data
Yaser Moarrab; Esmaiel Salehi; Mohammad Javad Amiri; Hassan Hoveidi
Abstract
Extended AbstractIntroductionThe global rise in urbanization and settlement of the majority of the world’s population in urban areas create opportunities and challenges for improving the quality and sustainability of life. Potential of cities for meeting the basic needs of people has become an ...
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Extended AbstractIntroductionThe global rise in urbanization and settlement of the majority of the world’s population in urban areas create opportunities and challenges for improving the quality and sustainability of life. Potential of cities for meeting the basic needs of people has become an important part of recent scientific and political debates. Covering only a small area of land, cities are responsible for many global environmental problems such as carbon emissions, energy and resource consumption, biodiversity degradation, and ecosystem degradation. They also convert natural forests to human settlements, farms, roads, gardens, and other human-made land uses, leaving many direct and indirect effects on natural conditions and ecological functions of upstream and downstream in forests (such as changes in quantity and quality of water, changes in water flow in rivers, changes in climatic condition and habitat quality). These structural and functional changes undermine environmental services provided by ecological infrastructure and threaten the environmental security of cities and their sustainable development. Therefore, urban managers and experts have always sought a suitable way for urban planning to regulate the structure of cities, support the stability of ecosystem and its performance, and maintain the ecological security of cities. Case studyLavasanat is a district in Shemiranat County in Tehran province of Iran, which is located in the northeast of Tehran. MethodsThe present study analyzes temporal-spatial changes of land use / land cover and then, uses InVEST 3.7.0 model to evaluate temporal-spatial changes of land uses. Results & DiscussionChanges occurring in the reference period were depicted in maps prepared for various land cover / land use classes. Validation of image classification shows a total accuracy of 95.72%, 96.26% and 95.32% and a Kappa coefficient of 0.948, 0.943 and 0.936 for classifications in 2000, 2010 and 2020, respectively, which is acceptable and indicates the compatibility of classified land uses and reality. Classification of images using maximum likelihood algorithm showed the presence of five classes of residential areas (urban area, villages, industries and roads), barren lands, pastures, water bodies and green space in the region.Land use maps and information derived from satellite images indicate that residential areas have experienced a growing trend due to increasing population, demand for land and consequent growth of urbanism, while green space had a decreasing trend during the reference period. Development of residential areas and reduction in green space are quite evident between 2010 and 2020. According to the present trend of land use change, there will be a sharp decline in green space in the coming years. Pastures experienced a decreasing trend from 2000 to 2010. However, it faces an increasing trend from 2010 to 2020 since more green areas were converted into pastures. Barren lands experienced a decreasing trend from 2000 to 2020. ConclusionThe present paper offers the results of modeling water production services in Lavasanat Basin in different decades. Results indicate that the water production in the entire Lavasanat basin equals 2641734.816 cubic meters in 2000, 3318950.915 cubic meters in 2010 and 7737201.215 cubic meters in 2020. Of these volumes, 1677926.367 cubic meters in 2000, 2287145.055 cubic meters in 2010, and 4908786.651 cubic meters in 2020 belonged to residential areas. This class contained an area of 4820578.505 square meters in 2000, 6885513.787 square meters in 2010 and 10407948.705 square meters in 2020 in the whole basin.The results obtained from InVEST scenario building model and water production model showed that the increasing trend of human-made land uses in the study area has a significant impact on increasing water production and, consequently, increases runoff. In fact, water production has experienced a growth rate of 1.25 or 125% from 2000 to 2010, and a growth rate of 2.33 or 233% from 2010 to 2020. Thus in 20 years, water production has increased by 2.92 (292%). The volume of water production in residential areas has increased by 1.36 times (136 %) from 2000 to 2010, 2.14 times (214 %) from 2010 to 2020 and 2.92 times (292%) in 20 years. Also, the total area covered by residential land use has grown 1.42 times from 2000 to 2010 (142 %), and 1.51 times (151%) from 2010 to 2020. Therefore, an increase of 2.15 or 215% was observed in residential areas over this 20 year period.
Arastou Zarei; Reza Shahhoseini; Ronak Ghanbari
Abstract
Extended Abstract
Introduction
As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal ...
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Extended Abstract
Introduction
As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal and spatial scales, and thus a complete description of LST require measurements involving spatial and temporal frequencies. Hence, climatological, meteorological, and hydrogeological studies require having access to wide scale information about spatial changes of air temperature. Since the LST product of SLSTR uses linear split-window algorithm, the present study has used nonlinear split-window algorithm to estimate LST in Sentinel-3 images. Linearity of the radiation transfer equation in linear algorithm and some approximations used in split-window algorithms (such as transfer approximation as a linear function of vapor value) result in considerable errors because of which nonlinear algorithm is used in the present study. Using linear split-window algorithm to estimate LST in tropical climates also leads to a high level of error. The present study seeks to estimate LST using a nonlinear split-window algorithm and data retrieved from Sentinel-3 in different seasons of 2018 and 2019. The results are also evaluated using temperature product of MODIS and SLSTR.
Materials & Method
A time series of sentinel-3 images retrieved from 2018 to 2019 was used as research data. Data were collected by Sentinel-3 SLSTR sensors operated by the European Space Agency (ESA). Obviously, images shall be radio-metrically corrected before calculating physical land surface parameters such as temperature, emissivity, reflectance and radiance, albedo, and etc. To reach this goal, it is necessary to omit or minimize the effect of atmosphere, epipolar geometry of sensor, sunlight, topography, and surface characteristics while estimating surface parameters in these images. The current study seeks to estimate LST applying a nonlinear split-window algorithm on Sentinel-3 data collected during different seasons of 2018 and 2019 and to evaluate the results using temperature product of MODIS, SLSTR, and in-situ data. Pearson Correlation Coefficient and Root Mean Square Error (RMSE) were also used as relative and quantitative criteria to evaluate the accuracy of the proposed method and determine the deference between temperature calculated by the proposed method and temperature product of MODIS and SLSTR sensor. Hence, four frames of LST product collected by MODIS, and SLSTR in April, June, and October, 2018 and January, 2019 were used to evaluate the proposed method.
Results & Discussion
The proposed method was also indirectly evaluated using temperature products of MODIS and SLSTR sensor. Applying parameters of mean and root mean square error, the evaluation has shown that the results obtained from the proposed method in the one-year reference period were more similar to the results obtained from MODIS sensor. Comparing nonlinear Split-Window algorithm and MODIS products, RMSE ranged from 1.21 to 2.46 and the highest and lowest accuracy belonged to winter and summer, respectively. Comparing this algorithm with the SLSTR product, RMSE ranged from 0.76 to 2.24 and the highest and lowest accuracy belonged to winter and summer, respectively. Proper performance of the algorithm in winter is due to the relative balance of atmospheric water vapour in this season. Comparing nonlinear modelling of atmospheric water vapour in the non-linear algorithm of a Split-window and the linear algorithm in SLSTR and MODIS products, the small difference between temperature calculated by the algorithm and the products can be justified. However, due to temperature fluctuations in summer, results obtained by the proposed method were not reliable enough compared to both temperature products. Generally, results obtained from the proposed method showed a higher correlation with the temperature product of SLSTR sensor, which is due to the similar spectral bands used in calculating the surface temperature. Relative comparison of the Split-Window and the MODIS product’s nonlinear algorithm showed a coefficient of determination ranging from 0.76 to 0.96, while comparing this algorithm with the SLSTR product showed a determination coefficient of 0.80 to 0.98. Comparing temperature obtained from the nonlinear Split-Window algorithm with SLSTR and MODIS temperature products, the proposed algorithm was relatively stable no matter which season was taken into account.
Conclusion
The present study seeks to estimate Land Surface Temperature using a nonlinear Split-Window algorithm and Sentinel-3 data collected in different seasons. Values obtained from the algorithm were validated using in-situ dataset retrieved from the meteorological station. They were also evaluated using temperature product of MODIS and SLSTR. To increase the accuracy level, temperature product of MODIS and SLSTR were also evaluated and compared with the in-situ dataset and provided good results. Generally, there is a significant difference between temperature values estimated by the NSW algorithm for different seasons especially summer. However, a similar trend was observed in temperature changes reported by SLSTR and MODIS, and the proposed algorithm in different seasons of the study area. Although, the nonlinear Split-Window algorithm showed a higher accuracy in spring and winter, overall results indicated that the proposed method was relatively stable no matter which season was taken into account. It can be concluded that LST estimation with nonlinear Split-window method and Sentinel-3 satellite data has an acceptable level of accuracy and thus, can be used in large scale environmental crises such as climate changes.
Majid Danesh; HosseinAli Bahrami; Roshanak Darvishzadeh; Ali Akbar Noroozi
Abstract
Extended Abstract
Introduction
Soil is considered to be dynamic and complex both spatially and temporally and thus, many physical, chemical and biological properties should be determined before assessing its quality. To reach this purpose, a large sample must be collected for laboratory tests which ...
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Extended Abstract
Introduction
Soil is considered to be dynamic and complex both spatially and temporally and thus, many physical, chemical and biological properties should be determined before assessing its quality. To reach this purpose, a large sample must be collected for laboratory tests which is both time-consuming and costly and requires lots of attention and precision. Compared to other components of soil, sand is closely related with the quality of soil and crop growth. Therefore, environmental modeling and digital soil mapping projects should pay special attention to this part of soil texture. However, large-scale detection, mapping and monitoring of sand content using common traditional sampling and usual laboratory analytical procedures are both time-consuming and costly due to the vast spatial variability of sand. Compared to laboratory-based and field spectroscopy, spaceborne and airborne remote sensing have a lower level of accuracy due to atmospheric effects, compositional and structural effects, lower spatial and spectral resolution, geometric distortions and spectral mixing. Hence, an appropriate technology is required to overcome these imperfections and study spatially variable factors. Lab Diffuse reflectance Spectroscopy (LDRS) which utilizes fundamental vibration, overtones and a combination of functional groups has been introduced as a promising tool for soil investigation. The present research uses proximal soil sensing technology to study sand content.
Materials and Methods
128 samples were collected from a soil depth of 20cm in accordance with stratified randomized sampling method and supplementary data (geology, pedology, land use, etc.). The samples were then divided into two subsets: calibration subset with 96 and validation subset with 32 samples. Afterward, definitive calibration model was developed and reviewed with two & four latent variables in accordance with R, R2, RMSE, RPD and RPIQ indices using multivariate regression analysis-PLSR method, LOOCV cross-validation technique and preprocessing algorithms such as spectral averaging (spectral reduction method), smoothing and 1st derivative (Savitzky-Golay algorithm).
Results & Discussion
The estimating model indicated that out of the seven latent variables, the first two and four variables can provide the best estimate of the volume of sand in 96 calibration samples and the 32 validation subset. Since more than 60% of the variance of sand variable and 95% of the variance of spectral variables can be concentrated in these selected factors, the predicting model was calibrated based on the first four LVs and the full LOOCV procedure. The best model was calibrated with these features: Rc=0.76, R2C=0.57, RMSEc= 9.77 and SEc of about 9.82. The correlation coefficients (R) between sand contents and the effective spectral bands were calculated and equaled UV-390nm= 0.46, Vis-510 to 540nm about 0.53, 680 to 690 about 0.55, NIR- 950 to 970 about 0.67 and 1100nm= 0.70, SWIR- 1410 nm=0.76, 1860 to 1900 about 0.76, 2180 to 2220 about 0.77 indicating that the selected spectral bands (spectral ranges) with the maximum R contents were the most effective independent predictors in the present modeling process. Furthermore, the most influential spectral domains in the modeling process were determined as follows: UV-390 nm, Vis-440-540 nm, NIR- 740-990 nm, SWIR- 1430-1890, 1930, 2190-2240, 2330-2440 nm which was in agreement with previous studies. The quality of the calibrated sand predicting model was evaluated with Hotelling, Adjusted leverage and residual variances tests. The model was validated based on 32 independent samples. General characteristics of the validation process for LV=4 were Rp= 0.82, R2p= 0.67, RMSEp= 8.83, SEp= 8.92 and bias= -0.93 and Rp= 0.83, R2p= 0.68, RMSEp= 8.68, SEp= 8.72 and bias= -1.26 for LV=2.
Conclusion
Results indicate that the final model was capable of predicting sand contents and thus for two factors (LV=2): RPDc= 1.51, RPIQc= 2.44, RPDp= 1.78 and RPIQp= 2.45 were obtained while for four factors (LV=4): RPDc= 1.54, RPIQc= 2.48, RPDp= 1.75 and RPIQp= 2.41 were reached. A RPIQ of more than 2 shows that the model is capable of estimating soil sand content in Mazandaran province using data collected through diffuse reflectance spectroscopy. Since a new generation of hyperspectral remote sensors with high spectral resolution is now available, results of the present study can be the starting point for more accurate mapping of sand particles in soil texture using RS platforms. However, proximal spectroscopy must be more thoroughly investigated. Determining and detecting the key wavelengths in the modeling process can enhance the upscaling operation and the new airborne/satellite hyperspectral sensors and thus result in more precise mapping of the soil texture. Finally, the VNIR-DRS technology was proved to be potentially capable of estimating soil sand content in Mazandaran province. The present model and key spectral domains identified in the present study can make a basis for future studies investigating the sand content in very large-scale samples using airborne/satellite hyperspectral data. This shows the importance of LDRS and its role in identifying optical wavelengths which will be used in space-borne data (upscaling process).
Hassan Emami; Seyyed Ghasem Rostami
Abstract
Extended Abstract
Introduction
Unmanned Aerial System (UAS) photogrammetry now provides a low-cost, fast, and effective approach to real-time acquisition of high resolution and digital geospatial information, as well as automatic 3D modeling of objects, for a variety of applications including topographical ...
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Extended Abstract
Introduction
Unmanned Aerial System (UAS) photogrammetry now provides a low-cost, fast, and effective approach to real-time acquisition of high resolution and digital geospatial information, as well as automatic 3D modeling of objects, for a variety of applications including topographical mapping, 3D city modelling, orthophoto generation, and cultural heritage preservation. UASs are known by a variety of names and acronyms, including aerial robots or simply drones, with UAV and drone being the most commonly used terminology. Because of the versatility of their on-board Global Navigation Satellite System (GNSS) navigation systems and inertial measurement unit (IMU) sensors, UASs open up new options for photogrammetric projects. In this research, the ability of four different state-of-the-art and professional drone-based software packages, including AgisoftMetashape, InphoUASmaster, Photomodeler UAS, and Pix4D Mapper, to generate a high density point cloud as well as a Digital Surface Model (DSM) and true orthoimage over barren, residential, green space, and uniform textured areas in urban and exurban areas is investigated.
Methodology
The following are the major processes in this study: image acquisition, point cloud, DSM, DEM generation, and accuracy assessment. Data planning and acquisition are the initial steps in commencing any project. The overlapping images are initially obtained using four data sets with distinct surface feature attributes and camera kinds with different shooting situations. The data sets that must be acquired include pictures taken with FC6310 (8.8 mm), NEX-5R (5.2 mm), and Canon IXUS 220HS (4.3 mm) cameras at varied flight heights and spatial resolutions ranging from 52 to 246 m. The four data sets, two of which are connected to Iran and two of which are related to other nations, were chosen from barren, residential, green space, and uniform texture areas. GPS coordinates for these photos must also be recorded using a GPS device. This is done to geo-reference the images for improved model accuracy. The calibration of the camera must also be addressed, and its characteristics and readings must be determined at the start of the project. The images will be calibrated first in order to determine camera pose estimate. The following stage is to compare survey measurements to model measurements in order to assess the overall correctness of the 3D model. The correctness of the point cloud, DSM, and 3D textured model is next evaluated. The accuracy evaluation evaluates the orientation correctness, and measurement uncertainties in the various modeling procedures. Finally, the various products of the mentioned software packages were statistically and qualitatively evaluated.
Results and discussion
The outcomes of this study demonstrate the ability of commercial photogrammetric software packages to do automatic 3D reconstruction of numerous attributes across urban and exurban regions using high quality aerial imagery. This assessment employs a variety of visual and geometric measurements to assess the quality of produced point clouds as well as the performance of the four software packages. According to the visual quality findings, AgisMesh software performs better in 3D modeling of all varieties of surfaces in all locations, but badly in the reconstruction of building edges in urban regions. Pix4D software, on the other hand, performs poorly in areas with uniform texture but excels at recognizing height changes and reconstructing building site boundaries. In terms of visual outcomes, the other software falls somewhere in the middle. In quantitative tests, they were tested first with checkpoints and then with randomly selected points in three distinct classes of urban and exurban regions. Check point findings revealed that the root mean square error (RMSE) in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by choosing random locations revealed that UASmas had an accuracy of 1.83, 1.20, and 2.74 cm, respectively, in three residential, barren, and green space zones. In addition to the 6.90, 2.96, and 7.24 cm accuracy of the PhUAS, the Pix4D was 4.72, 3.46, and 3.59 cm more accurate than AgisMesh software in the three stated classes. Table 1 displays the assessment findings based on the RMSE criterion.
Conclusions
The findings of this study indicate the capacity of specialist drone-based photogrammetric software packages to automatically reconstruct 3D features from high quality aerial images over desolate, residential, green space, and uniform texture environments. In this study, all conditions and parameters in all software were regarded the same, and owing to the similarity of statistical parameters, number of points, and so on in various products, only the discrepancies and their differences were discussed in depth. Various visual and geometric parameters are utilized in this evaluation to analyze the quality of generated 3D point clouds, DSM, and true orthophoto. AgisMesh offers a simple and easy user interface in general and visual assessment, and it is possible to describe and execute data from any camera, even unknown models, without utilizing coordinate images by utilizing powerful processing methods. In contrast, the UASmas program has a highly complex user interface, and the user must be familiar with all of the concepts of photogrammetry as well as the camera parameters file, which is not readily set. It is possible to manually alter restricted processing results in Pix4D. As a result, faulty results are not obtained in regions with the same texture, while production points in other areas are of poor quality. When compared to the other three applications, PhUAS fared poorly aesthetically and geometrically. The user must enter many parameters or thresholds in the processing phases. Therefore, the user must be sufficiently informed of the specifics of photogrammetric and machine vision algorithms to understand that the quality of software output is largely reliant on these factors. Furthermore, check point findings revealed that theRMSE in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by picking random points revealed that UASmas has an accuracy of 3.51 cm, PhUAS has 10.45 cm, and Pix4D was 6.87 cm more accurate than AgisMesh in three residential, barren, and green space regions. Taking into account all of the benefits and evaluations of visual and geometric correctness, the performance and accuracy of AgisMesh, UASmas, Pix4D, and PhUAS may be ranked from one to four, accordingly.
Amir Hosein Shokri; Saied Sadeghian
Abstract
Introduction Recently, cadastre has become a suitable platform for global partnership in management of land and its assets. Due to ever- increasing population, spatial organization of citiesis considered to be one of the most important issues in national development planning. This indicates the necessity ...
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Introduction Recently, cadastre has become a suitable platform for global partnership in management of land and its assets. Due to ever- increasing population, spatial organization of citiesis considered to be one of the most important issues in national development planning. This indicates the necessity of using 3D land information systems since theenvironment, quality, ownership and other benefits of lands do not only change horizontallyany moreand height is also a decisive and vital factor.Therefore, 3D cadastreis used as abasis for integrating information into a complete and efficient information storage system. This system is usedtomanage scarce land resources and plays a key role in achieving future legal and managerial success in the field ofreal-estate. Designing and implementing a system capable of displaying the third dimension (height) is very complex. Common methods of producing 3D cadastral models include land surveying, classical aerial photogrammetry, high-resolution satellite imagery, and so on. Recently, the advent of drones has provided a suitable platform for large-scale cadastral mapping. Collecting high resolution images, processing withstructure from motion(SFM) method, multi-stereo vision (MSV), and dense 3D point cloud with a high resolution of about a few centimeters are the main advantages of these tools. Recentstudies in this field indicate high capabilities of UAV-based photogrammetry method for the production and updating of cadastral maps. Materials and Methods Due to the applied nature of the present study, guideline for the spatial information production using photogrammetric method published by Tehran Municipality and other Surveying and Mapping guidelines published by the National Cartographic Center of Iran have been used to produce 3D cadastral modeland reach relatively real results. The study area is Khosban village in MiyanTaleqan rural district, in the central district of Taleqan County, Alborz Province, Iran. Necessary information was collected using an eBee Plus survey drone with a SODA camera (designed for professional photogrammetric applications). Besides, exterior orientation parameters were measured using the preciseinertial measurement unit (IMU), global navigation satellite system (GNSS) Antenna withreal-time kinematic (RTK) and post-processing kinematic (PPK) techniques and triangulation was performed using these parameters. To increase the accuracy, reduce hidden areas and achieve more accurate 3D models, 75%longitudinal and transverse overlappingwere considered for the images. Image processing was performed using Pix4dmapper and Metashape software and products such as orthomosaic, dense 3D point cloud, and digital surface model were produced. To prove thegeometric accuracy of triangulation, 8 ground control points were used, and32 checkpoints were also used for the final evaluation of 3D models. Results and Discussion 3D cadastre implementation was performedin the present paperusing UAV based photogrammetry without any ground control points. According to the results of triangulation, the maximum root mean square error in the X-component was reported 3.21 cm, the Y-componentwas reported2.86 cm, and the Z-component was reported 3.96 cm using Pix4dmapper and Metashape software. Moreover, 32 sample checkpoints were used for the final evaluation of the 3D models and data collected from these points were compared with the reference data. Results indicated the occurrence of maximum root mean square error in the horizontal components (X, Y) of 0.2 and 0.21 meter respectively, and 0.27 meter in the height component (Z). A correlation coefficient of about 1 represents high geometric accuracy of the 3D models produced using UAV based photogrammetry. Conclusion 3D cadastre can be used as a tool for improving land management and related issues. Due to structural complexity and ownership issues,most developed countrieshave not yet fully implemented 3D cadastre. However, these countries are always looking for ways to achieve such a system. So far in our country, the issue of 3D cadaster has only been pursued in academic studies and no practical stephas been taken to implement this system. Unfortunately, technical dimension and preparation of 3D models are only a part of 3d cadastre and legal issues occurring due to insufficient understanding of the third dimensionand its complexity alsolead to failure in the implementation of 3D cadaster.
Mahvash Naddaf; Seyyed Reza Hosseinzadeh; Jose Martin; Naser Hafezi; Mahnaz Jahadi; Kapil Malik
Abstract
Extended AbstractIntroductionMining (especially surface) is one of the major causes of land and environmental degradation globally. Environmental impacts such as deforestation, landscape degradation, alteration of stream and river morphology, widespread environmental pollution, siltation of water bodies, ...
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Extended AbstractIntroductionMining (especially surface) is one of the major causes of land and environmental degradation globally. Environmental impacts such as deforestation, landscape degradation, alteration of stream and river morphology, widespread environmental pollution, siltation of water bodies, biodiversity loss, etc., have been noted to be associated with mining. Surface deformation is the biggest problem in open cast mines and their surrounding areas due to mining activities. Surveying engineers study the amount of displacement in open pit mines by using leveling to calculate the amount of displacement and determine it. These methods are expensive and time consuming. Satellite images are considered as an important tool for land resource management due to the wide view that provide of an area and also due to its regular repetitive coverage. Interferometric Synthetic Aperture Radar (InSAR) is a useful tool in the study of surface displacements. The SAR interferometry concept has been introduced in the last 1980s.The objective of this study as an academic research is monitoring deformation using Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) method for managing a very rich iron ore resource in the eastern part of Iran named Sangan, near the Afghanistan boundary. MethodologyIn this paper, surface deformation calculation based on the processing of PS-InSAR technique (Persistent Scatterers SAR Interferometry) have been carried out. For this study, according to the availability of data for study area 47 SLC images of Sentinel-1A covering the study area during the period of October 7, 2014 –July 7, 2020 are downloaded from European Space Agency website. Sentinel-1A acquired images with a swath width of 250 by 180, with revisiting time 12 days within the IW data acquisition mode, it is reduced to six days if the images acquired by the Sentinel-1B satellite are available. Sentinel-1 has launched on 4th April 2014 by ESA.PS includes following steps:Master image selection,Co-registration data,Reflectivity map generationAmplitude stability index,Persistent Scatterers Candidate selection (PSC),PS point selection,Multi-image sparse grid phase unwrapping,Atmospheric phase screen estimationRemoval and PS phased readingDisplacement estimation. Study areaSangan Iron Ore Complex (SIOC) is located at latitude N 34°24’ to 34°55’ longitude E 60°16’ to 60°55’ in the Khorasan-e-Razavi Province, North-Eastern Iran. The iron ore deposit is about 20 km Northeast of Sangan town at about 1650 meters above sea level. Sangan Iron Ore Mines (SIOM) is one of the largest mineral areas in Iran, and also considered to be one of the Middle East’s richest deposits which are located in a rectangular area with 26km length and 8km width. Results and DiscussionIn this paper, the 47 scenes of IW SLC Sentinel-1A images, spanning the period from October 7, 2014–July 7, 2020 are accumulated displacement map and the time series of the deformation derived. The PS were selected on the basis of the ASI threshold value of 0.7, which signifies the stability of target points. The LOS displacement was improved by using APS and atmospheric phase delay correction. Later, the LOS displacement velocity on PS locations was estimated. The temporal coherence of all the selected PS was also tested. The PS points having ASI value of 0.7 and above, and temporal coherence of 0.9 and above, gave a relatively stable estimation of LOS velocity. We have identified 215377 Scatterers points. By imposing the standard threshold of 0.7 on ensemble coherence value, this amount decreased dramatically to 52449 PS points. These factors make the chosen technique suitable for studies of surface deformations. The results showed that the deformation velocity in this area is -4.8 mm/yrs and maximum displacement-30mm. In order to verify the results, we collected the Total Station data and PS data for analysis and comparison. Due to the lack of data in the plain, the Total Station data is related to downslope areas and as a result, uplift of area has been used to validation the results. It has been observed that for the same area the Total Station value shows good agreement with the PS- InSAR result. However, there may be some errors due to the fact that the data are not synchronous and that the nature of the impression is different. ConclusionIn the present study, PS-InSAR technique and C-band sentinel-1 data have been used for surface deformation monitoring in open cast mines of Sangan-Khaf, Khorasan Razavi. It can be concluded that monitoring the deformation of mined surfaces using traditional monitoring techniques such as field surveys and using Total Station, especially in large study areas, is time consuming. Since in using the interferometry methods in the study of open pit mines, the area covered by SAR images is much larger, so the use of this method will reduce costs. The results were assessed and validated using leavening data has been observed that, for the same area, the levelling value shows good agreement with the PS- InSAR result.
Sara Attarchi; Najmeh Poorakbar
Abstract
Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational ...
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Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational Land Imager (OLI), provides multi-spectral images with 30-meter resolution. Sentinel 2 was launched in 2015 with a multispectral sensor called MSI which captures images with different spatial resolutions (10m to 60m). The secret mission of Landsat satellites started in the 1970s and they have the longest archive of satellite images collected from the Earth. Sentinel 2 offers higher spatial, spectral and temporal resolutions and therefore it is important to compare the compatibility of Sentinel 2 and Landsat 8 images. OLI and MSI sensors both operate in the optical region, thus weather conditions can impose some limitations on their data acquisition. In such circumstances, data collected by a compatible and similar sensor can replace the cloud-covered images.
Generally, spectral features of new sensors are designed in such a way toconform to the corresponding bands of the previous sensors. The present study compares the corresponding bands of MSI and OLI sensors. The efficiency of both sensors in the classification of a heterogeneous and complex region has also been investigated.
Materials & Methods
Three near-simultaneous pairs of Landsat 8 and Sentinel-2 scenes were obtained to conduct a comparative study. Images were acquired in August 2017, November 2017, and July 2018.Minudasht - in northern Iran- was selected as the study area because of the presence of different land cover classes including rainfed agricultural lands, irrigated agricultural lands, forests, residential areas, and bare lands.Thescenes were processed for further analysis. First, the scenes were atmospherically corrected. In the next step, spatial resolution of MSI bands was resampled to 30 m, and each pair of mages were geometrically co-registered. To do so, 10 tie points were selected, and scenes were co-registered usingthe first-degree polynomial method. RMSE values were reported 2.5 m, 2.4 m, and 2.8 m for August 2017, November 2017, and July 2018, respectively. To investigate the similarities and differences of the sensors’ spectral content, the correlation between corresponding bands of the two sensors was estimated.
Then, images were classified using the support vector machine (SVM) algorithm. Five distinct land cover classes were found in the region including rainfed agricultural land, gardens and irrigated agricultural land, forests, residential areas, and bare lands. The training samples were selectedfromthe land use map and high-resolution Google Earth images. Approximately 300 training samples were selected for each land cover class. The accuracy of classification results was compared to verify the efficiency of two sensors in land cover mapping. Independent validation samples were selected for each class. Overall accuracy, commission error, and omission error were calculatedbased on the confusion matrices.
Results & Discussion
The reported correlation coefficientfor all corresponding bands was higher than 0.8. Results indicate a high level of similarity between the two sensors. Similar findings were reported by previous studies. Overall classification accuracy ofOLIimagescollected in August 2017, November 2017, and July 2018 was 91. 35 %, 89.60 %, and 93.12%, respectively. Overall classification accuracy ofMSI images collected inAugust 2017, November 2017, and July 2018 was 94.76 %, 95.55 %, and 94.07%, respectively. As it is obvious, Sentinel 2showed a higher performance in comparison to Landsat’s, because of its higher spatial resolution. A medium spatial resolution image collected from a complex landscape is often composed of mixed pixels, since different land cover types exist in one pixel. As the image’s spatial resolution improves, the dimensions of each pixeldecrease. Therefore, the number of mixed pixels will decrease and a higher classification accuracy will be expected.
Conclusion
Results confirm the similarity of two sensors in land cover classification. However, the findings could not be extended to other applications. MSI sensorslacka thermal bandand thus are not applicable when such a feature is needed (for an instance inthe retrieval of land surface temperature). In such applications, MSI cannot substitute OLI. For further studies, it is necessary to compare the performance of these sensors in different regions, since different land cover types may impactclassification results. Findings of the present study may raise attention to the differences between Landsat 8- OLI and Sentinel 2 MSI. Further studies can be conducted to investigate the differences between these two sensors. The possible similarities of othersimilar sensors can also be a topic for further investigations.
Saied Sadeghian; Asghar Milan Lak; Hamed Ahmadi Masine; Roohollah Karimi
Abstract
Extended Abstract
Introduction
Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ...
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Extended Abstract
Introduction
Applying GPS/IMU data in aerial triangulation has increased the strength of photogrammetric block and reduced the number of ground control pointsneededfor block adjustment. Systematic errors in data used fortriangulation reduce the accuracy of the process and make ground control pointsnecessarydespitetheexistenceof GPS/IMU data. Therefore, reducing systematic errorsin data naturally increases the accuracy of triangulation and reduces the number of ground control points required forblock adjustment andthe number of crossstrips used to eliminate systematic errorsin GPS data.
Materials
Digital images captured by the National Cartographic Centerof Iran from an area in Fars province usingUltraCam-Xpcamera in2010 were used in the present study to investigate the roleof self-calibration parameters in the reduction of ground control points and cross strips requiredfor block adjustmentin aerial triangulation. The intended block consists of 58 images and four strips; two of which are cross strips. Control points in this block include eight horizontal control points, eight vertical control points and eight full control points. Each image has a dimension of 11310 by 17310 pixels, a pixel dimensionof 6 microns, afocal length of 10500 microns, an end lap of 70%, and a side lap of 30%. Theregion has an average elevation of 760 m. Given the focal length, flight height and pixel dimensions, ground resolution is around 12 centimeters. Each image covers anarea of 2077.2 mlength and 1357.2 mwidth on the ground.
Methodology
The present study investigates theroleof self-calibration parameters, such as elimination of systematic error in GPS/IMU data and image sensor,in increased accuracy oftriangulation, and reduced number of ground control points and cross strips required for block adjustment. To reach this aim, optimal self-calibration parameters are determined using a genetic algorithm and the identified parameters are used in the bundle block adjustment. Variance components estimation method was used to solve the problem of equationsinstability. This method not only stabilizes the equation, but also determines the optimal weight matrix during the adjustment process.
Results and Discussion
Since images at a scale of 1:2000 were used in the present study, maximum RMSE equals 60 cm and maximum residual errorsequal 1.2 m. Using additional parameters to eliminate systematic errors results in an acceptable maximum error at the control points, but absence of additional parameters results in an unacceptable maximum error at the horizontal and vertical control points even in the presence of crossstrips. In addition to the evaluation of horizontal and vertical errors at the ground control points, horizontal and vertical RMSE of the checkpointsare also used to evaluate the geometric accuracy of aerial triangulation. Again, applying additional parameters keeps the RMSE at a much lower level than the accepted limit, while absence of additional parameters results in a horizontal and verticalRMSE higher than the accepted limit even in the presence of cross strips. It should be noted that using cross strips reduces RMSE at the vertical component.
Conclusion
Results indicated that using self-calibration parameters and reducing errorsin data used for the adjustment process decreases the number of control points and cross strips required for block adjustment.Using optimal self-calibration parameters(even in the absence of control points) resultsin a maximum RMSE of 0.143 m at the checkpoints, while absence of these parameters results in a maximum RMSE error of around one meter with or without cross strips. Genetic algorithm is capable of determining optimal self-calibration parameters. It is also capable of optimizing nonlinear functions. Therefore, it is not necessary to linearize the equations before determination of self-calibration parameters, which reduces the amount of necessary calculations. Variance components estimation can also be used along with the bundle block adjustment method to stabilize the equations and determine the optimal weight matrix. As a result, it is suggested to take advantage of these three methods, i.e. block adjustment, stabilization and optimal weight matrixdetermination, simultaneously.
Mohsen Pourkhosravani; Ali Mehrabi; Sadegh Karimi; Mina Azizi
Abstract
Extend AbstractIntroductionEnergy is considered to be one of the most important factors affecting the development of human societies and also an essential parameter in economic and social development along with the quality of life. Population growth, rising living standards, the risk of global ...
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Extend AbstractIntroductionEnergy is considered to be one of the most important factors affecting the development of human societies and also an essential parameter in economic and social development along with the quality of life. Population growth, rising living standards, the risk of global warming caused by greenhouse gas, acid rain, environmental problems and threats to human health, lack of fossil energy sources and rising energy consumption have increased interests in renewable energies. Solar energy has been used as a source of renewable energy for a long time. As one of the safest, most efficient and most economical sources of energy, it has the potential to become the main energy source in the near future (Dincer, 2000: 157). Due to the high number of sunny days, Iran is among the countries receiving the highest level of solar radiation in the world. With 240 to 250 sunny days per year, approximately 80 percent of the country receives an average annual solar radiation of 4.5 - 5.4 kWh / m² (Moghadam et al, 2011: 107). In this regard, the present study seeks to evaluate and monitor radiant energy reaching the surface of Sirjan basin. Materials & MethodsThe study area, Sirjan Basin, is located between 28 degrees and 46 minutes and 50 seconds to 29 degrees and 58 minutes and 1 second northern latitude, and 55 degrees and 11 minutes and 20 seconds to 56 degrees and 32 minutes and 40 seconds eastern longitude. It includes 18481 square kilometers with an average altitude of 1710 meters above sea level. Descriptive-analytical method has been used in the present applied research. Data are collected using library and documentary research methods (from information and statistics offered by different organizations) or extracted from satellite images. Solar radiation energy reaching to the surface of the study area has been evaluated using three methods including Angstrom experimental model, Solar Analyst method in GIS and Remote Sensing. Results & DiscussionAngstrom experimental model indicates that the maximum amount of energy directly received by the basin at low latitudes (28 degrees and 50 minutes) is 73370-73436 watts per square meter. This decreases as we move toward higher latitudes reaching 72836-72903 watts per square meter in the northern parts of the basin (latitude 29 degrees and 50 minutes). Monitoring solar radiation energy reaching the surface with GIS Solar Analyst (solar radiation analysis method) shows that the lowest amount of radiant energy reached the surface in January (between 14000 to 144039 watts per square meter). Also, the maximum amount of radiant energy reached the surface in July (between 111000 to 252000 watts per square meter). Remote sensing technique also shows that the amount of instantaneous radiation received in Sirjan basin reaches its minimum during winters and only a limited part in the west of the study area receives 4.498 to 8.436 watts per square meter. The maximum amount of instantaneous radiation received in summers is 597.6 to 845.6 watts per square meter, which is received in a large part of the west, northwest and southwest of the basin. ConclusionMonitoring radiant energy reaching the surface of Sirjan basin using experimental Angstrom model shows that the highest level of energy received in the southern parts of the basin is around 733370 to 73436 watts per square meter. This is reduced moving toward the northern parts of the basin. Moreover, solar radiation analysis method (Solar Analyst in GIS) shows that the highest amount of solar energy in Sirjan Basin is received in July with 200000 to 252000 watt-hours per square meter , June with 170000 to 248341 watt-hours per square meter, May with 190000 to 247627 watt-hours per square meter and August with 190000 to 234500 watt-hours per square meter, respectively. These values are recorded in eastern, northeastern and southeastern parts of the basin. Results indicate that the eastern half of the basin in which the cities of Balvard, Tekiye, Saadatabad and Pariz are located, receives the highest amount of solar radiation energy especially in summer. Remote sensing technique shows that the highest amount of instantaneous radiation received in summer is 597.6to 845.6 watts per square meter which is recorded in the western, northern, northwestern, southern and southern parts of the region including the villages of Pariz, Saadatabad, Balvard in the central strip and Khatunabad, Mahmoudabad, Najafabad, Malekabad and Golestan. The same is also recorded in other seasons, though with a decreasing trend. The highest level of instantaneous radiation is received in these parts of the basin.
Mehdi Bazargan; Mohammad Ajza Shokouhi
Abstract
Introduction Nowadays, theft -especially residential burglary-is considered as one of the most common and frequent crimes in many countries of the world, including Iran. As such, it has become a pervasive and serious problem with various social, economic, and security-related aspects. Investigating ...
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Introduction Nowadays, theft -especially residential burglary-is considered as one of the most common and frequent crimes in many countries of the world, including Iran. As such, it has become a pervasive and serious problem with various social, economic, and security-related aspects. Investigating geographical dimensions of this crime facilitates the process of exploring this phenomenon. Space and its special features play an important and undeniable role in crime commitment, because space has always been considered as one of the most important factors in commitment of financial crimes such as residential burglary. Spatial analysis and geographical investigation of crimes seek to provide a spatial presentation of criminal actions, crime dispersion, and crime hotspots. This type of crime analysis basically aims to provide a model for decreasing crime commitment in urban spaces. Accordingly, the present research seeks tomodel spatial diffusion of residential burglary crimes in MashhadusingHogstrand’s spatial diffusion theory. Materials and methods The present study is performed based on descriptive-analytic and qualitative methods. The research sample includes cases of residential burglary committed in Mashhad in the 2011-2017period. Data analysis was performed using ArcGIS software. Case study area includes Mashhad, with an area of about 35187 hectares, a population of more than 3057679, and a population density of 87 people per hectare. Results and discussion Police reports in Mashhad suggest that the highest crime rates belong to the 2nd and 3thdistricts, and the lowest rates belong toSamen (around Razavi Shrine), the 12th, and 8thdistricts. 70% of crimes in Mashhad are committed in informal settlements including the 2nd, 3th, 4th, 5th, 6th, 7th, and 10thdistricts. However, only 10.6% of the city area and 29.3% of its population belong to these districts. Furthermore, the highest crime rates have been reported in 2017. In 2011, only two major crime hotspots were observed in Mashahd (in the 2nd and 3thdistricts). Results suggest that crimes have spread from one place to anotherin Mashhad, which indicates a close relationship between crime and distance factor. In other words, proximity to a crime hotspothas resulted in rapid spread of crimes, and due to the short distance, nearby places have been affected more quickly. Informal settlements of Mashhad are located in eastern, northern, and northeastern districts,which contain 99% of crime hotspots. This indicates that spatial autocorrelation of crimes in informal settlements of Mashhad is relatively high, which has led to formation of crime hotspots in these districts. However, moving from marginalized areas towards southern districts of Mashhad (more prosperous regions), spatial correlation of crimes decreases, and lead to formation of 99% of cold spots. Conclusion The present research has investigated the spatial diffusion pattern of crimes in Mashhad in 2011-2017period.To reach this end, crime hotspots were investigated by quantitative methods such as Kernel density, Moran coefficient, and crime hotspot analysis. Results suggest that the highest crime rates are reported in the 2nd and 3thdistricts, while the lowest rates are reported in Samen (around Razavi Shrine), the 12th, and 8th regions. In fact, 70% of crimes in Mashhad are committed in informal settlements including the 2nd, 3th, 4th, 5th, 6th, 7th, and 10thdistricts. Moreover, statistics indicate that for every100000 people,anaverage of 75/2 cases of crimes have been reported in the 2011-2017period.Results of Moran coefficient for spatial diffusion of crimes indicated the presence of a cluster distribution of crimes in Mashhad. Meanwhile, spatial diffusion pattern of crimes in Mashhad suggests that the first crime hotspots were formed in northern, eastern, and northeastern districtsof Mashhad, and crimes have spread from these to other districts (more central and prosperous regions such as the 8th and 9thdistricts). In fact, investigations suggest that crimes are spreading from informal settlements to other regionsof Mashhad, and acompatible spatial diffusion pattern of crimes exists in this city.
Zahra Bahari Sojahrood; Mohammad Taleai
Abstract
Extended Abstract
Introduction
The most important challenge in urban land use planning is the spatial organization of urban activities and functions based on the needs of urban society. Extracting the current rules that exist in the city not only adds local conditions to the standard values mentioned ...
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Extended Abstract
Introduction
The most important challenge in urban land use planning is the spatial organization of urban activities and functions based on the needs of urban society. Extracting the current rules that exist in the city not only adds local conditions to the standard values mentioned in various instructions (Habib et al, 1999; Shiea 2018; Saeedinia 2004) but also makes it possible to analyze with comparing the existing conditions of the city with the standards. There is some research to examine the current situation of the city. Most of these studies have used statistical methods (Hosseinzadeh et al. 1399; Omidipour et al, 2017; Mohammadnejad et al. 2012).
A few of them have utilized data mining methods, but none of these studies examine existing patterns between one type of land use with other land uses. In addition, the method used in this research is a new method that tries to use the capabilities of association rules and decision trees in exploring co-located patterns by combining these methods.Therefore, considering the importance and necessity of addressing this issue, the purpose of this research is to explore the current situation of urban land use by using data mining methods to discover the current patterns in the location of land uses in the vicinity and at different distances.
Finally, providing rules derived from these models may help planners and managers to understand the current status of land use appropriately and improve urban land-use plans by utilizing them in combination with standards and rules based on expert knowledge.
Materials & Methods
Spatial association rules
Association rules discover the laws of interdependence between the data of a large database. In other words, patterns that are frequently repeated in the data set are identified and used to explain the rules of dependence (Han & et al, 2011: 54; Li 2015). The rules of the association in which one of the propositions in the premise or sequence contains a spatial relation are called spatial association rules (Geissen & et al, 2007: 277-287, Mennis & et al, 2005: 5-17).
Decision Tree
The decision tree is one of the most powerful and common techniques for classification and prediction. Among the algorithms used to construct the decision tree, the most important is the C5 algorithm which is the developed ID3 algorithm.
Methodology
A n*l transaction matrix is generated. Where n is the number of available features and l represents the number of types of land use studied, which is 19 in this article. The elements of this matrix can be zero or one.
To fill the transaction matrix, we first consider the distance and apply buffer analysis for all the features in the land use layer. Then, for each feature, we intersect the buffer layer of that feature with the land-use layer and extract all the features that appeared at the intersection. Arc GIS software was used to perform spatial analysis.
Then, to extract the current rules of land use in the urban environment, the a priori algorithm is selected as one of the association rules algorithms, and the C5 algorithm is selected as one of the decision tree algorithms.
In this research, the user data of neighborhood 4, district 5 of Tehran Municipality, including 1065 property plots, were used.
Results & Discussion
In this step, the proposed model for deriving the rules of land use dependence based on the current situation of land use in the study area is implemented step by step and the results are presented.
According to existing standards, three distances are considered to extract spatial rules with an apriori algorithm. After extracting the rules, they are compared with the values of approved standards in urban land use planning. Vicinity and compatibility are examples of indicators in common standards for locating and determining land use for the land. Using the extracted rules, the indicators are examined.
Due to the lack of extraction of some rules by association rules, for example, not extracted rules related to therapeutic land uses within 300 meters from residential land uses, we use the decision tree algorithm to extract related rules in more detail. The graphs obtain from the decision tree shows which land uses are effective for predicting and categorizing specific land uses, based on the current status of the land uses located in the case study area.
Conclusion
The purpose of this paper is to data mining the current status of urban land uses to extract the rules of neighborhood and proximity of different land uses. Using the proposed model in this article, it is possible to extract the existing rules of land uses in detail and as well as to evaluate its compliance with conventional standards and criteria in urban land use planning.
Akram Sadeghbeygi; Kamran Moravej; Mohammad Amir Delavar
Abstract
Extended Abstract Introduction In the last few decades, thematic maps and models were usually assessed using Kappa index of agreement. The index gives us the relative observed agreement among raters (identical to accuracy), but lacks any useful information to make practical decision making about ...
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Extended Abstract Introduction In the last few decades, thematic maps and models were usually assessed using Kappa index of agreement. The index gives us the relative observed agreement among raters (identical to accuracy), but lacks any useful information to make practical decision making about the model’svalidity easier. In other words, Kappa index does not provide an explanation about classification quality or an idea about increasing theaccuracyof the predicted map. Moreover, the index does not explain the causes of disagreement.Thus, giving indices of agreement without any interpretation will not be satisfactory. Today, new complementary methods are required to show the quantitative and spatialagreement and disagreement between two maps. It is necessary to show how a modeled map can be produced with better accuracy. The present study seeks to introduce and explain concepts of agreement and disagreement components with an example. Finally, these components are introduced as a useful method for the validation of digital maps. Materials and Methods An area of 410 hectares which belongs toZanjanUniversity was used to express the findings of this study. The area is located 5 km before the beginning of Zanjan-Miyaneh Road at 48.4° eastern latitude and 36.68° northern longitude. A digital soil mapin which probability distribution of different soil classes is obtained using multinomial logistic regression algorithm and a reference soil mapproduced with the conventional methods are usedto explain the concepts and investigate the spatial and quantitative agreement and disagreement indices. Validation and calculation of quantitative and spatial agreement and disagreements are performed using IDRISI software (SELVA version). To simplify the process, two maps with a grid structure (3 x 3) are introduced as a reference map and a predicted map. The reference map is used for spatial and quantitative evaluation and validation of the predicted map cells. Each map contains 9 cells and each grid cell has a membership value of either white or gray categories. Results and Discussion In the validation process of two maps, most researchers seek to find answers to two important questions: 1- How much agreement is there between the cells of each mapping class group? And 2- How much agreement is there between the map used in modeling and the reference map regarding the position of the cells in each class? The present study expresses agreement between the two soil maps using an index of (M (m)) which equals 60.69%. With an average level of quantitative and spatial information about different classes of the digital soil map (DSM), the H (m) index equals46.4%. Results indicate that if the produced map is modified or rearranged (provided that the level of quantitative information remains unchanged but the amount of spatial information increases), the agreement between the maps increases dramatically and reaches 87.17%. Quantitative and spatialagreement and disagreement between the digital and traditional soil maps also equal 61% (M(m) = 61%) and 39%, respectively. The DSM accuracy can be increased to 87% (P (m) = 87%) compared to thetraditional soil map through spatial modification of cells(without changing quantitative information). Conclusion Evaluating the accuracy and validity of digital maps are considered to be an important and sensitive stepof research projects. Therefore, introducing more accurate indices is very important. According to the results of the present study, displayingquantitative and spatialagreement and disagreement in the form of a matrix and according to the different levels of quantitative and spatial information can be a new strategy to verify modeling methods. The method presented here not only introduces and interprets sources of (quantitative and spatial)error, but also provides information on the possible ways of reducing these errors. Thus, introducing the amount of error without any scientific interpretation cannot be useful for predicted maps. Unfortunately, researchers does not concur on how to report agreement and disagreement. However, it seems thatwhen it comes to explaining errors and finding a method to reduce such errors,the components of disagreement and its related parameters are more useful than agreement component and its indices. Therefore, it is recommended tointerpretdisagreement components before other components of agreement. The advantage of this method is that complex analyses can be reported in a simple form. Finally, this assessment and validation method is expected to be used in different studies as an appropriate and alternative method.
Geographic Data
Keyvan Mohammadzdeh; Sayyed Ahmad Hosseini; Mehdi Samadi; Ilia Laaliniyat; Masoud Rahimi
Abstract
Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms ...
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Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms affect the flow in water bodies, sediment transport, soil production, and climate at a local and regional scale. Identification and classification of landforms are among the most important purposes of geomorphological maps and also a fundamental step in the process of producing such maps. Geomorphologists have always been interested in achieving a proper and accurate classification of landforms in which their morphometric properties and construction processes are clearly indicated. The present study has attempted to develop a new method and identify the relationship between morphometry of landforms and surface processes using a multi-scale and object-based analysis. Extraction and classification of landforms are especially important in mountainous areas, which are considered to be dynamic due to their special physical and climatic conditions. These areas are often remote and sometimes unknown. Mountainous topography has also made them difficult to access. However, they are of great importance due to their impact on the macro-regional system. Because of this significant importance, Maku County was selected as the study area.
Materials and methods
Maku County is located in northwestern Iran (West Azerbaijan Province) which borders Qarasu River and Turkey in the north, Aras River and the Republic of Azerbaijan in the east, Turkey in the west, and Shut County in the south. This County is located between 44° 17' and 44° 52' east longitude and 39° 8' and 39° 46' north latitude. The present study takes advantage of satellite images (sentinel-2A) with a spatial resolution of 10 m, derivatives of DEM layer (slope, maximum curvature, and minimum curvature, profile and plan curvature) and object-based methods to identify and extract landforms of the study area precisely.
Discussion and results
The present study applies various functions and capabilities of OBIA techniques to extract landforms precisely. These functions include texture features (GLCM), average bands in the image, geometric information (shape, compression, density, and asymmetry), brightness index, terrain roughness index (TRI), maximum and minimum curvature, texture, and etc. The image segmentation scale was first optimized in the present study using ESP tools and objects of the image were created on three levels (9, 17, and 27 scales). In the next step, sample landforms were introduced, membership weights were calculated and defined for the classes in accordance with the fuzzy functions, and finally, 14 types of landforms were extracted using object-oriented analysis.
Conclusion
Fuzzy method includes boundary conditions, defines membership function, and constantly considers landform changes in class definition. Thus, it seems to be ideal for the purpose of the present study. The present study used two types of data (data derived from satellite imagery and DEM layer) along with OBIA approach to extract landforms. Classification of landforms based on fuzzy theory makes it possible to collect more comprehensive information from the earth's surface. Results indicate that fuzzy object-based method has classified landforms with an accuracy of 87% and a kappa index of 85%. Considering the resolution of the images applied in the present study, all features were extracted with an acceptable accuracy except for debris. This can be attributed to the fact that debris is usually accumulated in a small area on steep mountainsides, and thus remains hidden from satellites in nadir images. OBIA approach shows a high efficiency because it can combine spectral characteristics of various types of data (i.e. images and DEM data) and their derivatives while analyzing the shape of the segment, and size, texture and spatial distribution of segments based on their class and other neighboring segments.
Seyyed Ghasem Rostami; Hassan Emami
Abstract
Extended AbstractIntroductionVarious religions, including Islam, Judaism, Hinduism, and Chinese, have utilized lunar calendars for chronology. Methods for forecasting the first sighting of the new lunar crescent existed as early as the Babylonians, and maybe earlier. The Babylonians reasoned that the ...
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Extended AbstractIntroductionVarious religions, including Islam, Judaism, Hinduism, and Chinese, have utilized lunar calendars for chronology. Methods for forecasting the first sighting of the new lunar crescent existed as early as the Babylonians, and maybe earlier. The Babylonians reasoned that the lunar crescent can be seen with the naked eye under two conditions at sunset. First, the moon is older than 24 hours, and the moon's lag time is greater than 48 minutes. Fotheringham and Maunder developed standards for the seeing of the crescent moon at the beginning of the nineteenth century, and Bruin used his own criteria in 1977. Schaefer recently addressed crescent visibility extensively and integrated weather conditions into his work. Yallop then utilized the same database that Shaffer developed in 1997, but he overhauled some of the observation records extensively. Furthermore, many Muslim astronomers had developed their own criteria and published them in their literature. Despite the fact that different study organizations have created different criteria, there are still mistakes in the best time to forecast the crescent moon sighting. The use of old and conventional observations in modeling is one of these limitations, as is the use of non-uniform and heterogeneous observations. The Yallop criterion, for example, forecasts the visibility of the crescent moon for older crescents pessimistically. The Odeh criterion, on the other hand, forecasts young crescents with optimism. New Iranian criteria, such as the phase and altitude criteria (Mirsaeed criterion) and the triangular model (Iran criterion), have been presented in Iran. The goal of these criteria is to find the best timing between sunset and the first sighting of the crescent moon. Bruin, Schaefer, and Yallop have spent the last four decades developing the notion of the best moment. Because, after sunset, the sky darkens and the conditions for seeing the narrow crescent improve, while the moon approaches the horizon and the conditions for viewing the crescent moon worsen. Because the thickness of the atmosphere along the horizon is 3.7 times more than that of the zenith, the moonlight travels a greater distance than it did just a few minutes before. As a result, the sky towards the horizon is red or orange, and the crescent is not visible in this part of the sky. Material and Methods The objective of this study is to verify the rate of sky darkening in various regions and its influence on modeling the crescent visibility parameters of the moon, as well as to identify the best time to find out. To that end, 268 observational reports gathered from different divisions of Iran during the previous 20 years (2000-2021) were used to model the lunar crescent sighting. The proposed models are based not only on an examination of 20-year data to provide all effective tidal frequencies of the moon (the minimum period of moon’s notation motion is 18.61 years), but also on the use of sky-changing parameters such as local darkening rate and local sun occultation epoch time, the effect of the moon's distance from Earth, and the altitude of the moon from the horizon. The darkening rate of the sky factor was confirmed using various parameters and variables such as each point's geodetic latitude. Furthermore, unlike prior studies, the proposed models are developed using categorized observational reports with the least amount of error and can forecast the crescent sighting time in the presence of the sun (daylight time). The statistical correlation between the waiting time of each observation and the effective parameters in the lunar crescent visibility was studied in the first step. Following that, the parameters with the highest correlation values were chosen as the key quantities for modeling. After that, 17 alternative mathematical models with 2, 3, 4, and 5 parameters were implemented and tested, and the coefficients of the final two models (two and five parameter models) were determined using the least squares method as the suggested models. Results As a simple model, the two-parameter model can forecast crescent visibility with an average root-mean-square error (RMSE) of 4.7 minutes. The five-parameter model, on the other hand, was a more full and accurate model than the prior model, which was tested in two separate situations. They were evaluated over data for perigee distances of moon orbit (less than 375 thousand km) and observations for apogee distances of moon orbit (distance more than 390 thousand km) in the first and second cases, respectively. The findings of the 5-parameter model revealed that the first and second forms of the model had an average RMSE of 3.6 and 4.0 minutes to forecast the best time to see the crescent moon with the naked eye, respectively. Conclusion The results revealed that the best period to observe the crescent moon is from 32 minutes after sunset to 12 minutes earlier than sunset owing to the angular separation of the moon from the sun (10 to 20 degrees) and the difference in the altitude of the moon from the sun (5 to 20 degrees). When a result, as the local darkening epoch time increases, so does the waiting epoch time. In other words, the lunar crescent appears earlier in the northern part of Iran than in the southern half.
Reza Parhizcar isalu; Khalil Valizadeh Kamran; Bakhtiar Faizizadeh
Abstract
Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting ...
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Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting and exploring sources of this energy using modern and low cost methods –as a replacement for land surveying methods-can help planners and authorities working in the field of energy. In this regard, thermal remote sensing with a vast coverage of the earth’s surface, and the possibilityof calculating land surface temperature using satellite imagery plays an important role as a new economic tool.Mapping land surface temperature is a key point in achieving geothermal anomalies and different algorithms play an important role in land surface temperature estimation. Therefore, identifying potential sources of geothermal energyusingremotely sensed thermal data is a challenging and yet interesting subject.
Materials and Methods
The present study takes advantage of images received from OLI and TIRS sensors (Landsat 8) to estimate land surface temperature, analyze thermal anomalies, and identify areas with potential geothermal resources in Meshkinshahr.The images were retrieved fromUSGSin Geo TIFF format.Envi 5.3, eCognition 9.1, MATLAB and ArcMap 10.4.1 were used to prepare, process and analyze the images.Moreover, meteorological data received fromMeshkinshahr station was collected from the General Department and Meteorological Center of Ardabil Provincewith the aim of identifying the optimal algorithm for calculation ofland surface temperature. Data wascollected for a one-day period (31/08/2017), i.e. the same day Landsat 8 passed over the areaunder study.
Results and Discussion
The present study sought to identify areas with potential geothermal resources using thermal remote sensing and a combination of surface temperature and thermal anomaly models. In order to calculate thermal anomaly, an observational thermal image is required, which is in fact the same land surface temperature calculated using Split Window and Mono Window algorithmsfor the image received from the satellite thermal band at the moment of collecting images. It should be noted that the land surface temperature calculated with these algorithms was evaluated using statistical data recorded in the temperature monitoring station. Results indicated higher accuracy of Split Window algorithm (3 ° C difference). Since, temperature obtained from this algorithm was more consistent with the actual temperature, its results were used as the observational thermal image.A thermal model was also defined to model factors responsible for heat variation from one pixel to another one. These two images were calculated and subtracted to reach the thermal anomaly image.In order to identify thermal anomalies caused by undergroundfactors heating the earthsurface, other factors responsible for increasing/decreasinglandsurfacetemperature should be normalized in the image. Thus, the effect of parameters such as solar energy, environmental degradation and evaporation on land surface temperature obtained from split window algorithm was investigated and finally, areas with heat anomalies and evidences indicating the presence of geothermal resources around themwere selected as areas with potential geothermal resources.Results indicate that inthe area surroundingSabalanmountains,two regions with 5.5 and 10.05 hectares in the northern and northeastern parts of Moyelvillage, a1.4 hectares area in the southwestern part of Qutursouli Spa, and the southern part of the Qinrjah Spa with an area of 1.1 hectare had potentialgeothermal resources and a high potential for exploration of geothermal resources.
Conclusion
The presence of hot springs, a geothermal power plant and other evidences shows that Ardabil Province and especially Meshkinshahr city has the potential for geothermal energy production as one of the major sources of new and environmentally friendly energies.However, no effective studies have been performed to identify these resources using modern and low-cost methods including thermal remote sensing.Therefore, the present study for the first time took advantage ofGIS and remote sensingto identify areas appropriate for geothermal energy extraction inMeshkinshahr city and concluded that remote sensing studies on Landsat 8 satellite images have a high efficiency for identifying areas with potential geothermal resources. Thus, areas identified in the present study have a strong spatial correlation with the geothermal evidences founded in the region.
Yousef Ebadi; Akram Eftekhary; Hekmatollah Mohammad Khanlu; Majid Fakhri
Abstract
Introduction As an important type of precipitation, snow is especially important in the hydrological cycle. This importance can be examined and analyzed from several aspects such as water supply in other seasons. The most important aspect is the possibility of creating hazards for human beings and human ...
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Introduction As an important type of precipitation, snow is especially important in the hydrological cycle. This importance can be examined and analyzed from several aspects such as water supply in other seasons. The most important aspect is the possibility of creating hazards for human beings and human infrastructure (snow avalanches, floods during seasonsof snowmelt). Therefore, it is necessary to study the snow phenomenon and its covered surfaces in winter. Monitoring the changes in this important climatic phenomenon has always been considered important by researchers and planners. Remote sensing methods have revolutionized the field of natural environment monitoring since their inception. Snow depth is an example of what can be monitored and evaluated by remotely sensed data and techniques. Materials & Methods The present study seeks to evaluate the efficiency of several important remote sensing indices in monitoring snow depth, andalso to introduce and evaluate a proposed spectral index. To reach this aim, satellite images of Landsat 8 and Sentinel 2 have been used. These images were received from the relevant portal and used to calculate snow indicesafterinitial corrections. Four spectral indices were usedto extract snow covered surfaces. These indices include: NDSI - S3 - NDSII - SWI. These indices are based on reflection from snow covered surfaces in light reflection and absorption spectra of snow covered surfaces.Light reflection from snow covered surfaces in the visible spectra and absorption in the short infrared spectrum allow automatic detection and extraction of snow covered surfacesin remote sensing multispectral images. The above mentioned indices have the ability to extract snow, but they fail to differentiatebetween snow and other related phenomena such as water (in the absorption band) and light-color salt marshes (in the reflection band) and thus, similarity of the spectra occurs. This spectral mixing which occurs due to the similarity of the reflections, cannot be eliminated even when threshold limits are defined. Thus, the extracted snow cover includes not only snow, but also other similar zones. To solve this problem and extract snow covered surfaces correctly,a new index is presented in this paper based on principal component analysis (PCA) and the first component of the set, and short wave infrared (SWIR) spectrum reflection.Using the first component of the set with the highest variance makes the difference between reflectance of snow and similar phenomena visible and thus, solves the issue of spectral mixing to a very large extent. The proposed new index called PCSWIRI is also evaluated and validated along with 4 other indices in the present paper. Results & Discussion Spectral indices introduced in the previous section were examined and evaluatedusing 7 sets of images (4 Landsat images and 3 sentinel 2images) captured in different days of winter from the main study area (Lake Urmia in the northwest) and two other study areas. The results indicate efficiency of the proposed index in the extractionof snow covered surfaces. The proposed index has improved the accuracy of snow cover extractionin the whole collection of images. This increased accuracy has been confirmed withstatistical evaluation criteria, such as kappa coefficient, overall accuracy and in the visual review of indices(comparing to the composition of the original image). The main study area includes Lake Urmia, an important geographic feature containing water and salt and a mixture of the two, which makes its spectrum similar to snow. This lake is incorrectly identified by other indices as a snow covered surface. Like the main study area, the first study and assessment area contains salt covered zones (salt lake). Despite the spectral similarity between snow and salt,the proposed index has been able to distinguish between this phenomena (in both regions) and snow and to extract only realsnow covered surfaces. In addition, visual review of existing water bodies (Dam Lake) and 5 evaluated indicesindicates higher accuracy of the proposed index. In order to automate the process of calculation in the proposed spectral indices, a software was also providedbased on MatLAB. Conclusion The findings of the present study indicates higher accuracy and efficiency of the proposed index (PCSWIRI) for snow cover extraction. Snow cover maps are very useful in various hydrological, climatic, precipitation-runoff modeling studies, and etc. Therefore, increasing the accuracy of snow cover maps is of great importance and results inimprovedaccuracy and reliability of modeling processes.
Hamid Bayat Barooni; Mojtaba Ezam; Abbasali Aliakbri Bidokhti; Masoud Torabi Azad
Abstract
Extended AbstractIntroductionThe Caspian Seaclassed as the world’s largest lake, lies between Europe and South Western Asia (between 45.43°to 54.20°longitude east and 36.33°to 47.07°latitude north). The Caspian Sea level has changed widely over time. These changes have occurred ...
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Extended AbstractIntroductionThe Caspian Seaclassed as the world’s largest lake, lies between Europe and South Western Asia (between 45.43°to 54.20°longitude east and 36.33°to 47.07°latitude north). The Caspian Sea level has changed widely over time. These changes have occurred gradually and incrementally leading to landward and seaward migration of the coastline. Therefore, it is very important to study and predict futurechanges of the Caspian Seacoastline. Today, experts in atmospheric and marine physics from all around the world consider the Caspian Sea as a natural dynamic model of oscillatory processes in watersurface.High annual rate of water level changeshas made oscillatory processes of this lake different from those of oceans. With the advent of satellite altimetry in 1973, highly accuratemonitoring of sea level has been made possible. The present study seeks to investigate the trend of dynamic topography changes in the Caspian Sea and determine the effects of changes in thesea level on the southern coastline. MethodologyVarious sets of satellite data have been used in the present study. Long-term average ofglobal sea level data was obtained from MSS_CNES.CLS15. Covering a period of 20 years (1993 to 2012),these datasets are produced based on information received from different satellitealtimeters. Mean sea level is calculated foreach point of the network created atthe Caspian Sea (with a distance of 0.25°). The correlation between altimetry data and sea level changes is calculated using gravity changes. Investigating these changes leads us to equipotentialgeomagnetic surfaces called geoid. Geoid is an equilibrium surface of the Earth’s gravitational field showingapproximately the average leveloffree water. Mean sea level does not coincide with geoid and theirdifference at any given point is called absolute dynamic topography. In this study, GOCE model was used to calculate geoid value at every point of the network created at 1′distance from the Caspian Sea. Aviso Altimetry dataset was used to obtain sea level anomaly data. Mean sea level was obtained by adding dynamic topography mean to geoid height.In order to obtain average dynamic sea topography,MDT values were calculated for all the points created in the Caspian Sea. Afterwards, sea level anomaly was added to the mean dynamic sea topography to obtain absolute dynamic topography. Daily SLA data of the Caspian Sea were extracted with a resolution of 0.25° from AVISO and CNES.CLS15 SLA ultrasound satellites and interpolated at the specific location created on the Caspian Seanetwork with a resolutionof 1′.Aabsolute dynamic topography were calculated on a daily basis. These calculations were repeated for a 20 year period (7305 days) from 1993 to 2012 using MATLAB and in this way, a complete database including the Caspian Sea surface topographic datawas obtained for this period. ResultFollowing the calculation of the mean ADT data obtained fromall over the Caspian Sea, time series of daily Sea Level Fluctuations were extracted. These time series indicated that despite the positive trend of the Caspian Sea water level changes in both 1993-1995 and 2000-2005 periods, the overall trend of water level changes over the 20-year period is negative. Moreover, examining sea level changes over this 20-year period shows thatthe highest altitude (-25.914m) has occurred on June 1st, 1995, while the lowest altitude (-27.20) has occurred on November 26th, 2012. In addition, March 20th, 2002 and June 29th, 2005 have experienced two abrupt changes of -26.843m and -26.26m in the time series. In this time series, an upward trend is observed until June 1st, 1995, while a decreasing trend of 93 cmis observed from March 20th, 2002 over a period of approximately 7 years. Between March 20th, 2002 to June 29th, 2005 (a period of approximately 3 years), we observe a decreasing trend of 61 cm. Over a 7-year period (until late 2012), we also observe a 97cm decreasing trend. Altimetry data received from three stations located in the Caspian Sea are used to verify the results obtained from the above mentioned method. Examination of these values and comparing them with the values obtained from the method used in the study confirms the resulting trend. In orderto investigate the shoreline changes caused by changesin the Caspian Sea water level,the southern shoreline of the Sea is mapped based on the obtained trend.Days with the highest and lowest sea level over the 20-year study period were extracted from satellite images. Mapping and overlayingthe coastlines based on the information related to these two time series, changes have been observedthroughthe Caspian coastlines. However, these changes are more significant in the South Eastern Gorgan Bay (Miankale) due to the smaller slope of the South Eastern Caspian Sea compared to other areas of the Sea. ConclusionInvestigating changes of the Caspian Sea level shows anegativetrend of changes, with a -1.287 m difference between thehighest and lowest altitudes. Of course, the trend has not always been negative over these years. For an instance, a positive trend was observed from 1993 to1995 and from 2000 to 2005. Results indicate that the Caspian Sea dynamics of water level fluctuations changes rapidly and long-term prediction of the Caspian Sea water level cannot be very accurate. However, it can be concluded that the Caspian water level changes will continue its decreasing trend in the future. This negative trend of sea level changes has resulted in the seaward migration of the Caspian coastline, which has began in 1995 and still is present today. This has resulted in drying up of more than 12850 hectares of the GorganGulf.
Hadi Fadaei; Mahdi Modiri
Abstract
Extended Abstract
Introduction
Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types ...
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Extended Abstract
Introduction
Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types of resolution in digital ground elevation models.
üArea resolution: The dimensions of the length and width of each cell in the pixel grid is a digital elevation model that shows the minimum dimensions of the topographic features taken on the ground.
ü Height resolution: represents the minimum elevation dimensions that the digital elevation model is able to display. For example, in the digital model of ground elevation with a resolution of 30 meters, elevation features less than 30 meters are not visible.
The digital elevation model can be prepared for a region with different accuracy. The high accuracy of the digital elevation map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1- Forward 2- Vertical 3- Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the elevation of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the digital elevation model generated by the PRISM sensor of ALOS satellite in comparison with the digital elevation model of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan).
Method
The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of digital elevation model from PRISM sensor stereo images from Alves satellite and its evaluation is with digital model extracted from ASTER image. The digital SRTM model has a spatial resolution of 90meters, the digital ASTER model has a spatial resolution of 15 meters and the digital elevation model obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, elevation control points using Google Earth and GPS have been examined. The algorithms used in this method to extract elevation information are the same as the algorithms used in the photogrammetric method. Elevation digital models are made from satellite images taken in pairs. The accuracy of digital elevation models of this method is perfectly proportional to the scale or resolution of satellite images.
Results & Discussion
In this study, we evaluated the digital elevation model from stereo satellite images of ALOS/PRISM satellite and compared it with the digital model of ASTER elevation and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a digital elevation model prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with digital models extracted from stereo ASTER satellite and digital models extracted from SRTM. The results obtained from the digital elevation model are the accuracy of the digital elevation model produced by the pair of ASTER satellite images using a correlation between the two images of 0.47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the digital model is less than the size of pixels, i.e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for digital model of elevation extracted from ASTER and PRISM and ground observations are 7.46, 8.77, 3.66 and 6.8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1.9 meters and in the transverse direction of 2.3 meters and the distance between the pixels of the digital model is 3 meters high. Therefore, the accuracy of the digital model extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision digital elevation model in all borders of the country, which uses a digital elevation model produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images.
Conclusion
The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The elevation accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the digital model of SRTM elevation in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the digital elevation model produced from the stereo images of the PRISM sensor is higher than the digital elevation and SRTM digital models, i.e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the digital elevation model with accuracy.
Milad Alizadeh Badresh; Farhad Hosseinali
Abstract
Extended Abstract
Introduction
Cultivation Pattern is a roadmap that shows which, how much, when, and where crops should be cultivated given the constraints and available resources. Cultivation pattern program determines appropriate crop types in accordance with the climatic condition of the province ...
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Extended Abstract
Introduction
Cultivation Pattern is a roadmap that shows which, how much, when, and where crops should be cultivated given the constraints and available resources. Cultivation pattern program determines appropriate crop types in accordance with the climatic condition of the province and thus ensures the sustainability of agricultural products, food security, and optimal utilization of resources, capabilities and potentials of each region. Review of the related literature indicates that AHP and TOPSIS methods are among the most widely used methods in decision making and prioritization. Moreover, previous studies have shown that AHP method is suitable for qualitative data and TOPSIS method is suitable for quantitative data, whereas both quantitative and qualitative factors are involved in determining the cultivation pattern. Therefore, the present study has utilized a larger number of criteria (nine criteria), and combined AHP and TOPSIS models in an attempt to make use of their strengths and avoid their weaknesses. Linear programming was also used with four scenarios. In one of the scenarios, two lowest ranking crops in TOPSIS method were eliminated. The present study has innovatively utilized these models and a larger number of criteria simultaneously to determine the cultivation pattern. It also has precisely identified the appropriate crop for each plot of land using SWOT tables.
Materials & Methods
The case study was located in Qeyghaj plain in west Azerbaijan province. In accordance with the geographical location and climate, wheat, barley, alfalfa, sugar beet, rapeseed, potato, maize and fodder corn are mostly cultivated in the area which have been considered as alternatives for cultivation in each plot of the present study. The present study has begun with evaluating the slope and aspect of each cultivation plot. Then, crops are ranked and optimal crops are selected based on various criteria and using a combination of AHP and TOPSIS models and different decision matrices. Afterward, the maximum and minimum appropriate volume of crop production is determined using linear programming in accordance with the maximum profit. Finally, the most suitable crops for each land parcel are determined using SWOT tables.
The present study has proposed a multicriteria decision model which includes the strong points of AHP and TOPSIS models and avoids their weaknesses. In order words, relative weights were obtained from AHP pairwise comparisons and also the compatibility index was evaluated using AHP model while crop alternatives were ranked using TOPSIS model. The hierarchical structure included the goal, nine criteria used to evaluate the strategies and eight strategies (options).
Matrices used in pairwise comparisons were all obtained from experts' opinions. These include comparisons made to determine the weight of each criteria to be used in the TOPSIS model, as well as pairwise comparisons made between options which could not be quantitatively compared. Then, the general structure of the hierarchical model was developed in Superdecision software and the final weights, compatibility index of each matrix and quantity of each product were obtained based on each of the indicators. The values were then entered into the TOPSIS model and used to rank the crops, compare different options and select the best crop.
Results and Discussions
In the first step, a slope map was produced for the study area using digital elevation model based on which an aspect map was also produced. In accordance with these maps, the physiological suitability of the study area for the cultivation of eight crop types was evaluated. Results indicate that the study area is physiologically very suitable for cultivation of alfalfa, suitable for wheat, barley and canola and fairly suitable for the other four remaining crops.
Then, pairs were compared in hierarchical analysis using expert opinions and the weights of criteria and crops were obtained. Then, weights were assigned to each alternative (crops) and decision criterion (nine selected criteria) using the TOPSIS model and more appropriate products were selected. A decision matrix was first created in TOPSIS. Some criteria such as economic index were initialized directly in accordance with the available quantitative values whilst the values of some other criteria (such as temperature whose quantitative values cannot be obtained) were initialized using the results of AHP.
In the next steps, a weighted normalized matrix was developed and positive and negative ideals were found. The distance between positive and negative ideals was calculated and then the ideal solutions were obtained. Finally, the score obtained by each alternative or similarity index was calculated. The closer similarity index is to one, the superior that alternative will be. Linear programming is a method in mathematics that finds the minimum or maximum value of a linear function on a polygon. The present study seeks to reach maximum profit under various restrictions such as water restriction, restrictions on area under cultivation and maximum and minimum amount of cultivated crop. Water restriction included all surface and subsurface resources for crop cultivation. Crop coefficients were defined as the need for crop irrigation. Water constraints included the constraints assigned to allocated water in spring, summer, autumn and total amount of allocated water.
Three scenarios were developed with or without the previously mentioned constraints. Then the goal function was changed in accordance with the MOTAD method and another scenario was developed. The scenarios are explained as follows:
Scenario 1 (Without any restrictions on the minimum and maximum crop yield): In this case, the goal was reaching the maximum profit and the restriction included the lowest amount of water consumption, regardless of the requirements in the study area. In this scenario, variables x1 (wheat), x5 (Canola) and x8 (fodder corn) were included in the cultivation pattern. Consequently, farmers' income was maximized and the amount of water consumption was reduced. However, obtained results were not acceptable in accordance with the regional and national policies since cultivation of most crop types will thus be stopped.
Scenario 2 (locally acceptable size and local farming customs and the restrictions assigned by the agriculture office): the present scenario seeks to maximize profit, satisfy requirements of the area and achieve the goals of the agriculture office. All crops are included in the cultivation pattern. Therefore, minimum and maximum cultivation restrictions have been used in addition to water and land restrictions.
Scenario 3 (not cultivating some water-intensive crops): As previously mentioned, Poldasht agriculture office has introduced reduced cultivation of some low yielding crops or even stopping the cultivation of such crop types as one of its main goals. Corn and potato are highly water -intensive with a low yield in the study area and thus gain one of the lowest ranks. Therefore, potato and corn were removed to determine the cultivation pattern of the region in their absence.
Scenario 4 (MOTAD approach): MOTAD is a linear programming approach aiming to maximize the profit whose objective function equals the sum of deviations between total gross income and the expected income based on the average gross income of the sample. Linear programming with MOTAD requires having access to income gained from each crop type in previous years. Restrictions such as fund and manpower restriction must also be considered. The statistical period used in MOTAD approach starts in 2011 and lasts till 2016.
Income values in MOTAD approach lead to a constraint relation. Just as the previous scenarios, water and land constraints are considered in this approach and fertilizers and pesticides restrictions have not been taken into account.
Conclusions
Based on the collected information, available parameters, SWOT analytical model and tables developed for each field, a suitable crop was selected for each farm (parcel). Accordingly, 112.3 hectares was identified as suitable for the cultivation of wheat, 59.9 hectares for barley, 32.1 hectares for alfalfa, 37.6 hectares for sugar beet, 85.7 hectares for Canola, 15.5 hectares for potato, 13.2 hectares for Maize and 63.7 hectares for fodder corn. In this case, the resulting profit equaled 23, 503,410,000 Rials and the water consumption equaled 2,542,293.8 cubic meters which shows 2,052,120,000 Rials increase in profit and 90,770.6 cubic meters decrease in water consumption as compared to the present cultivation pattern.
Comparing the profit and water consumption in each of the five models and the current cultivation pattern, it can be concluded that the pattern obtained from the SWOT analytical model is more feasible since it includes various parameters and particularly farmers' opinions.
Mojtaba Yamani; Arefeh Shabanieraghi; Seyed Mohammad Zamanzadeh; Abolghasem Goorabi; Nafiseh Ashtari
Abstract
Extended Abstract
Introduction
Climate changesare considered to be the most important event of the Quaternary period largely reflected in the geomorphology and sedimentology of the period.Paleogeomorphology helps us to understand past climate changes and predict future changes. Depending on the ...
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Extended Abstract
Introduction
Climate changesare considered to be the most important event of the Quaternary period largely reflected in the geomorphology and sedimentology of the period.Paleogeomorphology helps us to understand past climate changes and predict future changes. Depending on the Quaternary periods, closed pitlakes are called cold or rainy period lakes.Some of these lakes have completely dried up, others are temporary lakes that change into playas in the dry season, and others have been larger in the past. Researchers can identify pluvial lakes in today’s arid regions, because of the variety of factors and complex processes involved in their formation.Mighan Playa is located in the central and southwestern areas of Markazi province. It includes seasonal and saline Tozlogol Lake, and alluvial plains.
Methodology
The present study used evidences of playa lake sediments as well as geomorphological evidences(lake terrace) to investigate the extent of MighanLake in Quaternary period. Data included datacollectedfrom library sources, statistical data, field surveys, sedimentary samples, sedimentary evidences, climatic data, remote sensing data received from Landsat TM satellite, ETM, and SRTM digital elevation models(SRTM 90 meters, and Dem10 meters).Initially, previous studies and environmental characteristics of the area were analyzed. Then, lake terracewas investigated to find geomorphic evidences of Pluvial Lakes in Quaternary period. To do so, probable ranges of the lake Terrace were determined using satellite imagery, geological maps, and elevation data of digital models. Probable area was divided into several distinct zones, and finally an area was identified in the western part of the lake and based on the elevation of this zone, the extent of the lake catchment in Quaternary period was determined. During fieldwork, samples were collected from the mountain slope line toward the Playa and lake shore, and then granulometrytests were performed on the 14 collected samples to determine the amount and type of sediments.Sedimentary and graphical analysis were also performed based on Folk classification. The percentage of clay and sand in the new samples collected from the region containing this mountainous area, lake coast and deeper parts of the lake were determined and attributed to past sediments. In this way, the information could be used to determine the extension of lake sedimentsin the past.Based on sedimentary logs (Arak Groundwater Studies Report, Central Water department of Markzani Province), sedimentology studies and percentages (clay-sand-gravel) of present-day samples collected from deep sections of Playa andelevated areas of sediment pits were interpolated in GIS environment and a map of the lake extension in the Mighan catchment areawas prepared.Subsequently based onpaleogeographic studies, paleontological climate of the area and sedimentation rate calculated by Pedrami in 1993, a map was produced to show the extent of sediments and the lake progressions and regressions in the past.
Discussion
The stratigraphic and sedimentary evidences of logs in the margins of Mighanpit indicates changes in wet and dry periods. Type and size of sediments reflect the climatic conditions in each period, while high percentage of clay sediments reflects lake conditions. Paleontological sedimentological maps of the area show that the clay sediments were more concentrated in the southwestern, western and northwestern regions. Uplift of the Talkhab fault in the northeastern regionhas resulted in tectonic asymmetry of the pitand concentration of sediments in the western and southern parts. According to Krinsley, Bubeck, Pedrami and etc. Lake Mighan has been larger in the past. However, none of these researchers have determined the extent of lake water in the past. In this study, the extent of the lake was determined by reconstruction of clay sediments and using geomorphological evidencescollected from the lake shorelines (lake terrace) near Mighan village (Mashhad). Results indicated a height of 15 m in Quaternary period.
Conclusion
Sedimentary and geomorphologic evidences indicated that compared to the present playa level, the Lake fluvial was more permanent and vast in the past, but this extension differs in different directions and shows significant differences due to the tectonic location of the area.
Mojdeh Ebrahimikia; Ali HosseiniNaveh
Abstract
Extended Abstract
Introduction
Today, orthophotos are one of the most widely used products in the field of spatial information, and they are often created from aerial or satellite images, so paying attention to their accuracy and quality is essential in order to have both geometric and radiometric ...
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Extended Abstract
Introduction
Today, orthophotos are one of the most widely used products in the field of spatial information, and they are often created from aerial or satellite images, so paying attention to their accuracy and quality is essential in order to have both geometric and radiometric information. The point clouds and the digital surface model used to build them are the two most important aspects that affect the quality of these images. On true orthophotos, there are some distortions on the structural edges of buildings, which is due to defects in these areas in the point cloud used in the digital surface model. This problem is greater for orthophotos that have been made from UAV images in urban areas because of their lower altitude. Additionally, because of the presence of occluded regions and radiometric changes between overlapping images, approaches for creating point clouds based on image matching are unable to produce complete point clouds and contain flaws, particularly towards the outer edges of objects with high height differences. Before interpolation of the point cloud and preparation of the digital surface model and then preparation of orthophotos of it, it is necessary to complete the point cloud in areas with defects. Some studies have shown that adding edge points has the effect of decreasing the distortion of true orthophotos. In this study, a new method for completing point clouds is proposed and explained in detail.
Materials & Methods
In this study, the imaging of the Yazd region was done with a Phantom 4 drone equipped with a DJI camera. The SfM algorithm has been used to calibrate the camera, estimate the internal and external camera parameters, and produce images without distortion and low-density point clouds, and SGM has been used to produce dense point clouds. In the proposed method, the purpose is to complete the incomplete points of the building. Assuming that the points on the roof of each building are predetermined, without noise, and have incomplete edges, these point clouds were used to complete them, and then added to the existing point clouds in their actual coordinates. The final point cloud was used in the preparation of digital models to produce irregular and then regular surfaces and in the preparation of true orthophotos using camera parameters and undistorted images. One of the images with buildings marked as numbers 1 to 4 was selected to perform tests and prepare orthophotos.
Results & Discussion
The lack of structural edge points on any roof, which is the distance between severe height differences between levels, causes the greatest amount of distortion on the edge of the roof and around it. Adding these points with edge line recognition and reconstruction algorithms to the point cloud improves the resulting digital surface model. Since the quality and accuracy of the digital elevation model directly affects the resulting orthophoto, using a more accurate digital elevation model improves these images. These point clouds have been modified in the proposed method, and quantitative and qualitative comparisons demonstrate improved results in eliminating distortion in the majority of the buildings studied. The reasons for the superiority of the proposed method over previous methods include determining and calculating a more complete and precise form of the roof of each building and considering the outermost edges of the buildings.
Conclusion
The biggest amount of distortion on the edge of the roofs and their surroundings is caused by the lack of points on the structural edge of each roof, which is the boundary between dramatic height variations between the levels. By integrating these points with algorithms for recognizing and repairing edge lines, the resulting digital elevation model will be improved. This study presented a new method for completing the point cloud that enhanced the quality of true orthophoto edges, which was tested on a large number of building images and compared to the results of existing methods. In addition to implementing a new method for improving point clouds for orthophoto creation, the degree of distortion on the selected edge of four buildings has been greatly reduced when compared to the previous method. The success of the results with the latest proposed method of true orthophoto enhancement indicates an improvement of about 62% and 55% in the distortion decreasing of the structural edges and maintaining their coordinate accuracy.
The proposed method did not uniformly reduce the distortions at the structural edges, and future advanced models could possibly improve it.
Faeze Shoja; Mahmood Khosravi; Ali Akbar Shamsipour
Abstract
Introduction
North Indian Ocean (NIO), which includes the Bay of Bengal(BoB) and the Arabian Sea (AS),is one of the tropical oceans and therefore, prone to the formation of the tropical cyclones (TC). On a global scale, approximately 7% of the tropical cyclones are formed in this area. Studies ...
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Introduction
North Indian Ocean (NIO), which includes the Bay of Bengal(BoB) and the Arabian Sea (AS),is one of the tropical oceans and therefore, prone to the formation of the tropical cyclones (TC). On a global scale, approximately 7% of the tropical cyclones are formed in this area. Studies indicate an increase in the frequency of remarkably powerful cyclonesin the Arabian Sea in recent years.In the period between May 16 and 27, 2018, two very strong cyclones called Sagar and Mekunu, affected southwestern and western regions of the Arabian Sea. The present study aims to determine the role of large-scale environmental parameters affecting the tropical cyclogenesis during the life period of these two storms.
Data and Methodology
The current study collects data, including the location of cyclones occurrence, tropical cyclone track, the minimum sea level pressure, and maximum wind speed from the report prepared by the India Meteorological Department. Requiredoceanic and atmospheric parameters, including U and V components of wind (at 200 and 850 hPa levels), relative humidity (at 600 hPa level), sea surface temperature (SST), sea level pressure (SLP), air temperature, pressure, and specific humidity at 23 levels of pressure (levels of 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 700, 775, 850, 925, 1000 hPa) were also extracted from the reanalyzed dataof ECMWF (European Centre for Medium-Range Weather Forecasts)on a daily basis and with the spatial resolution of 0.5°longitude and 0.5° latitude. In order to achieve the goal of the research, first, the values of large-scale environmental parametersplaying a crucial role in TC formation, including absolute vorticity (at 850 hPa level), vertical wind shear, potential intensity, and relative humidity, were calculatedusingGRADS and MATLAB. The related maps were also plotted and analyzed. Then, the genesis potential index of days before the storms occurrence wascalculated for different regions of the Arabian Sea, and the likely areas for cyclone occurrence were predicted based on the index. Finally, some anomaly maps were produced for the atmospheric parameters affecting cyclogenesis, and changes in these parameters were examined in the life period of the storms as compared to the normal climatological conditions.
Results and Discussion
Results indicated that the storms track coincided with the regions in which maximum relative humidity and maximum absolute vorticity occur.During cycloneSagar, relative humidity in areas affected by the cyclone reached over 80%. During the formation period ofcycloneMekunu,maximum relative humidity was observed in the area between 0°N to 10°N and 50°E to 80°E- the area dominated byMekunucyclone. Spatial distribution of environmental variables, such as temperature, sea level pressure, and vertical wind shear indicates that the favorable values of these parameters have been concentrated in the areas affected by the cyclones in all three phases of their formation, intensification, and dissipation.
Although, vertical wind shear did not considerably change in different parts of the Arabian Seaduring the life cycle of Sagar, its minimum levelwas reported in the Gulf of Aden. Similarly, with the increase in wind speed duringcyclone Mekunu on May 25, the minimum vertical wind shear moved to the northern latitudes and its value ranged from 6 to 12 m/s in the western Arabian Sea. The maximum absolute vorticity is observed in the Gulf of Aden during the life cycle of Sagarcyclone, and these conditions continue until cyclone’s dissipation. Also duringcycloneMekunu, maximum absolute vorticity was observed in the areas affected by thecyclone. Affected by the maximum sea surface temperature, potential intensity indexreached a value of more than 70 m/s in regions affected by the storms (20-degree north latitude). Spatial distribution of GPI values collected from the days before the cyclones occurrence indicated that there is a strong correlation between the spatial distribution of this index and the occurrence of cyclones. Furthermore, the storm track also coincided with the increase in this index,so that highest GPI values were concentrated in areas dominated by cyclones Sagar and Mekunu.Analysis of anomaly maps revealed that compared to the long-term average,sea surface temperature and relative humidity have increased in the area affected by tropical cyclones and sea level pressure and vertical wind shear have decreased.
Conclusion
Findings of the present research indicated that dynamic and thermodynamic parameters have provided the most favorable cyclogenesis conditions in the areas affected by the storms. In other words, the cyclone had moved to the direction in whichenvironmental parametersexhibited the best threshold levels. Therefore, it is possible to predict the occurrence of tropical cyclones in the northern latitudes of the Arabian Sea, especially in the Gulf of Oman,based on the changes in large-scale environmental parameters in different parts of the Arabian Sea.
Geographic Data
Mojtaba Ghadiri Masoum; Hamid Afshari
Abstract
Extended AbstractIntroductionNowadays, tourism is widely accepted as a fundamental basis of development. As a sector of economy, tourism is considered to be one of the most important activities of contemporary human beings, which not only makes dramatic changes to the landscape, and political, economic, ...
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Extended AbstractIntroductionNowadays, tourism is widely accepted as a fundamental basis of development. As a sector of economy, tourism is considered to be one of the most important activities of contemporary human beings, which not only makes dramatic changes to the landscape, and political, economic, and cultural condition, but also transforms lifestyle of many individuals. The contemporary world considers tourism as one of the most important sectors of the tertiary industry which affects job creation and income generation, results in significant economic growth, and consequently provides the prerequisites for sustainable development of different societies. Iran is among the top 10 countries of the world in terms of tourist attractions, possessing many sites with potential attractions. Thus, tourism can be considered as an effective tool in dealing with economic problems of the country. As the basis of sustainable development, tourism can solve some problems of the country and thus, development of its infrastructure results in optimal allocation of available resources. The present study seeks to investigate the overall condition of tourism infrastructure in Markazi province. Previous studies in France, Austria, Switzerland, the United Kingdom, Ireland, Thailand and Japan indicate that their tourism sector has developed rapidly and now aids other sectors of the economy. Therefore, a comprehensive analysis of necessary infrastructure for the development of this industry can result in a more dynamic rural economy. Materials & MethodsThis applied study has a descriptive-comparative design and its study area includes all counties of Markazi Province. Library method and questionnaires were used for data collection. Statistical data and information were collected from the General Directorate of Cultural Heritage, Tourism and Handicrafts of Markazi Province and the statistical yearbook (2015) of this province. In accordance with Delfi method and targeted sampling, related indices were sent to 17 rural development experts and specialists via Email. It should be noted that some of these experts had previous experience in tourism. Finally, 10 completed questionnaires were received. PROMETHEE multifunctional decision-making model was also used to determine the overall condition of counties in Markazi Province in relation to tourism infrastructure.Since the present study seeks to classify counties in Markazi province, the first function of this technique has been used. An appropriate weight is first assigned to each of the 20 indices of tourism infrastructure using Delphi method. Then, these weights are evaluated and measured along with the value of each component and option in Visual PROMETHEE software. Structural equation modeling was used to investigate relations between variables more comprehensively. SPSS 26 and Smart PLS 3 software were also used to analyze the data. Results & DiscussionFindings indicate that Arak with a value of 0.7739 has ranked first among the counties. Several factors can be the reason: First, as the capital of the province, Arak possesses better facilities, larger population, etc. Second, as the main access road connecting neighboring provinces, Arak has developed more than other counties. With a value of 0.4673, Saveh has the second rank. Saveh also contains the access road connecting some of neighboring provinces and is located near Tehran. Thus, a strong industrial town has developed in this county attracting many workers with different ethnicities seeking employment and income. Due to these factors, relatively good facilities have developed in Saveh. With a value of 0.3536, Shazand has ranked third. Due to its proximity to Arak (the capital of the province), this county has attracted large industries such as petrochemical industry along with suitable facilities and infrastructure. Khomein (0.3166), Delijan (0.0168), Mahalat (-0.1023), Tafresh (-0.1634), Khandab (-0.3002), Zarandieh (-0.3266), Farahan (-0.3320), Ashtian (-0.3514) and Komijan (-0.3523) are next in rank.Analyzing the relationships between variables indicates that service-related components (0.279) and transportation-related components (0.096) have the most powerful direct influence on the level of development and other variables are next in rank. ConclusionFindings of the present study and previous studies indicate that centrality and population can be considered as influential factors resulting in easier access to desirable and appropriate facilities in different countries of the world. However, such a difference is not observed between different regions in developed countries due to their integrated development. Developing countries such as Iran lack such an integrated development environment and thus, the condition in provincial capitals is much more different from other counties. As indicated in the present study, the level of development in Arak was much higher than other counties of Markazi province. Therefore, an appropriate plan is required for other counties to achieve sustainable development, and especially sustainable tourism development.
Abolfazl Ghanbari; Sadra Karimzadeh; Sedighe Taraneh
Abstract
Extended AbstractIntroductionDespite higher standards of living in urban areas, rapid growth of urbanization has caused some problems such as development of dense and unplanned residential areas, environmental pollution, lack of access to services and amenities, increased gap between social classes and ...
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Extended AbstractIntroductionDespite higher standards of living in urban areas, rapid growth of urbanization has caused some problems such as development of dense and unplanned residential areas, environmental pollution, lack of access to services and amenities, increased gap between social classes and etc. Manifested as severe differences between living standards in different parts of cities, these affect the quality of urban life. Quality of life is considered to be one of the most important dimensions of sustainable urban development. The desire to improve the quality of life in a particular space, for a particular individual or group is one of the main concerns of planners. Failure to identify factors affecting the quality of life in various human settlements will have unexpected and unfortunate consequences. With a decrease in citizens' life satisfaction, society will gradually lose its productive and capable labour force. The present study primarily seeks to find a way to objectively study and evaluate the quality of life in urban areas using remote sensing technology and GIS. Therefore, it investigates the quality of life in Zahedan and identifies possible factors improving life quality. Methods and MaterialThe present study applies a descriptive-analytical methodology. Statistical data were collected from census data of Iranian Statistics Center and maps were retrieved from Zahedan detailed plan-related service centers. Satellite images were also used. The present study applies four indicators to study the quality of life: economic, social, and environmental indicators along with access to service providing centers. Cronbach's alpha method was used in SPSS to determine the reliability of the questionnaire resulting in a coefficient of 0.723 for the previously mentioned indicators which shows high reliability of the instrument. The validity of the questionnaire was also investigated using experts' opinions. Collected data and factor analysis for economic and social variables were performed using SPSS. Criteria were weighted using Super Decision software and ArcGIS was used to combine and model the layers. Satellite images were retrieved from Google Earth Engine. Results and DiscussionIn order to investigate the socio-economic inequalities affecting quality of life, 16 parameters were extracted from the available census data and used to assess the socio-economic situation in the study area. Correlated parameters were combined using factor analysis to represent a single index. A specific name was then assigned to each factor. Indicators were normalized and aligned for the modeling stage. Fuzzy membership functions (Large, Small and Liner) were used to normalize the indicators in ArcGIS. Each index is then multiplied by the weight obtained from ANP method, and integrated using GAMMA fuzzy command. Spatial distribution of urban blocks in the central parts of the first district ranked higher in terms of economic and social indexes of life quality. Environmental indexes and access to service providing centers have a more desirable status in the second district. Parameters such as economic participation rate , housing status, air pollution and health centers had the largest impact on quality of life. Moran's spatial autocorrelation index shows a cluster pattern for quality of life in the study area. ConclusionFinal results show that access to service providing centers has the largest impact on quality of life. In general, the second district ranks higher than the first district in terms of quality of life. This city faces various economic and social limitations, while having access to many facilities: Recent droughts, universities and higher education institutions, mutual borders with neighboring countries and a large number of immigrants from Afghanistan. It is also facing hot and dry climate, a decrease in vegetation cover and an increase in temperature level. The freeway located in the western part of the study area, urban expansion toward the western parts, increased constructions and increased urban density due to proximity to university centers and finally heavy traffic have caused air pollution. Also, public service centers are not evenly distributed. These are some of the most important causes of low quality of life in the study area.
Morteza Heidarimozaffar; Morteza Shahavand
Abstract
Introduction Iran is mostly located in arid and semi-arid regions, and groundwater is its only water resource. The present study introduces a method based on spatial zoning evaluation which takes advantage ofFuzzy Logic and Geospatial Information System to design possible sites for an underground dam, ...
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Introduction Iran is mostly located in arid and semi-arid regions, and groundwater is its only water resource. The present study introduces a method based on spatial zoning evaluation which takes advantage ofFuzzy Logic and Geospatial Information System to design possible sites for an underground dam, and ranks them according to their suitability. The usability of this method for the construction of an underground dam in Kabodarahang Plain in the north of Hamedan Provincewas evaluated in the present study. Materials & Methods Groundwater use is considered to be a solution of water scarcity in arid and semi-arid regions. Lack of sufficient financial resources and adequate technology as well as specific physical conditions make it difficult to provide clean water in arid areas of most developing countries. Over the past few years, underground dams has been considered as a way to overcome water scarcity in arid and semi-arid regions. The present study seeks to identify suitable locations for the construction of an underground dam in Kabodarahangplain in north of Hamadan province using fuzzy logic in GIS environment. As one of the case study areas of Qareh Chai River,KabodarahangPlain isthe largest plain of Hamedan province with an area of 3448 square kilometers and an average height of about 1789 meters above the sea level.It is located between 48°14ˊ 51 ̋ to 49° 5ˊ 11 ̋ eastern longitude and 34° 50ˊ 6 ̋ to36°14ˊ 31 ̋ Northern latitude. To reach the goal of the present study, effective parameters in the constructionof underground dam, such as land slope, positionof wells, springs and aqueducts, rivers channels, positionof faults, location of villages and cities, position of paths and the thickness of alluvium were collected from the study area. Based onthe possibility of performing different spatial analyses in geographic information system environment, zoning ofKaboodarahang plain was evaluated from the point of view of an underground dam construction usingfuzzy logic and GIS tools in the present study. Results & Discussion Similar to membership in classical series,“And” operatorin Fuzzy Logic is used when two or more different criteria can help in solving an issue. This operator extracts the minimum membership level of pixel units in a specified positionand use it in the final map.Fuzzy multiplication operator multiplies membership level of pixel units in specified positionsof different factors and use the result in the final map. This operator is used when mapsof different criteria have a subtractive effect on each other.Fuzzy gamma operator is the general form of algebraic multiplication of fuzzy multiplication and addition operators to the power of gamma. It is used when increasing and decreasing effects are present in the relations between different criteria. Following the preparation of layers in Arc Map software, Euclidean distance operator and interpolation based on triangulation method were used to convert parameters to raster layers. Based on the background research and standards used, the criteria maps were combined using fuzzy operators. Using Fuzzy membership operator, an area of 3342 hectares, using fuzzy multiplier operator an area of2393 hectare (around one percent of the study area) and using the fuzzy gamma operator, an area of 35574 hectares (10.32% of the study area) was selected as having a very good potential for underground dam construction.Slope Map is also one of the most important criteria in determining areas appropriate for underground dam construction. It is suggested to use a larger-scale topographic map to improve the accuracy and increase the possibility of errors. Intelligent algorithms can also be used to determine the threshold level for standardization of the criteria. Since different organizationswork in the field of data collection, it is also suggested to providea suitable mechanism to assess the potential of other plains through consultation and coordination with other relevant organizations. It is recommended to use other parameters and factors affecting the selection of suitable areas for the construction of underground dams, such as soil type or physical and chemical properties of soil in future studies. Conclusion Zoning maps prepared by fuzzy logic in GIS environment can be used to determine the appropriate location forthe constructionof underground dams. Fuzzy operators provide special conditions which make them more reliablecompared to traditional methods.Appropriate areas for construction of underground dam were identified in GIS environment. A decision making model can also be produced based on the input parameters.It is suggested to enter general information of the area to perform the initial investigation of potential areas and then add field study information to complete the model.
Mehrdad AhangarCani; Mohammad Reza Malek
Abstract
Extended Abstract
Introduction and Objective
Road traffic accidents impose numerous social, economic, and cultural costs upon various societies, especially developing countries. Identification of accident blackspots is a method proposed to deal with car accident risks. Among various events associated ...
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Extended Abstract
Introduction and Objective
Road traffic accidents impose numerous social, economic, and cultural costs upon various societies, especially developing countries. Identification of accident blackspots is a method proposed to deal with car accident risks. Among various events associated with transportation network, road traffic accidents play a significant role, because of their specific features, including high frequency, high intensity and the chance of direct involvement of all members of the community.This problem is more conspicuous in developing countries such as Iran. The present study aims to identifyaccidentblackspotsand to prepare risk map for road trafficaccidents in Babol city using volunteered geographic information.
Materials and methods
According to the characteristics of the study area, the present study takes advantage of criteria such as distance from population centers, proximity to city squares, distance from footbridges, and proximity to road intersections to identifyaccidentblackspotsand a prepare risk map for roadtraffic accidents in Babol city. Accident blackspots detected by volunteered geographic information, along with the criteria determined by applying analytic hierarchy process (AHP) and analytic network process (ANP) were compared in a pairwise manner, and their respective weight was calculated to showtheir specific level of impact. Ultimately, a risk map was produced for the risk of road traffic accidents obtained from each method. In order to evaluate the accuracy of the identified accident blackspots obtained from volunteered geographic information, as well as the accuracy of susceptibility maps, ROC curve and Kappa Coefficient were applied to police official records.
Results and Discussion
According to the findings, Jame Mosque shopping center, Shahabnia shopping center, intersection of Farhangstreet and Velayat square were identified as the most accident-prone areas in Babol city. Also, among the prespecified criteria, distance from population centers and distance from intersections are considered to be the most important criteria, respectively. Results obtained from the evaluation criteria indicatedhigh accuracy of volunteered geographic information, and thus it is concluded that this kind of information can be effective in determining the accident blackspotsinBabol city. Also, the ANP method works better than AHP method in preparing the risk map of accidents.
Conclusion and Future works
Due to the large number of road accidents, especially in developing countries,the issue of accident blackspotsand providing a risk map for road trafficaccidents are an essential part of roads safety. In the present study, volunteered geographic information was used, along with multivariate decision-making methods of analytic hierarchy process (AHP) and analytic network process (ANP) to identifyaccident blackspots based on number, causes and severity of accidents and to develop a risk map for driving accidents in Babol city. Moreover, the criteria of distance from population centers, proximity to the city squares, distance from the footbridges, and adjacency to intersections were used to determine accident blackspotsand to prepare a risk map for driving accidents in Babol city. According to the results, Jame Mosque shopping center, Shahabnia shopping center, Farhang intersection and Velayat square were identified as the most accident-prone points in Babol city. Also, distance from population centers and distance from intersectionswere identified as the most important criteria, respectively. Evaluation criteria demonstrated that volunteered geographic information can be effective and accurate in determining accident blackspotsinBabol city. Also, the ANP method worked better than AHP method in preparing the risk map of driving accidents. The method proposed in this study to identify accident blackspots and preparedriving accidents risk maps can be generalized to other areas. Basedon the characteristics of specific routes, other criteria such as arc radius, longitudinal slope can alsobe used. It is also suggested that the results of other methods used for investigation ofaccidentblackspotsand production of risk maps based onvolunteered geographic information (VGI) are compared with the results of the present study.
Najmeh Neisany Samany; Ali Asghar Alesheikh; Zahra Abedi
Abstract
Extended Abstract
Introduction
Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations ...
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Extended Abstract
Introduction
Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations in areas which increase users of this system in different parts of the city. The present study seeks to identify suitable places for the construction ofproposedbus stations in the 6th district of Tehran municipality using GIS functions, Analytic Network Process and Allen’s temporal model.Proposedstationswere then optimized.
Materials & Methods:
Based on necessary investigations about the 6th district of Tehran, 17 indicators were identified: access criterion (sub criteria: business, administrative, medical, religious, educational and sports centers, and urban facilities, subway, roads), demographic criterion (sub criteria:population and employeesdensity) and traffic status (sub criteria: BRT lines, one way and two way streets, street width, traffic load, slop of the area and kind of road).
At the first phase, questionnaires were distributed among 35 experts of transportation and traffic. Based on the results of DEMATEL questionnaires and their analysis in MATLAB, the severity of relationship between the criteria were calculated and pairwise comparison questionnaires were designed.
Using DEMATEL technique, the presence or absence of a relationship between the aforementioned criteria and sub criteria was investigated. As a decision makingtechnique based on pairwise comparison, DEMATEL uses experts’ judgments to extractelements of a system and find a systematic structure for them using the principles of graph theory. This technique provides a hierarchical structure of the factors of the system along with their corresponding relationship, and determines the effect of these relations in the format of numerical scores. DEMATEL technique is used to identify and investigate the mutual relationships between criteria and to produce a map of network relations.
The ANP model not only calculates the relationship between the criteria, but also the relative weight of each criterion. The result of these calculations make a supermatrix, from which it is possible to derive dependency between each criterion and selection and their weights. An increase in this weight shows higher priority, so it is possible to choose the best option. (Saa’ti, 2003)
It is possible to calculate ANP process in both Super Decision and and ANP-solver software. After calculating weight of the criteria, spatial layers are created in GIS software and finally suitable digital layer is created through integration of the criteria. The obtained digital layer shows the best spatial zones for the construction of bus stations in the study area.
Results & Discussion:
Time and place are inseparable parts of each phenomenon in our world. Since, the first step of processing and analyzing a phenomenon in spatial information systemsismodeling, creating a model with necessary capabilities to include temporal dimension is inevitable. One of the main requirements of spatio-temporal modelling is the ability to investigate the topological temporal -spatial relations betweendifferent phenomena. The present study used Allen’s Interval Algebra to extract all relations between different dimensions of time. These include 3 relations between two temporal events, 6 relations between one event and a time mode, and 13 relations between two time modes.
Based on Allen’s model, the rush hours were investigated and common temporal – spatial features of each station were obtained. New stations were proposed based on existing stations and the desirable layer, and a desirable time was determined for the buses to pass stations based on land uses around the stations, the rush hours of each land useand common temporal – spatial features of each station (based on Allen’s model).
Conclusion
Results indicate that the ANP and Allen model can only search a very small number of possible answers and reach the required answer. 6thdistrict of Tehran municipality covers an area of 1557.65 hectares, from which 18.10% are in a suitable condition, 21.41% are relatively suitable, 30.45% are moderate, 23.88% are relatively improper and 6.17% are completely improper.
281.923 hectares of the district has no problem regarding the access criterion and donot need a station. This district has 185 bus stations and 61 new stations are proposed (a total number of 246).
From the aforementioned 246 stations, 17 stations do not have a common schedule, 87 stations have a common point in their schedule, 89 stations have 2, 42 have 3, 10 stations have 4 and one station have 5 common points in their schedule.
In terms of time,42.28% stations are in a suitable condition, 36.18% are relatively suitable, 17.07% are moderate, 4.07% are relatively improper and 0.41% are completely improper.
Accordingly it is recommended that a bus should pass every 5 minutesfrom stations with 5 and 4 common points in their schedule.For stations with 4 common points in their schedule, this time reaches 10 minutes.Stations with two common points in their schedule need a bus every 15 minutes and stations with 1 common point in their schedule need a bus every 20 minutes.
Saeed Farzaneh; Mohammad Ali Sharifi; Seyedeh Samira Talebi
Abstract
Extended Abstract
Introduction
In recent years, the development of the country in the space industry and the ability of building, launching and infusion of satellites in the lower orbit has put the limited number of countries with such technology. In order to complete the entire cycle of the space ...
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Extended Abstract
Introduction
In recent years, the development of the country in the space industry and the ability of building, launching and infusion of satellites in the lower orbit has put the limited number of countries with such technology. In order to complete the entire cycle of the space industry, the satellite navigation and control, which has been neglected since the beginning of the movement of space science in the country, has to be considered specially. The attitude determination in one sentence is the application of a variety of techniques for estimating the attitude of spacecrafts. In dynamic astronomy, the attitude determination is the the process of controlling the orientation of an aerospace vehicle with respect to an inertial frame of reference or another entity such as the celestial sphere, certain fields, and nearby objects, etc.
A spacecraft attitude determination and control system typically uses a variety of sensors and actuators. Because attitude is described by three or more attitude variables, the di®erence between the desired and measured states is slightly more complicated than for a thermostat, or even for the position of the satellite in space. Furthermore, the mathematical analysis of attitude
determination is complicated by the fact that attitude determination is necessarily either underdetermined or overdetermined.
Materials and methods
Attitude determination typically requires finding three independent quantities, such as any minimal parameterization of the attitude matrix. The mathematics behind attitude determination can be broadly characterized into approaches that use stochastic analysis and approaches that do not. This paper considers a computationally efficient algorithm to optimally estimate the spacecraft attitude from vector observations taken at a single time, which is known as single-point or single-frame attitude determination method. There have been a number of attitude determination algorithms that compute optimal attitude of a spacecraft from various observation sources (known as the Wahba’s problem), and each of the methods has advantages and limitations in terms of accuracy and computational speed. The most popular are: the very important ˆq-Method, the most popular TRIAD and QUEST, SVD, FOAM, and ESOQ-1, the fastest ESOQ-2, and many others approaches introducing new insights or different characteristics, for instance, the EAA, Euler-2, Euler-ˆq, and OLAE.
Results and discussion
Since star detection algorithms can provide more than two stars, the star detector field of view often consists of two or more stars that are passed through the identification algorithms will be detected, those star vectors that have measurement errors can be compensated by using more than two stars. Methods such as the QUEST algorithm usually optimize an error function to the minimum optimal. In fact, the QUEST algorithm estimates the optimum specific eigenvalue and vector for the problem described in the Q_method method without the need for complex numerical calculations. The fact that the QUEST algorithm retains all the computational advantages of a fast definitive algorithm while maintaining the desired result efficiency underscores why it is typically used.
Conclusion
Simulation results showed that the traid and quest algorithms with shuster method attitude determination algorithm can be an efficient alternative over the eight tested algorithm in terms of computational efficiency for singularity-free attitude representation.
Kazem Borhani; Ashraf Azimzadeh Irany; Amirhosein Elhami
Abstract
Extended Abstract Introduction Emergency shelters built based on multifunctional spaces are one of the main components of crisis management, which is carried out for various purposes by transferring people from hazardous or damaged areas to safe areas. Providing suitable spaces for the accommodation ...
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Extended Abstract Introduction Emergency shelters built based on multifunctional spaces are one of the main components of crisis management, which is carried out for various purposes by transferring people from hazardous or damaged areas to safe areas. Providing suitable spaces for the accommodation of refugees, establishing safe routes, warning and informing people about the possibility of danger, transfer and return planning and supporting are the main components of multi-purpose spaces. These components are defined according to the dimensions and scope of danger. Building multi-purpose land uses and paying special attention to them in urban development projects will help in optimization of crisis management processes, and create mechanisms to guarantee citizen security and achieve sustainable urban development. Paying attention to emergency shelters built based on multi-purpose land uses in military towns is of more importance and should be planned during peacetime. In fact, special attention should be paid to selecting and organizing multi-purpose spaces in these cities. Saravan, the center of Saravan city, is a town in Sistan and Baluchestan Province. It is a military town in southeastern Iran with a strategic importance from security and military point of view. Given its strategic location, the necessity of security and defense planning of the city based on the principles of passive defense is quite clear. Aspreviously mentioned,utilizing multi-purpose land uses as a strategy based on passive defensecan be considered an appropriate solution for defense planning of the city. Due to the spatial nature of data as well as multi-objective and multi-criteria nature of decision making, spatial analysis and site selection of multi-purpose land uses needs to be performed using a combination of geographic information system and multi-criteria decision making methods. Materials & Methods The presentapplied studyis performed using descriptive-analytical method. Data was collected from library and documentary sources and land use maps. Some information was also collected from the Statistical Center of Iran. ArcGISwas used to analyze collected data and produce relatedmaps including land use maps, mapsof sensitive urban centers such as military barracks, etc. According to the nature of the research and its field of study,geographical information system (GIS) was integrated with fuzzy multi-criteria decision making methods in the information analysis phase. Results & Discussion In order to analyze collected data, various criteria and indicators were first determined for selection of spaces and multi-purpose land uses based on studies conducted. Then, a special weight is allocated to each criterionusing verbally generated fuzzy methods andaccording to experts’ opinions and then SAW model was used to combine related GIS layers. Finally, the zoning map of Saravan city has been presented as an appropriate example for creating multi-purpose spaces and land uses. Indicators of site selection for multi-purpose land uses have been scored by experts to determine their effect and importance in spatial analysis of multi-purpose spaces in Saravan city. The opinions of 26 experts have been collected to determine this weight. The indicators were converted to GIS layers and presented after the integration of the final map. Conclusion Due to its strategic location and security-related issues, Saravan needs defense planning. Undoubtedly, shelters are needed to protect people againstthe enemy’s attacks in case of war. The necessity of paying attention to this issue has increased the importance of research on defense planning and passive defense in the field of urban planning. Passive defense in border towns focuses on defense planning and reduces the number of casualties. Shelters and multi-purpose spaces are also of this type. With the aim of spatial analysis and site selection for multi-purpose land uses, and in order toutilize existing land uses for urban defense planning, the present study has identified multi-purpose land uses in Saravan. Using geographic information system and multi-criteria decision making methods, these land uses have been located and appropriate situations have been identified for creation of multi-purpose spaces. Results indicated that usingexperts’ views, along with geographic information system, and multi-criteria decision making methods could be an appropriate way fordefense planning and site selecting for multi-purpose land uses. According to the final map which is produced using SAW model for locating and planning multi-purpose land uses, different appropriate areas exist for the location of multi-purpose uses. These areas are specified in the final map of Saravan. According to this map, northeastern areas of the city is considered to be suitablebased on all criteria. It can be concluded that the areas obtained fromGIS are scattered throughout the city and responsible organizations can use the final map for site selection. The largest area with the most suitable condition is located in the northeasternregion, and the southeastern, southern and southwestern regions seem appropriate. A vast region of the city (beginning in the northwestern region and reaching southeastern region) is in poor condition in terms of the criteria examined. Due to the 10 levels of classification used in the final map, the map shows different conditions even in this region, and more suitable situationscan be selected for multi-purpose land usescompared to other regions.
Majid Fakhri; Amin Faraji; Mehdi Aliyan
Abstract
Extended Abstract
Introduction
In recent years, protecting infrastructure, especially critical infrastructure, has become increasingly important because the economy of a region and the well-being of its inhabitants depend on the continuous and reliable operation of its infrastructure. These infrastructures ...
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Extended Abstract
Introduction
In recent years, protecting infrastructure, especially critical infrastructure, has become increasingly important because the economy of a region and the well-being of its inhabitants depend on the continuous and reliable operation of its infrastructure. These infrastructures are like arteries for survival of urbanism damaging .Some of infrastructures can have devastating effects on security, economy, and society at the regional and national levels. There are different systems and infrastructures in different countries, including Communication, electricity, gas and oil, banking and finance, transportation, water supply and government services infrastructure, which are critical infrastructures.
A review of various types of infrastructures shows that energy infrastructure is more important and plays a more significant role in comparing with other types of infrastructure.
Maintaining the security of this infrastructure against attacks and threats is one of the priorities of securing a country. One way to ensure security is to measure the spatial vulnerability of infrastructure. This article assesses the capacity of Yazd province against the vulnerability of energy infrastructure.
Materials & Methods
The information for this research has been extracted by documentary methods (including books, scientific articles, reports, etc.) as well as using the country's infrastructure database. Then, GIS layers of the energy infrastructure of Yazd province, including electric transmission network, electric plant, gas transmission lines, gas pressure regulation stations, oil transmission lines, oil products transmission lines, oil and gas storage tank and gas stations were examined.
The next step was ranking the importance of infrastructure elements with the DEMATEL model. Then, the infrastructure elements of Yazd province were prioritized with the analytic network process(ANP) model.
The next step was to prepare maps and GIS layers for each of the infrastructure elements ,by preparing them in Arc GIS and the priorities of the network analysis process model ;sothe final vulnerability map of the province was prepared.
Results& Discussion
After calculations of supermatrix coefficients, the results show the importance of these infrastructures in providing services to people and other infrastructures, as well astheattractiveness for each infrastructure element. Gas transmission network with the value of 0.1003, oilproducts transmission lines with the value of 0.0988, oil and gas tank with the value of 0.0995, have the most weight and importance, and gas stations with the value of 0.0485 has the least importance in comparing to other energy infrastructures in the Yazd province.
The results show that the central part of Yazd province is more vulnerable thanthe other part of province, because moreenergy infrastructuresareestablished inthe central part of Yazd province. Examination of the results on a smaller scale show thatthe vulnerability of energy network infrastructure inYazd,Meybod, Mehriz and Sadooghis high,butinBahabad, Khatam, and Abarkoohis low.
Conclusion
The results show that distribution of infrastructure in the Yazd province has not beenin a good model. The central part of the province is more vulnerable than the peripheralareas so that more than half of the infrastructure of the energy network (55%) is in very vulnerable zone and 18% of the infrastructure is in highly vulnerable zone;thus, observing the teachings of passive defense in the province deserves more importance.
