Extraction, processing, production and display of geographic data
Sara Sheshangosht; Hossein Agamohammadi; Nematollah Karimi; Zahra Azizi; Mohammad Hassan Vahidnia
Abstract
Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh ...
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Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh glacier’s snout is known as one of the most dynamic parts of glaciers in Takht-e-Soliman height due to the yearly advance and retreat of glacier movement causing substantial volumes of various glacial deposits to collapse into their downstream areas. Nowadays, the advancements of satellite imagery, aerial photos, and Unmanned Automated vehicles (UAV) pave the path for accurately extracting and evaluating these changes. Therefore, the objectives of this research are: (a) evaluating the use of new and cost-effective technologies (UAVs) in comparison to satellite imagery for monitoring glacier changes, (b) identifying spatiotemporal glacier elevation changes, and (c) evaluation of the elevation change rate of the Alamkooh glacier snout from 2010 to 2020 using high spatial resolution remotely sensing data. In this context, the elevation changes of the snout of Alamkooh Glacier, as the hazardous activist part of this glacier, were assessed using Digital elevation models (DEMs) differences of 2010, 2018, and 2020.
Materials and Methods
Alamkooh Glacier is located on the northern hillside of Alamkooh Summit in the Takht-e-Soliman region. The snout of this glacier is situated in a steep valley known as Lizbonak and its high activity changes the shape and morphology of this area. In this paper, spatial and temporal elevation changes of Alamkooh Snout were identified and evaluated using DEMs subtraction derived from aerial laser scanning (LiDAR) data in 2010, and from images captured by UAV in 2018 and 2020. Before elevation change analysis, the DEMs obtained through UAVs in 2018 and 2020 were carried out using approximately 40 and 20 ground control points, respectively. The resulting outputs displayed a reliable accuracy of around 15 cm for these DEMs. In addition, for assessing elevation changes precisely, the all of extracted DEMs were preprocessed and orthorectified and then subsequently subtracted pairwise. Then after, the accuracy of elevation changes was appraised based on non-glacial area elevation change. The outcomes of elevation change in this region signify a high level of accuracy in the 10-year time span. According to the results, the average and standard division elevation change of non-glacial area was ±0.05 cm and 0.34 cm respectively. Moreover, the average error assessment on the non-glacial area indicates that within eight years from 2010 to 2018 the average error was ±0.16 cm, and within two years it was ±0.11 cm from 2018 to 2020.
Result and discussion
Results of DEMs pairwise differences show significant elevation changes in this part of Alamkooh Glacier from 2010 to 2020. The average and the maximum elevation change rates in this period are -0.8 (m/yr.) and -2.31(m/yr.) respectively. The major elevation changes in the snout of Alamkooh happened in the initial period from 2010 to 2018 where the yearly and the maximum mean elevation change rates were -1.03 (m/yr.) and –2.77 (m/yr.) respectively. On the contrary, the elevation changes from 2018 to 2020 were lower than the first period whereas the yearly mean elevation change was about +0.1 (m/yr.) and the maximum elevation change rate was -1.85 (m/yr.). The positive rate of elevation change from 2018 to 2020 is due to debris and ice cubes flowing from upstream and accumulation downstream. Moreover, the Spatial analysis of elevation changes results show a heterogeneous distribution whereas the most significant elevation change in the snout of Alamkooh glacier has occurred predominantly across and along the largest existing valley rather than being evenly spread out across the entire area. The elevation change domain in this valley is between +1.3±0.05 to -23.05±0.05 and the average elevation change of in ten years from 2010 to 2020 is about -8.01 ± 0.05 meters. These changes mostly were negative with decreasing and eroding rates. In contrast, the elevation changes in other valleys only occurred at the exit area of the glacier and just the entrance of the snout area, and the margins did not show a considerable change. When considering all valleys in the snout of Alamkooh the elevation changes distribution across the snout varies between +0.45 to -13.2 (m) with an average of -7.8 (m) which is less than alongside changes at the main valley.
Conclusion
The results show elevation changes in the Almakooh snout do not have constant rate and largely fluctuate in different years and regions. The maximum elevation changes occurred from 2010 to 2018 and along with the main steepest valley. The main valley plays a vital role in elevation change analysis and flowing debris down. This area is also known as the depletion area of the Alamkooh glacier and its drastic elevation changes are caused due to ice and snow melt. The tremendous historical flood of the SardAbrood River occurred in June 2011 was created and affected by elevation changes in this area. Therefore, the tongue of Alamkooh Glacier is considered one of the most dangerous areas regarding natural hazards, and morphological change studies require precaution regarding approaching or visiting this area. This research also confirms that using time-series of remote sensing data such as UAV and Lidar images is very helpful and cost-effective data for identifying, extracting, and monitoring the spatiotemporal changes of glaciers, debris flow directions, and natural hazards.
Geographic Information System (GIS)
Abolfazl Ghanbari; Mostafa Mousapour; Habil Khorrami hossein hajloo; Hossein Anvari
Abstract
Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of ...
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Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of urban areas is one of the main drivers of global changes that have important direct and indirect effects on environmental conditions and biodiversity. In the process of physical development of the city, due to the transformation of natural and semi-natural ecosystems into impermeable surfaces, it often causes irreversible environmental changes. One of the new approaches in urban planning is the use of remote sensing techniques and geographic information system. The emergence of remote sensing and machine learning techniques offers a new and promising opportunity for accurate and efficient monitoring and analysis of urban issues in order to achieve sustainable development. The process of processing satellite images can generally be divided into two approaches: pixel-based image analysis and object-based image analysis. The pixel-based analysis technique is performed at the level of each pixel of the image and uses only the spectral information available in each pixel. On the other hand, the object-based analysis approach is performed on a homogeneous group of pixels, taking into account the spatial characteristics of the pixels. One of the basic problems in urban remote sensing is the heterogeneity of the urban physical environment. The urban environment usually includes built structures such as buildings and urban transportation networks, several different types of vegetation such as agricultural areas, gardens, as well as barren areas and water bodies. Therefore, in the pixel-based processing approach, the existence of heterogeneity in the urban biophysical environment causes spectral mixing and also spectral similarities in the classification operation of satellite images in such a way that in a place where a pixel is If the surrounding environment is different, it causes Salt and Pepper Noise. Therefore, according to the problems in the pixel-based processing approach, the aim of this research is to compare the accuracy of machine learning algorithms based on object-based processing of satellite images in extracting the physical development area of Hamedan city using Sentinel 2 satellite image.Materials & Methods: The remote sensing data used in this research is a multi-spectral satellite image with a spatial resolution of 10 meters from the Sentinel 2 satellite, including bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared) related to the date is the 23 of August 2023 in the city of Hamadan. The image of the Sentinel 2 satellite was downloaded from the website of the European Space Agency. In ENVI software, the pre-processing operation was performed on the satellite image. Then, in the eCognition software, the segmentation process was performed based on the appropriate scale, shape factor, and compression factor with the aim of producing image objects. After segmenting and converting the image into image objects, using machine learning classifiers based on object-oriented processing of satellite images including Bayes classification algorithms, k-nearest neighbor, support vector machine, decision tree and random trees, the classification process was carried out and maps of urban physical development area were produced. After the segmentation operation and the production of visual objects, three classes of built-up urban land, vegetation and barren land were defined, and some of the built objects in the segmentation stage were selected as training points and some were selected as ground Truth points.Results & DiscussionAfter downloading the satellite image from the website of the European Space Organization, in order to apply the radiometric correction of the image and also with the aim of matching the value of the gray levels of the image with the value of the real pixels of the terrestrial reflection, the gray levels are converted to radiance and then, using atmospheric correction, to coefficients. They became terrestrial reflections. In order to apply radiometric correction, Radiometric Calibration tool was used, and to apply atmospheric correction, FLAASH model was used in ENVI software. In order to classify the satellite image based on machine learning algorithms based on object-based processing, eCognition software was used. The satellite image of the study area, which was pre-processed and saved in TIFF format, was called in the environment of this software and saved as a project. In order to produce visual objects, segmentation operations were performed in different scales, shape factor and compression ratio to reach the most appropriate segmentation mode. In this step, the multiple resolution segmentation method was used to segment the image. The most appropriate segmentation included the scale of 100 and the shape factor of 0.6 and the compression factor of 0.4. Because in scales higher than 100, the construction of the visual object was not done correctly, so that several distinct complications were placed in one piece, and in scales less than 100, in some cases, one complication was placed in several pieces. In order to classify the generated image objects, machine learning algorithms were defined separately and after training each algorithm, the classification operation was performed. In this step, the classification was done based on the nearest neighbor method and by selecting the average and standard deviation parameters for each image band. After producing a map of the city physical development range through machine learning classifiers based on object-based processing of satellite images, the classification accuracy of each of the used algorithms was calculated. In order to calculate the accuracy of the above algorithms in eCognition software, using selected ground Truth control points, the overall accuracy and kappa coefficient were calculated for each of the algorithms.Conclusion:Based on the results of the research, it is possible to produce a map of Hamedan's urban physical development using machine learning algorithms based on object-based processing of satellite images with acceptable accuracy. Also, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and kappa coefficient of 0.96 provided more accuracy.
Extraction, processing, production and display of geographic data
Qadir Ashournejad
Abstract
Extended AbstractIntroductionRemote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts ...
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Extended AbstractIntroductionRemote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts in geospatial science is one of the dimensions of this development. The land cover product is one of these products that is used more than other remote sensing products. When presenting these products, their qualitative and quantitative characteristics, including their global accuracy, are also published. Expressing the accuracy of these products globally makes it necessary and necessary to re-evaluate their accuracy regionally for the users of these products in different regions of the world.Materials & MethodsIn this research, the accuracy of the European Space Agency's Copernicus Global Land Service (CGLS), GlobeLand30 and Esri's land cover product were evaluated for regional use in the north of Iran - Mazandaran province. After calculating the area of the classes for each of the land cover products, Pearson's correlation coefficient was used to calculate the correlation between them. For quantitative evaluation, the error matrix was used as one of the most common ways to evaluate the accuracy of land cover products. This method is based on the comparison of classified data and ground reality data. Also, the categorized random sampling method was used to select 1329 evaluation samples in Mazandaran province. For visual evaluation, three areas with dimensions of 6 x 6 km were selected.Results & DiscussionThe regional accuracy evaluation of the studied products shows opposite results compared to the global accuracy of these products. Based on the global accuracy reported for the studied products, the highest accuracy is calculated for the Esri product at 86%, followed by GlobeLand30 and CGLS at 83-85 and 80%. Meanwhile, based on the regional accuracy obtained from the results of this research, the highest regional accuracy for the CGLS product has been calculated at 84% and then for GlobeLand30 and Esri products at 81 and 75%. In evaluating the regional accuracy of the classes, all three studied products (CGLS, GlobeLand30 and Esri) have acceptable accuracy (above 90%) in the classes of snow and ice (100, 100 and 100%), forest (90, 95 and 98 percent), water (96, 94 and 90 percent) and impervious surface (94, 91 and 90 percent). For the agricultural class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers.In the 3 classes of shrubland, Impervious surface and wetland, the accuracy results are less than other classes for all three land cover products and in the amount of (29, 0 and 13 percent), (65, 66 and 42 percent) and (67, 38 and 0 percent).Conclusion By evaluating and comparing the regional accuracy of three CGLS products, GlobeLand30 and Esri, this research answered the question of whether the accuracy stated in global land cover products can be trusted for regional studies and planning. The results show that the regional accuracy of CGLS, GlobeLand30, and Esri are 84, 81, and 75 percent, respectively, compared to their global accuracy (80, 83, 85, and 86 percent). These results show the difference obtained for the Esri product more than the two products CGLS and GlobeLand30. Meanwhile, the remote sensing data used for the Esri product (Sentinel-2 data) and its pixel size (10 meters) are of higher quality and quantity than the other two products. In fact, these results show that only paying attention to the type of data used and the global accuracy is not enough to use products in regional scales and requires evaluations before using them.In addition, by evaluating the classes of each product and comparing them, the need for this evaluation before using these products seems necessary. The results showed that in the evaluation of the regional accuracy of the classes, all three studied products had an accuracy of over 90% in the classes of snow and ice, forest, water areas and human construction. For the agricultural land class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers. In the 3 classes of shrubland, herbaceous cover and wetland, the results show lower accuracy than other classes for all three land cover products. Significant results were also obtained in the visual evaluation, and it seems necessary to pay attention to this evaluation before the applications where it is important to pay attention to a particular class.
Remote Sensing (RS)
Nastaran Nazariani; Asghar Fallah
Abstract
Extended Abstract Introduction Estimation of forest habitat characteristics is a necessary issue in order to collect information for sustainable forest management (Ahmadi et al., 2020). Data collection methods require a lot of time and money. Therefore, it is always tried to use ...
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Extended Abstract Introduction Estimation of forest habitat characteristics is a necessary issue in order to collect information for sustainable forest management (Ahmadi et al., 2020). Data collection methods require a lot of time and money. Therefore, it is always tried to use complementary methods, with lower costs and acceptable accuracy, using the achievements obtained in various scientific fields (Sivanpillai et al., 2006). Sentinel 2 is a new generation optical satellite for Earth monitoring developed by the European Space Agency with new spectral capabilities, wide coverage and good spatial and temporal resolution for data continuity and enhanced Landsat and Spot missions (Wang et al., 2017). When the size of the population is not very large, the application of each of the simple random, classification and systematic methods leads to a more or less similar result. But when the size of the community increases, these methods are associated with problems such as: preparing a sampling framework, high cost of surveying sample units with high dispersion and preparing a sampling plan from units far from each other (Zubair, 2007). The cluster method is one of the recommended methods for large areas in which instead of one sample plot, several sample plots are harvested in one part of the study area (Yim et al., 2015). Among the researches done on the mentioned subjects are the research of Kleinn (1994), Ismaili et al. (1396), Behera et al. (2021), Sibanda et al. (2021), Praticò et al. (2021), Nazariani et al. (1400) and Dabija et al. (2021). Although studies on estimating quantitative forest characteristics using distance measurement data and nonparametric algorithms in Zagros forests may have been done extensively, the effect of main and artificial bands to estimate canopy characteristics and density (number Per hectare) using Sentinel 2 images in the forests of Watershed Orfi Olad Ghobad Koohdasht with the aim of selecting the optimal cluster design to save time and money to achieve forest inventory has not been reported, so in this study, we tried to investigate this issue. Materials and methods In order to conduct the present study, a part of the Zagros forests located 35 km north of Koohdasht city, named Watershed Olad Ghobad was selected. Sampling points were determined in a regular-random manner using a grid with dimensions of 600 × 500 meters. Then, at each sampling point, 16 different cluster sampling designs with four circular and square subplots were designed and implemented. The radius of the circular subplots was 15 meters, the diameter of the square sample was 37 meters and the distance between the subplots was 60 meters. Then, the information on the characteristics of the number per hectare and canopy of trees including the number, of two large and small canopy diameters per sample was measured. In this study, Sentinel 2 sensor images related to August 6, 2021, equivalent to summer 1400, were used at the L1C correction level. This level of correction is geometrically error-free due to the reference ground and because their reflection is at the upper level of the atmosphere. In the present study, four bands (2-blue band, 3-green band, 4-red band, and 8-near-infrared band) of this sensor with a resolution of 10 meters were used. In general, Sentinel 2 image preprocessing operations involve radiometric and geometric correction. The image processing also includes various operations such as grading, texture analysis, band integration, and fabrication of plant features (Naghavi, 2014). In addition to the main bands, artificial bands were created by applying appropriate processing, which was used in the modeling process. Spectral values equivalent to ground plots were extracted from the main and artificial bands and used as an independent variable in the models. In order to evaluate and fit the regression models, 25% of the data were randomly selected (Lu et al, 2004) and excluded from the evaluation data set. The validity of statistical models was evaluated using the coefficient of determination of the mean squared error squared, bias, mean squared error, and squared percentage. In total, ArcGIS software was used to implement the sample parts on the image, ENVI software was used for image processing and STATISTICA software was used for modeling.ResultsIn this method, during data validation, the results showed the characteristic of number per hectare of cluster 16 and the characteristic of canopy cover of cluster 15 with a coefficient of explanation (0.66) and (0.59), respectively, it has the highest accuracy. The results obtained from the application of the nearest neighbor algorithm with four criteria of Euclidean distance, Euclidean square, Manhattan, and Chapichev showed that for the number of characteristics per hectare, the Euclidean distance criterion with cluster 16 and for the canopy characteristic of the Euclidean distance criterion with cluster three, respectively (R2 = 0.59 and RMSE=5.70%) and (R2 = 0.62 and RMSE= 12.30%). The accuracy and efficiency of the support vector machine algorithm are influenced by the type of kernel used. The results of different kernels by considering different cluster sampling designs in the backup vector machine method showed for the characteristic number of linear kernel trees and 13 cluster sampling designs with an explanation coefficient of 0.72 and for the canopy characteristic. The linear kernel and the cluster sampling design of seven with a coefficient of determination of 0.65 have the best results. Evaluation of the artificial neural network model showed that the MLP algorithm is more suitable than the RBF algorithm in estimating the studied characteristics with its high accuracy and average squared percentage. Based on this, among the 16 designs used with the MLP algorithm, they showed the most suitable results for the number of characteristics per hectare of cluster six with a coefficient of reflection of 0.86 and for the canopy characteristic of cluster 10 with a coefficient of reflection of 0.76, respectively. Based on the values of the coefficient of explanation and the lowest squared percentage of the mean squares of error, the most appropriate model was selected from the four types of algorithms studied in modeling and the results showed both characteristics of the artificial neural network model respectively (with MLP algorithms MLP 80-20-1 and MLP 80-11-1) presented optimal results with explanation coefficients of 0.86 and 0.76.Discussion and conclusionThe modeling results with four studied algorithms for the canopy characteristic showed that the artificial neural network model algorithm with a cluster sampling design of 10 with an explanation coefficient of 0.76 was the most suitable method. The results are consistent with the study (Yim et al., 2015;) and show the superiority of using cluster sampling, nonparametric modeling of the artificial neural networks and Sentinel 2 images in the structure of the forest ecosystem. Yim et al. (2015) acknowledged that in natural environments, the correlation between sub-plots and habitat conditions in terms of their shape and size should be more sensitive to forest structure. According to the study of Sivanpillai et al. (2006) in poorer masses, due to the presence of more gaps in the canopy, absorption and distribution occur. In contrast, Dabija et al. (2021) compared support vector machine and stochastic forest algorithms for canopy mapping using Sentinel-2 and Landsat 8 satellite imagery to evaluate regional and spatial classification and development in three different regions. Catalonia, Poland, and Romania paid. The results showed that Sentinel-2 satellite images were better than Landsat 8 data inaccuracy (8-10%) in land cover classification and radial-based support vector algorithm than in random forest with accuracy (6-7%). Function. Nazariani et al. (1400) also had the stochastic forest algorithm as the most suitable model for estimating the canopy characteristic, which is not consistent with the results of the present study. The reason for the difference can be found in the type of algorithm obtained and the accuracy achieved.
Remote Sensing (RS)
Mahsa Jahanbakhsh; Ali Esmaeily
Abstract
Extended AbstractIntroduction In recent years, we have seen the importance and high demand of lithium (Li) due to its many applications, for example in the production of rechargeable lithium batteries, which are mainly related to the global markets of electric vehicle manufacturing ...
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Extended AbstractIntroduction In recent years, we have seen the importance and high demand of lithium (Li) due to its many applications, for example in the production of rechargeable lithium batteries, which are mainly related to the global markets of electric vehicle manufacturing to achieve a healthy environment and more suitable transportation. Due to this high demand, the identification of new lithium reserves is very important and the investigation of its identification and zoning methods has been the focus of many researchers, and the use of remote sensing data and image processing techniques in the detection of lithium due to cost reduction of earth exploration has increased, greatly.In this research, using modern methods, a general and intelligent approach was presented, so that with the least time and cost, after selecting the bands of the desired satellite images and zoning the area of Degh Ptergan, in Zirkoh city, South Khorasan province, as a possible area for the existence of lithium reserves, modeling was done by the supervised machine learning method, and the relative importance of the variables was determined using the trained model.Also, the relative importance of the variables was determined by the trained model, and the ability of each of the remote sensing techniques to achieve this goal has been challenged.Materials& Methods Here, 13 bands of Sentinel-2 images and the region of 12 known lithium mines around the world were used as lithium presence areas, so that, by going through steps, suitable data for modeling were produced. In this way, by using the boundaries of these mines, samples were produced that can be used as input for modeling algorithms. The maximum entropy algorithm was used to model the distribution of lithium samples. Since the correlation between the input variables reduces the performance of the model and makes it difficult to interpret the results of the modeling, first, the correlation between the input variables was calculated and those with a high correlation were discarded. So that, 16 variables were used as input in the maximum entropy algorithm and finally a suitable model was obtained with the AUC (Area Under the Curve) criterion of 0.706 and by it, the study area of Degh Patregan, located in the province South Khorasan, Iran was zoned and two possible areas containing lithium resources were identified.To determine the relative importance and contribution of the input variables in the prediction map of lithium minerals, the Jacknife method was implemented. According to this method, the variables B10, B06/B08, B06/B07 and B01/B10 have a high relative importance, which shows that they have more information than the other variables. Then classic remote sensing techniques including color composition, band ratio, principal component analysis and SAM was done to zone the study area, too. The results of maximum entropy modeling were compared with these techniques and the high ability of the maximum entropy algorithm was determined.Results & Discussion According to the results and prediction maps related to the classical methods, it showed that although some of these methods approximately identified the areas specified by the maximum entropy algorithm, but they had problems that is emphasized on the development of more suitable remote sensing algorithms to describe the changes associated with lithium minerals. The maximum entropy algorithm with its unique options is a powerful tool for extracting the features of satellite images and expresses their hidden information more clearly. The accuracy of this method was compared with classical techniques and it was able to provide a more appropriate classification with a low noise and with a Kappa coefficient of 0.8775 and an overall accuracy of 0.9435, and identified two areas with the possibility of the presence of lithium minerals in the study area.Conclusion & SuggestionsIn the present research, the study area of Degh Patergan, located in South Khorasan province, Iran, was zoned, whereby two possible areas containing lithium resources were identified and the ability of classical remote sensing methods and maximum entropy algorithm was challenged. The method discussed in the research may be used as a cost-effective and technological solution with priority over field mapping for mineral exploration in remote border areas with difficult access, also an automatic approach with the maximum entropy algorithm was presented for the exploration of different mineral resources, which can be used for other exploration as well. Therefore, it is suggested to be used in different areas and to explore different sources.
Hadi Soleimani Moghadam
Abstract
Extended Abstract
Introduction
Recent scientific and technological development have provided comfort and well-being for communities while also resulting in new challenges such as environmental pollution. As a source of environmental pollution, fossil fuels emit toxic gases into the air while burning ...
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Extended Abstract
Introduction
Recent scientific and technological development have provided comfort and well-being for communities while also resulting in new challenges such as environmental pollution. As a source of environmental pollution, fossil fuels emit toxic gases into the air while burning and thus trap heat in atmosphere, increase air temperature and result in wide-ranging climate changes. As the most significant source of energy, the sun can provide us with a proper alternative to fossil fuels. Related information can be collected through direct measurement of solar energy using devices such as a pyrometer. So far, various approaches such as remote sensing have been adopted to universalize solar irradiance maps. Due to their high accuracy and speed in estimating net radiation, remote sensing techniques can be an appropriate alternative to old experimental methods. Having access to precise information on the amount and intensity of solar radiation at low latitudes, including Iran is essential for the development of solar sites. The present study assesses solar energy and the feasibility of generating solar power or a photovoltaic (PV) system in rural areas of Joveyn County.
Materials and method
Elevation and related maps, sunshine hours, direct and indirect radiation, and total radiation were first collected and calculated. GIS-based solar radiation analysis was conducted in the present study and a zoning map was generated showing total solar radiation in 113 villages of Joveyn County. Atmospheric transmittance and diffuse radiation were extracted from the total radiation and extraterrestrial radiation of the studied stations. Then the annual radiation received in 2017 was estimated using the radiation analysis method and 30-meter resolution Digital Elevation Model (DEM) of the study area. Finally, the feasibility was assessed based on the consumption requirements of the villages and solar energy production capacity in the study area.
Discussion and results
GIS radiation analysis sub-program was used to zone the total solar radiation in Joveyn County. Atmospheric transmittance and diffuse radiation were then estimated separately using the radiation recorded in each station and entered the model to determine the radiation. Elevation and related maps, sunshine hours, direct and indirect radiation, and total radiation were first collected and calculated in the present study. The highest altitude was recorded in the southern parts of the study area including Jalambadan, Ramshin, and Bid rural areas.
Sunshine duration was the most important climatic parameter in the present study. Except for the southern elevations, the study area generally experienced long sunshine hours. The longest sunshine duration was observed in spring with an average of 1177.81 W/m², while the shortest was in winter with an average of 904.269 W/m². Tarsak village and Ghaem town have experienced the longest sunshine hours. The highest direct solar radiation was observed in the southern elevations of Joveyn County. Results indicate that the highest amount of direct solar radiation is observed in spring in rural areas of Karimabad, Rahmatabad, and Beyhagh, while the lowest is received in autumn.
The highest amount of total radiation was observed in Jalambadan and Rahravi Bidkhor villages in spring, while the lowest was observed in autumn. Observed differences in radiation and altitude show that both parameters were affected by topographic conditions such as degree and aspect of slope and obstacles blocking direct radiation. Results indicated that Rahravi Bidkhor, Kalateh Fazel, and Bidkhor have received the highest total radiation throughout the study area.
Finally, the total radiation potential was calculated. Accordingly, the highest solar radiation energy potential was recorded in Helamabad and Qale-e-Now villages. Results indicated that solar energy can be utilized in scattered and sparsely populated rural areas. Potential measurement map showed that 89.07% of the study area had an excellent potential, 8.58% had very good potential, and 2.33% had good potential. Finally, wind speed and direction were also evaluated. The highest wind speed was observed in the western and northwestern regions of the study area which results in a high potential for wind energy harvesting. Moving from east toward the study area, the potential decreases.
Conclusion
The present study has measured solar radiation reaching the Earth’s surface using the solar energy analyzer function of remote sensing and GIS with the aim of assessing the feasibility of using photovoltaic systems in the study area. Results indicated that solar radiation of the study area is between 27605 and 383675. Since a 1000 watts per square solar radiation is needed for photovoltaic cells, solar radiation in the study area has the necessary potential for solar photovoltaic systems. The wind speed potential in the study area decreases from west to east. Therefore, construction of wind power plants in the western parts of the study area is possible and economical. Moreover, environmental conditions show that solar panels can be installed and solar energy can be utilized in the mentioned region.
Consistent with the present study, Sherbafian (2008) has assessed the feasibility of using solar energy in four provinces of Khorasan, Gilan, Qazvin, and East Azerbaijan, and concluded that Iran enjoys a high potential for solar energy generation. Findings are also consistent with Safaei et al. (2015) who have studied the potential of clean energy production in Esfarayen city.
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.
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.
Mohammad Ghasem Torkashvand; Mostafa Mousapour
Abstract
Extended Abstract
Introduction
The snow cover is one of the quickest changing phenomena on the earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and biogeochemical as well as human activities. Precise estimate ...
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Extended Abstract
Introduction
The snow cover is one of the quickest changing phenomena on the earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and biogeochemical as well as human activities. Precise estimate of snow cover is regarded as one of the fundamental operations in precipitation. Thus, monitoring the snow-covered surfaces hasspecial importancefor the perspective of climatic, ecologic and hydrologic studies. The researchers believe that remote sensing data can lead to better assess from the snow-covered areas than traditional topography methods. Therefore, nowadays, in efficient management of water resources, remote sensing data aims to achieve exact information on snow-covered areasis applying operationally. Satellites are suitable tools to measure the mentioned areas since high snow reflection creates a good contrast with other natural surfaces except clouds. This research is conducted to compare the performance of Cornell functions of support vector and object-oriented Fuzzy operators in estimating the desired areas in Almabolaq Mountain, Asadabad.
Material & Methods
The data used in this research are the bands with 10 m spatial segregation of 2B Sentinel satellite including bands 2, 3, 4 and 8 on 6th March 2020. To classify Cornell functions of support vector machine and compute their accuracy, ENVI software was implemented. The eCognation software was used to partition and categorize those with the same object-oriented Fuzzy operators. Separating similar spectral sets and classifying those with the same spectral behaviour are regarded as satellite information classification. In other words, categorizing the photo pixels, and allocating one pixel to one class or phenomenon are the mentioned classification. Support vector machinethat is one of the most common classifiers in learning machine, which divides data using an optimum separation super plate. One of the important advantages of support vector machine is the ability to deal with high dimensional data using almost less training samples for remote sensing applications. Objective analysis is an advanced technique of image processing which is used to assess the digital images and typical conflicts of basic pixel classification based on different methods. Traditionally, pixel-based analysis can be done by available data of each pixel whereas object-based analysis considers a set of similar pixels called objects or image objects. It regards adjacent pixels with the same information value as one distinct unit called piece or segment. In fact, pieces are the areas produced by one or few homogeneous criteria in one or few dimensions of a specific space, so that the pieces have extra spectral information in each band, mean, maximum and minimum amounts, variance, etc. as compared to single pixels. Combining the object-oriented and Fuzzy methods provides the classification of image pieces with a specific membership degree. In this process, image pieces with different membership degrees are classified in more than one class, so according to the membership degree, image piece classification is done leading to the increased final precision.
Results & Discussion
In this research, after preparing satellite images in SNAP software using Sen2Cor, radiometric correction was conducted on the images. To prepare the classification map of Cornell functions of support vector machine, TIFF satellite images were called by ENVI software. Using the shape file of the case study, the area cutting operation was done. Afterwards, two classes of snow and non-snow regions were created to pick up the training points, so based on imagery processing, training points were specified for each class. To classify support vector machine algorithm, linear, polynomial, radialand sigmoid Cornell functions were applied,soclassification maps were separately produced. To draw the classification map of object-oriented Fuzzy operators, satellite images pre-processed in previous stages were called by eCognation software, then they were defined as a project. Afterwards, two mentioned classes were defined to do the classification process, for each class, the desired Fuzzy operator was determined. For suitable classification, it was done in various scales and weight coefficients of shape and compactness. Scale 75, shape 6.0 and compactness 8.0 presented suitable classification. The training samples, parameters of lighting, mean and standard deviations were chosen as distinct features of classes for object-oriented classification. Using the nearest adjacent neighbor algorithm, object-oriented classification was done for each of the Fuzzy operators. After drawing the snow-covered areas through Cornell functions of support vector machine and object-oriented fuzzy operators, the accuracy of classification was computed.
Conclusion
The results indicate that AND algorithm showing the logic share and minimum return value out of Fuzzy values is the highest accuracy (98%) and to classify digital images,the object-oriented processing methods of satellite imagery enable more precision due to the data related to texture, shape, position, content and geometrical features as compared to Cornell functions of support vector machine.
Geographic Information System (GIS)
Sakine Koohi; Asghar Azizian
Abstract
Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts ...
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Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). Materials & MethodsASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. Results & DiscussionFindings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. ConclusionResults indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.
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.
Geographic Data
Mehran Maghsoudi; Mohamad Fathollahzadeh; Hamid Ganjaeian
Abstract
Extended Abstract
Introduction
Surface winds move and transport soil particles on the ground and thus, affect the intensity of erosion to a great degree (Tage Din et al, 1986: 118). Various studies have found a decreasing trend for surface wind speed in different parts of the world in recent years. ...
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Extended Abstract
Introduction
Surface winds move and transport soil particles on the ground and thus, affect the intensity of erosion to a great degree (Tage Din et al, 1986: 118). Various studies have found a decreasing trend for surface wind speed in different parts of the world in recent years. This decrease has been more widely reported in mid-latitudes (McVicar et al, 2008). Continuous drought in consecutive years is one of the factors that can reduce soil moisture and stop the growth of vegetation cover. (Hereher at el, 2009). Iran is located in the arid belt of the world and two thirds of its total area is located in these arid regions (Maghsoudi, 2006). Previous studies have shown that 17 provinces of the country are affected by wind erosion, among which Kerman faces a more severe conditions. Iran has more than 20 relatively large ergs and several small ergs covering an area of approximately 36,000 square kilometers (Mahmoudi, 1991). The present study investigates different characteristics of winds and its effects on morphology and displacement of sand dunes using Sentinel-2 optical and Sentinel_1 radar images.
Materials and Methods
Due to the lack of any synoptic station in the Lut Desert, related data including wind direction and speed were collected from 6 neighboring stations (Bam, Dehsalm, Zabol, Shahdad, Nusratabad and Nehbandan). Then, a wind rose and a sand rose graph were prepared for each station using WR Plot and Sand Rose Graph software. Resultant force vector acting in the displacement of sands and formation of sand dunes was determined. Following an examination of wind characteristics in the study area using Sentinel-2 optical images collected in the 2016 - 2019 reference period, changes of sand dunes and direction of their movements were also analyzed. In order to investigate vertical displacement in the region, radar interference method and SBAS time series have been used. This method only uses pairs of images in which vertical component of the baseline is less than its critical value, and also have a minimum baseline time. 45 Sentinel_1 radar images were used in the present study to measure radar interference.
Results
Recorded data in Dehsalm, Nehbandan, and Nosrat Abad stations indicate that winds blowing in these stations affect the Lut Desert. The prevailing wind recorded in Dehsalm station blows in northwest to southeast direction of the Lut Erg, while in Nehbandan station, the prevailing wind blows in north to south direction of this Erg. The prevailing wind in Nosrat Abad station blows in southeast to northwest direction of this erg. Sand rose graphs show that DPt in Dehsalam station equals 422.6 and in Nehbandan station equals 484.2. Since both DPts are more than 400, wind in this region has a high energy level and is potentially capable of sand displacement. Changes of sand dunes and direction of their movements were analyzed using Sentinel-2 and Sentinel-1 images in 2016-2019 reference period.
Discussion and Conclusion
Hourly wind speed and direction data in Nehbandan, Dehsalam, and Nosratabad stations were investigated in the present study to evaluate their impact on geomorphological changes in the Lut Erg and its sand dunes. Results indicate that the prevailing wind in these stations blows in north, northwest and southeast direction towards the Lut Erg, respectively. Investigating wind speed changes in Nehbandan station shows that during the last 34 years, average monthly wind speed in this station has decreased from 3.7 meters per second in 1986 to about 2.2 meters per second in 2020, which means a 1.5 meters per second decrease has occurred during this period. Apart from wind speed and direction data, Sentinel-2 optical images were also used to monitor changes in sand dunes of the Lut Erg. Results indicate that during the 2017 - 2018 reference period, most changes have occurred in the sand dunes of the northwest and northeast regions and the margins of this erg, while in the 2018 - 2019 reference period, most changes have occurred in the northwest and southeast regions of the Lut Erg. Analysis of satellite images indicates that the direction of wind force vectors is consistent with the direction of sand transport vector. In other words, sand dune changes in the Lut Erg have occurred under the influence of winds blowing in northwest and southeast directions, which is consistent with the direction of the sand transport vector in plots prepared for the three stations (Nehbandan, Dehsalam, and Nusrataba).
In order to validate the results of wind direction and speed analysis and remote sensing of optical images, vertical displacement of the erg surface was measured in 4-year periods using Sentinel_1 radar images and SBAS time series. In general, southern parts of the Lut Erg and especially sand dunes in these parts have experienced an increase in elevation, while the northern parts of Erg have experienced a decrease in elevation. This can be due to erosion and deposition of sediments in the southern regions of the Lut Erg, which is consistent with the sand rose and wind rose graphs prepared for the region .
Hadi Ghafourian; Seyed Hossein Sanaei Nejad; Mahdi Jabbari Nowghabi
Abstract
Extended Abstract Introduction Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, ...
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Extended Abstract Introduction Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, and industrymanagement. Due to the problems related to generalization of point precipitation to regional precipitation, alternative methods have been proposed forthe measurement of this variable. In many cases, short reference period, inadequate density of stations and poor quality of data collected from precipitation measurement networks have challenged the analysis of this climate variable. In order to overcome these problems, it is necessary to identify alternative sources, evaluate and use them to estimate the amount of precipitation. The present study primarily seeks to evaluate precipitation data from the TMPA and provide calibration data for arid, semi-arid, Mediterranean, humid, and very humid regions of Iran on a monthly scale. Materials and Methods In the present study, monthly precipitation data of 15 synoptic stations in 5 regions of Iran (arid, semi-arid, Mediterranean, humid and very humid) were selected as reference data and monthly precipitation data from the TMPA (3B43-v7) were corrected based on them. To ensure reliability of results and reduce errors,stations were selectedrandomly from 15 separate provinces with different topographic conditions. A 20-year reference period (1998-2017) was selected for the study. Collected satellite data have a monthly temporal resolution and a spatial resolution of 0.25 degrees covering 50th parallel south to 50th parallel north. Table 1 shows features of the selected stations and their corresponding pixels. Pre-processing included quality control, homogeneity test, and data accuracy test. Usinga long-term reference period of 20 years, different statistical criteria to evaluate satellite data and a correction relationindependent from ground data are among the advantages of this research. In this study, a more efficient method is used to determine errors and one of the most modern methods of calibration is also used. Followingthe application of log transformation and multiplicative model, monthly C parameter was calculated to rectify satellite data collected from different climates. Results were evaluated using R2 (Coefficient of Determination), MBE, MAE and RMSE. Results and Discussion Findings indicated that the distribution of initial data obtained from TMPA satellite in a monthly scale is similar to the distribution of pattern obtained from ground data (due to a correlation of above 75% (R2>0.6)). Satellite data collected from arid areas are usually overestimated, while data collected from humid areas are generally underestimated. However, determination coefficients (R2) of different climates show a strong correlation between these two sources of data. The initial TMPA data have estimated the monthly precipitation of Bam, Piranshahr and Abali stations with the least amount of error. The highest level of errors were obtained from Marivan, Bandar Anzali, and Koohrang stations. In other words, the highest level of errors have occurred in the very humid region. Calibration of TMPA data collected from the 5 different climates indicated that correction of TMPA monthly data would improve valuesestimated from satellite images. Mean bias error (MBE) was reduced by 88.7, 95.3, 68.4, 38.4 and 63.9 percentin arid, semi-arid, Mediterranean, humid and very humid climates, respectively. Values of the correction parameter (C) in the arid climate indicate that a reduction factor has been applied to rectify satellite data collected in each month of the year. In the semi-arid climate, reduction factorswere obtained for each months of the year. A reduction factor is also required to rectify data collected in the warmest months of the year (June, July, and August) in the Mediterranean climate. Due to the low precipitation of these months, overestimation seems reasonable in these areas. A reduction factor should also be applied in the humid climate for 6 months of spring and summer. Considering the precipitation rate in these areas, decreasing precipitation rate in these seasonsresults in overestimation and error. Due to the significant precipitationrate in the cold months of the year (autumn and winter), decreasing factorand underestimation are expected to occur. In the very humid climate, a reduction factor should be appliedin the warmest months of the year (June, July, and August). Due to the low precipitation rate of these months and higherfrequency of cloudy days, overestimation will be reasonablein these areas. Due to underestimationin the coldest months of the year (autumn and winter), coefficients higher than one must be corrected. Conclusion Based on the results, the model used to correct precipitation in all 5 climates have reduced errors in precipitation measurement. However, this improvement was more obvious in arid and semi-arid climates. Sincea large part of Iran havean arid and semiarid climate, this calibration model is highly recommended. In addition, the final correction model does not depend on ground data and thus, applying the calibration modelto areas other than the specified stations will also be useful.
Mohammad Hossein Rezaei Moghaddam; Keyvan Mohammadzade; Majid Pishnamaz Ahmadi
Abstract
Extended Abstract
Introduction
With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of ...
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Extended Abstract
Introduction
With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of water, damming, increasing demand for agricultural products, pollution anddegradationofthe environment. Therefore, monitoring water bodies and retrievingrelated information are essential for management of environmental issues and decision making in this field. Accurate recognitionof water bodiesiscrucialin many applied fields, such as environmental monitoring, production of land cover and land use maps, flood risk assessing and monitoring, and drought monitoring.Modern methods such as object-oriented processing take advantage of remote sensing capabilities to make accurate and precise recognition of water bodies possible. Classical methods on the other hand, cannot accurately classify satellite imagery with similar spectral information merging into each other. This reduces the accuracy of pixel-based classification methods. Therefore, object-oriented processing of satellite images is used in the present study to obtain precise maps for the identification of waterbodies.
Materials and methods
A part of Aji Chai River, near the city of Khajeh in Harris County, has been selected as the study area. The total study area included 28 square kilometers. Based on the aim of the present study, the study area was selected in a way to contain linear features, arable lands, and other topographical and human-madefeatures (shading factor) which interfere with the extraction of water bodies and reduce the classification accuracy. Object oriented methods (the closest neighbor and fuzzy object-oriented methods) were used in the present study to identify and extract water bodies from high resolution images (Sentinel 2A imagery).
Discussion and results
Different functions used in OBIA techniques,such as GLCMtextual features, average number of bands in the image, geometric information (shape, compression and asymmetry), and normalized difference vegetation index(NDVI) were used in the present studyto precisely extract land cover. Moreover, algorithms with the highest membership degree in the class of water bodies were considered as effective factors in classification. Usual methods of extracting and monitoring water bodies use spectral information of pixels, and therefore, have limited ability in distinguishing water bodies from linear features, such as roads, clouds, shaded regions, and residential areas. These methods also have limited capabilities in mountainous areas, especially when they are required to separate water from snow. In other words, these methods cannot separate water bodies from regions with lower albedo. Therefore, the present study takes advantage of object-oriented methods (the nearest neighbor and fuzzy methods) and evaluate their effectiveness in the extraction of water bodies.
Conclusion
In this study, the nearest neighbor and fuzzy object-oriented methods were used to extract water bodies and their efficiencies were compared. To improve the results in the nearest neighbor method, the separation space between the samples was optimized using the FSO algorithm, then the water bodies were extracted with 95% accuracy and a Kappa coefficient of 93%. Findings of the present studyindicated that this method cannot distinguish water bodies from shaded regions, and linear featuressuch as roads, and residential areas, and categorizes these features as water bodies, which reduces the accuracy of the final results. In the next step, water bodies were once more extracted using object-oriented fuzzy model. In this method, membership degrees were first calculated for each sampleand then applied in the classification procedure. High accuracy of the results of this method (overall accuracy of 98% and a kappa coefficient of 96%) indicated the superiority of this method over the previous one (nearest neighbor). In this method, water bodies are completely distinguished from linear features such as roads, as well as shaded regions, clouds and residential areas. The results of this study can be generalized to other rivers and water bodies. Compared to classical methods, object-oriented methods are more time efficient and accurate.
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.
Mina Arast; Abolfazl Ranjbar; Khodayar Abdolahi; Sayed Hojjat Mousavi
Abstract
Introduction Evapotranspiration is one of the most important parts of the water cycle (Boegh and Soegaard 2004). Precise prediction of actual evapotranspiration () is essential for various fields, such as agriculture, water resource management, irrigation planning and plant growth modeling. Therefore, ...
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Introduction Evapotranspiration is one of the most important parts of the water cycle (Boegh and Soegaard 2004). Precise prediction of actual evapotranspiration () is essential for various fields, such as agriculture, water resource management, irrigation planning and plant growth modeling. Therefore, accurate determination of actual evapotranspiration has always been a major concern of experts in these fields. Due to the limited number of weather stations and the fact that collecting ground information is both time consuming and expensive, remote sensing and satellite imagerycan be a suitable tool in determination of actual evapotranspiration (Brisco et al., 2014). Satellite productions are usually divided into images with low, medium and high spatial resolution (Rao et al., 2017). Surface energy balance is a method usually used in combination withremotely sensed spatial data for estimation. Information collected from various sources, such as remotely sensedimageries and meteorological data, are used in this method. The present studyinvestigatesspatial distribution on different scales (from field- to regional-) using remotely sensed imagerieswithdifferent spatial and temporal resolution. TheSurface Energy Balance System (SEBS) is one of the most important methods used for the estimation of in remotely sensed images (Ochege et al., 2019). This model needs thermal maps produced using satellite images. Daily maps produced with RS are usually very large, and their pixelsize is usually so large that it can provide the spatial diversity found in the basins with respect to the errors (Mahour et al., 2017). Material and Methods In order to estimate the actual evapotranspirationin satellite images collected from Zayanderud basin,the effects of Co-Kriging downscaling of surface temperature (LST) were investigated in June 2017 using two different methods.To reach this aim, we first applied a co-kriging downscaling method to a low-power LST product collected from MODIS at 1000 meters. Then based on the results and using the SEBS system, the daily was obtained from images with average spatial resolution (250 m).In the second method, map produced usinghigh resoultion satellite imageswas downscaled to medium resolution (250 m). For both methods, 250 m resolutionMODIS NDVI products were used as co-variables.Then, validation was performed using Landsat-8 imagery, and land surface temperature was extracted from its thermal bands. SEBS algorithm was used to determine in Landsat 8 30-meter resolutionimagery. Accuracy of measurements wasexamined based on a comparison between down scaledLST and maps (250 meterresolution). Results and Discussion In the present study, mean LST equals 3/312 K (SD = 1.74) and average daily equals 12.5 mm / day (SD = 0.86). In the downscaling phase, the relationship between LST parameters and and vegetation index(as a co-variable)was investigated.Moreover, to investigate the relation betweenhigh resolution variables and NDVI, we re-sampled LST and variables from a 1000 mresolution to 250 mresolution.In250 mresolution, there is a negative linear relation (r=-0.85) between LST and NDVI, but the relation betweenand NDVI is positive (r = 0.80). Thus, lower LST (> 305k) indicates more vegetation (NDVI >0.3) inthe region, while higher LST results in lower NDVI or lack of vegetation. As a result, more vegetation can be observed in regions with higher(12 mm/day). Results indicated that the difference between average downscaled-SEBS (12.56 mm/day) and reference (13.11 mm/day) is negligible. The RMSE between the reference and the downscaled equaled 1.66 mm/day (r = 0.73), and RMSE between the reference LST and the downscaled LST equaled4.36 K (r = 0.78). Thus,values obtained from two downscaling methods were similar, but the obtained from downscaled LST showed a higher spatial variation. Therefore, LST has greatly influenced the production of maps using remotely sensing images, and Co-Kriging downscaling has been useful for providing daily maps with intermediate spatial resolution. Conclusion Evapotranspiration downscaling using the co-kriging method is not significantly different from the SEBS product and the results are similar. The results of -SEBS method isalso acceptable, but the derived from the SEBS algorithm is more variable due to the LST downscaling.
Sayyad Asghari Saraskanroud; Imanali Belvasi
Abstract
Introduction
The sun is known as the source of energy, the origin of life, and the origin of all other energies. The global solar radiation is one of the fundamental structures of any climatic range. Hence, recognition of the features and the prediction of these basic structures have a great impact ...
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Introduction
The sun is known as the source of energy, the origin of life, and the origin of all other energies. The global solar radiation is one of the fundamental structures of any climatic range. Hence, recognition of the features and the prediction of these basic structures have a great impact on energy-related planning. One way to gainaccess to the solar energy information is the direct measurements of solar energy by measuring devices such as Pyranometer and Pyrheliometer Unfortunately, the measurement of the solar radiation is not always carried out in many parts due to the high cost, maintenance and the need for the equipment calibration. Remote sensing techniques can be an appropriate alternative to the experimental and old methods in this field due to the high accuracy and speed in predicting the net radiation values. In general, remote sensing models have a better performance in estimating solar radiation, and can be used as one of the suitable and low cost tools for estimating solar radiation. Considering the importance of solar radiation as a clean, availableand free of any environmental destructive pollutants, identifying the radiation areas to be introduced to the relevant authorities is essential and the aim of the research. In this research, it was attempted to study the feasibility of utilizing solar energy in the region of Alashtar County using the SEBALalgorithm and remote sensing technology.
Materials and Methods
To investigate and study the feasibility of using solar radiation energy, the Landsat-8 satellite images over a 12-month period of the year 2017, 1: 50,000 digital topographic maps of the Armed Forces Geographic Organization and the climatic data of the study area including temperature, precipitation, wind speed and the number of sunny days were used. The ENVI software was used to perform the calculations related to SEBALmodel and the ArcGIS software was used to implement the model. In this study, the feasibility of using solar energy in Salsala city was studied using SEBALalgorithm and remote sensing technology. In this method, the instantaneous values of pure radiation are obtained by measuring the sun’s incident radiation from the cloudless images and using surface albedo, surface emission and surface temperature. In this method, instantaneous values of pure radiation are obtained by measuring the sun’s incident radiation from cloudless images using surface albedo, surface emission and surface temperature. After calculating the parameters of the SEBAL algorithm, the net surface radiation flux was calculated.
Discussion and Results
The results showed that the average maximum short-wave radiation was 996 watts per square meter in June and the minimum was 460 watts per square meter in January, while the highest amount of net radiation in September was calculated to be 602 watts per square meter and the lowest amount in January was calculated to be 261 watts per square meter. Also, the highest percentages of net radiation distribution in the ranges of 0-200, 200-400, 400-600, 600-800 and 800-1000 watts per square meter were in August, November, April, September and June. The highest percentage of net radiation distribution was in the range of 600-800 watts per square meter with 69.86% of total net radiation in September and the lowest percentage was in the range of 800-800 watts per square meter in January.
Conclusion
In order to carry out the research, the Landsat 8 ETM satellite images for the 12 month period of the year 2017 were provided. But, since the images of February, March and December were completely cloudy, they were not used. Then the preprocessing operation in ENVI software was used on all bands of images. The amount of pure radiation in the study area was calculated in watts per square meter in January to November in ENVI software environment and by the utilization of SEBAL algorithm, using the prepared images (Table 2). The results of Table (2) show that the average maximum input shortwave radiation is 996 watts per square meter in June, the lowest amount input is 460 watts per square meter in January, the highest output long wave radiation is 539 watts per square meter in July and the lowest output is 391 watts per square meter in January. Finally, the highest amount of net radiation reaching the surface of the Earth was 602 watts per square meter in September and the lowest amount was 261 watts per square meter in January. The highest percentage of net radiation in the range of 600-800 watts per square meter was 69.86% in September 2017 and the highest percentage of net radiation in the range of 600-400 watts per square meter was 60.12% in January 2017.
The difference in the amount of net radiation reaching the ground in the study area is due to the difference in the angle of the sunlight and the number of sunny hours in different months of the year.
The results obtained from of the information in Tables 2 to 11 prove this fact. Also, given the sensitivity of the photovoltaic cells that are sensitive to the solar radiation from the radiation threshold of up to 1000 watts per square meter and receive them, it can be concluded that solar radiation in the city of Alshtar has the potential to implement the solar photovoltaic plans in 9 months of January to November.
Hassan Emami; Hassan Shahriyari
Abstract
Extended Abstract Introduction Forests play numerous critical roles in nature. They stabilize and fertilize soil, purify water and air, store carbon, and nurture environments abundant in biodiversity. Moreover, forests offer numerous job opportunities and hidden wealth toany economy. Unfortunately, wildfires ...
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Extended Abstract Introduction Forests play numerous critical roles in nature. They stabilize and fertilize soil, purify water and air, store carbon, and nurture environments abundant in biodiversity. Moreover, forests offer numerous job opportunities and hidden wealth toany economy. Unfortunately, wildfires have turned into a serious natural risk nowadays. Wildfires are a natural disaster threatening forests and ecosystem, from local to global level. Evaluating the risk of wildfires is an important factor in fire management. This can be performed at different spatial and temporal scales: global and local; short term, and long-term. At global scales, it can contribute to the establishment of general guidelines for fire management at continental level, while at local scales,it is more suitable for resources focusing on preventing specific fires in small regions. Long-term estimation addresses general, more permanent planning of firefighting resources, which is related to the more structural factors affectingwildfires or their spread, such as topography or terrain characteristics, vegetation structure, human activities or weather patterns. Materials & Methods Wildfire risk has become a major concern in recent years, particularly in areas where human settlements are in close proximity to forests. Wildfire origin canbe determined largely by environmental factors. However, fire related data is either unavailable, or mostly incomplete. Thus, reaching an overall annual estimate of wildfires is difficult. Some common methods are used toestimate the risk ofwildfires, including qualitative methods, quantitative methods based on specialized knowledge (multi-criteria evaluation techniques), regression techniques (linear regression and logical regression), and artificial neural networks. Wildfire initiation and spread depend on several important factors, including precipitation, presence of ignition elements, factors like topography, temperature, thunder, spreading of fuel, relative humidity, wind speed, and etc. The present study integrates data produced by remote sensing with data received from geographic information system. It also takes advantage of LDCM satellite imagery, and digital elevation model, along with natural/human factors such as wind speed and direction, vegetation, land surface temperature, slope, proximity to roads and residential areas. The present study seeks to quantify environmental and human elements effective in occurrence and spread of wildfires in the protected jungles of Arasbaran. To this end, a risk zone map was produced for the area, along with a map for areas with 50% risk. In the present study, the final map of risk zone was produced using the Fire Risk Index (FRI) and spatial statistics method. Results & Discussion In the present study, factors such as land cover type, slope, distance from residential area, distance from the road, and elevation were taken into account. During the process, different indices were assigned to each class of these factorsbased on their sensitivity to fire or their flammability. Land cover was one of the most important factors affecting the occurrence of wildfires. Slope was another important factor with a significant influence on the spread of fire. This natural factor affects fire spread and fire intensity. Proximity of human settlements to jungles is another important factor which sometimes threatsjungles. Therefore, forests in proximity of human settlements face a higher risk of wildfires. Elevation is another important topographical factorclosely related to wind behaviour, with a significant role in fire spreading. In Arasbaran forest, northern, eastern, and north-easternareas are more elevatedand thus, more prone to wildfires. In this study, a combination of environmental and human factors was applied to produce fire hazard maps along with a map for areas with 50% risk of wildfire. Conclusion Occurrence and spread of wildfires depends on many factors, some of which are more important and play a more significant role in these fires. A risk zone map was produced for wildfiresusing an integrated method consisting ofremote sensing and GIS methods. Risk zone was divided into 5 areas, i.e. very low, low, average, high, very high.Results indicate that the methodology presented based on a combination of RS and GIS techniquesin this study, is a reliable approach and tool for the prevention and mitigation of forest fires. They are also useful for all active institutes working in crisis management and emergency services, while helping jungle protectingorganizations to prevent fires or manage them. In addition, quantitative results indicate that vegetation index with a correlation of 58.36%, and slope with a correlation of 38.38 are the most affective factors, and other parameters are in the next ranks.Moreover, land cover, land surface temperature, direction, and slope with 29.20%, 29.11%, 21.93% and 19.75% normalized correlation coefficient respectively, have the highest correlation with the map of fire risk zone. In addition, results of evaluating 50% risk zone map indicate that around 17% of the study area have a high fire risk and more than 50% of the area is located in a high fire risk zone. In addition to environmental elements, results indicate that proximity to the road was the most affective factor in the occurrence of fire. Quantitative results showed that roads and residential areas were at least 32% and at most 68% correlated with fire risk in the study area.
Elahe Khesali; Mohammadreza Mobasheri
Abstract
Extended Abstract Introduction Frost causes a lot of damage to the agricultural sector every year.From the meteorological point of view, when the temperature drops below a certain value, frost occurs. This threshold may vary from one crop to the other. Not much research has been done to predict frost ...
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Extended Abstract Introduction Frost causes a lot of damage to the agricultural sector every year.From the meteorological point of view, when the temperature drops below a certain value, frost occurs. This threshold may vary from one crop to the other. Not much research has been done to predict frost using remote sensing technology. Most of the models used to predict frost have been provided by climatologists, geographers and meteorologists based on data collected at meteorological stations.The measurements at meteorological stations are at a point and the number of these stations are limited. Therefore, depending on the surface coverage and texture around the station, the air temperature would only be valid in certain and limited distance from the stations. On the other hand, satellite images have relatively acceptable spatial resolution specially for using in the environmental studies.This indicates the necessity of using remote sensing data in many occasions including frost prediction.This work tried to predict areas at risk of frost using the NEAT method in the state of Georgia, USA. For this purpose, the MODIS satellite data and the data collected in meteorological stations of AEMN network are used. Materials and Methods The State of Georgia, in the southern part of the United States between latitude of 30o31’ to 35o north, and longitude of 81o to 85o53’ west with an area of 154077 square kilometers, was chosen for this case study.The reason for choosing this region was merely because of accessibility and availability of surface collected data mostly in cultivating and agricultural zones. In this study, data collected in 10 AEMN stations from 2005 to 2015 were used for modeling and evaluation. Also, data collected in 68 stations of AEMN were used for evaluation of model for two different periods. The satellite images used in this study is collected by Moderate Resolution Imaging Spectroradiometer (MODIS) on board of Terra and Aqua platforms. The MODIS products used in this study consist of LST (MOD11 and MYD11), lifted index (MOD07 and MYD07), total precipitable water (MOD05 and MYD05), and normalized differential vegetation index (MOD13). Also, in this study, to estimate air temperature in each 1 by 1 km grid box, the method developed by Mobashari et al. (2018) was used. The method offered an accuracy of 2.33 °C and a correlation coefficient of 0.94. Khesali and Mobasheri, 2019 presented Near-surface Estimated Air Temperature (NEAT) model in which extrapolation coefficients for air temperature to the next hours are calculated. To increase the accuracy of the NEAT model, it was recalculated using AEMN data at Aqua and Tera passing times. The methodology in this study consists of the following steps. • Selection of study area and collecting temperature data from AEMN meteorological stations, • Reproducing NEAT model coefficients usinga set of AEMN data, • Evaluating NEAT equation using another set of AEMN data, • Receiving and preparation of MODIS products and calculation of air temperature at the passing time of Terra and Aqua, • Applying NEAT to the MODIS images, • Producing Frost map using temperatures estimated by NEAT • Evaluation of frost prediction accuracy Results and Discussion In order to implement the model, Two periods were selected: 3–9 December 2006 and 3–11 April 2007 in which severe crop damage across the southeastern United States has happened (Prabha and Hoogenboom, 2008). First, the NEAT model coefficients are calculated using the AEMN network data, and evaluated for air temperature extrapolation to the next hours. Then, the air temperature was extracted using MODIS products for Aqua and Terra night time sensors. Finally, the NEAT model was applied to the air temperature extracted from satellite images, and the nighttime temperature was predicted from approximately 22:30 pm to 7:30 am of next day at 15 minute intervals. Then in the extracted images the air temperature was classified into two degreeintervals. Areas with temperatures below zero degrees Celsius are considered frost zones. Data from 68 AEMN network stations were used for evaluation. Statistical parameters like RMSE and variations of User Accuracy and Overall Accuracy were analyzed over the night. The RMSE value for all data, which is 13,840, is estimated to be 2.5 degrees. This parameter has an increasing trend from the satellite passing time to 6 hours and varies from 0.1 to 2.5 degrees Celsius. The results show the effectiveness of the proposed model in frost prediction. Conclusion In this study, AEMN meteorological data and MODIS satellite images were used for frost prediction. The study area is located in the Georgia state in the southeast of the US. Using the Neat model, air temperature is extrapolated during night in 15 minute intervals. Air temperature maps for two periods of time are produced. The results and accuracy assessment parameters show the ability of the proposed model in air temperature prediction and its effectivenessin frost prediction
Mohsen Shaterian; Seyed Hojjat Mousavi; Zahra Momenbeik
Abstract
Extended Abstract Introduction Knowing type and percentage of each land use and land cover are considered to be a fundamental need for understanding and managing an area. Given the ever-increasing changes in land use, managers and experts need to be aware of past changes and developments. This is because, ...
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Extended Abstract Introduction Knowing type and percentage of each land use and land cover are considered to be a fundamental need for understanding and managing an area. Given the ever-increasing changes in land use, managers and experts need to be aware of past changes and developments. This is because, policy making and solving existing problems require detecting changes and determining the trend of changes over time. Satellite data is one of the quickest and least expensive methods available based on which researchers can produce different land use map. In this regard, Landsat Satellite imageries are one of the most important data sources used to study different types of land use and land cover changes, such as deforestation, agricultural expansion and urban growth. Extracting information from satellite imagery through classification is one of the most widely used methods. One of the most important applications of remote sensing data is for investigating and discovering changes in phenomena with a spatial-temporal nature (i.e. phenomena whose position and status changes over time). In fact, change detection is the process of identifying and determining the type and extent of land cover or land use in a given period of time based on remote sensing images. The present study seeks to monitor land use changes in Shahr-e Kord during the period of 1985 to 2017, and to prepare land use maps of the area using Landsat satellite imageries. Materials & Methods In the present study, satellite imageries received from TM, ETM+, and OLI sensors of Landsat satellites in 1985, 2000, 2015, and 2017 were extracted from the United States Geological Survey (www.usgs.gov) and analyzed using different remote sensing software and geographical information systems like ENVI 4.7 and ArcGIS 10.4. In order to produce land use changes map, error correction was first performed. Then, images were processed using supervised classification method and maximum likelihood algorithm, which based on previous studies have a higher accuracy compared to other algorithms. In order to classify land use/land covers, a training sample was produced for each land use based on field observations, topographic maps (1:25000) produced by Iran National Cartographic Center, Google Earth imageries, and visual study of the imageries. Then, classification results were corrected using auxiliary data, visual interpretation, experiential knowledge, and GIS techniques. Prior familiarity with the region, visual study of imageries, previous experience and field operations revealed that following land uses exist in the region and are detachable on the images as well: a) urban, b) agricultural, c) industrial, d) meadow, e) airport, and c) other land uses (including pasture, rocky areas and areas without any specific land cover). Confusion or error matrix –including overall accuracy, producer’s accuracy, user accuracy and kappa coefficient- was also used to evaluate the accuracy of the classification. Also, urban land use changes were monitored using image differentiation functions. Results & Discussion After production of land use maps based on imageries received in 1985, 2000, 2015, and 2017, area of the six land cover classes was obtained. Results indicate that during these four periods (1985 to 2000), urban, industrial, agricultural and airport land uses have increased to 13, 111.7, 5.2 and 3.4 km2 (1.26, 10.16, 0.51 and 0.4 % increase) respectively, while meadows and other land uses have faced a decreasing trend. In other words, it can be concluded that most changes during this 15-year period occurred in meadows and other land uses. Since development of the airport have resulted in destruction of a large part of meadows, this land use have faced more severe changes. Land use changes from 1985 to 2017 indicate that 7.8 km2 of agricultural lands were transformed into urban land use, 1.4 km2 to industrial land use, 1.08 km2 to airport and 7.7 km2 to other land uses. Also, 20.5 km2 of other land uses were transformed into urban land use, 203.1 km2 to agricultural land use, 0.03 km2 to dried meadows, 0.17 km2 to airport and 14.5 km2 to industrial land use. 2.8 km2 of meadows were also transformed into agricultural land use, 0.05 km2 to industrial land use and 2.04 km2 to airport. During this period, urban and industrial land uses have remained unchanged. Conclusion Generally, results indicate that urban, industrial and agricultural land uses have developed over time, and these land uses have always had a positive increasing trend. While meadows and other land uses have had a decreasing and negative trend. This is due to the construction of Shahr-e Kord Airport, uncontrolled exploitations, digging wells and drought phenomena, which have led to a decrease in the level of water in aquifers and destruction of natural ecosystem in this region. In this way, previous meadows have turned into the source of intense dust generation in the city, which is a sign of desertification and ecosystem destruction. Due to drought and water scarcity in recent years, new deep wells have been dug with the aim of supplying water. This have occurred despite the critical condition of the meadows, and thus, have resulted in repeated protests by farmers and livestock farmers. Dramatic decrease in other land uses, including pastures, can also be attributed to recent droughts in Iran and intense dust generation. Increased population, increased human pressure on natural resources and also development of agricultural lands are among other causes of the present situation. Based on existing maps and satellite imageries, Shahr-e Kord is developing towards North and North West. In some areas, this development has occurred in pastures. Therefore, due to very high population density in the region which is still increasing, and also ongoing migration of villagers to the city, supplying appropriate accommodation and occupation for this population requires finding new suitable locations for urban and industrial development of the city. This development process should happen with correct management and according to the goals of sustainable development.
Hekmatollah Mohammad Khanlu; Mahdi Modiri; Elahe Khesali; Hamid Enayati
Abstract
Introduction
Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal ...
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Introduction
Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal infrastructure of coastal countries. Providing proper hydrographic services ensures safe and efficient sailing. Thus, development of hydrographic services on the national level can improve safety of mariners, and protect people’s lives and belongings on the sea, while providing some facilities for the protection of marine environment. The advancement of space technologies in recent years has increased the speed of spatial information production and facilitated sea monitoring.
Materials and Methods
Different methods are used for bathymetry. Lyzanga et al (1978) used a linear combination of the logarithm of corrected radiance ratio. This method is based on the simplification of Beer's physical model in which a linear equation of five unknowns is obtained for two bands. In 2006, Lyzanga et al. presented an improved version of their model. Using Tow-Bands Reflection Ratio, Stampf et al (2003) not only reduced the number of unknown variables in Lyzenga method, but also decreased the sensitivity of depth determination to different substrates. In this method, the difference between absorption properties of green and blue bands is used. TCarta is a global supplier of geospatial products. The company generated Satellite Derived Bathymetry (SDB) dataset by accurately extracting water depth from multispectral imageries received from the European Space Agency’s Sentinel-2 Satellite. The resulting bathymetric data had a point spacing of 10 meters, while measuring up to a depth of 15 meters. Data covered a 30-square kilometer area around Preparis Island on the Bay of Bengal.
The present article used images received from Sentinel-2 in 7 different periods for depth determination, and 1: 25,000 ADMIRALTY Nautical Charts for accuracy evaluation. Following the assessment of water transparency in received images, the 12/15/2018 image was used for depth determination. Case study area contains around 130 km along the Port of Salalah, Oman.
Results and Discussion
In order to implement the model, it is necessary to separate land from water in images using NDVI, NDWI, MNDWI and AWEI indices. The NDVI index has been used in this project. NDVI is primarily used to estimate vegetation cover, but since this index exhibits a negative value in areas covered with water, this property is used to provide a mask for separating land from water. In this step, 68 control points and 68 check points were selected from the existing ADMIRALTY map. The DN values of the corresponding pixels of the selected points were extracted from four 10-meter bands of Sentinel-2 images. The control and checkpoints and the DN value of their corresponding pixels were extracted in 4 separate files, then these 4 files were logged into the Bathymetry software and the parameters of LMR and Stumpf methods were calculated. The root mean square error (RMSE) and correlation coefficient (CC) were used to assess geometric accuracy. In order to extract necessary parameters for each model, RMSE= 2.15 m and CC= 92.5% were calculated at depth distances of 0 to 20m. Results indicates higher accuracy and stronger correlation of LMR findings. Therefore, this method was used for depth determination between 0 to 20 meters. The 5 parameters extracted from the Bathymetry software and the corresponding pixel values of the four bands with 10-meter resolution extracted from the Sentinel-2 image (received from the on 12-15-2018) were used as input. Linear Regression Model was applied to transform 4 bands of Sentinel-2 image into depth. The output of the model (depth) was presented as the Substrate DEM of the coasts of Port of Salaleh, Oman.
Conclusion
Hence, it can be concluded that Remote Sensing technologies can be used for depth determination and sea monitoring at critical times (during wars or other periods of insecurity) for an acceptable time period. It also provides an appropriate context for bathymetry of inaccessible coastlines and monitoring of strategic widespread water zones. In this way, the depth of sea bed in shallow areas is extracted using spectral analysis of satellite data and different models.
Milad Soltani; Adel Soltani; Mahin Kalehhouei; Karim Solaimani
Abstract
Introduction
Drought is a serious danger with very extensive impacts on the soil, economy, and the threat to the livelihood and health of local communities. This disaster as an unpleasant climatic phenomenon that directly affects the communities through restrictions on access to water resources, causes ...
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Introduction
Drought is a serious danger with very extensive impacts on the soil, economy, and the threat to the livelihood and health of local communities. This disaster as an unpleasant climatic phenomenon that directly affects the communities through restrictions on access to water resources, causes high economic, social and environmental costs. Meteorological drought indicators are calculated directly from meteorological events such as precipitation, and in the absence of these data, drought monitoring will not be useful. Due to the fact that meteorological drought indicators are only valid for a single location and do not have the required spatial resolution and are also dependent on weather station information, and these stations are often distributed distantly, the reliability of these indicators has been questioned. Given the characteristics of satellite data such as spatial and temporal resolution, extensive coverage of studied areas, and direct investigation of vegetation status by satellite indices, many studies have been carried out for drought modeling using this technology and these indicators. Over the past four decades, far-reaching drought monitoring tools have been widely developed and drought monitoring models are widely proposed, which are generally based on vegetation indices, surface temperature, humidity and reflection in the visible and infrared regions. These include leaf water content index, vegetation cover index and temperature - drought - vegetation index. Therefore, remote sensing techniques can be a useful tool in drought monitoring. The purpose of this study is to monitor drought and vegetation health in the city of Kermanshah using LANDSAT satellite imagery. For this purpose, first, by examining the rain-gauging and synoptic data of existing stations and using the standard precipitation index model, the driest year and one wet year were selected as the sample. In this study, two years of 2015 and 2016 were selected as the dry and wet years and then, the vegetation cover of the region was compared with the Landsat images. To use these images, it is first necessary to make sure that there is no geometric error. For this purpose, the road vector layer was used, which was revealed that the images have geometric errors. Images with less than a half-pixel error were corrected geometrically using 21 and 24 auxiliary points. The adaptation of the vector layers with the roads existing in the image indicated the accuracy of the correction. At the next stage, the driest year and one wet year were selected as samples by examining the rain-gauging and synoptic data of the existing stations and by using the standardized model of rainfall index. At the next stage, the Temperature Condition Indices and Vegetation Health Index (VHI) were compared in two wet and drought periods were studied in order to determine the differences of these indices during a dry year and a year with high precipitation. For this purpose, each of the aforementioned indices was built using the LANDSAT-8 imagery, and the stages of building these indices were subsequently presented. The required pre-processing and processing as well as the geometric and radiometric corrections were first performed on the satellite images. Then, temperature condition indices, vegetation status index and vegetation health index were prepared for drought monitoring. Considering that, the meteorological drought indices are only valid for a single location and lack the required spatial resolution and are also dependent on the information of the meteorological stations, and these stations are often distributed far apart from each other, the reliability of these indexes has been questioned. Satellite data characteristics like high spatial and temporal resolution, extensive coverage of the study areas, and direct survey of the vegetation status by the satellite indexes have led to a large number of studies on drought modeling using this technology, and the confirmation of the use of these indices. The aim of this study is to determine the moisture, heat and health of the vegetation using the LANDST images. Thus, the results of the study in the next stage indicated that the LANDSAT images and the built indices have the required capabilities to monitor drought. The results of this research can be a proper option for decision-makers to effectively supervise, examine and resolve the drought conditions and double the necessity of profile definition. Supplementary studies are suggested for spatial drought monitoring by satellite imagery through ground measurements of the quantitative changes in the coverage and temperature of the earth’s surface. There are limitations in the use of NDVI and satellite thermal bands. These include weather and cloud conditions that should be considered. Using maps obtained from the drought monitoring and evaluating indices can help improve drought management programs and play a significant role in reducing the effects of drought. Using vegetation health status index, it was determined that the vegetation status has had a lot of changes during drought compared to the wet period, hydrological drought has had a major share in the destruction of vegetation and drying of the lakes and, consequently, the abandonment of agricultural lands and the lack of access to alternative water resources, as well as the lack of groundwater resources or the lack of alternative surface water resources have intensified, and it seems that, this part of Iran will face numerous problems if the drought continues in the coming years and the appropriate methods are not used to deal with it. Also, given that the water resources of the region are going to decrease in the coming decades, the necessity of using comprehensive water management methods in all sectors, including the reserve, transfer and distribution sectors seems very essential and inevitable. Finally, it is expected that the trend of destruction of vegetation decreases in the future by applying proper management practices, sustainable water distribution, regional negotiations, methodical agriculture as well as the establishment of optimal hydrological conditions.
Manuchehr Farajzadeh; Mohsen Ahadnejad; Hadi Targholizadeh
Abstract
Extended Abstract
Introduction
Due to the large dimensions of earthquake damages and losses, more rapid procedures are required to identify damaged buildings. Field studies and old procedures are no longer efficient enough because of being time consuming, costly and requiring lots of workforce. ...
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Extended Abstract
Introduction
Due to the large dimensions of earthquake damages and losses, more rapid procedures are required to identify damaged buildings. Field studies and old procedures are no longer efficient enough because of being time consuming, costly and requiring lots of workforce. This research seeks to identify the buildings damaged by earthquakes through analyzing the spectral response of urban houses to the reflective bands and effective factors, before and after the earthquake, for recognizing buildings damaged in the earthquake and compare the results of the reflective bands with each other, and then, determine the most efficient band among them. The earthquake stricken city of Bam was selected as the case study of this research. In order to identify the damaged urban houses, satellite imagery and remote sensing reflective bands were considered for detecting the changes, distinguishing the bands, and analyzing the spectral reflection profile.
Materials and Methods
The high resolution Quick bird satellite, photographed the city of Bam just eight days after the earthquake on January 3, 2004. The satellite also had taken a clear image of Bam about three months before the earthquake on September 30, 2003, that, with regard to the objectives of the research and the capabilities of the images taken, these Quick Bird satellite images were selected to study and investigate in this field. The method of this research is to analyze the spectral reflection profile and the factors affecting it. Since the multi-spectral remote sensing is a set of reflective, emissive or backscattering energy from the study area in electromagnetic multi-spectral bands, the aim of this research is to describe why terrestrial phenomena show different responses to the electromagnetic spectrum, and to analyze their spectral curve as well. To this end, we established an analytical strategy to achieve a better interpretation of the blue (band 1: 450 - 520 nm), green (band 2: 520 - 600 nm), red (band 3: 630 - 690 nm), and infrared (band 4: 760 - 900 nm) reflective bands. And the earthquake stricken city of Bam was selected as the case study of this research in order to identify the damaged urban houses by analyzing the spectral reflection profile and factors affecting it.
Results and Discussion
Urban housing is composed of various materials (concrete, asphalt, metal, plastic and soil) by man in various ways for building houses. When earthquake strikes, these houses might be destroyed. Therefore, satellite multi-temporal images before and after the earthquake were selected as data for analyzing the electromagnetic spectrum curve of the study area. In this research, the vulnerability of urban houses is different from one place to another. Therefore, educational samples of the case study from different parts of the city such as those which have been completely destroyed, partially destroyed or have remained intact, were selected. Then, the spectral response analysis of the urban houses was carried out in 4 blue (band 1: 450 - 520 nm) green (band 2: 520 - 600 nm) red (band 3: 630 - 690 nm) and Infrared (band 4: 760 - 900 nm) reflective bands before and after the earthquake in order to identify effective factors and the bands independent of these factors comparing with other bands. The results show that, before the earthquake occurs, some factors such as shadows cause a sharp decrease in the reflection in all bands, the atmospheric scattering at short wavelengths with increasing spectral reflection, the angle of sunshine, type of material, the surface smoothness or roughness of the surface, the time of the day, the height and texture had a great impact on the 3 blue, green and red reflective bands. Infrared band with a rectangular shape in spectral curve is a band independent of the aforementioned factors (with the exception of the shadow and surface smoothness of the materials).
Conclusion
The results obtained from analyzing the spectral response of the urban houses in four reflective bands (Blue, Green, Red and Infrared) indicated that in general, the urban houses had high reflection and shadows had less reflection before the earthquake. After the earthquake, urban houses showed an irregular and significant reduction in spectral reflection, and the spectral reflection curve was irregular as well. However, the method of analyzing the spectral reflection profile is a point estimation method and does not result in a map, and this method is often used to check the accuracy of other methods.
Hamid Ebrahimy; Aliakbar Rasuly; Ahmad Ahmadpour
Abstract
Extended Abstract
Introduction
Land use is one of the most important indicators of economic and social development in urban areas, and has resulted in extensive changes in available structures and procedures of these areas. Therefore, human activities are known as one of the main principles and components ...
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Extended Abstract
Introduction
Land use is one of the most important indicators of economic and social development in urban areas, and has resulted in extensive changes in available structures and procedures of these areas. Therefore, human activities are known as one of the main principles and components of change in land use. Generally, land use changes are inevitable product of interactions between human activities and environmental elements. Remote sensing technology with capabilities such as providing update and reliable information about natural and urban areas, digital processing of satellite imageries, providing the possibility of temporal and spatial comparing of different phenomena, diversity of products, and etc. is considered to be a powerful tool in improving the efficiency of urban management. Consequently, remote sensing data are used to determine type, quantity and location of land use changes. Moreover, remote sensing technology is used extensively in land use maps all over the world. Many models have been applied to predict land use changes, which due to the complex, dynamic, and non-linear nature of the issue gained little attention. However, CA-Markov model, which is a combination of Markov chain and cellular automata, is commonly considered to be an appropriate and good method for spatial-temporal modelling of land use changes. In the present study, land use changes were investigated for a 15-year period in Shiraz using object- based image analysis. Then, a land use map was produced using cellular automata-Markov (CA-Markov) model to predict land use changes in the study area in 2020.
Material & Methods
The present study includes two main phases. In the first phase, land use map of Shiraz was produced using Fuzzy object based analysis of satellite imageries. In the second phase, modeling and predicting of land use changes in 2020 were performed. Landsat imageries of the study area in 2005, 2010 & 2015 were used in this research. After preprocessing and preparing the imageries, segmentation procedure was performed as the first stage of object based classification using multiresolution segmentation algorithm. The nearest neighbor algorithm was used for object based classification of satellite imageries. Classification conditions were defined in accordance with each class properties, and classification was performed based on fuzzy operators of the classification operation. In CA-Markov model, the possibility of changing from one class of land use to another was calculated using transfer matrix table. Then, land use map of future years will be predictable in accordance with the transfer probability matrix, and desired time interval.
Result & Discussion
In this study, scale parameter of 10, shape index of 0.4, and compactness index of 0.2 were extracted as the optimum conditions for segmentation. Apart from spectral data, information regarding the location, context, texture, normalized difference vegetation index, enhanced vegetation index, and digital elevation model were also used to improve the efficiency of classification phase. The results of model validation shows an overall accuracy of 89% and kappa coefficient of 0.87. Therefore, the results of CA-Markov model shows a very good potential for predicting land use changes in Shiraz. Thus with the adjustment and calibration of model parameters and based on land use maps of 2010 and 2015, Shiraz land use in 2020 was predicted.
Conclusion
Due to the complexity of modeling dynamic changes in urban land use, utilizing efficient and update methods of data analysis is crucial. Therefore, satellite imageries and object based image analysis techniques were used to prepare land use map of Shiraz based on the data collected over a 15 year period. By considering the defined land use classes (residential area, barren lands, street network and urban green space), optimum image segmentation parameters were found. Then, classification conditions were defined for each class using the nearest neighbor algorithm and fuzzy operators. In this way, image classification was performed. By analyzing land use changes during the 20-year period, we understand that residential area has increased from 38 square kilometers in 2005 to 142 square kilometer in 2020. Additionally, green space area faced a reduction of 4 km in the first 5 years of the period, while in the next 15 years green space area shows an increasing trend.
Mehdi Najafi Alamdari; Masoud Torabi Azad; Ali Hakimi
Abstract
Extended Abstract
Introduction
The Mean Dynamic Topography (MDT) of the seas is a quantity which comes from subtracting the Geoid Height (GH) from the Mean Sea Surface (MSS) at every point on the sea. The direction of geostrophic currents is obtained through the calculation of the MDT slope relative ...
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Extended Abstract
Introduction
The Mean Dynamic Topography (MDT) of the seas is a quantity which comes from subtracting the Geoid Height (GH) from the Mean Sea Surface (MSS) at every point on the sea. The direction of geostrophic currents is obtained through the calculation of the MDT slope relative to the Geoid. In this research, a series of GOCE geopotential coefficients resulted from the 4 year collection of GOCE observations was used to estimate the reference geoid height in the Persian Gulf, the Oman Sea and the Indian Ocean, i.e., in the area of interest. Two MDT models data were available at the time of performing this research: Denmark Technical University’s model, named ‘Mean Dynamic Topography of Denmark Technical University 2010’ (MDT_DTU_2010) which has been available on a geographical grid of 2 arc minutes spacing (Knudsen & Andersen, 2010). This model is based on the mean sea surface topography model MSS_DTU_2010 and the 2 month of GOCE geopotential data for the Geoid as the reference surface. The second model is the Mean Dynamic Topography Centre National d'Etudes Spatiales collecte localisation satellites 2009 (MDT_CNES_CLS09) with 15 minutes resolution (Rio et al, 2011). This model contains the east-west and north-south geostrophic current components with itself as well. It is based on MSS_CLS01 (Hernandez and Schaeffer, 2001) and 4.5 years of GRACE geopotential data used for the reference geoid.
Materials and Methods
In this research a new Mean Dynamic Topography (MDT) model with the name of MDT_IAU_TN_2014 is presented. Also, the surface permanent current vectors in a grid with 2 minutes resolutions is computed in the Persian Gulf, the Oman Sea and the north of Indian Ocean. This MDT is formed by a Mean Sea Surface (MSS) model computed from 6 altimetry satellites data (Topex/Poseidon, Jason 1 and 2, ERS 1 and 2 and Geosat Follow-On) and GOCE satellite data with 21 and 4 years ranges in 1992-2013 are calculated. The first step for the Mean Sea Surface (MSS) computation is to calculate the mean of Sea Surface Heights (SSH) along the repeated (in time) sub-tracks of altimetry satellites over the years available in the area of interest. The mean value of SSHs over time in a same track is then called Mean Height (MH). The Basic Radar Altimetry Toolbox (BRAT) version 3.1.0 was used for the MH computation. The correction term includes the tidal periodic variations, physical earth corrections such as troposphere, ionosphere, and sea state biases. All of these corrections are considered from the satellite handbooks T/P (AVISO/ALTIMETRY, 1996), J1 (AVISO and PODAAC USER HANDBOOK, 2012), J2 (OSTM/Jason-2 Products Handbook, 2001), ERS (RA/ATSR products - User Manual, 2001), GFO (GEOSAT Follow-On GDR User's Handbook, 2002). Among altimetry satellites, T/P (J1 and J2) has the highest orbit and longest data sets so it has been selected as a reference for corrections.
Results & Discussion
To homogenize the spectral of MSS and the Geoid, a truncated Gaussian filter with 1.386 degree radius has been used. MDT results have been compared with two global model and have 0.033 and 0.051 RMS of differences in order. Among altimetry satellites used in this research, J2 and GFO satellites have the ability to measure shallow waters. Hence, the data provided by these satellites in shallow waters, i.e. Persian Gulf are valuable. MHS differences between E1 and T/P are larger than the MHS of other satellites, because there are differences between the two missions, i.e., there are 8 km distances between E1 sub-tracks at equator but long repeatability period of 35 days of data acquisition time and T/P sub-tracks spacing are 315 km at equator and short repeatability period of 9.9 days. Also, the orbit elevations are different: T/P at altitude of 1336 km and E1 at altitude of 785 km. Inclusion of E1 data in the MSS_IAU_TN_2014 solution would globally decrease the RMS difference of the solution relative to the MSS_CNES_CLS_2011 model from 0.4 m (without E1 data) to 0.1 m. This improvement by the E1 data is probably due to the higher resolution of the data in the region of interest.
Conclusion
Changing the filtering radius of 1.386 degree down to lower degrees until 1 degree would increase the MDT_IAU_TN_2014 differences (relative to the MDT_DTU_2010) and MDT_CNES_CLS09 from 0.033m and 0.051m RMS up to larger values. At the 1.386 degree, the differences are minimum. For filtering radiuses of more than 1.386 degree the MDT surface would become unreasonably much smoother and the RMS difference would increase. Geostrophic and Ekman velocity currents using 22 years data of surface wind has been calculated. Total currents of the released model in this research have been compared with OSCAR in-situ data and have 0.047 and 0.031 meter RMS of differences in North-South and East-West current components. The total currents from MDT_IAU_TN-2014 model vary between 0 to 0.61 m/s in the north Indian ocean region. The comparison shows that all three models show almost the same range of variations in the region of interest. SLA an In-Situ data could be used to make the MDT_IAU_TN_2014 independent from any other models. The lack of In-Situ data in the region of interest forced MDT_IAU_TN_2014 to use MDT_DTU_2010 to cover filtered parts. Also using other gravity models with higher Spherical harmonic coefficients degree and orders such as EIGEN-6c and EGM08, would make filtering not needed in the dynamic modeling.
