Kosar Kabiri; Sayyed Bagher Fatemi
Abstract
Extended Abstract Introduction Different image fusion methodsprimarily seek to improve spectral and spatial content of the final result. However, the final fused image often suffers from some spectral distortions. Moreover, some image fusion methods are too slow. Image fusion using IHS transformation ...
Read More
Extended Abstract Introduction Different image fusion methodsprimarily seek to improve spectral and spatial content of the final result. However, the final fused image often suffers from some spectral distortions. Moreover, some image fusion methods are too slow. Image fusion using IHS transformation is known as a fast image fusion method. Unfortunately, the resulting image fused with IHS also suffers from some spectral distortions and therefore several versions of this method have been developed. Defining weights of each band for generation of the intensity component is one of the main problems discussed in the literature. Spectral response curves are used as one of the major sources for defining relative weight of each spectral band. Scientific reports indicate that spectral response curves can improve the quality of the final fused image. Weights of each individual band is often calculated based on the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands. But, information like the non-overlapping areas of the curves are also considered to play a role in the calculation of the weights. The present comparative studyinvestigatesthe potential of using this information. Materials & Methods A multi-spectral Geoeye-1 satellite image with 2 meter spatial resolution, four spectral bands and the corresponding panchromatic band with a spatial resolution of0.5 meter were used to test the idea. Seven variants of the FastIHS fusion method have been developed based on different approaches of intensity component estimation using the information obtained from spectral response curves. The test methods have been compared with the original FastIHS image fusion method. The only difference of these methods was in the way they calculate the weights of each band. The seven tested methods included: 1) ratio of the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands and multispectral response curves, 2) the ratio of the area of the multispectral band’s response curves and the area of the panchromatic band’s response curve, 3) the inverse of the distance between the central wavelength of the panchromatic and multispectral response curves, 4) the ratio of the overlapping area of the spectral response curves of the panchromatic and multi-spectral bands and the area of the panchromatic response curve, 5) the ratio of the non-overlapping area of the panchromatic and multi-spectral response curves and the area of the multispectral response curves, 6) ratio of the overlapping area of both panchromatic and multi-spectral response curves and the area of the panchromatic response curve minus the area of the multispectral response curves, 7) the ratio of the panchromatic and multispectral response curves’non-overlapping area and the area of the multispectral response curves multiplied by the ratio of the area of the multispectral response curve and the area of the overlapping regions of the panchromatic and multispectral response curves. Results & Discussion In order to evaluate the fused images, four criteria were used, including ERGAS, RMSE, Correlation Coefficient, and edge correlation with panchromatic band. In order to calculate edge correlation Coefficient, a Sobel filter was applied on the panchromatic and fused bands. Then, the correlation coefficient between the individual filtered spectral bands and the filtered panchromatic bands was calculated. All eight methods were ranked based on the four evaluation criteria. Because of the inconsistencies in the ranking results, the four criteria have been merged and a new ranking method was obtained based on the final results. Based on this final ranking, the fifth method is in the first rank and the second method is in the eighth rank. Therefore, the sorted list of the methods based on the final ranking is: IHS5, IHS3, IHS6, IHS1, IHS4, IHS7, FastIHS, and IHS2. As the ranking shows, almost all tested methods have a higher level of accuracy as compared to the base method (FastIHS). Conclusion The results indicates that using the information obtained from the spectral response curves can improve the final results of the FastIHS image fusion. This information can improvethe fusion speed and reduce spectral distortions of the final fused image. Unfortunately, the spectral feature of the data is preserved and the total number of detected edges is decreased. Spectral response curves are directly tied with the physics of the imaging, therefore using their information can produce some natural fused images with better visualization and enhanced spatial contents.
Zahra Banimostafavi; Saeed Farzaneh; Mohammad Ali Sharifi
Abstract
Extended Abstract:
Introduction
Nowadays, engineering structures face many threats. Natural and human activities can result in deformation and displacement of dams, bridges, and towers. As a result, any crack in the body of these structures is important and may have dangerous consequences. To prevent ...
Read More
Extended Abstract:
Introduction
Nowadays, engineering structures face many threats. Natural and human activities can result in deformation and displacement of dams, bridges, and towers. As a result, any crack in the body of these structures is important and may have dangerous consequences. To prevent catastrophes, the behavior of these structures should be monitored permanently during the construction phase and after opening.Nowadays,thebehavior of engineering structures such as dams, power plants, and towers is considered to be especially important. Three different methods are usually used to measure such behavior: classical, satellite and precise instruments.
Materials and methods
Modern equipment is considered to be a crucial factor in controllingpossible changes and preventing human errors. Therefore, different sensors are installed in the structure to measure tensile and shear flexibility during the construction phase. Moreover, data received from these sensors is analyzed permanently during the service life to ensure sustainability of the structure. These tools make internal analysis of these structures possible. Analyzing the behavior of engineering structures is considered to be one of the most important tasks in the field of geodesy. Inaccurate analysis of displacements can have deadly effects. Various methods are used to measure such displacements, which are divided into two categories: robust and non-robust methods based upon the results of the epoch adjustment. To find deformations, a geodetic network should be defined in the first step. If two epochs are not measured in the same datum, the results will not be reliable. Displacement can be measured in two ways: Absolute and Relative. In the absolute method, some points are considered to be stable, while in the relative network, all points are considered to be unstable, and the problem is solved based upon this hypothesis. The method of relative network is used in the present study. Regarding network geometry, displacement analysis is performed using two methods:single and combinatorial. Moreover, displacement analysis is divided into two categories of robust and non-robust methods. Iterative Weighted Similarity Transformation (IWST)and Minimum L1 norm are among robust methods which calculate the matrix of displacement by minimizing the first and second norm. Global Congruency Test (GCT) is a non-robust statistical method used to determine unstable points in geodetic networks. Robust and GCT are among classical methods used to discover unstable points in geodetic networks, while Simultaneous Adjustment of Two Epoch (SATE(is a new method used to achieve this purpose. Combinatorial methods are also considered to be a suitable alternative method used for detecting unstable points in a geodetic network. In our previous study, “evaluation of single-point methods used fordetecting displacement in classical geodetic networks”, single-point methods of detecting unstable points were investigated and the SATE method was selected as the optimal method. Unlike single-point methods, these methods examine all points of the geodetic network simultaneously to discover unstable points.
Results and discussion
The strong dependence of these methods on the network geometry makes discovery of all unstable points impossible. Combinatorial methods are considered to be a suitable alternative method used to detect all unstable points in the geodetic network. These methods does not have a strong dependence on scale and the network geometry. Multiple Sub Sample and M-split methods are classified in this category. These methods can detect unstable points efficiently. The present study takes advantage of simulated datato evaluate combinatorial methods such as Multiple Sub Sample (MSS) Angles, MSS-distance difference, and M-split and compare them with the SATE method with the aim of choosing the optimal method. Then, unstable points in the real network of Jamishan dam in Kermanshah Province will be discovered using the identified optimal method.
Conclusion
The present study identifies the best method between single and combinatorial methods. The best method can detect most unstable points and has the lowest dependence on geometry, scale and other factors influencing the results.According to the results, Multiple Sub Sample with distance difference is selected as the best method.
Masoud Taefi Feijani; Saeed Azadnejad
Abstract
Extended Abstract
1- Introduction
The present study primarily sought to present a new FCD model to eliminate two limitations of the initial FCD model.These limitations included the fact thatimplementing the initial FCD model for sensors without a thermal bandwas not possible, sincethe model ...
Read More
Extended Abstract
1- Introduction
The present study primarily sought to present a new FCD model to eliminate two limitations of the initial FCD model.These limitations included the fact thatimplementing the initial FCD model for sensors without a thermal bandwas not possible, sincethe model took advantage of a combination of shadow index and thermal index to detect black soil and calculate advanced shadow index.To overcome this limitation, we replaced thermal index with NLI and GNDVI indices, and combined shadow, BI, NLI and GNDVI indices to detect black soil and calculate advanced shadow index.Defining a global threshold for thermalindex used fordetectingblack soil was the next limitation of initial FCD model.Due to variations in regional climate and temperature, selecting a global threshold for the whole scene does not seem logical.Thus, a local thresholding process was used to define a threshold level for BI, NLI and GNDVI indices.In this regard, the study sitewas divided into 14 sections and an appropriate threshold wasselected for each section.A digital elevation model was also used to define a specific threshold level for forests in flat areasand elevated areas.
2- Materials & Methods
2.1 Study area and dataset description
The present study was performed within the basin of the Caspian Sea.Drainage basin is considered to be a standard unit ofstudy in environmental studies and thus due to the applied nature of the present study, the Caspian Basin was selected as our study site. In this study, a new FCD model was implemented for data collected from Landsat 5(1366) and Landsat 8 (1396).
2.2 Proposed approach
In the present study, an improved FCD model was obtained by adding two steps to the initial FCD model. In the following paragraph, these two steps will be explained.
2.2.1 Removing thermal index
The first limitation of the initial FCD model lies in the fact that implementing this model for data collected bysensors without thermal band is impossible, because advanced shadow index in the initial FCD model is calculated by combining shadow and thermal indices. Thermal index is only used to separate the shadow of vegetation cover from black soil.In order to overcome this limitationin the improved FCD model, thermalindex is replaced with NLI and GNDVI indices. In this way, black soil and vegetation shadows are separatedusingacombinationofshadow, BI, NLI and GNDVI indices.
The NLI index can be calculated using(1):
(1)
The GNDVI index is also calculatedusing(2):
(2)
2.2.2 Local thresholding
In the initial FCD model,black soil identification and shadow index improvement (advanced shadow index calculation) wereperformedusingthresholdingand based on the combination of shadow and thermal indices.In this model, a number is selected as the threshold of the heat index, and shadow index pixels with values less than this threshold are considered as black soil.Obviously, it is practically impossible to define a threshold and calculate advanced shadow index for large scale areas.
Localthresholding is a much more accurate method of thresholding, which is also used in the improved FCD model.In this method, image received from the study site was divided into 14 sections and a suitable threshold value was selected for BI, NLI and GNDVI indices in each section to calculate advanced shadow index.
Moreover, different thresholds were selected forforests in flat areasand elevated areas.In this regard, digital elevation model of the region was used to separate low-altitude and high-altitude areas.
3. Discussion& Conclusion
Results indicated that the proposed improved FCD model has provided a more accurate estimate of forest canopy density as compared to the initial FCD model.
According to the results, the overall accuracy and kappa coefficient of the initial FCD model were 86.24% and 68.43%, respectively.However, the improved FCD model had an overall accuracy of 96.98% and a kappa coefficient of 92.31% which confirms improved performance of the model.
Moreover, the statistical analysis of changes in the canopy densityindicated that the total area of Hyrcanian forests increased by about 161,963 hectares from 1366 to 1396. This includes an increase ofabout 79, 50 and 33 hectares in Mazandaran, Gilan and Golestan provinces, respectively.
Parisa Golshani; Yasser Maghsoudi; Hormoz Sohrabi
Abstract
Extended Abstract Introduction Estimation of forest Carbon stocks plays an important role in assessing the quantity of carbon exchange between the forest ecosystem and the atmosphere. Direct methods of measuring carbon stock are not economically efficient. Optical remote sensing methodsalso have limited ...
Read More
Extended Abstract Introduction Estimation of forest Carbon stocks plays an important role in assessing the quantity of carbon exchange between the forest ecosystem and the atmosphere. Direct methods of measuring carbon stock are not economically efficient. Optical remote sensing methodsalso have limited capability in predicting forest biomass, because the spectral response of optical images is related to the interaction between solar radiation and canopy, especially in mature forests. These obstacles limit the efficiency of optical sensors for forest biomass estimation. Recently, airborne data has received a great deal of scientific and operational attention for estimation of forest features. LiDAR data also faces challengessuch as limited efficiency in large areas, high costs and large data volumes. In contrast to the optical and LiDAR systems, SAR systems have some advantages, such as the possibility of data collection in any weather condition, penetration through clouds and canopy, and easy access. The potential of SAR images with quad polarization for the estimation of Iranian Hyrcanian forests biomass will be investigated. The main purpose of this study was to investigate the efficiency of ALOS-2 /PALSAR-2 backscattering coefficients andpolarimetric features in leaf-on and Leaf-off crown conditions, evaluate the linear regression model and select the most appropriate variables for biomass estimation. Material and methods The study area is located in a part of forests of Mazandaran province. The region forms a part of the deciduous broadleaf temperate plain forests. The forestsunder study was classified into 4 major types: (1) Forest reserve, (2) Natural forest, (3) Degraded forest and (4) Mixed species forest plantations. 115 circular sample plots (each including 0.1 hectares)were collected from the 4 different sites with various forest structures and biomasses. In each sample, tree species and diameter at breast height (DBH) of all trees with DBH > 7.5 cm were recorded. Allometric equations were used to convert tree diameter to biomass. The present study is based on polarimetric L-band PALSAR-2 data collected in spring and winter. Backscattering matrix was generated using the PALSAR data which consists of amplitude and phase information. Speckle noise filtering was performed using the Refined Lee adaptive filter. Following the filtering, all polarimetric features were extracted. After converting the SAR products to NRCS, geometric correction and georeferencingwere performed and the average backscattering coefficient (sigma naught value)was extracted for each sample plot by overlaying the AOI layers on corresponding SAR images. Finally, the relationship between forest biomass and backscattering intensity was investigated. Results and discussion The resultsvary regarding to the forest type, the range of biomass and forest canopy cover percentage.Forest type and biomass range as well as canopy cover percentage affect the scattering mechanism and correlations between biomass and SAR backscattering coefficient. Canopy cover percentageofthe 1stand 4thsites were over 90% and consequently, the sensitivity of HV backscatter value to biomass was higher than HH and VV. In the 2nd and especially 3rd sites, the correlation between HH backscattervalueand AGB was better than its correlation with HV backscatter. This is mainly because of the canopy structure in these sites which is not complete and the fact that the sensitivity of HH backscatter value to biomass is higher than HV. Results indicate in the 1st and 4th sites, the correlation between volume scatter component of decomposition methods with AGB was better than its correlation with double-bounce scatter component. In contrast, the double-bounce decomposition componentsexhibited the best results in the 2nd and 3rd sites. These findings are in agreement with the results obtained from the T3 matrix components. The least correlation value was observed between Freeman decomposition components and AGB. The volume scatter component of Cloude and also double-bounce component of Freeman did not provide suitable results. Results also indicate higher efficiency of images collected in spring as compared to those collected in winter.Linear regression results show that in the best possible situation, RMSE of the first forest habitat was 34.68 t/ha, and 30.09, 27.07 and 23.69 t/ha were estimated for the 2nd, 3rd, and 4th forest habitats, respectively. Therefore, it seems that classification of forests is necessary before biomass estimation. Conclusion The potential of PALSAR-2 data for Hyrcanian forest biomass estimation was assessed in this study. We demonstrated that L-band data are sensitive to the above-ground biomass (AGB) of Hyrcanianforestsand can be used to provide accurate estimates of biomass. Findings confirmed that decomposition methods are more efficient than backscattering coefficients for biomass mapping.
Sara Attarchi; Najmeh Poorakbar
Abstract
Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational ...
Read More
Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational Land Imager (OLI), provides multi-spectral images with 30-meter resolution. Sentinel 2 was launched in 2015 with a multispectral sensor called MSI which captures images with different spatial resolutions (10m to 60m). The secret mission of Landsat satellites started in the 1970s and they have the longest archive of satellite images collected from the Earth. Sentinel 2 offers higher spatial, spectral and temporal resolutions and therefore it is important to compare the compatibility of Sentinel 2 and Landsat 8 images. OLI and MSI sensors both operate in the optical region, thus weather conditions can impose some limitations on their data acquisition. In such circumstances, data collected by a compatible and similar sensor can replace the cloud-covered images.
Generally, spectral features of new sensors are designed in such a way toconform to the corresponding bands of the previous sensors. The present study compares the corresponding bands of MSI and OLI sensors. The efficiency of both sensors in the classification of a heterogeneous and complex region has also been investigated.
Materials & Methods
Three near-simultaneous pairs of Landsat 8 and Sentinel-2 scenes were obtained to conduct a comparative study. Images were acquired in August 2017, November 2017, and July 2018.Minudasht - in northern Iran- was selected as the study area because of the presence of different land cover classes including rainfed agricultural lands, irrigated agricultural lands, forests, residential areas, and bare lands.Thescenes were processed for further analysis. First, the scenes were atmospherically corrected. In the next step, spatial resolution of MSI bands was resampled to 30 m, and each pair of mages were geometrically co-registered. To do so, 10 tie points were selected, and scenes were co-registered usingthe first-degree polynomial method. RMSE values were reported 2.5 m, 2.4 m, and 2.8 m for August 2017, November 2017, and July 2018, respectively. To investigate the similarities and differences of the sensors’ spectral content, the correlation between corresponding bands of the two sensors was estimated.
Then, images were classified using the support vector machine (SVM) algorithm. Five distinct land cover classes were found in the region including rainfed agricultural land, gardens and irrigated agricultural land, forests, residential areas, and bare lands. The training samples were selectedfromthe land use map and high-resolution Google Earth images. Approximately 300 training samples were selected for each land cover class. The accuracy of classification results was compared to verify the efficiency of two sensors in land cover mapping. Independent validation samples were selected for each class. Overall accuracy, commission error, and omission error were calculatedbased on the confusion matrices.
Results & Discussion
The reported correlation coefficientfor all corresponding bands was higher than 0.8. Results indicate a high level of similarity between the two sensors. Similar findings were reported by previous studies. Overall classification accuracy ofOLIimagescollected in August 2017, November 2017, and July 2018 was 91. 35 %, 89.60 %, and 93.12%, respectively. Overall classification accuracy ofMSI images collected inAugust 2017, November 2017, and July 2018 was 94.76 %, 95.55 %, and 94.07%, respectively. As it is obvious, Sentinel 2showed a higher performance in comparison to Landsat’s, because of its higher spatial resolution. A medium spatial resolution image collected from a complex landscape is often composed of mixed pixels, since different land cover types exist in one pixel. As the image’s spatial resolution improves, the dimensions of each pixeldecrease. Therefore, the number of mixed pixels will decrease and a higher classification accuracy will be expected.
Conclusion
Results confirm the similarity of two sensors in land cover classification. However, the findings could not be extended to other applications. MSI sensorslacka thermal bandand thus are not applicable when such a feature is needed (for an instance inthe retrieval of land surface temperature). In such applications, MSI cannot substitute OLI. For further studies, it is necessary to compare the performance of these sensors in different regions, since different land cover types may impactclassification results. Findings of the present study may raise attention to the differences between Landsat 8- OLI and Sentinel 2 MSI. Further studies can be conducted to investigate the differences between these two sensors. The possible similarities of othersimilar sensors can also be a topic for further investigations.
Reza Parhizcar isalu; Khalil Valizadeh Kamran; Bakhtiar Faizizadeh
Abstract
Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting ...
Read More
Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting and exploring sources of this energy using modern and low cost methods –as a replacement for land surveying methods-can help planners and authorities working in the field of energy. In this regard, thermal remote sensing with a vast coverage of the earth’s surface, and the possibilityof calculating land surface temperature using satellite imagery plays an important role as a new economic tool.Mapping land surface temperature is a key point in achieving geothermal anomalies and different algorithms play an important role in land surface temperature estimation. Therefore, identifying potential sources of geothermal energyusingremotely sensed thermal data is a challenging and yet interesting subject.
Materials and Methods
The present study takes advantage of images received from OLI and TIRS sensors (Landsat 8) to estimate land surface temperature, analyze thermal anomalies, and identify areas with potential geothermal resources in Meshkinshahr.The images were retrieved fromUSGSin Geo TIFF format.Envi 5.3, eCognition 9.1, MATLAB and ArcMap 10.4.1 were used to prepare, process and analyze the images.Moreover, meteorological data received fromMeshkinshahr station was collected from the General Department and Meteorological Center of Ardabil Provincewith the aim of identifying the optimal algorithm for calculation ofland surface temperature. Data wascollected for a one-day period (31/08/2017), i.e. the same day Landsat 8 passed over the areaunder study.
Results and Discussion
The present study sought to identify areas with potential geothermal resources using thermal remote sensing and a combination of surface temperature and thermal anomaly models. In order to calculate thermal anomaly, an observational thermal image is required, which is in fact the same land surface temperature calculated using Split Window and Mono Window algorithmsfor the image received from the satellite thermal band at the moment of collecting images. It should be noted that the land surface temperature calculated with these algorithms was evaluated using statistical data recorded in the temperature monitoring station. Results indicated higher accuracy of Split Window algorithm (3 ° C difference). Since, temperature obtained from this algorithm was more consistent with the actual temperature, its results were used as the observational thermal image.A thermal model was also defined to model factors responsible for heat variation from one pixel to another one. These two images were calculated and subtracted to reach the thermal anomaly image.In order to identify thermal anomalies caused by undergroundfactors heating the earthsurface, other factors responsible for increasing/decreasinglandsurfacetemperature should be normalized in the image. Thus, the effect of parameters such as solar energy, environmental degradation and evaporation on land surface temperature obtained from split window algorithm was investigated and finally, areas with heat anomalies and evidences indicating the presence of geothermal resources around themwere selected as areas with potential geothermal resources.Results indicate that inthe area surroundingSabalanmountains,two regions with 5.5 and 10.05 hectares in the northern and northeastern parts of Moyelvillage, a1.4 hectares area in the southwestern part of Qutursouli Spa, and the southern part of the Qinrjah Spa with an area of 1.1 hectare had potentialgeothermal resources and a high potential for exploration of geothermal resources.
Conclusion
The presence of hot springs, a geothermal power plant and other evidences shows that Ardabil Province and especially Meshkinshahr city has the potential for geothermal energy production as one of the major sources of new and environmentally friendly energies.However, no effective studies have been performed to identify these resources using modern and low-cost methods including thermal remote sensing.Therefore, the present study for the first time took advantage ofGIS and remote sensingto identify areas appropriate for geothermal energy extraction inMeshkinshahr city and concluded that remote sensing studies on Landsat 8 satellite images have a high efficiency for identifying areas with potential geothermal resources. Thus, areas identified in the present study have a strong spatial correlation with the geothermal evidences founded in the region.
Mehrdad AhangarCani; Mohammad Reza Malek
Abstract
Extended Abstract
Introduction and Objective
Road traffic accidents impose numerous social, economic, and cultural costs upon various societies, especially developing countries. Identification of accident blackspots is a method proposed to deal with car accident risks. Among various events associated ...
Read More
Extended Abstract
Introduction and Objective
Road traffic accidents impose numerous social, economic, and cultural costs upon various societies, especially developing countries. Identification of accident blackspots is a method proposed to deal with car accident risks. Among various events associated with transportation network, road traffic accidents play a significant role, because of their specific features, including high frequency, high intensity and the chance of direct involvement of all members of the community.This problem is more conspicuous in developing countries such as Iran. The present study aims to identifyaccidentblackspotsand to prepare risk map for road trafficaccidents in Babol city using volunteered geographic information.
Materials and methods
According to the characteristics of the study area, the present study takes advantage of criteria such as distance from population centers, proximity to city squares, distance from footbridges, and proximity to road intersections to identifyaccidentblackspotsand a prepare risk map for roadtraffic accidents in Babol city. Accident blackspots detected by volunteered geographic information, along with the criteria determined by applying analytic hierarchy process (AHP) and analytic network process (ANP) were compared in a pairwise manner, and their respective weight was calculated to showtheir specific level of impact. Ultimately, a risk map was produced for the risk of road traffic accidents obtained from each method. In order to evaluate the accuracy of the identified accident blackspots obtained from volunteered geographic information, as well as the accuracy of susceptibility maps, ROC curve and Kappa Coefficient were applied to police official records.
Results and Discussion
According to the findings, Jame Mosque shopping center, Shahabnia shopping center, intersection of Farhangstreet and Velayat square were identified as the most accident-prone areas in Babol city. Also, among the prespecified criteria, distance from population centers and distance from intersections are considered to be the most important criteria, respectively. Results obtained from the evaluation criteria indicatedhigh accuracy of volunteered geographic information, and thus it is concluded that this kind of information can be effective in determining the accident blackspotsinBabol city. Also, the ANP method works better than AHP method in preparing the risk map of accidents.
Conclusion and Future works
Due to the large number of road accidents, especially in developing countries,the issue of accident blackspotsand providing a risk map for road trafficaccidents are an essential part of roads safety. In the present study, volunteered geographic information was used, along with multivariate decision-making methods of analytic hierarchy process (AHP) and analytic network process (ANP) to identifyaccident blackspots based on number, causes and severity of accidents and to develop a risk map for driving accidents in Babol city. Moreover, the criteria of distance from population centers, proximity to the city squares, distance from the footbridges, and adjacency to intersections were used to determine accident blackspotsand to prepare a risk map for driving accidents in Babol city. According to the results, Jame Mosque shopping center, Shahabnia shopping center, Farhang intersection and Velayat square were identified as the most accident-prone points in Babol city. Also, distance from population centers and distance from intersectionswere identified as the most important criteria, respectively. Evaluation criteria demonstrated that volunteered geographic information can be effective and accurate in determining accident blackspotsinBabol city. Also, the ANP method worked better than AHP method in preparing the risk map of driving accidents. The method proposed in this study to identify accident blackspots and preparedriving accidents risk maps can be generalized to other areas. Basedon the characteristics of specific routes, other criteria such as arc radius, longitudinal slope can alsobe used. It is also suggested that the results of other methods used for investigation ofaccidentblackspotsand production of risk maps based onvolunteered geographic information (VGI) are compared with the results of the present study.
Saeid Hamzeh; Afshin Amiri
Abstract
Extended Abstract Introduction As a type of mass movement involving slow or rapid movement of soil, rock material or both on the lower hillsides, landslide is under the effect of gravity.Landslide is recognized as one of the most common geological disasters causing worldwide damages and casualties.Landslide ...
Read More
Extended Abstract Introduction As a type of mass movement involving slow or rapid movement of soil, rock material or both on the lower hillsides, landslide is under the effect of gravity.Landslide is recognized as one of the most common geological disasters causing worldwide damages and casualties.Landslide susceptibility maps provide important and valuable information,including time scale of possible future landslides, which are usedfor predicting landslide hazards. Since predicting the time of landslide occurrence is beyond the capability of science and knowledge, identifying areas susceptible to landslide and ranking them can extensively restrict the damages caused by landslide. Therefore, it is essential to zone landslide risk and identify factors affecting it. Analytic Network Process(ANP) is aGIS-based Multi-Criteria Decision Analysis(GIS MCDA) method successfully applied to many decision-making systems. The present study seeks to evaluate landslide risk and achieve a zoning map for the sub-basin under study using ANP and Weighted Overlaymethods. Materials and Methods Based on the literature and using different experts’ viewpoint, criteria affecting landslide risk were identified and five major criteria including topography, land use and land cover, geology, hydrometry and infrastructure were selected. The selected criteria include the following sub-criteria: slope, slope direction, curvature, elevation, lithology, soil type, land use, vegetation density, distance from roads, distance from habitat, river and drainage density and precipitation. The effective factor layers were standardized and a specific scale was defined for their units.Then, each layer was assigned a weight based on its role and importanceusing Analytic Network Process.Proposed to modify Analytic Hierarchical Process(AHP), this method (ANP) relies on the analyses of the human brain for complex and fuzzy problems.Network Analysis Process generally includes the following steps: determining indicators, criteria and options;classifying identified criteria into clusters and elements; determining the relationship between clusters, elements and options; performing pairwise comparisons between clusters, elements and options, and finally calculating the final weight of elements and options. UsingWeightedOverlaymethod, these elements were then integrated with their related coefficients and the final landslide risk map was obtained. Results and discussion Each criteria and sub-criteria were weighted using Analytic Network Processmethod.Topographic and land cover criteria had the most and hydrographic criteria had the least impact on the landslide occurrence. According to the final map, most landslides have occurred in eastern and southern slopes at an altitude of 500 to 2,200 meters. Moreover, 17/31% of the study area was located in the very high-risk class and 33% in the high risk class (about half of the area has high potential of landslide). Previous landslide data were used to assess the landslide zoning map results. Results indicate that most landslides have occurred in the high risk class (about 35% of landslides) and only about 4% of landslides have occurred in the very low risk class. Conclusion Landslide is one of the natural hazards causing serious harms and problems for human life. Identifying the factors affecting landslide and zoning its hazard is especially important for the identification of risky and susceptible areas.So, landslides were selected as one of the main topics of the study with the aim of controlling and managing its hazards.The ANP network analysis method was used to model and predict landslide risk in this research.Each criteria and sub-criteria were weighted and overlapped to producethe map of relative landsliderisk.The lowest risk was observed in the northern parts of the region, and the highest landslide risk was observed in the northern hillsides with higher humidity.WeightedOverlaymethod and network analysis model were effective in predicting landslide susceptibility and producing landslide zoning map.
Mahdi Sedaghat; Hamid Nazaripour
Abstract
Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction ...
Read More
Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction results in the creation of centers susceptible to dust storms. With socio-economic impacts ranging from urban to intercontinental and from a few minutes to several decades, this can challenge regional development. The first estimate of potential dust sources is derived from the soil properties. With the reduction of surface soil moisture and the wind speedcrossing a certain threshold level, wind erosion process can cause the formation of dust storms. Field studies have proved that increasing the moisture content in soil from zero to about 3%, reduces the dust concentrationsignificantly. To understand the climatology of dust and develop related numerical predictive methods, continuous recording of dust storms is essential, which requires effective and continuous monitoring of the variations in surface soil moisture. Remote sensing technology is an effective method for calculating soil moisture. This technology was first used for the estimation of energy flux and surface soil moisture in the 1970s. To extract the surface soil moisture content, some remote sensing methods use surface radiation temperature and some others apply water transfer (soil/vegetation/air) (SVAT) model. Various indices have been developed for soil moisture monitoring, such as soil moisture (SM), soil water index (SWI), Temperature-Vegetation-Dryness Index (TVDI), Soil Moisture Index (SMI) and Perpendicular Soil Moisture Index (PSMI), all of which combine vegetation and surface temperature variables. Materials and Methods Soil moisture is considered to be a significant parameter in the exchange of mass and energy between the Earth surface and the atmosphere. Lack of soil moisture or decreased moisture in soil is considered to be a factoraccelerating the process of dust storm formation. During the previous decades, water stresses on the ecosystem of Hour-al-Azim have transformed this wetland into one of the main dust centers in the southwest Iran. Hour-al-Azim is one of the largest wetlands in southwestern Iran. This wetland is shared between in Iran and Iraq. It is located between N 30° 58´- N31° 50´ and E 47° 20´- 47° 55´. The Iranian part of this wetland encompassed an area of 64,100 ha in the 1970s, while in the 2000s, the area has decreased to only 29,000 ha. The present study aims to monitor the spatial-temporal variability of soil moisture in Hour-al-Azim wetland and to investigate the relation between these changes and dust storms in the southwest Iran. To reach this end, we used 8-day images obtained from the Aqua satellite in the period of 2003 to 2017 and also annual frequency of dust storms with a visibility of less than 1000 m in the period of1987–2017. A database consisting of 189 images of the red band, near-infrared band, and ground surface temperature (LST) was created, which contained 4 images per year (one image per season). The resolution of the red / near-infrared band data and daily LST values were 231.65 and 926.62 meters, respectively. Then, soil adjusted vegetation indices (SAVI) and perpendicular soil moisture index (PSMI) were extracted. SAVI index is used to reduce the effect of background soil on vegetation cover in semi-arid and arid environments with less than 30% vegetation cover.Compared to NDVI, SAVIwith L = 0.5reduces the effect of soil changes on green plants. In the next step, a trapezoidal method was used to calculate the PSMI index. In order to investigate changes in the soil moisture content of the Hour-al-Azim wetland, three time series obtained from regional mean of SAVI, LST and PSMI remote sensing indices and a time series consisting of the number of days with dust storms observed in the 9 stations were evaluated using simple linear regression test. Results and discussion Extracting Soil Adjusted Vegetation Index indicated that in the study period, the highest values of this index was observed with a regional mean of 0.15 on 4/7/2014 and the lowest values was observed with a regional mean of 0.08 on 1/1/2005. Land Surface Temperature survey showed that during the study period, the highest values of this index was observed with a regional mean of 54.42 ° C on 7/4/2010 and the lowest values was observed with a regional mean of 17.28 ° C on 1/1/2007. The regional mean of Perpendicular Soil Moisture Index indicates that despite winter is considered to bethe wettest season of the region, PSMI index with a regional mean of 0.2 has experienced the driest soil moisture conditionsat the beginning of winter (1/1/2016),while it had experienced the wettest soil moisture conditionsin the same season on 1/1/2009 with a regionalaverage of 0.13. Conclusion Finding of the present study indicate an increasing trend in the range of remote sensing indicators. The range of SAVI index is increasing, which means that the density of vegetation in the Wetland is decreasing. Perpendicular Soil Moisture Index values also show an increasing trend, indicating a decrease in soil moisture content. As a result of the decrease in soil moisture, the vegetation density also has decreased and the land surface temperature has increased. Results of statistical tests indicate the role of changes in environmental conditions of Hour-al-Azim wetland in the frequency of dust storms. Using findings of the present study, or studies such as Kim et al. (2017), it is possible to take advantage of soil moisture variations for the prediction of dust generation, its emission, and spread level.
SHadman Darvishi; Karim Solaimani; Morteza Shabani
Abstract
Extended Abstract Introduction Urbanization is a continuous process and the spatial patternsof urban growth havealways played an important role in the transformation of human life throughout history. Urban growth has two dimensions: demographic and spatial, meaning that with increased urban population, ...
Read More
Extended Abstract Introduction Urbanization is a continuous process and the spatial patternsof urban growth havealways played an important role in the transformation of human life throughout history. Urban growth has two dimensions: demographic and spatial, meaning that with increased urban population, the need for shelter increases and cities are faced with spatial growth. Expansion of cities in the spatial dimensions have several consequences,including changes in land use and land covers of areas surrounding cities.Land use change is currentlyone of the major concerns ofthe environmental approach, since land use changes in areas surrounding cities have led to changes in the economic structure of cities and the destruction of vegetation and agricultural lands as one of the main foundations of production in these areas. They have also seriously damaged other water resources, wildlife habitats, and resulted in the reduction of soil organic matter, changes in soil humidity and saltiness, increased energy consumption, increased urban heat islands, climate changes, as well as negative effects on the mental and physical health of urban residents. Nowadays, rapid growth in remote sensing technology and geographic information system, as well as the advancements in computer science and its application in environmental sciences and urban planning have created spatial modeling techniques such as Markov chain, Cellular Automata, intelligent neural networks and statistical models. Due to its dynamic nature, the capability of showing spatial distribution of land use changes, as well as its unique characteristics in modeling of natural and physical geographic featureson the ground and simpler adaptation with remote sensing data and GIS, a combination of Markov chain model and Cellular Automata are used as an important supporting toolfor decision making in urban planning and environmental sciences in many studies performedrecently. Over the past few decades, the population of Iranhas increased from 27 million in 1955 to 79 million in 2016. And according to the 2016census, 74 percent of the population lives in urban areas. In recent years, the population of Kurdistan province has experienced a 1.42% (2011 to 2016)average annual growth rate (especially in Baneh, Marivan and Saghez), which isaround 0.18% more than the average annual growth rate of the country (1.24%). Investigating census data shows that Baneh, Marivan and Saqezhave experienced a higher urban growth rate as compared to other cities in the province, and thus monitoring this growth and predicting its negative effects on the surrounding land use seems crucial.Destruction of vegetation and agricultural lands not only results in climate change, but also directly affect the lives of residents in the region. Therefore, understanding the growth rate is necessary for properplanning and managementofthese areas. Materials and Methodology Images received from Landsat in 1987, 2002 and 2017 were downloaded from the US Geological Surveywebsite and used in the present study. Google Earth images, land useand topography maps, and ground control points (GCP) were also used to perform imagepreprocessing, classification operations, and accuracy assessment. The study area includesBaneh, Marivan and Saqqez cities, which have recently experienced a high level of population growth. Considering the impact of population growth on increased rate of construction and physical development of urban areas, it is therefore necessary to study urban growth. In order to reduce the city’s impact on land use in future, it is necessary to modelurban growth. Using these models, planners can guide the urban development back to the optimal and appropriate routes and minimize the destruction of the land use.Image pre-processing in the present research was performed in ENVI5.3 environment. Then, using Maximum Likelihood algorithm, the images were categorized into five classes of water, residential areas, vegetation, agriculture and open spaces. Then, the overall accuracy of the classification maps was assessed using ground control points. To predict the urban growth, CA-Markov model was used in the IDRISI TerrSet software. Results and Discussion Findings indicate that the classified images have an accuracy of above 80%, and thus, land use maps of the study areas are valid.Investigations shows that the growth inMarivan and Baneh has most severely affected vegetation and agricultural land use. In the time period of 1987 to 2017, 897. 39 and 801 hectares of vegetation in Marivan and Banehhave been transformed into urban areas, respectively.During the same time period, 790.38 hectares of agricultural land in Marivan and 772.29 hectaresinBanehhave changed into urban areas. It is also important to note that unlike Saqez, the degradation of vegetation and agricultural lands in Marivan and Banehwas more severe than bare lands. In other words, bare landsinSaqez were more severely affected (as compared to vegetation and agricultural land), and about 1249,29 hectares of bare lands have turned into urban areas, while only 121.50 hectares of vegetation, and 509.04 hectaresof agriculture lands haveexperienced such a change.Also, results of the CA-Markov model showed that the growth of Baneh and Marivan cities in the 2017-2032 period will be in the Northeast and East directions, respectively. Results also indicate that this urban growth will affect agricultural and bare landsmore significantly. It is predicted that about 511.29 hectares of agricultural lands and 722.70 hectares of bare lands (in Baneh city) and 1080 hectares of agricultural lands and 2402.101 hectares of bare lands (in Marivan city) will turn into urban areas in this time period. Conclusion Based on the findings, it can be concluded that planning urban growth inthe study areas should be performed in a way that vegetation and especially the surrounding agricultural lands are preserved, and the negative effects of land use changesare minimized. Also,plannerscan apply the results of the present study in their future plansto guide the development of Baneh, Marivan and Saqeztoward optimal ways and reduce land use degradation.
Farshad Pazhooh; Farzaneh Jafari
Abstract
Extended Abstract
Introduction
Due to its specific geographical situation,Iranhas an especial precipitation pattern. In other words,despitehaving a precipitation equal to one-third of global average,Iran experiences a strong fluctuation in its rainfall regime. According to global classifications, floods ...
Read More
Extended Abstract
Introduction
Due to its specific geographical situation,Iranhas an especial precipitation pattern. In other words,despitehaving a precipitation equal to one-third of global average,Iran experiences a strong fluctuation in its rainfall regime. According to global classifications, floods are considered to be among the most important natural disasters. In recent decades, humaninterferencesin the environment and improper management of land usehave resulted in increasing severity and higher frequency of these natural disasters (Abbas ZadehTehrani et al., 2010: 78). Extreme floodingcaused by climate changeshave resulted in severe damages in different parts of the world during recent decades and the effects of these changes are more significant in dry environments (Negaresh et. al., 2013: 15). Increasing urbanization and constructions has naturally reduced permeable areasin different basins. The resulting impenetrable surfacesare incapable of absorbing the rainfall, and consequently, the total volume of runoff in the city has increased (TaheriBehbahani and Big Zadeh, 1996).
Materials and methods
Two typesofground level data and data collected from higher levels of the atmosphere were used in the present study:
A) Precipitation data collected during the first ten daysof April 2019 by stations in Western and South Western Iran obtained from the Iranian meteorological organization.
B) Data collected from higher levels of the atmosphere including revised geopotential heights, sea level pressure, meridian and orbital winds, omega and especial humidityobtainedfrom the National centre for environmental surveys at Colorado, USA.
For synoptic analysis, environment to circulation approach was used to detect heavy rainfall peak periods and then their synoptic dimensions were reanalysed in the spatial range of 10 to 70 degrees north latitude and 10 to 80 degrees east longitude. Based on the analysis ofprecipitation data, April5th and11th,2019 were selected as having the highest rainfall resulting in the highest level of flooding and damage in the western and southwest regions of Iran.
Results and Discussion
On April 5th,2019 most regions of Iran have receiveda rainfall of more than 20 mm. The maximum levels of rainfall wererecorded in Koohrangstation(187 mm), Izehstationin Khuzestan (155 mm) and Yasoujstation(151 mm). OnlySistan and Baluchestan, Kerman and South Khorasan Province have experienced a stable situation without any precipitation on this day. However, on April 11th,2019, the highest level of rainfall has occurred inwestern stations of the country. The maximumlevels of rainfallon this day were recorded inNahavand and Tuyserkan stations (Hamedan Province) and Noorabad(LorestanProvince) with 126 and 122 mm, respectively. Central and northwesternregions of the country have experienced the next highest level of rainfallfollowing western regions. Figures 1 to 3 show a part of precipitation values in the western and southwestern regions of Iran during rainfall peak periods. Precipitations in more than 16 provinces in the western, southwestern, and central regions of the country have damagedagricultural, economic and social sectors. More than 45 people were killed in thesedays.The highest number of deaths and injurieshas occurred in Shiraz. In the western parts of the country, Poldokhtar and Mamoualn were most severely damaged. Moreover, heavy rainfall and floodinghave damaged 700 thousand hectares of agricultural land and resulted in 4600 billion USDlosses. In the construction sector, the country has suffered from 1,600 billion USD losses (Hamshahri Newspaper, 1398).
Conclusion
The present study have focused on synoptic and thermodynamic analysis of systems causing pervasive, heavy and hazardous precipitation onApril 5th and 11th in the western and south western regions of the country. The synoptic and thermodynamic analysis of maps indicated that the contrast between the influence of southern and western low pressure fronts such as Saudi Arabia, Sudan and the Mediterranean on the southwestern areas of the country and the cold high pressure frontover the Caspian Sea have caused a strong pressure gradientand formed a strong front condition over the country and the region under study at the sea level. In the middle and upper atmosphere, deep multiple amplitudetroughsformed over the North Pole passed through Russia as bipolar and low pressureblocks, cyclonic centressettled over the eastern Mediterranean regions and the eastern half of the trough formed as a result of blocking settledover the western and southwesternregions of the country. These have resulted in severe, and widespread negative omega and divergence of warm and humid southern weather over the country and the region.
Mahnaz Hosseini Syahgoli; Ismail Soleimanirad; Mohammad Raouf Heydarifar
Abstract
Extended Abstract Introduction Today the costs of building and utilizing industrial centers are too high and thus,any disruption in these centers will have a negative effect on other urban functions.Hence, the presence of sensitive and vital land uses,especially heavy industries in the central urban ...
Read More
Extended Abstract Introduction Today the costs of building and utilizing industrial centers are too high and thus,any disruption in these centers will have a negative effect on other urban functions.Hence, the presence of sensitive and vital land uses,especially heavy industries in the central urban texture of Kermanshah is considered to be one of the most important issues and problems threateningthe city. Unfortunately, despite regulations of Housing and Urban Development organization which bansuch land uses within the city (due to their risks), they still continue their activities. Obviously, identifying the requirements of these land uses adjacency within Kermanshah decreases the vulnerability of the city. Therefore, it seems necessary to solve the problems caused by inconsideration of passive defense with gradual development of Kermanshah and begin the process of downsizing, scattering and decentralizing these industrial centers based on the principles of passive defensein future plans of the city to reduce the vulnerability of strategic and vital areas of city. Moreover, urban planners have always consideredsafety and security to be important issues in planning, constructing and designingurban residences. Policies of urbanplanning such as zoning regulations are important tools of risk management available for local governments. Experts in crisis management try to pay attention to urban land uses, their geographical and political importance, and the economic, military and political value of these places. In this regard, theytake advantage of methods and strategies to reduce vulnerability and damagesof these environmentsintimesofcrisis.Principles of passive civil defense are a set of basic and infrastructural actions implemented with the aim of achieving the goals ofpassivedefense such as reducing damages, limiting the capabilities of target identification systems and targeting capability of the enemy’s offensive facilities and thus increasing the costs of such attacks. Urban strategies may include strategic decisions playing a vital role in urban passive defense through urban decentralization, managing decisions made by urban managers and revising these decisions through executive strategies and criteria. General acceptance of passive defense and participation of all society members in the development of its fundamental principles and foundations are among the most important strategies that can save the society in possible crises. Previous studies suggest a poor understanding of passive defense even in cities of Iran which proves the necessity of more effort and planning in future. Switzerland, meanwhile, ranks first in passive defense in the world. Strategies and plans intended for the development of safety and security in cities should identify different levels of urban crises and analyze, evaluate, and manage potential crisis-causing factors. Identifying these factors will lead to planning, designing, and effectively managing crisis and increase security in cities. This can result in preventing crisis, or minimize the effects of possible crisis. Therefore, careful planning and designing, identifying vulnerable areas, zoning high-risk areas, and supporting organizations involved in crisis management and security can play a vital role in the sustainability of cities in times of crisis. The present study seeks toevaluatespatial importance of vital land uses. Materials&Methods This descriptive- analytical research take advantage of 10 information layers to analyze research data in GISusing fuzzy overlay functions and Gama Operator. Results & Discussion Results indicate that the 4th, 5th and 7th zones which are considered amongperiphery regions of Kermanshah city have a modern urban texture as compared to other 5 regions of the city. Also, these regions are in a better situation and condition in terms of passive defense as compared to other regions of the city because of easy access to open spaces, enough distance between neighboring land uses and etc. But, the 1st and 6thzonesare not in a suitable conditionas compared to the other six regions due to being located in thecrowded and dense regions of downtown. Conclusion Results indicate that five regions (out of 8) of the city have undesirable and unfavorable conditions in terms ofurban passive defense. This shows that Kermanshah is in a critical and inappropriate condition considering urban texture and structure in terms of passive defense, whichrequires the attention of urban planners.