Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Potato Fields Mapping Based on the Phenology Feature and Support Vector Machine Utilizing Google Earth Engine Platform Potato Fields Mapping Based on the Phenology Feature and Support Vector Machine Utilizing Google Earth Engine Platform 1 22 103148 10.48308/gisj.2023.103148 FA Salman Goodarzdashti Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran 0000-0002-8033-6510 Mohamad Seifi Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran Mahshid Kohandel Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran Davoud Ashourloo Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran Hossein Aghighi Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran Journal Article 2022 12 05 Introduction: Potato is the fourth most cultivated crop worldwide. In terms of its strategic role in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation and prediction at any scale. Although remote sensing (RS) approaches based on optical and/or microwave sensors have been widely employed to monitor cultivated lands (including crop area, conditions, and yield forecasting), the identification of potato planting areas using RS data and machine learning has not been much addressed. As a result, the present research addresses the literature gap by suggesting an effective potato mapping approach in Iran's main production center and tries to provide accurate information on the cultivated areas of this crop for the field of agricultural management. Material and Methods: Since most crops have specific spectral and temporal characteristics during their cultivation period, this research has presented a method to discriminate potato fields from other crops using time series images without explicit thresholding. Is. This method identified this product by using layers based on potato phenology and machine learning. We employed the ground truth data of the crop types from the studied site, which included a total of 1648 samples of potato fields and other crops, to optimize the internal parameters of the algorithm, train, and evaluate the model. A handheld GPS receiver was used to collect this data. This research employed Sentinel-2 satellite images and the Support Vector Machine (SVM) algorithm to map potato fields. To accurately identify potato fields, we prepared appropriate input layers, including the phenological index of the potato crop and the median statistical index of NDVI (time series of Sentinel-2 satellite images) at specific intervals. We used these layers as inputs to the SVM. We optimized the gamma and C values using the 5-fold cross-validation method to train the optimal model for SVM using the RBF kernel. We then used these values in the algorithm implementation process under the Google Earth Engine cloud computing platform. We assessed the efficacy of the suggested approach in the Iranian cities of Hamedan and Bahar, key sites for the cultivation of this particular crop. Results and Discussion: Based on the results, the optimal values for the internal parameters of the model (C = 70 and γ = 0.3) were calculated. We included these values in the RBF function to identify the cultivated areas of the potato crop. By implementing the classification algorithm and then applying the majority filter, a map of the areas under potato cultivation was prepared for the study area. This map showed the highest density of potato cultivation in the border area of two cities (northwest of Hamedan city and east of Bahar city). The calculated total area for potato farming was 4527.1 hectares in Hamedan city and 6088.3 hectares in Bahar city. The estimated overall accuracy and Kappa coefficient are 90.9% and 0.82 for Hamedan and 93.3% and 0.87 for Bahar, respectively. The present research's results demonstrate the effectiveness of the SVM algorithm in detecting potato cultivation areas, highlighting the potential of using indicators corresponding to potato phenology as distinguishing features for improved identification. Conclusion: By employing the SVM method, we effectively identified potato fields by utilizing layers of indicators that correspond to crop phenology. At the trial stage, it was demonstrated that this method can improve the potato acreage mapping process. Therefore, a similar approach can be evaluated for identifying other important crops in other regions. It is also suggested that the efficiency of microwave data and other machine learning algorithms be considered in future research. Introduction: Potato is the fourth most cultivated crop worldwide. In terms of its strategic role in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation and prediction at any scale. Although remote sensing (RS) approaches based on optical and/or microwave sensors have been widely employed to monitor cultivated lands (including crop area, conditions, and yield forecasting), the identification of potato planting areas using RS data and machine learning has not been much addressed. As a result, the present research addresses the literature gap by suggesting an effective potato mapping approach in Iran's main production center and tries to provide accurate information on the cultivated areas of this crop for the field of agricultural management. Material and Methods: Since most crops have specific spectral and temporal characteristics during their cultivation period, this research has presented a method to discriminate potato fields from other crops using time series images without explicit thresholding. Is. This method identified this product by using layers based on potato phenology and machine learning. We employed the ground truth data of the crop types from the studied site, which included a total of 1648 samples of potato fields and other crops, to optimize the internal parameters of the algorithm, train, and evaluate the model. A handheld GPS receiver was used to collect this data. This research employed Sentinel-2 satellite images and the Support Vector Machine (SVM) algorithm to map potato fields. To accurately identify potato fields, we prepared appropriate input layers, including the phenological index of the potato crop and the median statistical index of NDVI (time series of Sentinel-2 satellite images) at specific intervals. We used these layers as inputs to the SVM. We optimized the gamma and C values using the 5-fold cross-validation method to train the optimal model for SVM using the RBF kernel. We then used these values in the algorithm implementation process under the Google Earth Engine cloud computing platform. We assessed the efficacy of the suggested approach in the Iranian cities of Hamedan and Bahar, key sites for the cultivation of this particular crop. Results and Discussion: Based on the results, the optimal values for the internal parameters of the model (C = 70 and γ = 0.3) were calculated. We included these values in the RBF function to identify the cultivated areas of the potato crop. By implementing the classification algorithm and then applying the majority filter, a map of the areas under potato cultivation was prepared for the study area. This map showed the highest density of potato cultivation in the border area of two cities (northwest of Hamedan city and east of Bahar city). The calculated total area for potato farming was 4527.1 hectares in Hamedan city and 6088.3 hectares in Bahar city. The estimated overall accuracy and Kappa coefficient are 90.9% and 0.82 for Hamedan and 93.3% and 0.87 for Bahar, respectively. The present research's results demonstrate the effectiveness of the SVM algorithm in detecting potato cultivation areas, highlighting the potential of using indicators corresponding to potato phenology as distinguishing features for improved identification. Conclusion: By employing the SVM method, we effectively identified potato fields by utilizing layers of indicators that correspond to crop phenology. At the trial stage, it was demonstrated that this method can improve the potato acreage mapping process. Therefore, a similar approach can be evaluated for identifying other important crops in other regions. It is also suggested that the efficiency of microwave data and other machine learning algorithms be considered in future research.

https://gisj.sbu.ac.ir/article_103148_1e8fc0fc4416e57c6c956d728af5dcce.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Seismic Hazard Assessment Using Arithmetic-Weighted Overlay Method Based on Earthquake Potential Index (EPI), Southwest of Iran Seismic Hazard Assessment Using Arithmetic-Weighted Overlay Method Based on Earthquake Potential Index (EPI), Southwest of Iran 23 40 104067 10.48308/gisj.2023.229646.1133 FA Sasan Motaghed Dep. of Civil Engineering, Faculty of Engineering, Behbahan Khatam Al Anbia University of Technology, Behbahan, Iran Amin Nakhlian Dep. of Civil Engineering, Faculty of Engineering, Behbahan Khatam Al Anbia University of Technology, Behbahan, Iran Lotfolla Emadali Dep. of Civil Engineering, Faculty of Engineering, Behbahan Khatam Al Anbia University of Technology, Behbahan, Iran Nasrolla Eftekhari Faculty of Technology and Mining, Yasouj University, Choram, Iran Heshmatalla Mahmoudian Center of Monitoring Assessment and Prediction of Natural Disasters (MAP), Behbahan Khatam Al Anbia University of Technology, Behbahan, Iran Journal Article 2022 11 27 Introduction: In the arithmetic-weighted overlay method based on the earthquake potential index (EPI) to evaluate the seismic hazard of each region, historical earthquake data, spatial distribution and magnitude of past earthquakes, active tectonics (fault type and length), Fault density per earth surface, distance to active fault, distance to earthquake epicenters, slope, and topographical changes should be considered and corresponding layers are be created using GIS. This non-ergodic method solves the problems of attenuation relations and expression of inputs and outputs of hazard. Especially the method is very useful in preparing seismic hazard maps of large geographical areas with a rich history of seismic events. in This paper, the seismic hazard analysis for the southwestern region of Iran was conducted within a 400 km square centered on Behbahan city (located at longitude 50.2417° and latitude 30. 5985 ° N) using the arithmetic-weighted overlay method based on EPI . Methodology: The arithmetic-weight overlay method is based on the superposition of ranked spatial, geological and seismological information of the region with pre-determined weights. Earthquake potential index (EPI) is calculated using the following equation: (1) whrer, EPI is earthquake potential index, DEM is digital elevation model, Slope denotes the slope angle in degrees, Den_F is density of active faults, Den_Ev is density of earthquake foci, ML is earthquake magnitude, Dis_F is distance to active fault, Dis_epi_ev is distance to the epicenter of the earthquake, and i, j are the coordinates of the cell (longitude and latitude). Identifying areas with seismic potential and assessing seismic hazard requires considering the contribution of all parameters and their combination according to their relative importance. After preparing the necessary maps, according to the seismicity of the region (distribution of earthquake foci, seismic sources and active faults), tectonic features (layer age, tectonics), topography of the region (digital elevation model) and slope, EPI is determined. The studied area (a square measuring 400 km centered in Behbahan) ranges from a steep northeast slope with an altitude of 4418 meters to a gentle southwest slope (Persian Gulf area) with an altitude of -125 meters. Results and Discussion: Arithmetic-weighted overlay method was performed according to the earthquake potential index (EPI) in southwest Iran and the results were presented in the form of maps and tables. According to the results, parts of the eastern and northern cities of Khuzestan province and the southwestern cities of Chahar Mahal Bakhtiari, Kohgiluyeh Boyer Ahmad and Isfahan and the northern cities of Bushehr province are located in high EPI areas. The cities of Gachsaran, Behbahan, Omidiyeh, Behmai, Ramhormoz, Bagh Malek, Haftgol, Getund, Ardal, Kohrang, Farsan and Kabar are in the high risk area. In the studied area, the EPI ranges from 1.55 to 6.75. The estimated average value of EPI is 4.415 and the standard deviation is 1.94. These values indicate relatively large changes in the average seismicity in the region. The EPI values were estimated for all the cities of Khuzestan province and the EPIs were compared with the seismicity values of standard no. 2800. The results of the comparison are the concordance of the results in most cities and the greater seismic hazard by the 2800 standard in the cases of differences, which shows the reliability of the standard no. 2800 values. Conclusion: Arithmetic-weighted overlay method according to the earthquake potential index (EPI), is a new global method that can be used to assess non-ergodic seismic hazard. Based on the EPI results, the values of the 4th edition of the 2800 standard are sufficient for the seismic design of buildings. Introduction: In the arithmetic-weighted overlay method based on the earthquake potential index (EPI) to evaluate the seismic hazard of each region, historical earthquake data, spatial distribution and magnitude of past earthquakes, active tectonics (fault type and length), Fault density per earth surface, distance to active fault, distance to earthquake epicenters, slope, and topographical changes should be considered and corresponding layers are be created using GIS. This non-ergodic method solves the problems of attenuation relations and expression of inputs and outputs of hazard. Especially the method is very useful in preparing seismic hazard maps of large geographical areas with a rich history of seismic events. in This paper, the seismic hazard analysis for the southwestern region of Iran was conducted within a 400 km square centered on Behbahan city (located at longitude 50.2417° and latitude 30. 5985 ° N) using the arithmetic-weighted overlay method based on EPI . Methodology: The arithmetic-weight overlay method is based on the superposition of ranked spatial, geological and seismological information of the region with pre-determined weights. Earthquake potential index (EPI) is calculated using the following equation: (1) whrer, EPI is earthquake potential index, DEM is digital elevation model, Slope denotes the slope angle in degrees, Den_F is density of active faults, Den_Ev is density of earthquake foci, ML is earthquake magnitude, Dis_F is distance to active fault, Dis_epi_ev is distance to the epicenter of the earthquake, and i, j are the coordinates of the cell (longitude and latitude). Identifying areas with seismic potential and assessing seismic hazard requires considering the contribution of all parameters and their combination according to their relative importance. After preparing the necessary maps, according to the seismicity of the region (distribution of earthquake foci, seismic sources and active faults), tectonic features (layer age, tectonics), topography of the region (digital elevation model) and slope, EPI is determined. The studied area (a square measuring 400 km centered in Behbahan) ranges from a steep northeast slope with an altitude of 4418 meters to a gentle southwest slope (Persian Gulf area) with an altitude of -125 meters. Results and Discussion: Arithmetic-weighted overlay method was performed according to the earthquake potential index (EPI) in southwest Iran and the results were presented in the form of maps and tables. According to the results, parts of the eastern and northern cities of Khuzestan province and the southwestern cities of Chahar Mahal Bakhtiari, Kohgiluyeh Boyer Ahmad and Isfahan and the northern cities of Bushehr province are located in high EPI areas. The cities of Gachsaran, Behbahan, Omidiyeh, Behmai, Ramhormoz, Bagh Malek, Haftgol, Getund, Ardal, Kohrang, Farsan and Kabar are in the high risk area. In the studied area, the EPI ranges from 1.55 to 6.75. The estimated average value of EPI is 4.415 and the standard deviation is 1.94. These values indicate relatively large changes in the average seismicity in the region. The EPI values were estimated for all the cities of Khuzestan province and the EPIs were compared with the seismicity values of standard no. 2800. The results of the comparison are the concordance of the results in most cities and the greater seismic hazard by the 2800 standard in the cases of differences, which shows the reliability of the standard no. 2800 values. Conclusion: Arithmetic-weighted overlay method according to the earthquake potential index (EPI), is a new global method that can be used to assess non-ergodic seismic hazard. Based on the EPI results, the values of the 4th edition of the 2800 standard are sufficient for the seismic design of buildings.

https://gisj.sbu.ac.ir/article_104067_0a731813bc96c4e3244772585f528c29.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Land Use and Land Cover Classification by Combining GLCM, SNIC, and Machine Learning Algorithms in Google Earth Engine Environment (Case Study: Part of the Lands of North Mahabad, West Azerbaijan) Land Use and Land Cover Classification by Combining GLCM, SNIC, and Machine Learning Algorithms in Google Earth Engine Environment (Case Study: Part of the Lands of North Mahabad, West Azerbaijan) 41 60 104069 10.48308/gisj.2023.233271.1181 FA Mahdi Naderi Dep. of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran Journal Article 2023 09 28 Introduction: In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning, precision agriculture, and sustainable management of land resources. The availability of remote sensing data by providing different levels of spatial details, as well as the development of satellite image classification algorithms, has made object-oriented approaches more useful in land use and land cover (LULC) classification compared to traditional approaches. Therefore, in this study, an object-oriented approach using a combination of GLCM, SNIC, and machine learning algorithms is presented to classify the LULC of a part of the lands of North Mahabad, West Azerbaijan, in 2019 using satellite images in Google Earth Engine. Data and Methods: For this purpose, after preparing the initial dataset, which contains the bands of Sentinel-1 and Sentinel-2 images, the ALOS digital surface model, and NDVI, BSI, SAVI, and total scattering power (TSP) indices, two pixel-based and object-oriented approaches, as well as the random forest algorithm, were used to classify land use and land cover, and their results were compared to explain the best approach in terms of the accuracy of the various classes. In the object-oriented approach, textural measures were extracted by applying the GLCM matrix to the initial dataset. Due to the increase in the number of bands, the PCA method was used to reduce the dimensions of the image. Finally, by combining the PC1 layer and the segmentation layer obtained from the SNIC algorithm, the random forest algorithm was considered to produce land use and land cover maps of the study area. Results and Discussion: According to the research findings, the object-oriented approach performed better than the pixel-based approach in classifying various land use classes in the study area, with an overall accuracy and kappa coefficient of 86.40% and 0.8307, respectively, compared to 82.73% and 0.8028. The results of the accuracy evaluation criteria showed that the producer accuracy of most of the classes except for corn, fall irrigated vegetables, wheat, and barley irrigated in the object-oriented approach was higher than the pixel-based method, and their classification accuracy was more than 90%. Additionally, water, build-up, corn, and sugar beet classes have the highest user accuracy in the object-oriented LULC map. Conclusion: The findings showed that the appropriate determination of the super-pixel size of the SNIC clustering algorithm and the use of GLCM texture criteria effectively improved the performance of the proposed approach in land use and land cover classification Introduction: In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning, precision agriculture, and sustainable management of land resources. The availability of remote sensing data by providing different levels of spatial details, as well as the development of satellite image classification algorithms, has made object-oriented approaches more useful in land use and land cover (LULC) classification compared to traditional approaches. Therefore, in this study, an object-oriented approach using a combination of GLCM, SNIC, and machine learning algorithms is presented to classify the LULC of a part of the lands of North Mahabad, West Azerbaijan, in 2019 using satellite images in Google Earth Engine. Data and Methods: For this purpose, after preparing the initial dataset, which contains the bands of Sentinel-1 and Sentinel-2 images, the ALOS digital surface model, and NDVI, BSI, SAVI, and total scattering power (TSP) indices, two pixel-based and object-oriented approaches, as well as the random forest algorithm, were used to classify land use and land cover, and their results were compared to explain the best approach in terms of the accuracy of the various classes. In the object-oriented approach, textural measures were extracted by applying the GLCM matrix to the initial dataset. Due to the increase in the number of bands, the PCA method was used to reduce the dimensions of the image. Finally, by combining the PC1 layer and the segmentation layer obtained from the SNIC algorithm, the random forest algorithm was considered to produce land use and land cover maps of the study area. Results and Discussion: According to the research findings, the object-oriented approach performed better than the pixel-based approach in classifying various land use classes in the study area, with an overall accuracy and kappa coefficient of 86.40% and 0.8307, respectively, compared to 82.73% and 0.8028. The results of the accuracy evaluation criteria showed that the producer accuracy of most of the classes except for corn, fall irrigated vegetables, wheat, and barley irrigated in the object-oriented approach was higher than the pixel-based method, and their classification accuracy was more than 90%. Additionally, water, build-up, corn, and sugar beet classes have the highest user accuracy in the object-oriented LULC map. Conclusion: The findings showed that the appropriate determination of the super-pixel size of the SNIC clustering algorithm and the use of GLCM texture criteria effectively improved the performance of the proposed approach in land use and land cover classification

https://gisj.sbu.ac.ir/article_104069_77f3e692fa5c029622a9efaccc3f7308.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Integration of Multi-Sensor Data and Ground Observations in Order to Improve Accuracy and Spatial Resolution in Near-Surface Water Vapor Retrieval Integration of Multi-Sensor Data and Ground Observations in Order to Improve Accuracy and Spatial Resolution in Near-Surface Water Vapor Retrieval 61 78 104154 10.48308/gisj.2024.230453.1146 FA Mohamad Reza Talari Dep. of Geomatics, Faculty of Civil and Transportation Engineering, University of Isfahan, Isfahan, Iran Mina Moradizadeh Dep. of Geomatics, Faculty of Civil and Transportation Engineering, University of Isfahan, Isfahan, Iran Journal Article 2023 01 27 Introduction: Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, is of great importance. Since the atmospheric water vapor content in the near of surface is more and its temporal and spatial changes are more intense, the measurements of ground meteorological stations, despite their high accuracy, are not generalizable due to temporal and spatial limitations and point measurements. Therefore, it seems necessary to provide practical satellite-based methods to accurate and continuous retrieval of this parameter with appropriate spatial distribution. The aim of this research is to present four innovative and accurate methods to estimate the near surface atmospheric water vapor of Isfahan province in 2020 with a resolution of 1 km, through the integration of meteorological station data, sensor data and finally validating and comparing their performance. For this purpose, correcting the bias error of water vapor sensor data during the co-scaling stage and correcting the interpolation error of ground station observations was put on the agenda. Material and Methods: Different sensors measure water vapor with different sensitivities and spatial resolution. Therefore, it is necessary to provide methods based on the simultaneous use of diffferent sensor data and their integration to ground station observations, in order to simultaneously improve the accuracy and spatial resolution (1 km) of retrieved near surface water vapor. In the first method used in this research, the near surface water vapor is retrieved using the water vapor absorbing and non-absorbing bands of the MODIS, through the band ratio method and using ground observations. In the second method, first, observations of near surface water vapor of ground stations are converted to 1 km grid using the inverse distance interpolation (IDW) method. Then, during the steps of the proposed method and using the water vapor values estimated by the first method, the interpolation error in each pixel is removed. In the third method, the resolution of AIRS-derieved water vapor product is reduced to 1 km by combining MODIS data during an operation similar to the steps of the second method, with the difference that the AIRS sensor product is used instead of ground station observations. It is necessary to eliminate the bias error of near surface water vapor product of the AIRS during the co-scaling stage by first. Estimation of near surface water vapor using MODIS column water vapor product is the fourth method. Of course, due to the difference in content, it is necessary to unite the two sets and equate them with an approprite method. Results and Discussion: In order to model and validate the estimation of atmospheric near surface water vapor at a spatial resolution of 1 km using the different mentioned methods, 66.6% of the data were randomly used for training and the remaining 33.3% were used to evaluate the accuracy and validation. Finally, the implementation results of the methods have been compared with each other. The validation results of proposed methods show that the second method, which is based on the generalization of accurate observations of ground stations and removing their interpolation error, during integration with the water vapor values retrieved from first method, has the best performance (R2=0.55, RMSE=1.05 Gr/Kr). Conclusion: Considering the better performance of the second method in retrieving the mixing ratio of near surface water vapor with high accuracy and resolution of 1 km, and with the aim of using the capabilities of satellite-based products and data, it is recommended to combine them with each other and also with ground observations. Introduction: Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, is of great importance. Since the atmospheric water vapor content in the near of surface is more and its temporal and spatial changes are more intense, the measurements of ground meteorological stations, despite their high accuracy, are not generalizable due to temporal and spatial limitations and point measurements. Therefore, it seems necessary to provide practical satellite-based methods to accurate and continuous retrieval of this parameter with appropriate spatial distribution. The aim of this research is to present four innovative and accurate methods to estimate the near surface atmospheric water vapor of Isfahan province in 2020 with a resolution of 1 km, through the integration of meteorological station data, sensor data and finally validating and comparing their performance. For this purpose, correcting the bias error of water vapor sensor data during the co-scaling stage and correcting the interpolation error of ground station observations was put on the agenda. Material and Methods: Different sensors measure water vapor with different sensitivities and spatial resolution. Therefore, it is necessary to provide methods based on the simultaneous use of diffferent sensor data and their integration to ground station observations, in order to simultaneously improve the accuracy and spatial resolution (1 km) of retrieved near surface water vapor. In the first method used in this research, the near surface water vapor is retrieved using the water vapor absorbing and non-absorbing bands of the MODIS, through the band ratio method and using ground observations. In the second method, first, observations of near surface water vapor of ground stations are converted to 1 km grid using the inverse distance interpolation (IDW) method. Then, during the steps of the proposed method and using the water vapor values estimated by the first method, the interpolation error in each pixel is removed. In the third method, the resolution of AIRS-derieved water vapor product is reduced to 1 km by combining MODIS data during an operation similar to the steps of the second method, with the difference that the AIRS sensor product is used instead of ground station observations. It is necessary to eliminate the bias error of near surface water vapor product of the AIRS during the co-scaling stage by first. Estimation of near surface water vapor using MODIS column water vapor product is the fourth method. Of course, due to the difference in content, it is necessary to unite the two sets and equate them with an approprite method. Results and Discussion: In order to model and validate the estimation of atmospheric near surface water vapor at a spatial resolution of 1 km using the different mentioned methods, 66.6% of the data were randomly used for training and the remaining 33.3% were used to evaluate the accuracy and validation. Finally, the implementation results of the methods have been compared with each other. The validation results of proposed methods show that the second method, which is based on the generalization of accurate observations of ground stations and removing their interpolation error, during integration with the water vapor values retrieved from first method, has the best performance (R2=0.55, RMSE=1.05 Gr/Kr). Conclusion: Considering the better performance of the second method in retrieving the mixing ratio of near surface water vapor with high accuracy and resolution of 1 km, and with the aim of using the capabilities of satellite-based products and data, it is recommended to combine them with each other and also with ground observations.

https://gisj.sbu.ac.ir/article_104154_d64b86311686f6f81c6235268392956a.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Spatio-Temporal Analyses of Pedestrian Accidents Using Time Series and Differential Moran’s I, Case Study: Mashhad Spatio-Temporal Analyses of Pedestrian Accidents Using Time Series and Differential Moran’s I, Case Study: Mashhad 79 108 104155 10.48308/gisj.2024.228548.1112 FA Matin Shahri Dep. of Geoscience Engineering, Arak University of Technology, Arak, Iran Mohammad Amin Ghannadi Dep. of Geoscience Engineering, Arak University of Technology, Arak, Iran Journal Article 2022 08 29 Introdction: Pedestrians are considered the most vulnerable road users due to their lack of protective measures, making their safety crucial in transportation planning. Numerous studies have analyzed pedestrian accidents, focusing on predictive models, risk factors, and spatial-temporal patterns. These analyses highlight the importance of identifying high-risk areas and implementing preventive measures. Spatial and temporal autocorrelation effects are significant in understanding accident patterns, and methods like Moran’s I index and Kernel Density Estimation are commonly used. The study of Mashhad, Iran, emphasizes the impact of rapid socio-economic growth on traffic accidents and the need for targeted safety interventions to protect pedestrians. Materials & Mrthods: In this study, a time series exploratory analysis was used to examine pedestrian accidents on a monthly and hourly basis over a five-year period (2015-2019). Next, the presence of temporal autocorrelation and trends in pedestrian accidents were discussed. Then, using time series homogeneity analysis, the change points in the occurrence of accidents were examined. Finally, to extract spatial patterns of changes in pedestrian accidents during the study period, the differential Moran’s I index was applied. Results & Discussions: Using time series analysis, the temporal pattern and significant temporal autocorrelation in the monthly and hourly values of pedestrian accidents were confirmed. The results of the Mann-Kendall test, considering the effects of autocorrelation, also confirmed the presence of a significant trend in pedestrian accidents for different months of the year and different hours of the day. Additionally, through the homogeneity analysis of the time series using the Buishand test, the timing of sudden changes in accidents at different hours of the day (7:00-8:00 AM) and different months of the year (July and September) was identified. The results of using the differential Moran’s I index also showed significant spatial correlation in the changes in pedestrian accidents between the initial time period (2015) and the end of the analysis period (2019), identifying areas with significant changes. Conclusion: In this study, pedestrians, as one of the most vulnerable road users, were considered, and the changes in the occurrence of related accidents over a five-year period (2015-2019) were evaluated using time series analysis and differential Moran’s I spatio-temporal analysis in the metropolis of Mashhad. Significant temporal autocorrelation in monthly and hourly scales was also confirmed in the occurrence of accidents, showing a specific trend in pedestrian accidents in different months of the year and different hours of the day. Finally, the timing of monthly and hourly changes was identified. The results showed significant spatio-temporal autocorrelation in the changes in accidents between the initial (2015) and final (2019) time slices for different months. However, there was no significant spatio-temporal correlation for different hours, indicating that reducing the temporal scale leads to the loss of spatial correlations. The results of this study can serve as a first step in identifying and analyzing spatio-temporal patterns, identifying changes in pedestrian accidents, and allowing safety experts and decision-makers to evaluate the identified areas through local inspections. Introdction: Pedestrians are considered the most vulnerable road users due to their lack of protective measures, making their safety crucial in transportation planning. Numerous studies have analyzed pedestrian accidents, focusing on predictive models, risk factors, and spatial-temporal patterns. These analyses highlight the importance of identifying high-risk areas and implementing preventive measures. Spatial and temporal autocorrelation effects are significant in understanding accident patterns, and methods like Moran’s I index and Kernel Density Estimation are commonly used. The study of Mashhad, Iran, emphasizes the impact of rapid socio-economic growth on traffic accidents and the need for targeted safety interventions to protect pedestrians. Materials & Mrthods: In this study, a time series exploratory analysis was used to examine pedestrian accidents on a monthly and hourly basis over a five-year period (2015-2019). Next, the presence of temporal autocorrelation and trends in pedestrian accidents were discussed. Then, using time series homogeneity analysis, the change points in the occurrence of accidents were examined. Finally, to extract spatial patterns of changes in pedestrian accidents during the study period, the differential Moran’s I index was applied. Results & Discussions: Using time series analysis, the temporal pattern and significant temporal autocorrelation in the monthly and hourly values of pedestrian accidents were confirmed. The results of the Mann-Kendall test, considering the effects of autocorrelation, also confirmed the presence of a significant trend in pedestrian accidents for different months of the year and different hours of the day. Additionally, through the homogeneity analysis of the time series using the Buishand test, the timing of sudden changes in accidents at different hours of the day (7:00-8:00 AM) and different months of the year (July and September) was identified. The results of using the differential Moran’s I index also showed significant spatial correlation in the changes in pedestrian accidents between the initial time period (2015) and the end of the analysis period (2019), identifying areas with significant changes. Conclusion: In this study, pedestrians, as one of the most vulnerable road users, were considered, and the changes in the occurrence of related accidents over a five-year period (2015-2019) were evaluated using time series analysis and differential Moran’s I spatio-temporal analysis in the metropolis of Mashhad. Significant temporal autocorrelation in monthly and hourly scales was also confirmed in the occurrence of accidents, showing a specific trend in pedestrian accidents in different months of the year and different hours of the day. Finally, the timing of monthly and hourly changes was identified. The results showed significant spatio-temporal autocorrelation in the changes in accidents between the initial (2015) and final (2019) time slices for different months. However, there was no significant spatio-temporal correlation for different hours, indicating that reducing the temporal scale leads to the loss of spatial correlations. The results of this study can serve as a first step in identifying and analyzing spatio-temporal patterns, identifying changes in pedestrian accidents, and allowing safety experts and decision-makers to evaluate the identified areas through local inspections.

https://gisj.sbu.ac.ir/article_104155_2d583d718f45952918c7ce7f7466c144.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Remote Sensing Data Assimilation by Forcing Method in Simulation of Silage Maize Yield Using AquaCrop Model Remote Sensing Data Assimilation by Forcing Method in Simulation of Silage Maize Yield Using AquaCrop Model 109 128 104235 10.48308/gisj.2024.233912.1190 FA Elahe Akbari Dep. of Remote Sensing and Geographic Information System, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran Journal Article 2023 11 25 Introduction: An essential part of agricultural plans for maintaining and developing performance at the regional scale is the timely and accurate estimation and prediction of crop yield prior to harvesting using crop growth models. Modeling dynamic changes during crop growth helps researchers to plan crop management strategies to improve its yield. These models contain several parameters that should be calibrated according to the characteristics of the study area. Lack of spatial/geographic components in these models and parameter uncertainties lead to errors in the estimated outputs. Remote sensing data assimilation can be useful for solving this problem and evaluating the spatial variability in the lands, especially at the regional scale. Remote sensing can estimate the values of input variables of crop growth models such as the Leaf Area Index (LAI), canopy cover, biomass, and soil characteristics. Materials and Methods: To achieve accurate crop yield, it is possible to use crop growth models. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing stages and prior to cultivation over agricultural silage maize fields at the regional scale. To calibrate the Aquacrop simulation model through assimilation of remote sensing (RS) data, fCover biophysical variable was extracted from pixel-based RS data by developing GPR-PSO algorithm. Besides, to simplify the Aquacrop model, and to identify more sensitive parameters, the combined sensitivity analysis Morris and EFAST algorithms were employed. Finally, by assimilating the biophysical variable extracted by RS into the Aquacrop model, these more effective parameters were estimated through the forcing method, and compared the results with the results of no application of RS data. In order to calibrate the Aquacrop model, field sampling of soil (before planting) and crop during the growing season of silage maize, digital hemispherical photography (DHP) as well as measurement by destructive method for comparison, was performed in the fields of Qhale-Nou county located in the south of Tehran, in the summer of 2019. Results and Discussion: The results of assimilation of RS data in Aquacrop model compared to no application of RS data in this model showed that considering data assimilation of RS data leads to the increase in model calibration accuracy. As the results suggest, the output yield for the model with data assimilation was estimated with R2 values of 0.89 and 0.88 for calibration and evaluation, respectively. The superiority of RS data assimilation into the model as opposed to not its incorporating was also verified by improving the accuracy with increases in R2 values by 0.14 and 0.15 and decrese in Relative RMSE (RRMSE) values of 4.12 and 5.17 percent and RMSE of 2.5 and 2.4 ton/ha for calibration and evaluation, respectively. So, compared to RS data assimilation and without assimilation is associated with improving the model calibration process with RS data assimilation. Conclusion: The present study employed estimated fCover values obtained via RS data as observed state variables fed as input to the AquaCrop model for means of estimating the most effective parameters identified (via sensitivity analysis). The findings of this procedure indicate that RS data assimilation as a forcing method for model parameters estimating can increase the overall accuracy of the model. Moreover, the correlation between simulated and observed values was higher for the case of RS data assimilation as opposed to not incorporating such data. As these results suggest, RS data assimilation into the AquaCrop model can prove more successful and attain higher accuracies as opposed to not incorporating such data. Furthermore, this process of data assimilation can be used for estimating biophysical variables and calibrating crop growth models at the regional scale, with less time complexity and lower costs and more updated information. Introduction: An essential part of agricultural plans for maintaining and developing performance at the regional scale is the timely and accurate estimation and prediction of crop yield prior to harvesting using crop growth models. Modeling dynamic changes during crop growth helps researchers to plan crop management strategies to improve its yield. These models contain several parameters that should be calibrated according to the characteristics of the study area. Lack of spatial/geographic components in these models and parameter uncertainties lead to errors in the estimated outputs. Remote sensing data assimilation can be useful for solving this problem and evaluating the spatial variability in the lands, especially at the regional scale. Remote sensing can estimate the values of input variables of crop growth models such as the Leaf Area Index (LAI), canopy cover, biomass, and soil characteristics. Materials and Methods: To achieve accurate crop yield, it is possible to use crop growth models. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing stages and prior to cultivation over agricultural silage maize fields at the regional scale. To calibrate the Aquacrop simulation model through assimilation of remote sensing (RS) data, fCover biophysical variable was extracted from pixel-based RS data by developing GPR-PSO algorithm. Besides, to simplify the Aquacrop model, and to identify more sensitive parameters, the combined sensitivity analysis Morris and EFAST algorithms were employed. Finally, by assimilating the biophysical variable extracted by RS into the Aquacrop model, these more effective parameters were estimated through the forcing method, and compared the results with the results of no application of RS data. In order to calibrate the Aquacrop model, field sampling of soil (before planting) and crop during the growing season of silage maize, digital hemispherical photography (DHP) as well as measurement by destructive method for comparison, was performed in the fields of Qhale-Nou county located in the south of Tehran, in the summer of 2019. Results and Discussion: The results of assimilation of RS data in Aquacrop model compared to no application of RS data in this model showed that considering data assimilation of RS data leads to the increase in model calibration accuracy. As the results suggest, the output yield for the model with data assimilation was estimated with R2 values of 0.89 and 0.88 for calibration and evaluation, respectively. The superiority of RS data assimilation into the model as opposed to not its incorporating was also verified by improving the accuracy with increases in R2 values by 0.14 and 0.15 and decrese in Relative RMSE (RRMSE) values of 4.12 and 5.17 percent and RMSE of 2.5 and 2.4 ton/ha for calibration and evaluation, respectively. So, compared to RS data assimilation and without assimilation is associated with improving the model calibration process with RS data assimilation. Conclusion: The present study employed estimated fCover values obtained via RS data as observed state variables fed as input to the AquaCrop model for means of estimating the most effective parameters identified (via sensitivity analysis). The findings of this procedure indicate that RS data assimilation as a forcing method for model parameters estimating can increase the overall accuracy of the model. Moreover, the correlation between simulated and observed values was higher for the case of RS data assimilation as opposed to not incorporating such data. As these results suggest, RS data assimilation into the AquaCrop model can prove more successful and attain higher accuracies as opposed to not incorporating such data. Furthermore, this process of data assimilation can be used for estimating biophysical variables and calibrating crop growth models at the regional scale, with less time complexity and lower costs and more updated information.

https://gisj.sbu.ac.ir/article_104235_aab781062c44340ea76241c5e6ba06c8.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 A Combined Approach Utilizing Geographic Information Systems and Spatial Equity Indicators to Prioritize the Development of Bus Rapid Transit Lines until 1410 (Case Study of Isfahan, Iran) A Combined Approach Utilizing Geographic Information Systems and Spatial Equity Indicators to Prioritize the Development of Bus Rapid Transit Lines until 1410 (Case Study of Isfahan, Iran) 129 150 104237 10.48308/gisj.2024.232377.1166 FA Imam Baharloo Dep. of Science and Research, Faculty of Natural Resources and Environment, Dep. of Remote Sensing and GIS, Islamic Azad University, Tehran, Iran Aliakbar Matkan Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran 0000-0001-5394-4599 Alireza Vafaeinejad Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran Ahmad Khadim Al-Husseini Faculty of Geography, Islamic Azad University, Najaf Abad Branch, Isfahan, Iran Journal Article 2023 07 15 Introduction: In recent decades, rapid urbanization and insufficient urban planning have disrupted urban cohesion and spatial integrity. "The concept of 'spatial equity' has developed as an essential framework in urban planning discourse to alleviate spatial inequalities arising from urban fragmentation In urban environments, spatial inequalities, uneven distribution of services, and environmental challenges are often interrelated. This underscores the significance of addressing issues such as service allocation in urban spaces, particularly within the context of developing countries. In alignment with global trends and national macro-policies, the discourse on spatial equity has gained significant attention within the realm of Iranian urban studies, as it is recognized as a fundamental component for achieving sustainable development. A well-structured and extensive public transportation system is pivotal for urban life, facilitating public mobility and ensuring equitable access to essential services. Integrating spatial equity principles in public transportation development can significantly enhance system efficiency while improving citizens' access to urban service centers without extensive construction. Isfahan, Iran's third-largest metropolis, faces rising intra-city travel, declining quality of life, environmental pollution, and disrupted spatial equity due to population growth and physical expansion. Material and methods: The metropolis of Isfahan, being the third-largest in Iran, has witnessed substantial population growth and physical development in recent years. Consequently, it now faces various challenges, such as an increase in intra-city travel, a decline in quality of life, worsening environmental pollution, and growing spatial disparities. The existing public transportation system is inadequate in meeting the needs of its residents, resulting in inefficient urban service management. As such, investing in and developing suitable public transportation infrastructure has become a pivotal strategy to address these concerns and foster a more sustainable and equitable urban environment. Assessing public transportation, a critical factor shaping urban structures, is essential for addressing these challenges. To improve equitable access to public transportation, comprehensive studies in Isfahan have proposed the establishment of 21 Bus Rapid Transit (BRT) lines. This research applies a multi-criteria decision-making approach, incorporating Shannon Entropy and COPRAS models, to prioritize BRT line development with a focus on spatial justice. The planning horizon for this study extends to 2031 (1410 in the Iranian calendar). A key novelty of this research lies in the integration of Geographic Information Systems (GIS) with spatial justice indicators to guide BRT line prioritization. Results and discussion: First, the performance of existing BRT lines was assessed using permeability, proximity, and accessibility metrics, while spatial equity was quantified using the Gini coefficient and Lorenz curve. Subsequently, the Gini coefficient for each proposed BRT line was calculated and compared with the current value to evaluate the potential impact of each line. The Shannon Entropy method was employed to assign weights to the importance of the criteria, prioritizing proximity, permeability, and accessibility, respectively. Finally, the COPRAS method was utilized to rank the 21 proposed BRT lines for development by 2031. Conclusion: Results suggest that, despite the Ayatollah Ghafari Terminal to Sheikh Saduq line (14.9 km) falling within the medium-length category, it should be considered the top priority for implementation when taking into account the other relevant criteria. Keywords: Spatial Equity, Public Transport, Shannon Entropy, Kopras, Lorenz Curve Introduction: In recent decades, rapid urbanization and insufficient urban planning have disrupted urban cohesion and spatial integrity. "The concept of 'spatial equity' has developed as an essential framework in urban planning discourse to alleviate spatial inequalities arising from urban fragmentation In urban environments, spatial inequalities, uneven distribution of services, and environmental challenges are often interrelated. This underscores the significance of addressing issues such as service allocation in urban spaces, particularly within the context of developing countries. In alignment with global trends and national macro-policies, the discourse on spatial equity has gained significant attention within the realm of Iranian urban studies, as it is recognized as a fundamental component for achieving sustainable development. A well-structured and extensive public transportation system is pivotal for urban life, facilitating public mobility and ensuring equitable access to essential services. Integrating spatial equity principles in public transportation development can significantly enhance system efficiency while improving citizens' access to urban service centers without extensive construction. Isfahan, Iran's third-largest metropolis, faces rising intra-city travel, declining quality of life, environmental pollution, and disrupted spatial equity due to population growth and physical expansion. Material and methods: The metropolis of Isfahan, being the third-largest in Iran, has witnessed substantial population growth and physical development in recent years. Consequently, it now faces various challenges, such as an increase in intra-city travel, a decline in quality of life, worsening environmental pollution, and growing spatial disparities. The existing public transportation system is inadequate in meeting the needs of its residents, resulting in inefficient urban service management. As such, investing in and developing suitable public transportation infrastructure has become a pivotal strategy to address these concerns and foster a more sustainable and equitable urban environment. Assessing public transportation, a critical factor shaping urban structures, is essential for addressing these challenges. To improve equitable access to public transportation, comprehensive studies in Isfahan have proposed the establishment of 21 Bus Rapid Transit (BRT) lines. This research applies a multi-criteria decision-making approach, incorporating Shannon Entropy and COPRAS models, to prioritize BRT line development with a focus on spatial justice. The planning horizon for this study extends to 2031 (1410 in the Iranian calendar). A key novelty of this research lies in the integration of Geographic Information Systems (GIS) with spatial justice indicators to guide BRT line prioritization. Results and discussion: First, the performance of existing BRT lines was assessed using permeability, proximity, and accessibility metrics, while spatial equity was quantified using the Gini coefficient and Lorenz curve. Subsequently, the Gini coefficient for each proposed BRT line was calculated and compared with the current value to evaluate the potential impact of each line. The Shannon Entropy method was employed to assign weights to the importance of the criteria, prioritizing proximity, permeability, and accessibility, respectively. Finally, the COPRAS method was utilized to rank the 21 proposed BRT lines for development by 2031. Conclusion: Results suggest that, despite the Ayatollah Ghafari Terminal to Sheikh Saduq line (14.9 km) falling within the medium-length category, it should be considered the top priority for implementation when taking into account the other relevant criteria. Keywords: Spatial Equity, Public Transport, Shannon Entropy, Kopras, Lorenz Curve

https://gisj.sbu.ac.ir/article_104237_6fda38a047794ffb35ea42f4e42d09ce.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Identifying Fire-Prone Areas in the Vegetation of Lorestan Province Using Infrared Images Identifying Fire-Prone Areas in the Vegetation of Lorestan Province Using Infrared Images 151 170 104367 10.48308/gisj.2024.234303.1196 FA Samad Khosravi Yegane Dep. of Geography, Faculty of Literature and Humanities Sciences, Lorestan University, Lorestan, Iran Hamed Heidri Dep. of Geography, Faculty of Literature and Humanities Sciences, Lorestan University, Lorestan, Iran Mostafa Karampour Dep. of Geography, Faculty of Literature and Humanities Sciences, Lorestan University, Lorestan, Iran Journal Article 2023 12 29 Introduction: In each region, drought conditions vary from moderate to severe and with different durations, which require continuous and operational monitoring. The longer a drought occurs, the greater its effects on vegetation and water resources, and the more severe the drought, which can limit human services and alter natural systems. The effects of drought include habitat destruction for wildlife and water quality, reduced access to water resources, etc. and as a result, disruptions such as fire incidents and other natural disasters increase. Vegetation in each region, especially in different regions of Lorestan province, is at risk of numerous fires every year due to the lack of rain and dryness of the environment. For this reason, the issue of revealing and identifying fire-prone areas in relation to the most important climatic element (rainfall) has been selected and carried out, which can facilitate appropriate and preventive measures to protect vegetation areas. In this research, a combined method has been used. Material and Methods: In this study, an attempt has been made to investigate the drought condition of vegetation in Lorestan province by using Suomi NPP infrared images using NDVI, VCI and TCI indices. The studied period of 2013-2021 corresponds to the first of April to the end of July (week of 13-26 AD) as a weekly average. The monthly average of Standard Precipitation Index (SPI) using precipitation data, the use of monthly precipitation data from Aligoderz, Durood, Khorramabad, Borujerd, Noorabad, Kohdasht and Azna weather stations was done to analyze the precipitation situation well and separate dry and wet months from each other. become Then the correlation coefficient of SPI index with each vegetation index (NDVI, VCI and TCI) is calculated. Results and Discussion: Based on the rainfall data recorded in the meteorological stations of Lorestan province, it can be said that there is no rainfall in the study area in the summer season (July, August and September) and only in the autumn, winter and spring seasons. Therefore, the water year in Lorestan province starts approximately from the third decade of September and continues until the second and third decade of June every year. This indicates the very dry air and lack of humidity. Dry air or lack of humidity and increase in temperature provide the necessary conditions for causing fire in the province. In this article, they put a dry season in the summer season of Lorestan province and August is the driest month of the year. Conclusion: The results of this research showed that the vegetation in Lorestan province is always facing the risk of fire and this is very high in the years when there is a lack of rainfall, in different months. It was proved that if there is a lack of rainfall in the first months of the water year, there is a risk of vegetation fire even in the cold months of the year, and this risk increases significantly in the hot months of the year, which is the case in 2021. there have been. SPI calculations showed that the months of July, August and September are negative in Lorestan province. The results show that the best indicator is based on satellite images for monitoring vegetation drought and fire risk in the study area (TCI). In the years 2013 and 2015, the highest fire risk occurred in the western and central regions of Lorestan province. In 2021, the most severe fire risk has occurred in vegetation in the studied area. Due to the large changes and dispersion of vegetation indicators effective in the occurrence of fires in terms of time and place, Spearman's non-parametric correlation has been used. Introduction: In each region, drought conditions vary from moderate to severe and with different durations, which require continuous and operational monitoring. The longer a drought occurs, the greater its effects on vegetation and water resources, and the more severe the drought, which can limit human services and alter natural systems. The effects of drought include habitat destruction for wildlife and water quality, reduced access to water resources, etc. and as a result, disruptions such as fire incidents and other natural disasters increase. Vegetation in each region, especially in different regions of Lorestan province, is at risk of numerous fires every year due to the lack of rain and dryness of the environment. For this reason, the issue of revealing and identifying fire-prone areas in relation to the most important climatic element (rainfall) has been selected and carried out, which can facilitate appropriate and preventive measures to protect vegetation areas. In this research, a combined method has been used. Material and Methods: In this study, an attempt has been made to investigate the drought condition of vegetation in Lorestan province by using Suomi NPP infrared images using NDVI, VCI and TCI indices. The studied period of 2013-2021 corresponds to the first of April to the end of July (week of 13-26 AD) as a weekly average. The monthly average of Standard Precipitation Index (SPI) using precipitation data, the use of monthly precipitation data from Aligoderz, Durood, Khorramabad, Borujerd, Noorabad, Kohdasht and Azna weather stations was done to analyze the precipitation situation well and separate dry and wet months from each other. become Then the correlation coefficient of SPI index with each vegetation index (NDVI, VCI and TCI) is calculated. Results and Discussion: Based on the rainfall data recorded in the meteorological stations of Lorestan province, it can be said that there is no rainfall in the study area in the summer season (July, August and September) and only in the autumn, winter and spring seasons. Therefore, the water year in Lorestan province starts approximately from the third decade of September and continues until the second and third decade of June every year. This indicates the very dry air and lack of humidity. Dry air or lack of humidity and increase in temperature provide the necessary conditions for causing fire in the province. In this article, they put a dry season in the summer season of Lorestan province and August is the driest month of the year. Conclusion: The results of this research showed that the vegetation in Lorestan province is always facing the risk of fire and this is very high in the years when there is a lack of rainfall, in different months. It was proved that if there is a lack of rainfall in the first months of the water year, there is a risk of vegetation fire even in the cold months of the year, and this risk increases significantly in the hot months of the year, which is the case in 2021. there have been. SPI calculations showed that the months of July, August and September are negative in Lorestan province. The results show that the best indicator is based on satellite images for monitoring vegetation drought and fire risk in the study area (TCI). In the years 2013 and 2015, the highest fire risk occurred in the western and central regions of Lorestan province. In 2021, the most severe fire risk has occurred in vegetation in the studied area. Due to the large changes and dispersion of vegetation indicators effective in the occurrence of fires in terms of time and place, Spearman's non-parametric correlation has been used.

https://gisj.sbu.ac.ir/article_104367_b3cc07b492018bdbb9fc7b88aa6facca.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 1 2025 05 22 Site Selection of Hezar-Masjed Mountain Water Transmission Tunnel Using by the Analytic Hierarchy Process, and Shannon Entropy Site Selection of Hezar-Masjed Mountain Water Transmission Tunnel Using by the Analytic Hierarchy Process, and Shannon Entropy 171 192 104790 10.48308/gisj.2024.236085.1222 FA Sara Kiani Dep. of Geography Geomorphology, Kharazmi University, Tehran, Iran Majid Dashti Sahel Omid Iranian Consulting Engineers, Tehran, Iran 0000-0002-6513-2712 Journal Article 2024 06 22 Introduction: In recent years, the digging of water transmission tunnels has faced various risks such as squeezing potential, hydrogeological risks, environmental effects, gas risks, and sensitivity analysis of construction costs, which has multiplied the importance of the correct site locating of these structures. However, selecting the tunnel route using modern methods of weighting the effective criteria and zoning in the geographic information system has been neglected. Materials and Methods: In the current study, the site selection of the Hezar-Masjed water transmission tunnel using the Analytic Hierarchy Process, and Shannon Entropy as a part of the water transfer project from the Hezar-Masjed mountains to Mashhad city, has been discussed. Accordingly, five components including social hazards, structural geology, hydrogeology, topography, and economic criteria have been considered. In the first step, the two criteria of distance from villages and distance from water sources, regardless of their discharge, were considered as criteria involved with the social dimension. Based on this, it is suggested the tunnel excavation at distances far from these two criteria. The second criterion of studies is assigned to geology and tectonics. Accordingly, due to the high-risk potential of tunnel excavation, the construction of these structures in a high density of faults is not recommended. Hydrogeological studies as the third criterion influencing factors, water inflow into the tunnel, or influencing factors, drying of surrounding water resources from tunnel excavation are important. Therefore, the catchment area of springs in the study area is mentioned as a hydrogeological criterion. In terms of topography, the topographic map of the study area was used to obtain the tunnel overburden thickness map. In this criterion, excavation in less overburden thickness provides more optimal conditions. Finally, the tunnel excavation near the water delivery point, Mashhad - Charmshahr Refinery No. 3, the economic criterion, was considered. Therefore, a criterion called the distance from the tunnel exit portal to the water delivery point has been proposed as the economic criterion of the project. The thematic map of stated criteria was prepared and classified in the ArcMap environment. Scoring the classes in each criterion was done using The Analytic Hierarchy Process. Finally, the weighting of the effective criteria in tunnel site selection was done by the Shannon's entropy method. The prioritization of the effective criteria on the site selection of the Hezar-Masjed water transfer tunnel shows that the distance from the springs is more important than other criteria. So, although the distance from the village is not considered an important factor compared to other criteria, the existence of the spring will complicate the situation due to its importance for the livelihood of the residents, and this means that the distance or proximity to the village cannot be an independent factor for the tunnel route. In the second criterion, the distance from the fault has special importance due to its direct effect on the stability of the structure. Other criteria are in intermediate importance conditions. Finally, by combining the prepared maps, the zoning of suitable areas for tunnel excavation, as well as the three main priorities of the proposed tunnel axis via engineering judgment, have been presented. Results and Discussion: The results show that the northwestern part of the study area is not suitable for drilling and it will cause risks. Therefore, the excavation priority is assigned to the middle and eastern part of the study area. More precisely, drilling priority is assigned to the middle areas because the points are closer to the water delivery site, closer to Mashhad city in the west of the study area. The results show that the best option to transfer water from the northern limb to the southern limb of the Hezar-Masjed mountains is to build an 8732-meter-long tunnel around Chenarsukhteh village, the central part of the study area, and a route closer to the water delivery point. It should be noted that after the site selection of the tunnel route and before its excavation, it is necessary to carry out comprehensive studies of hydrogeology, engineering geology, and geology through field visits, drilling boreholes, and related tests along the route. Conclusion: This study shows that the use of multi-criteria approaches and advanced zoning technologies can help to improve the decision-making process in large infrastructure projects. Introduction: In recent years, the digging of water transmission tunnels has faced various risks such as squeezing potential, hydrogeological risks, environmental effects, gas risks, and sensitivity analysis of construction costs, which has multiplied the importance of the correct site locating of these structures. However, selecting the tunnel route using modern methods of weighting the effective criteria and zoning in the geographic information system has been neglected. Materials and Methods: In the current study, the site selection of the Hezar-Masjed water transmission tunnel using the Analytic Hierarchy Process, and Shannon Entropy as a part of the water transfer project from the Hezar-Masjed mountains to Mashhad city, has been discussed. Accordingly, five components including social hazards, structural geology, hydrogeology, topography, and economic criteria have been considered. In the first step, the two criteria of distance from villages and distance from water sources, regardless of their discharge, were considered as criteria involved with the social dimension. Based on this, it is suggested the tunnel excavation at distances far from these two criteria. The second criterion of studies is assigned to geology and tectonics. Accordingly, due to the high-risk potential of tunnel excavation, the construction of these structures in a high density of faults is not recommended. Hydrogeological studies as the third criterion influencing factors, water inflow into the tunnel, or influencing factors, drying of surrounding water resources from tunnel excavation are important. Therefore, the catchment area of springs in the study area is mentioned as a hydrogeological criterion. In terms of topography, the topographic map of the study area was used to obtain the tunnel overburden thickness map. In this criterion, excavation in less overburden thickness provides more optimal conditions. Finally, the tunnel excavation near the water delivery point, Mashhad - Charmshahr Refinery No. 3, the economic criterion, was considered. Therefore, a criterion called the distance from the tunnel exit portal to the water delivery point has been proposed as the economic criterion of the project. The thematic map of stated criteria was prepared and classified in the ArcMap environment. Scoring the classes in each criterion was done using The Analytic Hierarchy Process. Finally, the weighting of the effective criteria in tunnel site selection was done by the Shannon's entropy method. The prioritization of the effective criteria on the site selection of the Hezar-Masjed water transfer tunnel shows that the distance from the springs is more important than other criteria. So, although the distance from the village is not considered an important factor compared to other criteria, the existence of the spring will complicate the situation due to its importance for the livelihood of the residents, and this means that the distance or proximity to the village cannot be an independent factor for the tunnel route. In the second criterion, the distance from the fault has special importance due to its direct effect on the stability of the structure. Other criteria are in intermediate importance conditions. Finally, by combining the prepared maps, the zoning of suitable areas for tunnel excavation, as well as the three main priorities of the proposed tunnel axis via engineering judgment, have been presented. Results and Discussion: The results show that the northwestern part of the study area is not suitable for drilling and it will cause risks. Therefore, the excavation priority is assigned to the middle and eastern part of the study area. More precisely, drilling priority is assigned to the middle areas because the points are closer to the water delivery site, closer to Mashhad city in the west of the study area. The results show that the best option to transfer water from the northern limb to the southern limb of the Hezar-Masjed mountains is to build an 8732-meter-long tunnel around Chenarsukhteh village, the central part of the study area, and a route closer to the water delivery point. It should be noted that after the site selection of the tunnel route and before its excavation, it is necessary to carry out comprehensive studies of hydrogeology, engineering geology, and geology through field visits, drilling boreholes, and related tests along the route. Conclusion: This study shows that the use of multi-criteria approaches and advanced zoning technologies can help to improve the decision-making process in large infrastructure projects.

https://gisj.sbu.ac.ir/article_104790_29cf7418aac352c40ea56f9e78a37e30.pdf