Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Trend Analysis of MODIS-NDVI Time Series and Its Relationship with Land Use Changes in Golestan Province Trend Analysis of MODIS-NDVI Time Series and Its Relationship with Land Use Changes in Golestan Province 1 20 104760 10.48308/gisj.2024.234882.1207 FA Morteza Dastigerdi Department of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural sciences and Natural Resources University, Sari, Iran. Mehdi Nadi Department of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural sciences and Natural Resources University, Sari, Iran. 0000-0003-0854-8380 Bahareh Shamgani Mashhadi Department of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural sciences and Natural Resources University, Sari, Iran. Journal Article 2024 02 24 Introduction: Human activities and climate change directly affect the earth's surface cover. In the northern part of the country, land use changes have emerged as a significant factor contributing to the destruction of vegetation in Hyrcanian forests. Unfortunately, this destruction has persisted over the past few decades due to human activities. Golestan province, known for its diverse climate and surface cover, has experienced noticeable changes in vegetation, illustrating the impacts of these activities. Therefore, it is crucial to monitor the dynamics of vegetation in order to gain a better understanding of how vegetation respond to human pressures. This knowledge is essential for preserving the Hyrcanian forests. Materials and Methods: The investigation and monitoring of vegetation cover in Golestan province involved the use of the Normalized Difference Vegetation Index (NDVI). To analyze vegetation trends over a 20-year period, 16-day combined MODIS-NDVI time series data (MOD13Q1) with a spatial resolution of 250 meters, were utilized. The research began with the acquisition of raw NDVI images in HDF format from the NASA website. These 920 images, from the 16-day time series, were then analyzed statistically. To standardize the images and facilitate further analysis, they were converted into 460 images through mosaicing and scaling between -1 and +1. The evaluation of serial trends was performed in IDRISI TerrSet software, where the images underwent a preprocessing step to remove seasonal anomalies. The changes in vegetation activity and their significance were subsequently analyzed using the non-parametric Mann-Kendall method. Results and Discussion: The results indicate that 20% of the studied area experienced a decrease in vegetation, while 80% exhibited an increase. Out of these, 5% had a significant decrease, and 50% showed a significant increase in vegetation, while the remaining had no discernible trend. Over the past 20 years, a total of 4088 square kilometers of vegetation has been lost. To analyze the impact of human activities on these changes, location maps of cities and main routes were utilized. The findings revealed that the northern plains of Golestan exhibited a greater reduction in vegetation due to easier land access for human activities, changes in land use, and urban development. The cities of Kordkoy, Bandar Gaz, Aqqala, Gorgan, Azadshahr, and Ramian experienced the most significant increase in vegetation cover, whereas the cities of Bandar Turkman, Gonbadkavoos, Minoodasht, and Kalaleh witnessed the most significant decrease. The main routes leading to Golestan province, including Gorgan-Bujnoord, Gorgan-Sari, Azadshahr-Semnan, Incheh Brun West Road, and Incheh Brun East Road, all showed a decline in vegetation in their vicinity. Due to the significant climatic diversity in Golestan province, surface cover changes were examined at specific locations where a substantial reduction in vegetation cover was expected. Conclusion: Based on the research conducted from 2001 to 2020, it can be concluded that the highlands of Golestan province, particularly the western highlands, have witnessed an increase in vegetation. This phenomenon is believed to be correlated with the rise in temperature resulting from global warming, which has created more favorable conditions for plant growth in these highland areas. In contrast, low-altitude regions such as plains, coastal areas, lower elevations, and especially areas surrounding cities and roads, have experienced a decline in vegetation cover. This decline can be attributed to human activities, including changes in land use and urbanization in the northern plains of Golestan. These changes signify the loss of extensive vegetation and the exacerbation of detrimental impacts from both human activities and natural factors. Although the reduction in vegetation in the highlands has been relatively slower compared to the lower regions, concerns arise regarding the destruction of vegetation in pristine forest areas. These developments highlight the urgent need for immediate attention and implementation of measures for sustainable management of natural resources and adaptation to climate change. Introduction: Human activities and climate change directly affect the earth's surface cover. In the northern part of the country, land use changes have emerged as a significant factor contributing to the destruction of vegetation in Hyrcanian forests. Unfortunately, this destruction has persisted over the past few decades due to human activities. Golestan province, known for its diverse climate and surface cover, has experienced noticeable changes in vegetation, illustrating the impacts of these activities. Therefore, it is crucial to monitor the dynamics of vegetation in order to gain a better understanding of how vegetation respond to human pressures. This knowledge is essential for preserving the Hyrcanian forests. Materials and Methods: The investigation and monitoring of vegetation cover in Golestan province involved the use of the Normalized Difference Vegetation Index (NDVI). To analyze vegetation trends over a 20-year period, 16-day combined MODIS-NDVI time series data (MOD13Q1) with a spatial resolution of 250 meters, were utilized. The research began with the acquisition of raw NDVI images in HDF format from the NASA website. These 920 images, from the 16-day time series, were then analyzed statistically. To standardize the images and facilitate further analysis, they were converted into 460 images through mosaicing and scaling between -1 and +1. The evaluation of serial trends was performed in IDRISI TerrSet software, where the images underwent a preprocessing step to remove seasonal anomalies. The changes in vegetation activity and their significance were subsequently analyzed using the non-parametric Mann-Kendall method. Results and Discussion: The results indicate that 20% of the studied area experienced a decrease in vegetation, while 80% exhibited an increase. Out of these, 5% had a significant decrease, and 50% showed a significant increase in vegetation, while the remaining had no discernible trend. Over the past 20 years, a total of 4088 square kilometers of vegetation has been lost. To analyze the impact of human activities on these changes, location maps of cities and main routes were utilized. The findings revealed that the northern plains of Golestan exhibited a greater reduction in vegetation due to easier land access for human activities, changes in land use, and urban development. The cities of Kordkoy, Bandar Gaz, Aqqala, Gorgan, Azadshahr, and Ramian experienced the most significant increase in vegetation cover, whereas the cities of Bandar Turkman, Gonbadkavoos, Minoodasht, and Kalaleh witnessed the most significant decrease. The main routes leading to Golestan province, including Gorgan-Bujnoord, Gorgan-Sari, Azadshahr-Semnan, Incheh Brun West Road, and Incheh Brun East Road, all showed a decline in vegetation in their vicinity. Due to the significant climatic diversity in Golestan province, surface cover changes were examined at specific locations where a substantial reduction in vegetation cover was expected. Conclusion: Based on the research conducted from 2001 to 2020, it can be concluded that the highlands of Golestan province, particularly the western highlands, have witnessed an increase in vegetation. This phenomenon is believed to be correlated with the rise in temperature resulting from global warming, which has created more favorable conditions for plant growth in these highland areas. In contrast, low-altitude regions such as plains, coastal areas, lower elevations, and especially areas surrounding cities and roads, have experienced a decline in vegetation cover. This decline can be attributed to human activities, including changes in land use and urbanization in the northern plains of Golestan. These changes signify the loss of extensive vegetation and the exacerbation of detrimental impacts from both human activities and natural factors. Although the reduction in vegetation in the highlands has been relatively slower compared to the lower regions, concerns arise regarding the destruction of vegetation in pristine forest areas. These developments highlight the urgent need for immediate attention and implementation of measures for sustainable management of natural resources and adaptation to climate change.

https://gisj.sbu.ac.ir/article_104760_b2a514f9aa864440c3d2d243bee0430d.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Futures Study on the Livability of Worn Texture in Urban Crises (Case Study: Imamzadeh Yahya Neighborhood, Tehran) Futures Study on the Livability of Worn Texture in Urban Crises (Case Study: Imamzadeh Yahya Neighborhood, Tehran) 21 38 104820 10.48308/gisj.2024.233760.1187 FA Ghazaleh Goodarzi Dep of Urban Planning, Faculty of Civil Engineering, Architecture and Art, Science and Research Unit, Islamic Azad University, Tehran, Iran Reza Lor Dep of Urban Planning, Faculty of Civil Engineering, Architecture and Art, Science and Research Unit, Islamic Azad University, Tehran, Iran Journal Article 2023 11 13 Introduction: With the growth of urban populations, attention to urban safety and security has become one of the primary priorities for city managers and urban planners. Urban crises, especially in historical and deteriorated fabrics, can pose serious threats to the safety and livability of neighborhoods and lead to widespread destruction. Due to their specific characteristics—such as physical deterioration, high population density, lack of adequate infrastructure, and weak urban services—deteriorated urban fabrics are among the most vulnerable areas when facing urban crises like earthquakes, floods, and other natural or social disasters. In recent years, various studies and programs have been conducted to rehabilitate and renovate these areas and improve the quality of life for their residents. However, the focus on the livability of these fabrics in the face of urban crises and the use of futures studies for crisis prediction and management have received less attention. On the other hand, defining multipurpose spaces and their balanced distribution within neighborhoods, particularly in deteriorated fabrics with historical value, can contribute to improving the quality of life before a crisis and enhancing crisis management afterward. Materials and Methods: The lack of integration of future research with planning and crisis management is one of the gaps that this research examines how to improve crisis management conditions in the future by using future research techniques and spatial models, and fills this gap with innovation based on predictive analysis and possible scenarios. This research focuses on preserving the livability of deteriorated urban fabrics during urban crises using futures studies, specifically concentrating on the Imamzadeh Yahya neighborhood. It employs scenario writing to explore possible options for the future livability of this neighborhood. By analyzing livability criteria during crises using ArcGIS software and multi-criteria decision-making techniques, and leveraging CommunityVIZ Scenario360 software, the study examines the impact of variables across three different scenarios. Ultimately, the optimal scenario for creating multipurpose spaces is proposed. Results and Discussion: The findings reveal that selecting the optimal scenario and analyzing various layers emphasize the importance of open spaces, connectivity arteries, and vacant areas in the deteriorated urban fabric of the Imamzadeh Yahya neighborhood for urban crisis management. Furthermore, the research using CommunityVIZ Scenario 360 demonstrated that the northern and northwestern areas of the neighborhood are identified as suitable locations for defining multipurpose spaces, which can be effective in spatial enhancement and maintaining livability after a crisis. Additionally, the results indicate that low-cost and minimal projects, such as the use of multipurpose spaces and the consolidation of small-scale functions, can assist in restoring and improving the livability of historical and deteriorated neighborhoods even in crisis conditions. The findings suggest that multipurpose spaces, by offering diverse services and enhancing resilience, can lead to improved quality of life and increased satisfaction among residents. Conclusion: The results of this study indicate that the inequitable distribution of services and facilities can challenge urban crisis management and significantly diminish both city resilience and quality of urban life. Each neighborhood, in accordance with its population size, requires access to essential functions such as commercial, educational, green spaces, recreational, and healthcare facilities to address residents' basic needs at least in the initial hours of a crisis. Transitioning from traditional planning methods to modern approaches, including the use of advanced models and software, alongside urban futures studies, can lead to significant improvements in post-crisis conditions. Introduction: With the growth of urban populations, attention to urban safety and security has become one of the primary priorities for city managers and urban planners. Urban crises, especially in historical and deteriorated fabrics, can pose serious threats to the safety and livability of neighborhoods and lead to widespread destruction. Due to their specific characteristics—such as physical deterioration, high population density, lack of adequate infrastructure, and weak urban services—deteriorated urban fabrics are among the most vulnerable areas when facing urban crises like earthquakes, floods, and other natural or social disasters. In recent years, various studies and programs have been conducted to rehabilitate and renovate these areas and improve the quality of life for their residents. However, the focus on the livability of these fabrics in the face of urban crises and the use of futures studies for crisis prediction and management have received less attention. On the other hand, defining multipurpose spaces and their balanced distribution within neighborhoods, particularly in deteriorated fabrics with historical value, can contribute to improving the quality of life before a crisis and enhancing crisis management afterward. Materials and Methods: The lack of integration of future research with planning and crisis management is one of the gaps that this research examines how to improve crisis management conditions in the future by using future research techniques and spatial models, and fills this gap with innovation based on predictive analysis and possible scenarios. This research focuses on preserving the livability of deteriorated urban fabrics during urban crises using futures studies, specifically concentrating on the Imamzadeh Yahya neighborhood. It employs scenario writing to explore possible options for the future livability of this neighborhood. By analyzing livability criteria during crises using ArcGIS software and multi-criteria decision-making techniques, and leveraging CommunityVIZ Scenario360 software, the study examines the impact of variables across three different scenarios. Ultimately, the optimal scenario for creating multipurpose spaces is proposed. Results and Discussion: The findings reveal that selecting the optimal scenario and analyzing various layers emphasize the importance of open spaces, connectivity arteries, and vacant areas in the deteriorated urban fabric of the Imamzadeh Yahya neighborhood for urban crisis management. Furthermore, the research using CommunityVIZ Scenario 360 demonstrated that the northern and northwestern areas of the neighborhood are identified as suitable locations for defining multipurpose spaces, which can be effective in spatial enhancement and maintaining livability after a crisis. Additionally, the results indicate that low-cost and minimal projects, such as the use of multipurpose spaces and the consolidation of small-scale functions, can assist in restoring and improving the livability of historical and deteriorated neighborhoods even in crisis conditions. The findings suggest that multipurpose spaces, by offering diverse services and enhancing resilience, can lead to improved quality of life and increased satisfaction among residents. Conclusion: The results of this study indicate that the inequitable distribution of services and facilities can challenge urban crisis management and significantly diminish both city resilience and quality of urban life. Each neighborhood, in accordance with its population size, requires access to essential functions such as commercial, educational, green spaces, recreational, and healthcare facilities to address residents' basic needs at least in the initial hours of a crisis. Transitioning from traditional planning methods to modern approaches, including the use of advanced models and software, alongside urban futures studies, can lead to significant improvements in post-crisis conditions.

https://gisj.sbu.ac.ir/article_104820_866a768b0a4755fa94d81183069d8f27.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Evaluation of the Performance of the Singular Spectrum Analysis (SSA) Algorithm in Reconstructing Missing Data with Different Intensities in the Hourly Land Surface Temperature Time Series Evaluation of the Performance of the Singular Spectrum Analysis (SSA) Algorithm in Reconstructing Missing Data with Different Intensities in the Hourly Land Surface Temperature Time Series 39 58 104819 10.48308/gisj.2024.235694.1219 FA Hadi Zare Khormizi Dep of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran. Mohammad Jafari Dep of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran. Hamid Reza Ghafarian Malamiri Depof Geography, Department of Environmental Planning, Yazd University, Yazd, Iran. Ali Tavili Dep of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran Hamidreza Keshtkar Dep of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran. Journal Article 2024 05 15 Introduction and Purpose: Generating Land Surface Temperature (LST) data with temporal and spatial continuity is in great demand for hydrology, meteorology, ecology, environment, and, etc. studies. Approximately, 60 to 75 percent of the Earth is covered by clouds at any given moment. Therefore, clouds, by creating an obstacle, absorb part of the thermal energy emitted from the earth by affecting thermal infrared energy, creating gaps and outliers in LST time series data. Removing the effect of cloud cover is always a common problem in the field of using satellite images. The purpose of this research is to evaluate the performance of Multi-channel Singular Spectrum Analysis (M-SSA) in order to reconstruct gaps and remove outlier data due to the cloud coverage in the hourly LST time series of the Meteosat-9 satellite. Materials and Methods: The study area in the present research was whole Iran. Also, the hourly LST time series of the SEVIRI sensor from the Meteosat-9 geostationary satellite in 2022 was used. At first, using SSA software and the Monte Carlo test, the window size and the number of significant components of an hourly LST time series were determined. Then, using the identified significant components, LST time series were reconstructed using M-SSA algorithm. Reconstruction error in clear sky conditions with available time series data and reconstruction error in cloudy sky conditions by creating artificial missing data (artificial cloud) with intensities of 10, 20, 30, ..., 90% in time series were evaluated using root mean square error (RMSE) and coefficient of determination (R2) statistics. Results: On average, in Iran, 25.5% of the hourly LST time series in 2022 was lost due to cloud cover, and the highest percentage of lost data was observed at the edge of the Caspian Sea. The results of analyzing the annual hourly LST time series in a window size of 96 hours with the Monte Carlo test showed that components 1 to 5 are significant components of this time series. These components control 97.5% of the LST time series variance. The frequency of the first, second-third, and fourth-fifth components are respectively 0, 0.042 and 0.083 cycles per image. The first component indicates annual periodic changes, the second and third components indicate 24-hour or daily temperature changes, and the fourth and fifth components indicate 12-hour periodic temperature changes. Based on the results, the RMSE and the R2 between the original and the reconstructed data in clear sky conditions were 1.38 and 0.99 Kelvin, respectively. Also, in cloudy sky conditions, the RMSE error up to the level of 80% of randomly lost data (artificial cloud) was always less than 2.1 Kelvin. Discussion and Conclusion: The main key to reconstructing time series with periodic behavior is to identify significant periodic components and trends. In hourly LST time series, annual, 24-, 12- and 8-hour periods are the most important components of the time series. These components are formed due to the rotation of the earth around itself and the sun and the deviation of its axis. Therefore, these components are generally the same for the reconstruction of hourly LST time series in the major part of the globe. Based on the findings, M-SSA algorithm can be effective in reconstructing lost data with large distance in LST time series due to consideration of periodic components and trends as well as using temporal and spatial correlation. One of the significant cases in reconstructing the effect of cloud cover in the present study and many other studies is the reconstruction of LST with the clear sky condition. Therefore, reconstruction of LST under cloud cover can be a challenge and suggestion for further studies in the future. Introduction and Purpose: Generating Land Surface Temperature (LST) data with temporal and spatial continuity is in great demand for hydrology, meteorology, ecology, environment, and, etc. studies. Approximately, 60 to 75 percent of the Earth is covered by clouds at any given moment. Therefore, clouds, by creating an obstacle, absorb part of the thermal energy emitted from the earth by affecting thermal infrared energy, creating gaps and outliers in LST time series data. Removing the effect of cloud cover is always a common problem in the field of using satellite images. The purpose of this research is to evaluate the performance of Multi-channel Singular Spectrum Analysis (M-SSA) in order to reconstruct gaps and remove outlier data due to the cloud coverage in the hourly LST time series of the Meteosat-9 satellite. Materials and Methods: The study area in the present research was whole Iran. Also, the hourly LST time series of the SEVIRI sensor from the Meteosat-9 geostationary satellite in 2022 was used. At first, using SSA software and the Monte Carlo test, the window size and the number of significant components of an hourly LST time series were determined. Then, using the identified significant components, LST time series were reconstructed using M-SSA algorithm. Reconstruction error in clear sky conditions with available time series data and reconstruction error in cloudy sky conditions by creating artificial missing data (artificial cloud) with intensities of 10, 20, 30, ..., 90% in time series were evaluated using root mean square error (RMSE) and coefficient of determination (R2) statistics. Results: On average, in Iran, 25.5% of the hourly LST time series in 2022 was lost due to cloud cover, and the highest percentage of lost data was observed at the edge of the Caspian Sea. The results of analyzing the annual hourly LST time series in a window size of 96 hours with the Monte Carlo test showed that components 1 to 5 are significant components of this time series. These components control 97.5% of the LST time series variance. The frequency of the first, second-third, and fourth-fifth components are respectively 0, 0.042 and 0.083 cycles per image. The first component indicates annual periodic changes, the second and third components indicate 24-hour or daily temperature changes, and the fourth and fifth components indicate 12-hour periodic temperature changes. Based on the results, the RMSE and the R2 between the original and the reconstructed data in clear sky conditions were 1.38 and 0.99 Kelvin, respectively. Also, in cloudy sky conditions, the RMSE error up to the level of 80% of randomly lost data (artificial cloud) was always less than 2.1 Kelvin. Discussion and Conclusion: The main key to reconstructing time series with periodic behavior is to identify significant periodic components and trends. In hourly LST time series, annual, 24-, 12- and 8-hour periods are the most important components of the time series. These components are formed due to the rotation of the earth around itself and the sun and the deviation of its axis. Therefore, these components are generally the same for the reconstruction of hourly LST time series in the major part of the globe. Based on the findings, M-SSA algorithm can be effective in reconstructing lost data with large distance in LST time series due to consideration of periodic components and trends as well as using temporal and spatial correlation. One of the significant cases in reconstructing the effect of cloud cover in the present study and many other studies is the reconstruction of LST with the clear sky condition. Therefore, reconstruction of LST under cloud cover can be a challenge and suggestion for further studies in the future.

https://gisj.sbu.ac.ir/article_104819_9e7ed42b9d31ef50ef8c9996f51eaa2b.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Assessment of Areas Susceptible to Desertification with Emphasis on Erosion Models Using Multi-Criteria Decision Analysis A Case Study (Sistan Suture Zone and Afghan Blocks) Assessment of Areas Susceptible to Desertification with Emphasis on Erosion Models Using Multi-Criteria Decision Analysis A Case Study (Sistan Suture Zone and Afghan Blocks) 59 78 104850 10.48308/gisj.2024.234399.1198 FA Sajjad Taleghani Dep of Geography, Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad , Iran Atefe Bardooei Dep of Geography, Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad , Iran Amir Hossein Najafi Dehjalali Dep of Geography, Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad , Iran Masoud Minaei Dep of Geography, Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad , Iran Journal Article 2024 01 09 Introduction: Desertification is one of the major challenges of today's world, threatening environmental sustainability. This phenomenon arises from land degradation in arid and semi-arid regions and can have serious consequences for the environment, economy, and society. Due to its geographic location in the dry and semi-arid belt of the world, Iran is at risk of desertification. To combat this phenomenon, it is essential to identify and assess the influential factors, determine vulnerable areas, and use models to evaluate this issue. The use of remote sensing technologies and Geographic Information Systems (GIS) can be beneficial in assessing and monitoring desertification. These technologies enable comprehensive and accurate examination of land cover changes and assist in the management and protection of at-risk areas. This study aims to identify areas susceptible to desertification in the eastern belt of Iran (Sistan Suture Zone and Afghan Blocks) using multi-criteria decision analysis models based on the Ordered Preferential Approach (OPA). Materials and Methods: The geological zone of Sistan and the Afghan Blocks, covering an area of over 106,000 square kilometers, is located in the eastern belt of Iran and includes parts of Sistan and Baluchestan and South Khorasan provinces. According to the De Martonne climate classification, this area falls within the arid and hyper-arid climate zones. Such conditions, along with vegetation degradation and the drying up of water resources, have made this region susceptible to desertification. In this study, to obtain a map of areas prone to desertification, wind and water erosion potential maps were first generated using the RWEQ and RUSLE models, respectively, in the study area. The results of these models, along with other indicators such as vegetation cover, soil salinity, land use, temperature, soil classification, bulk density, and climate classification, were weighted using a multi-criteria decision analysis model based on the Ordered Preferential Approach (OPA). Finally, a map of areas susceptible to desertification in the eastern belt of Iran was produced. Results and Discussion: The results of this study showed that the average wind erosion potential in the eastern belt of Iran is 64 kg per square meter. Notably, 16% of this area, primarily located in the eastern and southeastern parts, including the cities of Zabol, Saravan, and Khash, has a wind erosion potential exceeding 512 kg per square meter. In contrast, the average water erosion was found to be 24.36 tons per hectare, with the highest rates of water erosion exceeding 40 tons per hectare covering 34.5% of the study area, primarily in the northern region, including the city of Nehbandan in South Khorasan province and central parts of the area. Finally, the results of the multi-criteria decision analysis model based on the Ordered Preferential Approach indicated that the most significant factors identified by experts in recognizing areas susceptible to desertification in this region are wind erosion, vegetation cover, and soil salinity. The eastern and southeastern parts of the area are severely affected by desertification. Conclusion: Erosion in the eastern belt of Iran has multiple negative consequences, including reduced soil fertility and threats to livelihoods, food security, and public health. The degradation of vegetation, loss of water resources, and conversion of these areas into barren lands, particularly in the eastern half of Iran, which has faced extensive drought in recent years, have had the most significant impact on desertification. To deal with this problem, there is a need for management such as resource management, sustainable agricultural development and biodiversity conservation. These initiatives should be designed and implemented considering the specific conditions of each region and with the participation of local communities and experts. The results of this study indicate that the use of models based on the Ordered Preferential Approach can be effective in identifying vulnerable areas for the formulation of effective management plans. Additionally, incorporating indicators such as grazing management, population, and groundwater levels in future studies will facilitate a better assessment of desertification status. Introduction: Desertification is one of the major challenges of today's world, threatening environmental sustainability. This phenomenon arises from land degradation in arid and semi-arid regions and can have serious consequences for the environment, economy, and society. Due to its geographic location in the dry and semi-arid belt of the world, Iran is at risk of desertification. To combat this phenomenon, it is essential to identify and assess the influential factors, determine vulnerable areas, and use models to evaluate this issue. The use of remote sensing technologies and Geographic Information Systems (GIS) can be beneficial in assessing and monitoring desertification. These technologies enable comprehensive and accurate examination of land cover changes and assist in the management and protection of at-risk areas. This study aims to identify areas susceptible to desertification in the eastern belt of Iran (Sistan Suture Zone and Afghan Blocks) using multi-criteria decision analysis models based on the Ordered Preferential Approach (OPA). Materials and Methods: The geological zone of Sistan and the Afghan Blocks, covering an area of over 106,000 square kilometers, is located in the eastern belt of Iran and includes parts of Sistan and Baluchestan and South Khorasan provinces. According to the De Martonne climate classification, this area falls within the arid and hyper-arid climate zones. Such conditions, along with vegetation degradation and the drying up of water resources, have made this region susceptible to desertification. In this study, to obtain a map of areas prone to desertification, wind and water erosion potential maps were first generated using the RWEQ and RUSLE models, respectively, in the study area. The results of these models, along with other indicators such as vegetation cover, soil salinity, land use, temperature, soil classification, bulk density, and climate classification, were weighted using a multi-criteria decision analysis model based on the Ordered Preferential Approach (OPA). Finally, a map of areas susceptible to desertification in the eastern belt of Iran was produced. Results and Discussion: The results of this study showed that the average wind erosion potential in the eastern belt of Iran is 64 kg per square meter. Notably, 16% of this area, primarily located in the eastern and southeastern parts, including the cities of Zabol, Saravan, and Khash, has a wind erosion potential exceeding 512 kg per square meter. In contrast, the average water erosion was found to be 24.36 tons per hectare, with the highest rates of water erosion exceeding 40 tons per hectare covering 34.5% of the study area, primarily in the northern region, including the city of Nehbandan in South Khorasan province and central parts of the area. Finally, the results of the multi-criteria decision analysis model based on the Ordered Preferential Approach indicated that the most significant factors identified by experts in recognizing areas susceptible to desertification in this region are wind erosion, vegetation cover, and soil salinity. The eastern and southeastern parts of the area are severely affected by desertification. Conclusion: Erosion in the eastern belt of Iran has multiple negative consequences, including reduced soil fertility and threats to livelihoods, food security, and public health. The degradation of vegetation, loss of water resources, and conversion of these areas into barren lands, particularly in the eastern half of Iran, which has faced extensive drought in recent years, have had the most significant impact on desertification. To deal with this problem, there is a need for management such as resource management, sustainable agricultural development and biodiversity conservation. These initiatives should be designed and implemented considering the specific conditions of each region and with the participation of local communities and experts. The results of this study indicate that the use of models based on the Ordered Preferential Approach can be effective in identifying vulnerable areas for the formulation of effective management plans. Additionally, incorporating indicators such as grazing management, population, and groundwater levels in future studies will facilitate a better assessment of desertification status.

https://gisj.sbu.ac.ir/article_104850_b4e9fef44e63edf34ff1a0c5c83b5c58.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Flood risk Monitoring of June 1402 in Zanjan Province Using Sentinel-1 Images Flood risk Monitoring of June 1402 in Zanjan Province Using Sentinel-1 Images 79 92 104998 10.48308/gisj.2024.234929.1210 FA Abdullah Faraji Dep of Geography, Faculty of Literature and Humanities, Zanjan University, Zanjan, Iran. Nafiseh Rahimi Dep of Geography, Faculty of Literature and Humanities, Zanjan University, Zanjan, Iran. Journal Article 2024 02 27 Introduction: Proper flood management requires the exact location and time of flooding so that crisis management planners can reduce the risk of flooding with proper management by providing solutions. Studies in this field have been carried out by researchers with different methods such as the use of Sentinel 1 and Sentinel 2 satellite gauges and it has been proven that flood monitoring with the help of remote sensing is a suitable tool for the quick direction of the flooded area. It is used in the early management of natural disasters, especially floods. The purpose of this study is to prepare a map of the extent of water caused by the flood of June Zanjan 1402 with the help of Sentinel 1 images. This map can be used in the management and planning of land users in flood plains, raising the level of awareness and warning people about flood spots. In the region, the development of flood risk reduction plans, the preparation of comprehensive flood risk management plans, and the preparation of guidelines for dealing with and resilience to critical conditions are contracted. Materials and Methods: The research method was carried out in steps: in the first step it was collected with the help of Sentinel-1, then in the second step: SAR data were pre-processed. The third step: the images were post-processed in the ENVI environment, with the help of the tree algorithm, and in the last step: the images were converted into vector files. Results and Discussion: Examining the changes in flooding after seven days of flooding in the region shows that the highest water level in the northern regions of the province is in the vicinity of the main and sub-rivers of the Qezal-Ozen watershed, especially in Tarem city. It was land with 312/067192, after which more number of polygons were seen in the north of Zanjan city in the area of Qezl-Ozen aquifer basin, Lower Zanjanrud, Pare, Ghani-Biglo, higher aquifer with the area of 150/713193, which these aquifers has taken more It has occurred under the impact of tectonics in the region around Qezl-Ozen river. Most of the flood water was seen in Mahenshan, in the northern part of Mahenshan city, in Mahenshan and Uriad divisions with 375 polygons and the extent of flood water was 26/618086. In Ijroud city in Zarin Abad, in the direction of the Ijroud river, most of the flood water was in the direction of the Ijroud river with the extent of 21/06405 and with flooding of 24 polygon centers, in Abhar city, the water is from the flood in Soltanieh center in Zangan. The river (one of the branches of the Qezal Ozen River) with an area of 96 lands was seen as a face with 547 flood polygons around the river.In the flood of June 1402 in Zanjan province, the height of the area was a key factor in controlling the direction of the flood and the persistence of water on the ground (5-b). The amount of flood water receding at altitudes less than 500 meters was very low compared to higher altitudes, in such a way that water retention was not seen at altitudes above 1000 meters, in the high places of Zanjan such as parts of Tarem, Zanjan and Mahenshan after Atmospheric precipitation had started to flow faster from the low-lying and flat areas, so that after seven days of the flood, water retention was not seen in these heights. While at altitudes of less than 500 meters, which mainly included the low altitude areas of the Qezl-Ozen catchment and its main and tributary rivers, most of the runoff was collected in the topographic holes of Tarem and Zanjan, in these low-lying areas. The region caused widespread flooding and flooding even in the population centers of these regions. Conclusion: In the upcoming research, in order to measure the extent of water caused by the flood and to prepare a flood zoning map for the month of June in Zanjan province and to evaluate the factors affecting it such as height and vegetation, Sentinel-1 images were prepared for before and after the flood. It was processed and classified into three classes and analyzed, and it was found that the largest amount of flood that entered Zanjan province was from the north of the province, especially Tarem city. Also, the study of the height factor in the flooding of the region showed that the heights of less than 500 meters, which mainly included the sub-basins of Qezl-Ozen and the rivers around it, had a high potential for flooding. Flooding also showed that the grassland vegetation has increased the flood potential of these areas due to insufficient permeability of rainfall in these areas. Introduction: Proper flood management requires the exact location and time of flooding so that crisis management planners can reduce the risk of flooding with proper management by providing solutions. Studies in this field have been carried out by researchers with different methods such as the use of Sentinel 1 and Sentinel 2 satellite gauges and it has been proven that flood monitoring with the help of remote sensing is a suitable tool for the quick direction of the flooded area. It is used in the early management of natural disasters, especially floods. The purpose of this study is to prepare a map of the extent of water caused by the flood of June Zanjan 1402 with the help of Sentinel 1 images. This map can be used in the management and planning of land users in flood plains, raising the level of awareness and warning people about flood spots. In the region, the development of flood risk reduction plans, the preparation of comprehensive flood risk management plans, and the preparation of guidelines for dealing with and resilience to critical conditions are contracted. Materials and Methods: The research method was carried out in steps: in the first step it was collected with the help of Sentinel-1, then in the second step: SAR data were pre-processed. The third step: the images were post-processed in the ENVI environment, with the help of the tree algorithm, and in the last step: the images were converted into vector files. Results and Discussion: Examining the changes in flooding after seven days of flooding in the region shows that the highest water level in the northern regions of the province is in the vicinity of the main and sub-rivers of the Qezal-Ozen watershed, especially in Tarem city. It was land with 312/067192, after which more number of polygons were seen in the north of Zanjan city in the area of Qezl-Ozen aquifer basin, Lower Zanjanrud, Pare, Ghani-Biglo, higher aquifer with the area of 150/713193, which these aquifers has taken more It has occurred under the impact of tectonics in the region around Qezl-Ozen river. Most of the flood water was seen in Mahenshan, in the northern part of Mahenshan city, in Mahenshan and Uriad divisions with 375 polygons and the extent of flood water was 26/618086. In Ijroud city in Zarin Abad, in the direction of the Ijroud river, most of the flood water was in the direction of the Ijroud river with the extent of 21/06405 and with flooding of 24 polygon centers, in Abhar city, the water is from the flood in Soltanieh center in Zangan. The river (one of the branches of the Qezal Ozen River) with an area of 96 lands was seen as a face with 547 flood polygons around the river.In the flood of June 1402 in Zanjan province, the height of the area was a key factor in controlling the direction of the flood and the persistence of water on the ground (5-b). The amount of flood water receding at altitudes less than 500 meters was very low compared to higher altitudes, in such a way that water retention was not seen at altitudes above 1000 meters, in the high places of Zanjan such as parts of Tarem, Zanjan and Mahenshan after Atmospheric precipitation had started to flow faster from the low-lying and flat areas, so that after seven days of the flood, water retention was not seen in these heights. While at altitudes of less than 500 meters, which mainly included the low altitude areas of the Qezl-Ozen catchment and its main and tributary rivers, most of the runoff was collected in the topographic holes of Tarem and Zanjan, in these low-lying areas. The region caused widespread flooding and flooding even in the population centers of these regions. Conclusion: In the upcoming research, in order to measure the extent of water caused by the flood and to prepare a flood zoning map for the month of June in Zanjan province and to evaluate the factors affecting it such as height and vegetation, Sentinel-1 images were prepared for before and after the flood. It was processed and classified into three classes and analyzed, and it was found that the largest amount of flood that entered Zanjan province was from the north of the province, especially Tarem city. Also, the study of the height factor in the flooding of the region showed that the heights of less than 500 meters, which mainly included the sub-basins of Qezl-Ozen and the rivers around it, had a high potential for flooding. Flooding also showed that the grassland vegetation has increased the flood potential of these areas due to insufficient permeability of rainfall in these areas.

https://gisj.sbu.ac.ir/article_104998_de1536b2319074ad520b61db0ff360fe.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Estimation of Soil Organic Carbon Content at Various Moisture Levels Using Visible/Near-Infrared Spectroscopy Estimation of Soil Organic Carbon Content at Various Moisture Levels Using Visible/Near-Infrared Spectroscopy 93 110 104997 10.48308/gisj.2024.235669.1218 FA Mehrdad Aghaei Sadi Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran Saeid Minaei Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran Bahareh Jamshidi Smart Agricultural Research Department, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran HosseinAli Bahrami Dep of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran Saham Mirzaei Institute of Methodologies for Environmental Analysis, Italian National Research Council, Potenza, Italy Journal Article 2024 05 12 Introduction: Determination of organic-carbon-content variation in the field is crucial due to the importance of soil organic carbon content, including its role in increasing soil resistance against wind and water erosion. This study examines the ability of reflectance visible-near infrared (Vis/NIR) spectrometry for measurement and prediction of soil organic carbon content and the effect of the type of spectral preprocessing on the accuracy of multivariable predictive models was studied. Material and Methods: In this research, spectroscopy of soil samples was performed at 7 moisture levels in the interactance measurement mode in the 350-2500 nm spectral range using a contact probe. Spectrophotometry of 5 different sections of each soil sample was carried out and the data were processed and analyzed. Spectral data obtained from the spectrophotometer included unwanted information, background and noise in addition to the information of the samples. In order to arrive at accurate and reliable analytical models, pre-processing of the spectral data was required prior to regression model simulation. Multivariate calibration models of partial least squares (PLS) were developed based on the reference measurements and the information of the preprocessed spectra using a combination of different methods for assessment and prediction of soil organic carbon content. These included: smoothing (moving average (MA), and Savitzky-Golay (SG)); normalizing (multiplicative scatter correction (MSC), standard normal Variate (SNV)); as well as increasing the spectral resolution (first and second derivatives (D1, D2)). Results and Discussion: Results showed that NIR spectroscopy is a suitable method for measurement of organic carbon content in soil samples. Prediction utilizing the data analyzed using the PLS model based on SG + MSC, produced the best detection results. Thus, SG+MSC preprocessing (Rc2 =0.81, RMSEC = 0.239, Rp2 = 0.79, RMSEP = 0.252) is suitable for predicting the amount of soil OC with high accuracy (SDR= 3.191). Results showed that reflectance rate diminishes with increasing moisture content reducing the ability of the PLS model to predict organic carbon content. This is true across all the preprocessing methods. In addition, the determined index values and validation criteria showed that prediction of organic carbon content with the PLS model using SG+D1+MSC, SG+MSC, SG+MSC, SG+D1+MSC, SG+SNV and SG+SNV combinations gives the best detection results for the following moisture levels, respectively: 6, 12, 18, 24, 30 and 36%. Conclusion: Vis/NIR spectroscopy can be used as an alternative to conventional laboratory methods for soil organic-carbon-content determination. Results showed that the use of Vis/NIR spectroscopy for determination of soil organic carbon content can be considered in the site-specific management of fields, which can ultimately lead to saving inputs and reducing the pressure on the environment. . Introduction: Determination of organic-carbon-content variation in the field is crucial due to the importance of soil organic carbon content, including its role in increasing soil resistance against wind and water erosion. This study examines the ability of reflectance visible-near infrared (Vis/NIR) spectrometry for measurement and prediction of soil organic carbon content and the effect of the type of spectral preprocessing on the accuracy of multivariable predictive models was studied. Material and Methods: In this research, spectroscopy of soil samples was performed at 7 moisture levels in the interactance measurement mode in the 350-2500 nm spectral range using a contact probe. Spectrophotometry of 5 different sections of each soil sample was carried out and the data were processed and analyzed. Spectral data obtained from the spectrophotometer included unwanted information, background and noise in addition to the information of the samples. In order to arrive at accurate and reliable analytical models, pre-processing of the spectral data was required prior to regression model simulation. Multivariate calibration models of partial least squares (PLS) were developed based on the reference measurements and the information of the preprocessed spectra using a combination of different methods for assessment and prediction of soil organic carbon content. These included: smoothing (moving average (MA), and Savitzky-Golay (SG)); normalizing (multiplicative scatter correction (MSC), standard normal Variate (SNV)); as well as increasing the spectral resolution (first and second derivatives (D1, D2)). Results and Discussion: Results showed that NIR spectroscopy is a suitable method for measurement of organic carbon content in soil samples. Prediction utilizing the data analyzed using the PLS model based on SG + MSC, produced the best detection results. Thus, SG+MSC preprocessing (Rc2 =0.81, RMSEC = 0.239, Rp2 = 0.79, RMSEP = 0.252) is suitable for predicting the amount of soil OC with high accuracy (SDR= 3.191). Results showed that reflectance rate diminishes with increasing moisture content reducing the ability of the PLS model to predict organic carbon content. This is true across all the preprocessing methods. In addition, the determined index values and validation criteria showed that prediction of organic carbon content with the PLS model using SG+D1+MSC, SG+MSC, SG+MSC, SG+D1+MSC, SG+SNV and SG+SNV combinations gives the best detection results for the following moisture levels, respectively: 6, 12, 18, 24, 30 and 36%. Conclusion: Vis/NIR spectroscopy can be used as an alternative to conventional laboratory methods for soil organic-carbon-content determination. Results showed that the use of Vis/NIR spectroscopy for determination of soil organic carbon content can be considered in the site-specific management of fields, which can ultimately lead to saving inputs and reducing the pressure on the environment. .

https://gisj.sbu.ac.ir/article_104997_2275949358d46d9e59922551351f383e.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Performance Analysis of Support Vector Machine, Random Forest, and Maximum Likelihood Algorithms in Land Use Classification of the Metropolitan Area of Mashhad Performance Analysis of Support Vector Machine, Random Forest, and Maximum Likelihood Algorithms in Land Use Classification of the Metropolitan Area of Mashhad 111 132 105144 10.48308/gisj.2024.236864.1230 FA Sajedeh Baghban Dep of Geography, Faculty of Literature and Human Sciences, Ferdowsi University of Mashhad, Mashhad, Iran Mohammad Rahim Rahnama Dep of Geography, Faculty of Literature and Human Sciences, Ferdowsi University of Mashhad, Mashhad, Iran Mohammad Ajza Shokuhi Dep of Geography, Faculty of Literature and Human Sciences, Ferdowsi University of Mashhad, Mashhad, Iran Hossein Vahidi Dep of Geography, Faculty of Literature and Human Sciences, Ferdowsi University of Mashhad, Mashhad, Iran Journal Article 2024 09 10 Introduction: Considering that the value and usability of any map produced from satellite images depend on its accuracy, evaluating the accuracy of satellite image classification methods is of great importance. Therefore, this research aims to analyse the performance of Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood Classification (MLC) algorithms in identifying land use and land cover (LULC) in the metropolitan area of Mashhad. Numerous algorithms have been developed for satellite image classification to date, and their performance varies under different conditions. For this reason, this study first identifies the most commonly used algorithms through a review of previous research, and then, by assessing the characteristics of various classifiers, selects the three algorithms: Support Vector Machine, Random Forest, and Maximum Likelihood. There are various studies regarding the performance of different classification algorithms, each yielding different results. Given that multiple studies have shown that LULC mapping accuracy is related to time and location, and that each of these studies has emphasized the accuracy of different algorithms, their results cannot be generalized to the geographical conditions of Iran. On the other hand, there has not been sufficient research in the geomorphological conditions of Iran to assess the accuracy of classification algorithms, and most studies validating these algorithms have been conducted in case studies outside of Iran. Therefore, considering the differences in algorithm results under various conditions, examining the accuracy and performance of these algorithms focusing on the extensive and diverse metropolitan area of Mashhad may yield novel and noteworthy findings. Materials and Methods: The present research is applied in terms of purpose and descriptive-analytical in terms of nature. Data collection in this study has been conducted through a documentary-library method. In this study, images from the OLI sensor on the Landsat 8 satellite were used. The classification of satellite images was performed in two stages: preprocessing and processing. After assessing the accuracy of the classification using the Kappa coefficient, confusion matrix, coefficient of variation, and User's accuracy and Producer's accuracy coefficients, the best algorithm for classifying land uses in the metropolitan area of Mashhad was determined in five classes: 1- Built-up areas, 2- Barren land, 3- Mountainous areas, 4- Green spaces, and 5- Water bodies. Results and Discussion: The results from the evaluation of standard deviation (SD) and coefficient of variation (CV) regarding the area share percentage in a LULC class by various algorithms indicate that barren lands were classified with higher accuracy, while water bodies and green spaces were classified with lower accuracy. The examination of U_Accuracy and P_Accuracy coefficients shows that the overall accuracy of the classification for all studied algorithms falls within the range of good to excellent. However, a more detailed examination of these algorithms reveals that the greatest challenge in class identification lies in built-up areas, mountainous regions, and green spaces, whereas the identification of barren lands faces fewer challenges. The Kappa coefficient and analyses based on the confusion matrix also demonstrate the variation in accuracy among each LULC classifier. The differences in the accuracy of the classifiers used are marginal, but these slight variations hold significant importance in the context of LULC planning. Given that these marginal differences are evident in sensitive land uses such as built-up areas and green spaces, selecting an algorithm with the highest accuracy and lowest error is of special importance. Conclusion: The results of the Kappa coefficient evaluation and confusion matrix analyses indicate that the SVM approach has greater overall accuracy and a higher Kappa coefficient compared to RF and MLC methods. Specifically, the algorithms achieved overall accuracies of 0.93, 0.88, and 0.80, respectively. Therefore, Support Vector Machine demonstrates the highest accuracy and least error among the studied classifiers. Considering that numerous studies have shown that LULC mapping accuracy is related to time and location, it is suggested that future research analyse the accuracy of classifiers under different morphoclimatic and geomorphic conditions. Introduction: Considering that the value and usability of any map produced from satellite images depend on its accuracy, evaluating the accuracy of satellite image classification methods is of great importance. Therefore, this research aims to analyse the performance of Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood Classification (MLC) algorithms in identifying land use and land cover (LULC) in the metropolitan area of Mashhad. Numerous algorithms have been developed for satellite image classification to date, and their performance varies under different conditions. For this reason, this study first identifies the most commonly used algorithms through a review of previous research, and then, by assessing the characteristics of various classifiers, selects the three algorithms: Support Vector Machine, Random Forest, and Maximum Likelihood. There are various studies regarding the performance of different classification algorithms, each yielding different results. Given that multiple studies have shown that LULC mapping accuracy is related to time and location, and that each of these studies has emphasized the accuracy of different algorithms, their results cannot be generalized to the geographical conditions of Iran. On the other hand, there has not been sufficient research in the geomorphological conditions of Iran to assess the accuracy of classification algorithms, and most studies validating these algorithms have been conducted in case studies outside of Iran. Therefore, considering the differences in algorithm results under various conditions, examining the accuracy and performance of these algorithms focusing on the extensive and diverse metropolitan area of Mashhad may yield novel and noteworthy findings. Materials and Methods: The present research is applied in terms of purpose and descriptive-analytical in terms of nature. Data collection in this study has been conducted through a documentary-library method. In this study, images from the OLI sensor on the Landsat 8 satellite were used. The classification of satellite images was performed in two stages: preprocessing and processing. After assessing the accuracy of the classification using the Kappa coefficient, confusion matrix, coefficient of variation, and User's accuracy and Producer's accuracy coefficients, the best algorithm for classifying land uses in the metropolitan area of Mashhad was determined in five classes: 1- Built-up areas, 2- Barren land, 3- Mountainous areas, 4- Green spaces, and 5- Water bodies. Results and Discussion: The results from the evaluation of standard deviation (SD) and coefficient of variation (CV) regarding the area share percentage in a LULC class by various algorithms indicate that barren lands were classified with higher accuracy, while water bodies and green spaces were classified with lower accuracy. The examination of U_Accuracy and P_Accuracy coefficients shows that the overall accuracy of the classification for all studied algorithms falls within the range of good to excellent. However, a more detailed examination of these algorithms reveals that the greatest challenge in class identification lies in built-up areas, mountainous regions, and green spaces, whereas the identification of barren lands faces fewer challenges. The Kappa coefficient and analyses based on the confusion matrix also demonstrate the variation in accuracy among each LULC classifier. The differences in the accuracy of the classifiers used are marginal, but these slight variations hold significant importance in the context of LULC planning. Given that these marginal differences are evident in sensitive land uses such as built-up areas and green spaces, selecting an algorithm with the highest accuracy and lowest error is of special importance. Conclusion: The results of the Kappa coefficient evaluation and confusion matrix analyses indicate that the SVM approach has greater overall accuracy and a higher Kappa coefficient compared to RF and MLC methods. Specifically, the algorithms achieved overall accuracies of 0.93, 0.88, and 0.80, respectively. Therefore, Support Vector Machine demonstrates the highest accuracy and least error among the studied classifiers. Considering that numerous studies have shown that LULC mapping accuracy is related to time and location, it is suggested that future research analyse the accuracy of classifiers under different morphoclimatic and geomorphic conditions.

https://gisj.sbu.ac.ir/article_105144_3ab555101a8d7066ad6760646e058652.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Evaluating the Efficiency of InSAR Coherence in Crop Type Mapping Using Machine Learning Evaluating the Efficiency of InSAR Coherence in Crop Type Mapping Using Machine Learning 133 160 105853 10.48308/gisj.2025.238522.1249 FA Fatemeh Amiri Dep of Remote Sensing and GIS, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran Ali Shamsoddini Dep of Remote Sensing and GIS, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran 0000-0002-4559-7563 Mohamad Sharifikia Dep of Remote Sensing and GIS, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran Journal Article 2025 01 25 Introduction: The optimal use of agricultural land is a key concern for authorities due to agriculture's significant role in job creation, foreign exchange earnings, ensuring food security, and reducing reliance on imports. Gathering information about the spatial distribution and cultivated areas of various crops can enhance their efficient usage. One effective method for obtaining this information is through satellite imagery. Remote sensing technology, with its ability to provide high-resolution images and extensive spatial and temporal coverage, has become a dominant approach for crop type mapping. One of the remote sensing data that has recently received attention in the field of crop type mapping is the interferometric coherence images of synthetic aperture radar (InSAR). The sensitivity of these images to crop’s structure, making them valuable for monitoring and mapping crop types. In global literature, InSAR coherence images have been widely used in research related to agricultural products. However, in Iran, the use of coherence data for monitoring phenology and distinguishing different crops has not received much attention, despite its unique capabilities. Therefore, evaluating the efficiency of coherence data and its potential for adopting optimal agricultural management policies in Iran can be highly beneficial. Methodology: The main objective of this study is to evaluate the efficiency of machine learning-based InSAR coherence data for crop type mapping. To achieve this, a one-year time series of Synthetic Aperture Radar (SAR) data was compiled from Sentinel-1 phase information for the 2019 crop year, for the Ardabil plain, located to the west and northwest of Ardabil city. A network of SAR image pairs with short spatial and temporal baselines was created to produce coherence data. Field data were collected from 1,358 fields containing various crops. To avoid mixed pixels, a 10-meter buffer was established around the edges of each crop field. A total of 156,026 pixels from the coherence images were sampled and randomly divided into three groups: training (70%), validation (15%), and test (15%). To select the appropriate time interval for using coherence images, the phenological response of the crops to the InSAR coherence was analyzed. During the time interval, the phenological signals of the studied crops were compared with the signals of the built-up areas and bare soil to ensure that they were not mixed. Consequently, the multi-temporal InSAR coherence values in the selected time interval were used as input to the Support Vector Machine (SVM) classifier with different kernels to distinguish and identify the type of crops. Result: The study of the coherence time series values in the selected control areas revealed distinct differences in the coherence behavior of various crops when compared to one another, as well as in comparison to both built-up and bare soil areas. The InSAR coherence data match well with the main phenological stages of the crops. Among the different SVM kernels tested, the radial basis function (RBF) kernel achieved the highest overall accuracy of 59.69% during the validation phase, utilizing various combinations of the parameters c and gamma. In the testing phase, the crop type map produced using the SVM classifier with the RBF kernel reached an overall accuracy of 60.6%. This model performed best in identifying wheat and least effectively in identifying alfalfa. User accuracy was notably higher for wheat and potato plants, while it was lower for corn, broad bean, and alfalfa. Conclusion: Coherence images offer valuable insights for identifying and classifying crops in Iran. Leveraging machine learning techniques can enhance the utility of coherence data in monitoring and categorizing different crop types. Several factors influence the effectiveness of coherence images and the performance of classification algorithms, including the number of training samples available for each crop, the number of coherence features, the use of complementary data, sensor parallax (spatial baseline), topographical features (slope and aspect), the temporal resolution, and the classification algorithm. These characteristics should be carefully considered to optimize the analysis. Introduction: The optimal use of agricultural land is a key concern for authorities due to agriculture's significant role in job creation, foreign exchange earnings, ensuring food security, and reducing reliance on imports. Gathering information about the spatial distribution and cultivated areas of various crops can enhance their efficient usage. One effective method for obtaining this information is through satellite imagery. Remote sensing technology, with its ability to provide high-resolution images and extensive spatial and temporal coverage, has become a dominant approach for crop type mapping. One of the remote sensing data that has recently received attention in the field of crop type mapping is the interferometric coherence images of synthetic aperture radar (InSAR). The sensitivity of these images to crop’s structure, making them valuable for monitoring and mapping crop types. In global literature, InSAR coherence images have been widely used in research related to agricultural products. However, in Iran, the use of coherence data for monitoring phenology and distinguishing different crops has not received much attention, despite its unique capabilities. Therefore, evaluating the efficiency of coherence data and its potential for adopting optimal agricultural management policies in Iran can be highly beneficial. Methodology: The main objective of this study is to evaluate the efficiency of machine learning-based InSAR coherence data for crop type mapping. To achieve this, a one-year time series of Synthetic Aperture Radar (SAR) data was compiled from Sentinel-1 phase information for the 2019 crop year, for the Ardabil plain, located to the west and northwest of Ardabil city. A network of SAR image pairs with short spatial and temporal baselines was created to produce coherence data. Field data were collected from 1,358 fields containing various crops. To avoid mixed pixels, a 10-meter buffer was established around the edges of each crop field. A total of 156,026 pixels from the coherence images were sampled and randomly divided into three groups: training (70%), validation (15%), and test (15%). To select the appropriate time interval for using coherence images, the phenological response of the crops to the InSAR coherence was analyzed. During the time interval, the phenological signals of the studied crops were compared with the signals of the built-up areas and bare soil to ensure that they were not mixed. Consequently, the multi-temporal InSAR coherence values in the selected time interval were used as input to the Support Vector Machine (SVM) classifier with different kernels to distinguish and identify the type of crops. Result: The study of the coherence time series values in the selected control areas revealed distinct differences in the coherence behavior of various crops when compared to one another, as well as in comparison to both built-up and bare soil areas. The InSAR coherence data match well with the main phenological stages of the crops. Among the different SVM kernels tested, the radial basis function (RBF) kernel achieved the highest overall accuracy of 59.69% during the validation phase, utilizing various combinations of the parameters c and gamma. In the testing phase, the crop type map produced using the SVM classifier with the RBF kernel reached an overall accuracy of 60.6%. This model performed best in identifying wheat and least effectively in identifying alfalfa. User accuracy was notably higher for wheat and potato plants, while it was lower for corn, broad bean, and alfalfa. Conclusion: Coherence images offer valuable insights for identifying and classifying crops in Iran. Leveraging machine learning techniques can enhance the utility of coherence data in monitoring and categorizing different crop types. Several factors influence the effectiveness of coherence images and the performance of classification algorithms, including the number of training samples available for each crop, the number of coherence features, the use of complementary data, sensor parallax (spatial baseline), topographical features (slope and aspect), the temporal resolution, and the classification algorithm. These characteristics should be carefully considered to optimize the analysis.

https://gisj.sbu.ac.ir/article_105853_808ceb3bbca8c50d340b5fe068268044.pdf

Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 17 4 2026 01 21 Improving land Cover Classification in Mountainous Areas by Combining Sentinel-1 Images From Different Orbits and Assessing Radiometric Terrain Flattening Effects Improving land Cover Classification in Mountainous Areas by Combining Sentinel-1 Images From Different Orbits and Assessing Radiometric Terrain Flattening Effects 161 180 105953 10.48308/gisj.2025.239329.1258 FA Mohammad Ebrahimi Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran Mahmod Reza Sahebi Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran Journal Article 2025 04 05 Introduction: In recent years, land cover has been recognized as a key indicator for assessing climate change, ecosystems, and the management of natural resources. Growing challenges in acquiring up-to-date and accurate data have led to the adoption of modern remote sensing technologies. Among these, the Sentinel-1 Synthetic Aperture Radar (SAR) imagery has emerged as a reliable source for land surface characterization. These images are produced by a SAR system using active microwave technology, enabling effective operation in all weather conditions. Therefore, this technology proves to be an appropriate tool for generating reliable and detailed land cover maps. The aim of this research is to improve the classification of land cover by simultaneously utilizing images acquired from both the ascending and descending orbits of Sentinel-1. In addition, the study investigates the impact of applying radiometric terrain flattening corrections on the overall accuracy of the classification results. Materials and Methods: This study examined three different regions in Iran: Marand, Sari, and Chadegan, which were selected due to their varied land cover characteristics and the presence of mountainous areas. The data used consisted of Sentinel-1 satellite images with VV and VH polarizations from both ascending and descending passes. Preprocessing steps included applying orbit file, thermal noise removal, border noise removal, calibration, radiometric terrain flattening, speckle filtering, Range-Doppler terrain correction, and conversion to decibels. Additionally, the data were rescaled to a specific range using the min-max normalization method. The Random Forest (RF) algorithm was then employed to classify land cover into five classes: water, soil, vegetation, urban areas, and agriculture. Finally, the results were evaluated using overall accuracy, the kappa coefficient, and class-specific accuracy metrics. Results and Discussion: The results indicate that the simultaneous use of ascending and descending images without applying radiometric terrain flattening significantly improves classification accuracy across all study areas. For example, in Marand, the overall classification accuracy increased from 65.33% to 79.17%, representing an approximate improvement of 13%. In Sari, the combination of images raised the overall accuracy from 55.67% to 75.41%, while in Chadegan, it resulted in an approximate 12% increase from 56.88% to 68.06%. Regarding class-specific accuracy, in Marand, the vegetation class improved from 43.41% to 69.64%, and in Sari, the soil class accuracy increased from 19.57% to 46.40%. Numerical analysis suggests that combining images from different orbits provides complementary perspectives of the Earth's surface, helping to reduce distortions caused by viewing angles and topography. In addition, the results reveal that while radiometric terrain flattening can enhance the accuracy of certain classes when using a single image, its application in the combination of two images may cause excessive similarity between some classes, ultimately reducing overall performance. Conclusion: In conclusion, this research highlights the importance of concurrently using Sentinel-1 images from both ascending and descending orbits, particularly when radiometric terrain flattening is not applied, which plays a crucial role in enhancing the accuracy of land cover classification. The observed improvement in overall accuracy, ranging from 13% to 20% across different study areas, underscores the strong potential of this approach for land cover mapping. Moreover, the findings of this study demonstrate that the preprocessing methods employed for Sentinel-1 images have a significant impact on the accuracy and efficiency of classification models. In some cases, applying radiometric terrain flattening can lead to a decrease in both accuracy and efficiency. Therefore, optimally combining Sentinel-1 data from multiple orbital passes can lead to more accurate and reliable land cover maps. The approach presented in this study can thus serve as a valuable reference for future studies in the field of remote sensing, particularly those focused on improving land cover classification for environmental and agricultural applications. Introduction: In recent years, land cover has been recognized as a key indicator for assessing climate change, ecosystems, and the management of natural resources. Growing challenges in acquiring up-to-date and accurate data have led to the adoption of modern remote sensing technologies. Among these, the Sentinel-1 Synthetic Aperture Radar (SAR) imagery has emerged as a reliable source for land surface characterization. These images are produced by a SAR system using active microwave technology, enabling effective operation in all weather conditions. Therefore, this technology proves to be an appropriate tool for generating reliable and detailed land cover maps. The aim of this research is to improve the classification of land cover by simultaneously utilizing images acquired from both the ascending and descending orbits of Sentinel-1. In addition, the study investigates the impact of applying radiometric terrain flattening corrections on the overall accuracy of the classification results. Materials and Methods: This study examined three different regions in Iran: Marand, Sari, and Chadegan, which were selected due to their varied land cover characteristics and the presence of mountainous areas. The data used consisted of Sentinel-1 satellite images with VV and VH polarizations from both ascending and descending passes. Preprocessing steps included applying orbit file, thermal noise removal, border noise removal, calibration, radiometric terrain flattening, speckle filtering, Range-Doppler terrain correction, and conversion to decibels. Additionally, the data were rescaled to a specific range using the min-max normalization method. The Random Forest (RF) algorithm was then employed to classify land cover into five classes: water, soil, vegetation, urban areas, and agriculture. Finally, the results were evaluated using overall accuracy, the kappa coefficient, and class-specific accuracy metrics. Results and Discussion: The results indicate that the simultaneous use of ascending and descending images without applying radiometric terrain flattening significantly improves classification accuracy across all study areas. For example, in Marand, the overall classification accuracy increased from 65.33% to 79.17%, representing an approximate improvement of 13%. In Sari, the combination of images raised the overall accuracy from 55.67% to 75.41%, while in Chadegan, it resulted in an approximate 12% increase from 56.88% to 68.06%. Regarding class-specific accuracy, in Marand, the vegetation class improved from 43.41% to 69.64%, and in Sari, the soil class accuracy increased from 19.57% to 46.40%. Numerical analysis suggests that combining images from different orbits provides complementary perspectives of the Earth's surface, helping to reduce distortions caused by viewing angles and topography. In addition, the results reveal that while radiometric terrain flattening can enhance the accuracy of certain classes when using a single image, its application in the combination of two images may cause excessive similarity between some classes, ultimately reducing overall performance. Conclusion: In conclusion, this research highlights the importance of concurrently using Sentinel-1 images from both ascending and descending orbits, particularly when radiometric terrain flattening is not applied, which plays a crucial role in enhancing the accuracy of land cover classification. The observed improvement in overall accuracy, ranging from 13% to 20% across different study areas, underscores the strong potential of this approach for land cover mapping. Moreover, the findings of this study demonstrate that the preprocessing methods employed for Sentinel-1 images have a significant impact on the accuracy and efficiency of classification models. In some cases, applying radiometric terrain flattening can lead to a decrease in both accuracy and efficiency. Therefore, optimally combining Sentinel-1 data from multiple orbital passes can lead to more accurate and reliable land cover maps. The approach presented in this study can thus serve as a valuable reference for future studies in the field of remote sensing, particularly those focused on improving land cover classification for environmental and agricultural applications.

https://gisj.sbu.ac.ir/article_105953_190df2fa5ad406240e2c890a0ee8a5b4.pdf