Iranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Preparation of Distribution and Health map of Afforestation in Langroud County using Sentinel-2 Sensor Images and Field DataPreparation of Distribution and Health map of Afforestation in Langroud County using Sentinel-2 Sensor Images and Field Data12210445210.48308/gisj.2024.234675.1202FAFatemehAhmadlooResearch Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, I.R. IranSaeedehEskandariResearch Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, I.R. IranMahmoodBayatResearch Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, I.R. IranMehrdadMirzaeiForest Expert, Department of Natural Resources of Langroud, Guilan Natural Resources and Watershed Administration, Rasht, Iran.0009-0005-6371-7687ShahryarSobh ZahediResearcher Expert, Research Division of Natural Resources, Guilan Agricultural and Natural Resources Research and Education Center, AREEO, Rasht, IranAminNasrollahianForest Expert, Guilan Natural Resources and Watershed Administration, Rasht, Iran.Journal Article20240202<strong>Background and aim:</strong> Monitoring the current status of existing afforestation’s in management decisions is very important for the development of afforestation’s in the future. This study was conducted in order to monitor the area, distribution and health of afforestation’s in Langroud County, Guilan province. Sustainable management of current and future annual afforestation’s requires studies on the status of the afforestation’s with focusing on their health, and healthy afforestation’s can have greater environmental performance compared to unhealthy ones. The aim of this study is to prepare a map and survey of the areas of afforestation’s in Langroud County using ground surveys, Android GPS software (GFAMP), and Google Earth for the protection, enrichment, and development of afforestation’s in the future. The health of the stands is also assessed using Sentinel 2 sensor images and vegetation cover indices of SAVI, TNDVI, NDVI, and RVI.<br /><strong>Materials and methods:</strong> For this purpose, first, field surveys were done in the form of land points from the existing afforestation, and the distribution map of afforestation was prepared using the land surveys, GPS Fields Area Measure PRO application (GFAMP), and Google Earth system. Then the Sentinel 2 sensor image related to the growing season in Langroud County was prepared from the Copernicus site. From the Sentinel 2 sensor images, various vegetation indices such as NDVI, TNDVI, SAVI and RVI related to the growing season were extracted and their maps were prepared in the area of afforestation. In the following, the amount of each vegetation index was extracted at the points of land harvesting and the correlation of the values of each vegetation index (resulting from images of the growing season of Sentinel 2 sensor) with the health of afforestation (resulting from field harvesting) was investigated. For this purpose, Pearson's correlation coefficient was used. Then, the index that showed the highest correlation with the health of afforestation in Langroud County (NDVI) was selected as the most important index to estimate the health of afforestation and its regression relationship with the health of afforestation was obtained. In the following, using the map of the most favorable vegetation index, information of field survey and the relationship between these two cases, a health map of afforestation in Langroud County was prepared.<br /><strong>Results and discussion:</strong> Based on the findings of this study, a total of 66 afforestation plots were identified in this county that the total area of these plots was obtained using the 100% inventory method and ground control as 746.2 ha, which are mainly distributed in the southwest of the county. In addition, the results showed that the NDVI Index is the most favorable vegetation index for estimating the health of afforestation in Langroud County, which indicates the ability of this index to assess the health of trees during the growing season. After that, the SAVI index showed the highest correlation with tree health, which also shows the good ability of this index in monitoring tree health. While the two indices TNDVI and RVI showed a much lower correlation with tree health within the Langroud afforestation area, their use for assessing tree health in future studies is not recommended. Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The overall accuracy (OA) of the afforestation’s health map in this study was 80 %, the producer accuracy was 79 %, the user accuracy was 78 %, and the kappa coefficient was 0.73. These results indicate the optimal classification of the afforestation’s health map into different tree health and vigor classes. In general, the afforestation’s in this county are mostly in perfect health, but some areas of Kachlebon, Larzian, Gandom Bijaran, Ghazi Dasht, Khorsh Sara, and Chaksar (Chakdasht) are less successful due to the lack of conservation operations and the lack of guards in the early years.<br /><strong>Conclusion:</strong> The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans. The presented regression model, based on the high correlation of the NDVI index with the health of afforestation, allows for rapid estimation, quantitative, low-cost, affordable, and economic assessment of the health status of afforestation on a large scale and in inaccessible areas. The results of this research, by understanding the current status of afforestation, provide a good view of the capacity and potential of Langroud County for afforestation, considering the environmental and ecological conditions of this county.<strong>Background and aim:</strong> Monitoring the current status of existing afforestation’s in management decisions is very important for the development of afforestation’s in the future. This study was conducted in order to monitor the area, distribution and health of afforestation’s in Langroud County, Guilan province. Sustainable management of current and future annual afforestation’s requires studies on the status of the afforestation’s with focusing on their health, and healthy afforestation’s can have greater environmental performance compared to unhealthy ones. The aim of this study is to prepare a map and survey of the areas of afforestation’s in Langroud County using ground surveys, Android GPS software (GFAMP), and Google Earth for the protection, enrichment, and development of afforestation’s in the future. The health of the stands is also assessed using Sentinel 2 sensor images and vegetation cover indices of SAVI, TNDVI, NDVI, and RVI.<br /><strong>Materials and methods:</strong> For this purpose, first, field surveys were done in the form of land points from the existing afforestation, and the distribution map of afforestation was prepared using the land surveys, GPS Fields Area Measure PRO application (GFAMP), and Google Earth system. Then the Sentinel 2 sensor image related to the growing season in Langroud County was prepared from the Copernicus site. From the Sentinel 2 sensor images, various vegetation indices such as NDVI, TNDVI, SAVI and RVI related to the growing season were extracted and their maps were prepared in the area of afforestation. In the following, the amount of each vegetation index was extracted at the points of land harvesting and the correlation of the values of each vegetation index (resulting from images of the growing season of Sentinel 2 sensor) with the health of afforestation (resulting from field harvesting) was investigated. For this purpose, Pearson's correlation coefficient was used. Then, the index that showed the highest correlation with the health of afforestation in Langroud County (NDVI) was selected as the most important index to estimate the health of afforestation and its regression relationship with the health of afforestation was obtained. In the following, using the map of the most favorable vegetation index, information of field survey and the relationship between these two cases, a health map of afforestation in Langroud County was prepared.<br /><strong>Results and discussion:</strong> Based on the findings of this study, a total of 66 afforestation plots were identified in this county that the total area of these plots was obtained using the 100% inventory method and ground control as 746.2 ha, which are mainly distributed in the southwest of the county. In addition, the results showed that the NDVI Index is the most favorable vegetation index for estimating the health of afforestation in Langroud County, which indicates the ability of this index to assess the health of trees during the growing season. After that, the SAVI index showed the highest correlation with tree health, which also shows the good ability of this index in monitoring tree health. While the two indices TNDVI and RVI showed a much lower correlation with tree health within the Langroud afforestation area, their use for assessing tree health in future studies is not recommended. Of the current total area of afforestation, 400.56 ha are in full health status, 305.60 ha are in medium health status, and 40.04 ha are in unhealthy status. The overall accuracy (OA) of the afforestation’s health map in this study was 80 %, the producer accuracy was 79 %, the user accuracy was 78 %, and the kappa coefficient was 0.73. These results indicate the optimal classification of the afforestation’s health map into different tree health and vigor classes. In general, the afforestation’s in this county are mostly in perfect health, but some areas of Kachlebon, Larzian, Gandom Bijaran, Ghazi Dasht, Khorsh Sara, and Chaksar (Chakdasht) are less successful due to the lack of conservation operations and the lack of guards in the early years.<br /><strong>Conclusion:</strong> The results of this research will help the forest managers for quantitative and qualitative monitoring of afforestation and its continuity in certain time periods as well as future afforestation development plans. The presented regression model, based on the high correlation of the NDVI index with the health of afforestation, allows for rapid estimation, quantitative, low-cost, affordable, and economic assessment of the health status of afforestation on a large scale and in inaccessible areas. The results of this research, by understanding the current status of afforestation, provide a good view of the capacity and potential of Langroud County for afforestation, considering the environmental and ecological conditions of this county.https://gisj.sbu.ac.ir/article_104452_7df1ddda4a30f42ea4ea083d62fed3a8.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Risk Assessment of the Coastal Habitats of Larak Islands Using the InVEST ModelRisk Assessment of the Coastal Habitats of Larak Islands Using the InVEST Model233210458910.48308/gisj.2024.231946.1161FAMahmoodSinaiDepartment of biology,Marine Environment Research center,Chabahar Branch,Islamic Azad University,Chabahar,IranMajidAskari HosniDepartment of Marine Biology, Faculty of Science, Shahid Bahonar University, Kerman, IranAliSaberDeputy of Marine Environment and Wetlands, Iranian Department of Environment, Tehran, IranMostafaHassanianDepartment of biology,Marine Environment Research center,Chabahar Branch,Islamic Azad University,Chabahar,IranJournal Article20230604<strong>Background and Objective:</strong> Lark Island, due to its diverse topography and the presence of hills, rocky shores, and other coastal types, has special environments that have led to the creation of diverse wildlife habitats. In addition, the rocky shores and the existence of diverse coastlines have provided very suitable conditions for the presence of a wide range of animals and plants. However, in recent years, various factors have caused the coastal habitats of this island to be threatened.<br /><strong>Materials and Methods:</strong> In this study, coastal areas covered by natural assets such as coral, mangrove, and seaweed, which can be the habitat and breeding ground for various plant and animal species, were considered as "habitats" and other areas changed by human intervention were considered as "non-habitats". In this study, the InVEST HRA (Habitat Risk Assessment) model was used to investigate the effects of human activities on coastal and marine ecosystems. In the present study, due to the importance of the user's role in determining stressors and habitats, all stressors were identified through field monitoring and based on expert opinion and knowledge of the study area.<br /><strong>Results:</strong> Evaluation of the HRA Invest model output graph results shows that human structures and irresponsible tourism are considered as the main stressors in the intertidal zone and the increase in water surface temperature, maritime transport, trawling, marine extraction, oil spills, desalination, fishing activities, traditional coastal fishing with varying degrees in the subtidal zone. The results related to the output of the Lark Island intertidal habitat cumulative risk map are shown in Figure 3 and the cumulative risk map in the subtidal zone is shown in Figure 4. The coastal (intertidal) area of Lark Island has the lowest cumulative habitat stress rating of 4 and the highest cumulative rating of 13 and the average cumulative stress rating of 8.5. In the marine (subtidal) area of Lark Island, the lowest cumulative habitat stress rating is 0 and the highest cumulative rating is 30 and the average cumulative stress rating is 15. The results show that the cumulative risk in the subtidal zone of Lark Island is high on the northern, northeastern, and eastern shores of the island. On this island, the major habitats of importance, along with high surface water temperatures in the warmest month of the year, are generally concentrated in the north and east of the island, which results in a significant difference in habitat risk ratings in the northern and western parts of the island compared to the eastern and southern parts.<br /><strong>Conclusion:</strong> The results show that the development and implementation of monitoring, protection or restoration and reconstruction programs in different coastal areas of Lark Island should be based on development plans and also be applied in proportion to the level of threats in different areas. The stress rating in the subtidal area on the northern, northeastern and eastern coasts around Lark Island is in the relatively high and high range, based on the average threat of the subtidal area, it can be interpreted that in the current situation, the stress rating and threats are high. Therefore, coral and algal habitats in this area are under high stress and threat, and an intensive intervention approach such as designating marine protected areas and preventing the entry of pollutants into this area should be considered.<strong>Background and Objective:</strong> Lark Island, due to its diverse topography and the presence of hills, rocky shores, and other coastal types, has special environments that have led to the creation of diverse wildlife habitats. In addition, the rocky shores and the existence of diverse coastlines have provided very suitable conditions for the presence of a wide range of animals and plants. However, in recent years, various factors have caused the coastal habitats of this island to be threatened.<br /><strong>Materials and Methods:</strong> In this study, coastal areas covered by natural assets such as coral, mangrove, and seaweed, which can be the habitat and breeding ground for various plant and animal species, were considered as "habitats" and other areas changed by human intervention were considered as "non-habitats". In this study, the InVEST HRA (Habitat Risk Assessment) model was used to investigate the effects of human activities on coastal and marine ecosystems. In the present study, due to the importance of the user's role in determining stressors and habitats, all stressors were identified through field monitoring and based on expert opinion and knowledge of the study area.<br /><strong>Results:</strong> Evaluation of the HRA Invest model output graph results shows that human structures and irresponsible tourism are considered as the main stressors in the intertidal zone and the increase in water surface temperature, maritime transport, trawling, marine extraction, oil spills, desalination, fishing activities, traditional coastal fishing with varying degrees in the subtidal zone. The results related to the output of the Lark Island intertidal habitat cumulative risk map are shown in Figure 3 and the cumulative risk map in the subtidal zone is shown in Figure 4. The coastal (intertidal) area of Lark Island has the lowest cumulative habitat stress rating of 4 and the highest cumulative rating of 13 and the average cumulative stress rating of 8.5. In the marine (subtidal) area of Lark Island, the lowest cumulative habitat stress rating is 0 and the highest cumulative rating is 30 and the average cumulative stress rating is 15. The results show that the cumulative risk in the subtidal zone of Lark Island is high on the northern, northeastern, and eastern shores of the island. On this island, the major habitats of importance, along with high surface water temperatures in the warmest month of the year, are generally concentrated in the north and east of the island, which results in a significant difference in habitat risk ratings in the northern and western parts of the island compared to the eastern and southern parts.<br /><strong>Conclusion:</strong> The results show that the development and implementation of monitoring, protection or restoration and reconstruction programs in different coastal areas of Lark Island should be based on development plans and also be applied in proportion to the level of threats in different areas. The stress rating in the subtidal area on the northern, northeastern and eastern coasts around Lark Island is in the relatively high and high range, based on the average threat of the subtidal area, it can be interpreted that in the current situation, the stress rating and threats are high. Therefore, coral and algal habitats in this area are under high stress and threat, and an intensive intervention approach such as designating marine protected areas and preventing the entry of pollutants into this area should be considered.https://gisj.sbu.ac.ir/article_104589_e73b8c08effd7b64f110d0878306651f.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Reconstruction of ECOSTRESS Land Surface Temperature Using Deep Learning and Support Vector regression (Case study: Bar-Arieh and Latian watersheds)Reconstruction of ECOSTRESS Land Surface Temperature Using Deep Learning and Support Vector regression (Case study: Bar-Arieh and Latian watersheds)335410459010.48308/gisj.2024.233210.1180FAForoughAhmadinezhad BaghbanDepartment of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran, IranVahidMoosaviDepartment of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran, IranHamid RezaMoradiDepartment of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran, IranJournal Article20230924<strong>Introduction: </strong>Land surface temperature (LST) is a critical variable in regional and local planning, playing a key role in climate change studies, hydrological modeling, vegetation monitoring, urban heat island effects, urban development, global warming, agricultural conditions, and influencing plant growth rates and timing. As an essential input parameter for most environmental models, the accuracy of LST estimation directly impacts the reliability of model outputs. Therefore, a precise and optimized study of the spatiotemporal variations of LST under different weather and climatic conditions is necessary. However, due to the limited coverage of meteorological stations, remote sensing serves as a fundamental tool for acquiring large-scale meteorological data. LST derived from satellite imagery represents the average pixel temperature over a specific land surface area, calculated based on the thermal band radiance detected by the sensor. Despite its advantages, a major challenge in LST estimation is the lack of simultaneous high temporal and spatial resolution in satellite data, which complicates accurate temperature analysis.<br /><strong>Materials and Methods: </strong>This study utilized deep learning and machine learning techniques to generate daily LST maps from MODIS, VIIRS, and ECOSTRESS satellite imagery, focusing on data-gap periods in two watersheds: Bar-Arieh (Neyshabur, Razavi Khorasan Province) and Latyan Dam (Tehran Province). The ECOSTRESS sensor, with a high spatial resolution of 70 meters, was selected for its ability to provide detailed LST measurements. However, due to temporal gaps in ECOSTRESS data, reconstruction was necessary. Two prominent models were employed: Support Vector Regression (SVR), a widely used machine learning model for regression tasks, and Long Short-Term Memory (LSTM), a powerful deep learning model specialized in processing sequential and time-series data. For each watershed, three pairs of dates (six dates in total) were selected, ensuring that the images were temporally aligned for model training and validation. The models were fed with corresponding satellite data from MODIS, VIIRS, and ECOSTRESS to predict missing LST values. Performance evaluation was conducted using three statistical metrics: Root Mean Square Error (RMSE), coefficient of determination (R²), and Normalized Root Mean Square Error (NRMSE).<br /><strong>Results and Discussion: </strong>The comparative analysis of RMSE, R², and NRMSE values demonstrated the superior performance of the LSTM model over SVR in reconstructing LST values. In the Bar-Arieh watershed, the best results were obtained for June 17, 2020, with RMSE = 1.81°C, R² = 0.66, and NRMSE = 11.94%. For the Latyan Dam watershed, the most accurate predictions were recorded on June 28, 2019, with RMSE = 1.61°C, R² = 0.83, and NRMSE = 8.65%. The LSTM model's success can be attributed to its inherent ability to extract complex features from raw data while accounting for temporal dependencies, making it highly effective for time-series forecasting. Unlike traditional machine learning models, LSTM captures long-term patterns and nonlinear relationships within the data, enabling it to handle the inherent complexities of LST dynamics. The model's robustness was further validated by its ability to reconstruct LST for periods with missing data (3–4 days ahead), accounting for the temporal intervals between successive ECOSTRESS overpasses. Despite the challenges posed by the variability of factors influencing LST—such as land cover changes, atmospheric conditions, and diurnal temperature fluctuations—the model's predictions remained within an acceptable error range. The RMSE values (ranging between 1.5°C and 3°C) indicate a reasonably accurate estimation, given the natural variability of LST (0°C to nearly 35°C in the study areas). Additionally, the NRMSE values (mostly between 8% and 17%) confirm the model's reliability, especially considering the complexity of environmental processes.<br /><strong>Conclusion: </strong>The study highlights the effectiveness of deep learning, particularly the LSTM model, in reconstructing high-resolution LST data from multi-sensor satellite imagery. The evaluation metrics (R², RMSE, and NRMSE) consistently demonstrated LSTM's superiority over SVR, reinforcing its suitability for time-series-based environmental modeling. Given the dynamic nature of LST and the multitude of influencing factors, the model's ability to maintain low error margins (RMSE ~1.5–3°C) and high explanatory power (R² up to 0.83) underscores its potential for operational use in remote sensing applications. Furthermore, the normalized error values (NRMSE between 8% and 17%) suggest that the model performs reliably even under complex environmental conditions. These results are particularly significant for applications requiring high spatiotemporal resolution LST data, such as urban heat island monitoring, precision agriculture, and climate change studies. Future research could explore the integration of additional satellite datasets or hybrid modeling approaches to further enhance prediction accuracy. Overall, this study provides a robust framework for LST estimation in data-scarce scenarios, contributing to improved environmental monitoring and decision-making processes.<strong>Introduction: </strong>Land surface temperature (LST) is a critical variable in regional and local planning, playing a key role in climate change studies, hydrological modeling, vegetation monitoring, urban heat island effects, urban development, global warming, agricultural conditions, and influencing plant growth rates and timing. As an essential input parameter for most environmental models, the accuracy of LST estimation directly impacts the reliability of model outputs. Therefore, a precise and optimized study of the spatiotemporal variations of LST under different weather and climatic conditions is necessary. However, due to the limited coverage of meteorological stations, remote sensing serves as a fundamental tool for acquiring large-scale meteorological data. LST derived from satellite imagery represents the average pixel temperature over a specific land surface area, calculated based on the thermal band radiance detected by the sensor. Despite its advantages, a major challenge in LST estimation is the lack of simultaneous high temporal and spatial resolution in satellite data, which complicates accurate temperature analysis.<br /><strong>Materials and Methods: </strong>This study utilized deep learning and machine learning techniques to generate daily LST maps from MODIS, VIIRS, and ECOSTRESS satellite imagery, focusing on data-gap periods in two watersheds: Bar-Arieh (Neyshabur, Razavi Khorasan Province) and Latyan Dam (Tehran Province). The ECOSTRESS sensor, with a high spatial resolution of 70 meters, was selected for its ability to provide detailed LST measurements. However, due to temporal gaps in ECOSTRESS data, reconstruction was necessary. Two prominent models were employed: Support Vector Regression (SVR), a widely used machine learning model for regression tasks, and Long Short-Term Memory (LSTM), a powerful deep learning model specialized in processing sequential and time-series data. For each watershed, three pairs of dates (six dates in total) were selected, ensuring that the images were temporally aligned for model training and validation. The models were fed with corresponding satellite data from MODIS, VIIRS, and ECOSTRESS to predict missing LST values. Performance evaluation was conducted using three statistical metrics: Root Mean Square Error (RMSE), coefficient of determination (R²), and Normalized Root Mean Square Error (NRMSE).<br /><strong>Results and Discussion: </strong>The comparative analysis of RMSE, R², and NRMSE values demonstrated the superior performance of the LSTM model over SVR in reconstructing LST values. In the Bar-Arieh watershed, the best results were obtained for June 17, 2020, with RMSE = 1.81°C, R² = 0.66, and NRMSE = 11.94%. For the Latyan Dam watershed, the most accurate predictions were recorded on June 28, 2019, with RMSE = 1.61°C, R² = 0.83, and NRMSE = 8.65%. The LSTM model's success can be attributed to its inherent ability to extract complex features from raw data while accounting for temporal dependencies, making it highly effective for time-series forecasting. Unlike traditional machine learning models, LSTM captures long-term patterns and nonlinear relationships within the data, enabling it to handle the inherent complexities of LST dynamics. The model's robustness was further validated by its ability to reconstruct LST for periods with missing data (3–4 days ahead), accounting for the temporal intervals between successive ECOSTRESS overpasses. Despite the challenges posed by the variability of factors influencing LST—such as land cover changes, atmospheric conditions, and diurnal temperature fluctuations—the model's predictions remained within an acceptable error range. The RMSE values (ranging between 1.5°C and 3°C) indicate a reasonably accurate estimation, given the natural variability of LST (0°C to nearly 35°C in the study areas). Additionally, the NRMSE values (mostly between 8% and 17%) confirm the model's reliability, especially considering the complexity of environmental processes.<br /><strong>Conclusion: </strong>The study highlights the effectiveness of deep learning, particularly the LSTM model, in reconstructing high-resolution LST data from multi-sensor satellite imagery. The evaluation metrics (R², RMSE, and NRMSE) consistently demonstrated LSTM's superiority over SVR, reinforcing its suitability for time-series-based environmental modeling. Given the dynamic nature of LST and the multitude of influencing factors, the model's ability to maintain low error margins (RMSE ~1.5–3°C) and high explanatory power (R² up to 0.83) underscores its potential for operational use in remote sensing applications. Furthermore, the normalized error values (NRMSE between 8% and 17%) suggest that the model performs reliably even under complex environmental conditions. These results are particularly significant for applications requiring high spatiotemporal resolution LST data, such as urban heat island monitoring, precision agriculture, and climate change studies. Future research could explore the integration of additional satellite datasets or hybrid modeling approaches to further enhance prediction accuracy. Overall, this study provides a robust framework for LST estimation in data-scarce scenarios, contributing to improved environmental monitoring and decision-making processes.https://gisj.sbu.ac.ir/article_104590_461988b65b230becd2a1d5386a7983b3.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Effect of Digital Elevation Models Spatial Resolutions on Nash and Rosso Instantaneous unit Hydrograph Performance (case study: Kasilian Watershed)Effect of Digital Elevation Models Spatial Resolutions on Nash and Rosso Instantaneous unit Hydrograph Performance (case study: Kasilian Watershed)557410458810.48308/gisj.2024.233606.1184FAFaribaEsmaeiliDepartment of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, IranMehdiVafakhahDepartment of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, IranVahidMoosaviDepartment of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, IranJournal Article20231102<strong>Introduction</strong> <strong>and</strong> <strong>Goal</strong><br />The use of digital elevation model (DEM) enables the extraction of physiographic characteristics used in Nash and Rosso instantaneous unit hydrograph (IUH) models. Therefore, the spatial resolution of the DEM is effective on the performance of Nash and Rosso IUH models. Therefore, the current research aims to evaluate the impact of DEMs including ALOS PALSAR, ASTER, SRTM and GTOPO with a spatial resolution of 12.5, 30, 90 and 1000 meters respectively and the DEM obtained from the topographic map (TOPO) of the Iran Geological Organization with a scale of 1:25000 and a spatial resolution of 10 meters was done in Nash and Rosso models in Kasilian watershed.<br /><strong>Material and Methods</strong><br />Kasilian Watershed, with an area of about 66.43 km<sup>2</sup>, is located in the north latitude of 35°58'30" to of 36°07'15"and east longitude of 53°08'44"to of 53°15'42". The watershed has a humid climate based on Domarten climatic classification and average annual rainfall of 783.4 mm. Physiographic characteristics including Horton ratios were calculated for each of the DEMs. Finally, IUH dimensions were estimated based on Nash and Rosso models for 64 rainfall-runoff events and five different DEMs.<br /><strong>Results and Discussion </strong><br />The results showed that by reducing the spatial resolution of DEM, the evolution of the drainage network is lost, so that the maximum rank of the streamflow in TOPO and PALSAR ALOS DEMs is equal to six, and in ASTER its number is reduced to five. Only in SRTM and GTOPO, the number of streamflow ranks was significantly reduced to three. The parameters n and k are effective components in Nash and Rosso models, and in the Nash model, the amount of n was not significantly different in different DEMs. But the value of k has increased from 1.35 to 1.64 with the decrease of DEM spatial resolution. In the estimation of parameter n by Rosso's method, a constant trend is not seen in DEMs, so that in TOPO, ALOS PALSAR and ASTER, it was about six. While in SRTM, three and in GTOPO, four were estimated and the k parameter was different for each event and its value was different in different DEMs. The Nash method, the lowest and highest average relative error (RE) are related to the TOPO and GTOPO DEMs with values of 10.72% and 11.01% in the estimated runoff volume, respectively. The Rosso method, the average value of RE in the three TOPO, ALOSPALSAR and ASTER DEMs is almost similar in the estimated runoff volume. While the amount of RE in SRTM and GTOPO is higher than other DEMs. In fact, the results are close to each other due to the similar ability to extract the physiographic characteristics of the basin from these three models. Of course, it should be stated that with a small difference, the lowest amount of RE in estimating the runoff volume is related to the TOPO DEMs.<br /><strong>Conclusion</strong><br />ASTER DEMs with lower spatial resolution compared to TOPO and PALSAR ALOS has provided an acceptable stream network. However, SRTM and GTOPO have not provided a suitable stream network. In general, with the increase in the spatial resolution of the DEM, drainage density developed more dense and real, but main stream is somewhat developed in DEMs with lower spatial resolution. Different methods have been presented to estimate the values of n and k, and in this research, in order to investigate the effect of different DEMs in the Nash method, among the different methods of estimating its parameters, the experimental method has been used. In the experimental method, the physiographic characteristics of the watershed, such as the slope, the length of the main river and the area of the basin, play the main role in estimating the peak discharge. In relation to the peak flow estimation, the Nash model performed better and in estimating the runoff volume, the Rosso model performed better using the TOPO DEMs. In fact, it can be stated that the estimation of the peak discharge in the Nash method based on the experimental method has provided acceptable results in this watershed. according to the necessity of estimating the peak discharge in determining the dimensions of the IUH in the watershed to accurately simulate and control future floods, using the results of the present study can be of great help.<br /> <strong>Introduction</strong> <strong>and</strong> <strong>Goal</strong><br />The use of digital elevation model (DEM) enables the extraction of physiographic characteristics used in Nash and Rosso instantaneous unit hydrograph (IUH) models. Therefore, the spatial resolution of the DEM is effective on the performance of Nash and Rosso IUH models. Therefore, the current research aims to evaluate the impact of DEMs including ALOS PALSAR, ASTER, SRTM and GTOPO with a spatial resolution of 12.5, 30, 90 and 1000 meters respectively and the DEM obtained from the topographic map (TOPO) of the Iran Geological Organization with a scale of 1:25000 and a spatial resolution of 10 meters was done in Nash and Rosso models in Kasilian watershed.<br /><strong>Material and Methods</strong><br />Kasilian Watershed, with an area of about 66.43 km<sup>2</sup>, is located in the north latitude of 35°58'30" to of 36°07'15"and east longitude of 53°08'44"to of 53°15'42". The watershed has a humid climate based on Domarten climatic classification and average annual rainfall of 783.4 mm. Physiographic characteristics including Horton ratios were calculated for each of the DEMs. Finally, IUH dimensions were estimated based on Nash and Rosso models for 64 rainfall-runoff events and five different DEMs.<br /><strong>Results and Discussion </strong><br />The results showed that by reducing the spatial resolution of DEM, the evolution of the drainage network is lost, so that the maximum rank of the streamflow in TOPO and PALSAR ALOS DEMs is equal to six, and in ASTER its number is reduced to five. Only in SRTM and GTOPO, the number of streamflow ranks was significantly reduced to three. The parameters n and k are effective components in Nash and Rosso models, and in the Nash model, the amount of n was not significantly different in different DEMs. But the value of k has increased from 1.35 to 1.64 with the decrease of DEM spatial resolution. In the estimation of parameter n by Rosso's method, a constant trend is not seen in DEMs, so that in TOPO, ALOS PALSAR and ASTER, it was about six. While in SRTM, three and in GTOPO, four were estimated and the k parameter was different for each event and its value was different in different DEMs. The Nash method, the lowest and highest average relative error (RE) are related to the TOPO and GTOPO DEMs with values of 10.72% and 11.01% in the estimated runoff volume, respectively. The Rosso method, the average value of RE in the three TOPO, ALOSPALSAR and ASTER DEMs is almost similar in the estimated runoff volume. While the amount of RE in SRTM and GTOPO is higher than other DEMs. In fact, the results are close to each other due to the similar ability to extract the physiographic characteristics of the basin from these three models. Of course, it should be stated that with a small difference, the lowest amount of RE in estimating the runoff volume is related to the TOPO DEMs.<br /><strong>Conclusion</strong><br />ASTER DEMs with lower spatial resolution compared to TOPO and PALSAR ALOS has provided an acceptable stream network. However, SRTM and GTOPO have not provided a suitable stream network. In general, with the increase in the spatial resolution of the DEM, drainage density developed more dense and real, but main stream is somewhat developed in DEMs with lower spatial resolution. Different methods have been presented to estimate the values of n and k, and in this research, in order to investigate the effect of different DEMs in the Nash method, among the different methods of estimating its parameters, the experimental method has been used. In the experimental method, the physiographic characteristics of the watershed, such as the slope, the length of the main river and the area of the basin, play the main role in estimating the peak discharge. In relation to the peak flow estimation, the Nash model performed better and in estimating the runoff volume, the Rosso model performed better using the TOPO DEMs. In fact, it can be stated that the estimation of the peak discharge in the Nash method based on the experimental method has provided acceptable results in this watershed. according to the necessity of estimating the peak discharge in determining the dimensions of the IUH in the watershed to accurately simulate and control future floods, using the results of the present study can be of great help.<br /> https://gisj.sbu.ac.ir/article_104588_0ef06434114ec4a220b4cecd1e8f00c2.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Evaluating the Effect of Roughness Length Index on Modelling the Maximum Intensity of Urban Heat Islands Using Remote Sensing and Geospatial Information Systems (Case study: District 22, Tehran)Evaluating the Effect of Roughness Length Index on Modelling the Maximum Intensity of Urban Heat Islands Using Remote Sensing and Geospatial Information Systems (Case study: District 22, Tehran)7510010465610.48308/gisj.2024.234358.1197FAZahraKhadem AgheliFaculty of Engineering, Islamic Azad University (IAU), Ramsar Branch, Ramsar, IranSeyyed HassanHashemi AshkaMapping and GIS Department at Management and Planning Organization, Guilan, Rasht, IranSaharAlianFaculty of Civil Engineering Rahman Institute of Higher Education: Ramsar, Ramsar, IranJournal Article20240104<strong>Introduction</strong>: In recent year, modeling and identifying spatial distribution patterns of urban heat islands phenomenon with the aim of planning to face the effects of this phenomenon and predicting the provision of infrastructure needed to provide better thermal comfort to citizens has increased. Oke's model is one of the prominent models in this field that simulates the maximum intensity of the heat island based on the urban canyon’s aspect ratio index. The dependence of the Oke's model on the climatic and physical conditions of cities requires that this model be before being used in any urban area to be modified if needed. Considering the effects of aerodynamic resistance of urban canyons (roughness length) on the maximum intensity of the heat island, considering the index of this factor in the localization process of the model can affect the accuracy of the results. In this study, it has been tried to localize Oke's model in an area of the 22nd district of Tehran, and to investigate the effect of roughness length in this process. Preparation of temperature data of urban canyons is one of the important challenges in the modeling process. Research shows that the air temperature in the central and suburban areas at night is close to the land surface temperature (LST), and canyons ’s LST can be used as a convenient approximation of the air temperature. Therefore, in this study, it was tried to solve the problem of preparing temperature data by using satellite thermal sensors and using an appropriate LST retrieval algorithm. Calculating the geometric and aerodynamic strength indices of the canyons in the modeling process is complex and time-consuming due to the need to perform various spatial processing, and therefore, it is another challenge in this field. Geospatial information systems (GIS) with the ability to store the topological relationships of geographical features and analyze them can facilitate the calculation of these indicators. Therefore, in this study, geospatial information systems have been used.<br /><strong>Materials and Methods:</strong> In this study, in order to prepare the required temperature data, ASTER sensor data and meteorological data of the nearest meteorological station to the study area during the period of 2016 to 2022 were used. These data were processed in MATLAB software using a separate window algorithm (SWA) and the LST and the maximum intensity of surface heat islands in the study area were calculated. Then, canyons ’s aspect ratio and roughness length indicators and also, their simulated maximum intensity of heat island (based on Oke’s model) were calculated by processing digital maps in the ModelBuilder program in the ArcGIS software environment. After dividing the study area into training and check areas, localization of the maximum heat island intensity model was performed in two different cases. In the first case, the coefficients of the local model of the training area were calculated by considering the aspect ratio index. For this purpose, the canyons were classified into 11 different classes based on their aspect ratio index and their simulated and measured maximum intensity heat island were calculated and through regression analysis of these two sets of data, the localized Oke’s model was obtained. In the second case, the canyons of the training area were classified into two separate classes based on their roughness length index. Then, the first and second class were classified into 8 and 3 separate groups based on their aspect ratio. By calculating the simulated and measured maximum heat island intensity of each group and using regression analysis, the localized Oke’s model was determined for each of the two mentioned classes.<br /> <br /><strong>Results and discussion:</strong> by validating the obtained models in the check area, the values of , ρ, RMSE and MAE obtained from regression in the first case were 0.53, 0.73, 1.18 ± and 0.98, respectively, and in the second case, 0.80, 0.89, 1.05 ± and 0.87, respectively. Comparison of these results shows that the inclusion of the aerodynamic resistance index in the process of modeling the maximum intensity of the heat island, while increasing the correlation coefficients and the regression detection coefficient, has increased the accuracy of the results obtained from the local model and improved the model.<strong>Introduction</strong>: In recent year, modeling and identifying spatial distribution patterns of urban heat islands phenomenon with the aim of planning to face the effects of this phenomenon and predicting the provision of infrastructure needed to provide better thermal comfort to citizens has increased. Oke's model is one of the prominent models in this field that simulates the maximum intensity of the heat island based on the urban canyon’s aspect ratio index. The dependence of the Oke's model on the climatic and physical conditions of cities requires that this model be before being used in any urban area to be modified if needed. Considering the effects of aerodynamic resistance of urban canyons (roughness length) on the maximum intensity of the heat island, considering the index of this factor in the localization process of the model can affect the accuracy of the results. In this study, it has been tried to localize Oke's model in an area of the 22nd district of Tehran, and to investigate the effect of roughness length in this process. Preparation of temperature data of urban canyons is one of the important challenges in the modeling process. Research shows that the air temperature in the central and suburban areas at night is close to the land surface temperature (LST), and canyons ’s LST can be used as a convenient approximation of the air temperature. Therefore, in this study, it was tried to solve the problem of preparing temperature data by using satellite thermal sensors and using an appropriate LST retrieval algorithm. Calculating the geometric and aerodynamic strength indices of the canyons in the modeling process is complex and time-consuming due to the need to perform various spatial processing, and therefore, it is another challenge in this field. Geospatial information systems (GIS) with the ability to store the topological relationships of geographical features and analyze them can facilitate the calculation of these indicators. Therefore, in this study, geospatial information systems have been used.<br /><strong>Materials and Methods:</strong> In this study, in order to prepare the required temperature data, ASTER sensor data and meteorological data of the nearest meteorological station to the study area during the period of 2016 to 2022 were used. These data were processed in MATLAB software using a separate window algorithm (SWA) and the LST and the maximum intensity of surface heat islands in the study area were calculated. Then, canyons ’s aspect ratio and roughness length indicators and also, their simulated maximum intensity of heat island (based on Oke’s model) were calculated by processing digital maps in the ModelBuilder program in the ArcGIS software environment. After dividing the study area into training and check areas, localization of the maximum heat island intensity model was performed in two different cases. In the first case, the coefficients of the local model of the training area were calculated by considering the aspect ratio index. For this purpose, the canyons were classified into 11 different classes based on their aspect ratio index and their simulated and measured maximum intensity heat island were calculated and through regression analysis of these two sets of data, the localized Oke’s model was obtained. In the second case, the canyons of the training area were classified into two separate classes based on their roughness length index. Then, the first and second class were classified into 8 and 3 separate groups based on their aspect ratio. By calculating the simulated and measured maximum heat island intensity of each group and using regression analysis, the localized Oke’s model was determined for each of the two mentioned classes.<br /> <br /><strong>Results and discussion:</strong> by validating the obtained models in the check area, the values of , ρ, RMSE and MAE obtained from regression in the first case were 0.53, 0.73, 1.18 ± and 0.98, respectively, and in the second case, 0.80, 0.89, 1.05 ± and 0.87, respectively. Comparison of these results shows that the inclusion of the aerodynamic resistance index in the process of modeling the maximum intensity of the heat island, while increasing the correlation coefficients and the regression detection coefficient, has increased the accuracy of the results obtained from the local model and improved the model.https://gisj.sbu.ac.ir/article_104656_1c43aaa3eded450b3f635463941c5d4b.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Assessment and Prioritization of Urban Redevelopment Strategies for Deteriorated Areas Using Remote Sensing Technology and the BWM Method (Case Study: Zanjan, Iran)"Assessment and Prioritization of Urban Redevelopment Strategies for Deteriorated Areas Using Remote Sensing Technology and the BWM Method (Case Study: Zanjan, Iran)"10111810465510.48308/gisj.2024.233033.1177FAMohamad TaghiHeydariDepartment of Geography ,University of Zanjan , znajan, IranMohsenAhad NejadDepartment of Geography ,University of Zanjan , znajan, IranMohamadRasooliDepartment of Geography ,University of Zanjan , znajan, IranJournal Article20230904<strong>Background and Objective: </strong>Urban deteriorated fabrics represent one of the most pressing challenges to sustainable urban development in Iran, with wide-ranging impacts on physical infrastructure, social cohesion, economic vitality, and environmental quality. The city of Zanjan, as a growing urban center, has in recent decades witnessed a notable expansion of such deteriorated areas. These districts suffer from issues such as declining living conditions, inadequate urban services, vulnerability to hazards, social anomalies, weakened sense of place, and a drop in social capital. Moreover, previous planning efforts within the frameworks of comprehensive and detailed urban plans have largely failed to meet the needs of these areas or facilitate their revitalization. Against this backdrop, the present study aims to identify deteriorated urban fabrics in Zanjan, analyze their current conditions, and prioritize strategic intervention measures using a hybrid approach involving remote sensing technology, the DPSIR analytical framework, and the Best-Worst Method (BWM) for multi-criteria decision-making.<br /><strong>Materials and Methods: </strong>To achieve the research objectives, satellite data for the city of Zanjan was extracted using Landsat 8 imagery dated November 28, 2020. Spatial analysis of the data was performed using ENVI software, focusing on indicators such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and surface emissivity. Urban zones with high LST and low vegetation cover were identified as having a high potential for physical deterioration. The final classification divided the urban area into five deterioration classes. The first class, accounting for approximately 6.9% of the city’s area, was identified as the most deteriorated, largely overlapping with Zanjan’s historical core. The second class, covering around 10% of the city’s area, was in an emerging phase of deterioration.<br /><strong>Results and Discussion: </strong>In the second phase of the study, the DPSIR (Driving forces–Pressures–State–Impact–Response) framework was used to understand the causal chain of urban fabric deterioration and to formulate appropriate intervention strategies. This conceptual model enabled a comprehensive analysis of the factors driving and exacerbating urban decay. To identify and evaluate intervention strategies, expert opinions were collected from 20 urban planning professionals selected through snowball sampling. The strategies were then prioritized using the Best-Worst Method (BWM), which relies on pairwise comparisons to determine the relative importance of various options.<br /><strong>Conclusion</strong>: The findings indicated that among the proposed strategies, "citizen participation in the revitalization process" received the highest priority. This was followed by "economic revitalization of deteriorated areas" and "identification of zones at risk of deterioration" as the second and third most important strategies, respectively. Conversely, strategies such as "improving cleanliness," "increasing residential density," and "physical reconstruction" were ranked lowest in priority. This prioritization suggests that social and economic interventions are more effective than purely physical measures in the regeneration of deteriorated urban fabrics. Overall, the study demonstrates the efficacy of integrating remote sensing technologies, conceptual modeling, and multi-criteria decision-making to create a localized and effective framework for the identification, analysis, and strategic planning of urban regeneration efforts. This framework may serve as a practical foundation for urban managers, policymakers, and planners aiming for sustainable urban renewal in Zanjan and other comparable cities.<strong>Background and Objective: </strong>Urban deteriorated fabrics represent one of the most pressing challenges to sustainable urban development in Iran, with wide-ranging impacts on physical infrastructure, social cohesion, economic vitality, and environmental quality. The city of Zanjan, as a growing urban center, has in recent decades witnessed a notable expansion of such deteriorated areas. These districts suffer from issues such as declining living conditions, inadequate urban services, vulnerability to hazards, social anomalies, weakened sense of place, and a drop in social capital. Moreover, previous planning efforts within the frameworks of comprehensive and detailed urban plans have largely failed to meet the needs of these areas or facilitate their revitalization. Against this backdrop, the present study aims to identify deteriorated urban fabrics in Zanjan, analyze their current conditions, and prioritize strategic intervention measures using a hybrid approach involving remote sensing technology, the DPSIR analytical framework, and the Best-Worst Method (BWM) for multi-criteria decision-making.<br /><strong>Materials and Methods: </strong>To achieve the research objectives, satellite data for the city of Zanjan was extracted using Landsat 8 imagery dated November 28, 2020. Spatial analysis of the data was performed using ENVI software, focusing on indicators such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and surface emissivity. Urban zones with high LST and low vegetation cover were identified as having a high potential for physical deterioration. The final classification divided the urban area into five deterioration classes. The first class, accounting for approximately 6.9% of the city’s area, was identified as the most deteriorated, largely overlapping with Zanjan’s historical core. The second class, covering around 10% of the city’s area, was in an emerging phase of deterioration.<br /><strong>Results and Discussion: </strong>In the second phase of the study, the DPSIR (Driving forces–Pressures–State–Impact–Response) framework was used to understand the causal chain of urban fabric deterioration and to formulate appropriate intervention strategies. This conceptual model enabled a comprehensive analysis of the factors driving and exacerbating urban decay. To identify and evaluate intervention strategies, expert opinions were collected from 20 urban planning professionals selected through snowball sampling. The strategies were then prioritized using the Best-Worst Method (BWM), which relies on pairwise comparisons to determine the relative importance of various options.<br /><strong>Conclusion</strong>: The findings indicated that among the proposed strategies, "citizen participation in the revitalization process" received the highest priority. This was followed by "economic revitalization of deteriorated areas" and "identification of zones at risk of deterioration" as the second and third most important strategies, respectively. Conversely, strategies such as "improving cleanliness," "increasing residential density," and "physical reconstruction" were ranked lowest in priority. This prioritization suggests that social and economic interventions are more effective than purely physical measures in the regeneration of deteriorated urban fabrics. Overall, the study demonstrates the efficacy of integrating remote sensing technologies, conceptual modeling, and multi-criteria decision-making to create a localized and effective framework for the identification, analysis, and strategic planning of urban regeneration efforts. This framework may serve as a practical foundation for urban managers, policymakers, and planners aiming for sustainable urban renewal in Zanjan and other comparable cities.https://gisj.sbu.ac.ir/article_104655_f659e5e16c7b3a91a40e184d1cd3e600.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Assessment of Soil Salinity Variations in the Bonab Area Using Ground-Based and Remote Sensing-Derived DataAssessment of Soil Salinity Variations in the Bonab Area Using Ground-Based and Remote Sensing-Derived Data11913810476110.48308/gisj.2024.235513.1217FABitaHeydarzadehDepartment of Remote Sensing and Geographic Information System, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, IranHassanKhavarian NehzakDepartment of Remote Sensing and Geographic Information System, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran0000-0003-3646-5726AydaAbbasi -KaloDepartment of Soil Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, IranNikouHamzehpourDepartment of Soil Sciences, Faculty of Agriculture, University of Maragheh, Maragheh, IranJournal Article20240507<strong>Background and objective:</strong> Preventing soil salinization and managing agricultural irrigation heavily depend on accurate soil salinity estimation. Soil salinity represents a prevalent form of land degradation, characterized by its temporal and spatial evolution. Traditional methods like laboratory analysis and field surveys are inadequate for monitoring soil salinity due to their inability to keep pace with the rapid changes of this phenomenon and their associated high costs. Additionally, the methods used in assessing spatial changes should have the capability to respond to new questions and developments occurring in this field. To address this challenge, satellite imagery emerges as a valuable tool for continuous monitoring, given the sensitivity of electromagnetic signals to soil parameters, particularly in the surface layer directly linked to soil salt content. Numerous studies have been conducted on soil salinity, yielding different results based on ground samples and satellite imagery. Therefore, the attention of soil mappers is drawn to employing data and techniques capable of ensuring sufficient accuracy and reliability by eliminating image errors. Given the significance of this issue, the objective of this study is the evaluating and establishing a relationship between ground data and spectral indices extracted from Landsat satellite images in Bonab County.<br /><strong>Material and methods:</strong><br />In this study, three types of data were used: Landsat 7 and 8 satellite images with a 15-year interval, DEM imagery as auxiliary data for classification operations, and soil salinity samples collected from 74 different points at 500-meter intervals. These samples were collected from a 40-square-kilometer area in the fall of 2014. To assess the significance of ground samples with satellite images, 12 remote sensing spectral indices were utilized, and after necessary preprocessing (atmospheric, radiometric corrections, and applying a 3*3 filter), corresponding values to EC were extracted. Pre- and post-filter images were examined using regression methods. Subsequently, stepwise regression was employed to examine the relationship between independent variables and the dependent variable. All spectral indices were included as independent variables in the model. The results indicated that among these indices, NDWI and NDSI had the most significant correlation with ground samples. To create the soil salinity change map for the years 1999 to 2014, ground samples and the NDSI index were used. Additionally, using DEM data, ground data, and Landsat 8 imagery, a maximum likelihood classification map for 2014 was generated.<br /><strong>Results and discussion:</strong> Regression analysis between EC samples and spectral indices revealed that NDVI (0.45), NDWI (0.37), SI-T (0.43), and NDSI (0.41) had a more significant correlation with soil salinity compared to other indices. The use of filters improved the coefficient of determination for these correlations. Additionally, VSSI and BI indices showed the least significant correlation with ground samples. The soil salinity change chart indicates that in an area of approximately 40 square kilometers, the most significant soil salinity changes, covering 35.3 square kilometers, occurred from saline to highly saline land. The maximum likelihood classification map for 2014 shows that with the drying of Lake Urmia, the trend of increasing salinity in the region has intensified.<br /><strong>Conclusion:</strong> In this study, Landsat 7 (1999) and Landsat 8 (2014) imagery was utilized to assess the significant relationship and produce a soil salinity map between ground data and remote sensing spectral indices in Bonab County. The results demonstrated that all extracted indices had significant correlations with soil salinity data, with NDVI, NDWI, SI-T, and NDSI showing stronger correlations compared to other indices. Furthermore, the results of filtering showed that applying a filter to the index could improve research outcomes. The study emphasizes using satellite imagery for ongoing soil salinity monitoring due to its sensitivity and adaptability, outperforming traditional methods. Significant correlations between ground data and spectral indices like NDVI, NDWI, SI-T, and NDSI underscore their effectiveness in analyzing soil salinity dynamics. These findings provide valuable guidance for future research, advocating for filtering techniques to improve accuracy in assessing spatial changes in soil salinity. The findings of this study can serve as a useful guide for selecting data and satellite images in similar studies related to spatial changes in soil salinity.<br /> <strong>Background and objective:</strong> Preventing soil salinization and managing agricultural irrigation heavily depend on accurate soil salinity estimation. Soil salinity represents a prevalent form of land degradation, characterized by its temporal and spatial evolution. Traditional methods like laboratory analysis and field surveys are inadequate for monitoring soil salinity due to their inability to keep pace with the rapid changes of this phenomenon and their associated high costs. Additionally, the methods used in assessing spatial changes should have the capability to respond to new questions and developments occurring in this field. To address this challenge, satellite imagery emerges as a valuable tool for continuous monitoring, given the sensitivity of electromagnetic signals to soil parameters, particularly in the surface layer directly linked to soil salt content. Numerous studies have been conducted on soil salinity, yielding different results based on ground samples and satellite imagery. Therefore, the attention of soil mappers is drawn to employing data and techniques capable of ensuring sufficient accuracy and reliability by eliminating image errors. Given the significance of this issue, the objective of this study is the evaluating and establishing a relationship between ground data and spectral indices extracted from Landsat satellite images in Bonab County.<br /><strong>Material and methods:</strong><br />In this study, three types of data were used: Landsat 7 and 8 satellite images with a 15-year interval, DEM imagery as auxiliary data for classification operations, and soil salinity samples collected from 74 different points at 500-meter intervals. These samples were collected from a 40-square-kilometer area in the fall of 2014. To assess the significance of ground samples with satellite images, 12 remote sensing spectral indices were utilized, and after necessary preprocessing (atmospheric, radiometric corrections, and applying a 3*3 filter), corresponding values to EC were extracted. Pre- and post-filter images were examined using regression methods. Subsequently, stepwise regression was employed to examine the relationship between independent variables and the dependent variable. All spectral indices were included as independent variables in the model. The results indicated that among these indices, NDWI and NDSI had the most significant correlation with ground samples. To create the soil salinity change map for the years 1999 to 2014, ground samples and the NDSI index were used. Additionally, using DEM data, ground data, and Landsat 8 imagery, a maximum likelihood classification map for 2014 was generated.<br /><strong>Results and discussion:</strong> Regression analysis between EC samples and spectral indices revealed that NDVI (0.45), NDWI (0.37), SI-T (0.43), and NDSI (0.41) had a more significant correlation with soil salinity compared to other indices. The use of filters improved the coefficient of determination for these correlations. Additionally, VSSI and BI indices showed the least significant correlation with ground samples. The soil salinity change chart indicates that in an area of approximately 40 square kilometers, the most significant soil salinity changes, covering 35.3 square kilometers, occurred from saline to highly saline land. The maximum likelihood classification map for 2014 shows that with the drying of Lake Urmia, the trend of increasing salinity in the region has intensified.<br /><strong>Conclusion:</strong> In this study, Landsat 7 (1999) and Landsat 8 (2014) imagery was utilized to assess the significant relationship and produce a soil salinity map between ground data and remote sensing spectral indices in Bonab County. The results demonstrated that all extracted indices had significant correlations with soil salinity data, with NDVI, NDWI, SI-T, and NDSI showing stronger correlations compared to other indices. Furthermore, the results of filtering showed that applying a filter to the index could improve research outcomes. The study emphasizes using satellite imagery for ongoing soil salinity monitoring due to its sensitivity and adaptability, outperforming traditional methods. Significant correlations between ground data and spectral indices like NDVI, NDWI, SI-T, and NDSI underscore their effectiveness in analyzing soil salinity dynamics. These findings provide valuable guidance for future research, advocating for filtering techniques to improve accuracy in assessing spatial changes in soil salinity. The findings of this study can serve as a useful guide for selecting data and satellite images in similar studies related to spatial changes in soil salinity.<br /> https://gisj.sbu.ac.ir/article_104761_7342534e28c0e092b0f9cf802da3ff16.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Analysis of Methods, Challenges, and Perspectives in Rural Road Networks Detection with a Focus on Remote Sensing ImagesAnalysis of Methods, Challenges, and Perspectives in Rural Road Networks Detection with a Focus on Remote Sensing Images13917610545710.48308/gisj.2025.237413.1237FAAliJavadi MoghadamDep of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, IranAbbasKianiDep of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, IranJournal Article20241105Background and Objective: By advancement of remote sensing and deep learning technologies, the automatic identification of road networks, particularly in rural areas and secondary roads, has become feasible. Moreover, traditional mapping methods, due to their high cost and time-consuming nature, have been increasingly replaced by approaches based on remote sensing data and machine learning. Therefore, this study aims to comprehensively review research conducted on the preparation of road maps using remote sensing data, especially in recent years. In general, preparing a map of the road network involves various methods, and one of the reliable and cost-effective methods is automatic road detection using remote sensing images. Examination of various research results indicates that among the available automatic approaches, methods based on deep learning networks can provide acceptable and more reliable accuracies compared to other conventional methods. Considering the common width used in road construction, remote sensing images with different spatial resolutions have been used in research, each with its advantages and disadvantages. In general, for the type of route under study, which is the rural road network, satellite images with medium spatial resolution and free access cannot achieve high accuracy. Thus, satellite images can be integrated to enhance spatial resolution and improve detection algorithms. One way to integrate satellite images is through super-resolution algorithms. This study can serve as a reference for comparing and selecting methods for the automatic detection of road networks and for improving the spatial resolution of remote sensing images to assist in identifying narrow roads (such as rural road networks), so that researchers can select appropriate data and algorithms based on the objective and type of road network under study.<br /><br />Materials and Methods: This research aims to investigate the existing methods for road network detection and the utilization of satellite images with medium spatial resolution for this purpose. Initially, the data and methods applicable for generating a road network map were examined. Subsequently, the principles used in the field of road network detection using remote sensing images were described, and based on these principles, classification methods, segmentation, road index, and machine learning were implemented. Methods for improving the spatial resolution of satellite images were also investigated for employing satellite images with medium spatial resolution. Finally, the methods were reviewed in terms of input parameters, mechanism, and output, to identify their strengths and weaknesses and to utilize them optimally for various applications.<br /><br />Discussion and Analysis: According to the reviews of the examined articles from various Authentic journals in the field of road network detection, classification, segmentation, road index, and machine learning methods account for approximately 28%, 31%, 5%, and 36% shares, respectively. In recent years, classification and segmentation methods based on neural networks have been developed, encompassing a larger share (about 60%) of machine learning methods in general. Additionally, in super-resolution, investigations show that methods based on traditional techniques and deep learning account for approximately 44% and 56% shares, respectively, and most recently, deep learning-based approaches are under development.<br /><br />Conclusion: The investigations show that using deep learning models in road network detection provides better results than traditional methods and gradually replaces these methods. Deep learning models with the ability to extract complex features and reduce the need for human intervention have improved the accuracy and efficiency of the detection process. On the other hand, super-resolution techniques based on deep learning can solve problems arising from the lack of high-resolution images by increasing the spatial resolution of images. By preserving spectral features and reducing noise, these techniques can provide higher-quality images for road detection. One of the main challenges in road detection from satellite images is the presence of vegetation cover and shadows, which can lead to incomplete and inconsistent detection of roads. To improve this problem, techniques such as tensor voting have been proposed, which can complete and correct roads that have been incompletely detected. Overall, combining super-resolution and deep learning methods for identifying road networks provides a cost-effective and efficient approach to updating road maps. These approaches, by reducing the costs and time required for detection, can be widely used by researchers and professionals in various fields. Furthermore, with further development and improvement of these techniques, new solutions can be developed for road construction, maintenance, and planning.Background and Objective: By advancement of remote sensing and deep learning technologies, the automatic identification of road networks, particularly in rural areas and secondary roads, has become feasible. Moreover, traditional mapping methods, due to their high cost and time-consuming nature, have been increasingly replaced by approaches based on remote sensing data and machine learning. Therefore, this study aims to comprehensively review research conducted on the preparation of road maps using remote sensing data, especially in recent years. In general, preparing a map of the road network involves various methods, and one of the reliable and cost-effective methods is automatic road detection using remote sensing images. Examination of various research results indicates that among the available automatic approaches, methods based on deep learning networks can provide acceptable and more reliable accuracies compared to other conventional methods. Considering the common width used in road construction, remote sensing images with different spatial resolutions have been used in research, each with its advantages and disadvantages. In general, for the type of route under study, which is the rural road network, satellite images with medium spatial resolution and free access cannot achieve high accuracy. Thus, satellite images can be integrated to enhance spatial resolution and improve detection algorithms. One way to integrate satellite images is through super-resolution algorithms. This study can serve as a reference for comparing and selecting methods for the automatic detection of road networks and for improving the spatial resolution of remote sensing images to assist in identifying narrow roads (such as rural road networks), so that researchers can select appropriate data and algorithms based on the objective and type of road network under study.<br /><br />Materials and Methods: This research aims to investigate the existing methods for road network detection and the utilization of satellite images with medium spatial resolution for this purpose. Initially, the data and methods applicable for generating a road network map were examined. Subsequently, the principles used in the field of road network detection using remote sensing images were described, and based on these principles, classification methods, segmentation, road index, and machine learning were implemented. Methods for improving the spatial resolution of satellite images were also investigated for employing satellite images with medium spatial resolution. Finally, the methods were reviewed in terms of input parameters, mechanism, and output, to identify their strengths and weaknesses and to utilize them optimally for various applications.<br /><br />Discussion and Analysis: According to the reviews of the examined articles from various Authentic journals in the field of road network detection, classification, segmentation, road index, and machine learning methods account for approximately 28%, 31%, 5%, and 36% shares, respectively. In recent years, classification and segmentation methods based on neural networks have been developed, encompassing a larger share (about 60%) of machine learning methods in general. Additionally, in super-resolution, investigations show that methods based on traditional techniques and deep learning account for approximately 44% and 56% shares, respectively, and most recently, deep learning-based approaches are under development.<br /><br />Conclusion: The investigations show that using deep learning models in road network detection provides better results than traditional methods and gradually replaces these methods. Deep learning models with the ability to extract complex features and reduce the need for human intervention have improved the accuracy and efficiency of the detection process. On the other hand, super-resolution techniques based on deep learning can solve problems arising from the lack of high-resolution images by increasing the spatial resolution of images. By preserving spectral features and reducing noise, these techniques can provide higher-quality images for road detection. One of the main challenges in road detection from satellite images is the presence of vegetation cover and shadows, which can lead to incomplete and inconsistent detection of roads. To improve this problem, techniques such as tensor voting have been proposed, which can complete and correct roads that have been incompletely detected. Overall, combining super-resolution and deep learning methods for identifying road networks provides a cost-effective and efficient approach to updating road maps. These approaches, by reducing the costs and time required for detection, can be widely used by researchers and professionals in various fields. Furthermore, with further development and improvement of these techniques, new solutions can be developed for road construction, maintenance, and planning.https://gisj.sbu.ac.ir/article_105457_e0417c7805f8f4477ba15460340a3b75.pdfIranian Remote Sensing and GIS
Society / Shahid Beheshti UniversityIranian Journal of Remote Sensing and GIS2008-596617320251023Spatial and Temporal Analysis of Methane Pollutant Distribution in Metropolitan Areas Using Remote Sensing and Geographic Information Systems (Case Study: Isfahan Metropolis)Spatial and Temporal Analysis of Methane Pollutant Distribution in Metropolitan Areas Using Remote Sensing and Geographic Information Systems (Case Study: Isfahan Metropolis)17720010552310.48308/gisj.2025.238060.1243FAAhmadrezaAboutorabi BoarzabadiDep of Geography and Urban Planning, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, IranAliSadeghiDep of Geography and Urban Planning, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, IranDariushRahimiDep of Geography and Urban Planning, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, IranJournal Article20241224Background and Objectives: Air pollution is one of the major environmental and health challenges that has been exacerbated by industrial growth and increased human activity, particularly in large and industrial cities. Methane gas, as one of the most potent greenhouse gases, plays a significant role in global warming, climate change, and the deterioration of air quality. The sources of methane emissions include wetlands, livestock farming, agriculture, and landfill sites, with human activities contributing significantly to its increase. Measuring and monitoring air pollution often faces spatial and temporal limitations due to ground-based monitoring stations. In this context, satellite data, due to its wide coverage, cost-effectiveness, and ability to provide high spatial and temporal resolution data, is used as one of the most important sources of information for studying air pollution. This research utilizes data from the TROPOMI sensor on the Sentinel-5P satellite, which enables the measurement of methane concentrations in the atmosphere, as the primary data source. These data serve as the basis for spatial and temporal analysis of methane distribution in the Isfahan metropolitan area, providing an opportunity to closely examine the spatial and temporal patterns of this pollutant on a large scale. Despite the high importance of methane pollution, no comprehensive study has been conducted regarding the spatial and temporal distribution of this pollutant in Isfahan. The aim of this research is to conduct a comprehensive and systematic analysis of methane distribution in the city of Isfahan using satellite data and identify the relationship between atmospheric changes and methane variations to offer effective solutions for air pollution management and environmental quality improvement.<br /><br />Materials and Methods: This study aims to analyze the spatial and temporal distribution of methane concentration in the Isfahan metropolitan area using TROPOMI sensor data from the Sentinel-5P satellite over the period from 2019 to 2023. Satellite data were retrieved, processed, and analyzed using the Google Earth Engine platform. To examine the spatial distribution pattern of methane concentration, the Global Moran’s I index and G-statistic were applied to analyze clusters and determine the data dispersion. Additionally, the Gi-statistic was used to identify areas with the highest (hot spots) and lowest (cold spots) methane concentrations. Furthermore, the relationship between methane concentration and climatic factors such as temperature, air pressure, precipitation, and wind speed was evaluated through the calculation of Pearson’s correlation coefficient. Finally, the temporal trends of methane concentration were analyzed on a monthly, seasonal, and annual scale.<br /><br />Results and Discussion: The results from the analyses indicate an increasing trend in methane concentration in the Isfahan metropolitan area during the study period. This gas experienced the highest concentrations in the colder seasons, especially in industrial and agricultural areas. Spatial analyses revealed significant clusters of high concentrations in the northern regions, particularly in areas 4 and 7, as well as in the eastern areas, particularly in areas 12 and 15. These high methane concentrations were linked to activities such as livestock farming, agriculture, and landfill operations. In contrast, the southern regions, particularly areas 2 and 6, as well as some parts of the western areas, were identified as cold spots with lower concentrations. Furthermore, the assessment of the relationship between climatic parameters showed an inverse correlation between temperature and wind speed with methane concentration changes, while air pressure exhibited a positive and significant relationship with the gas concentration changes.<br /><br />Conclusion: The results of this study, based on high-precision satellite data analysis and advanced spatial measurement techniques, provide valuable information for air pollution management and urban planning. Accordingly, it is recommended that methane emission monitoring and control be prioritized during the colder seasons, with a focus on the identified hot spots. In this regard, optimizing industrial processes, efficiently managing waste in the eastern parts of Isfahan, and controlling methane emissions from northern livestock farms using modern technologies, including bioremediation methods, can play an effective role in reducing this pollutant. Additionally, the use of remote sensing data and advanced predictive models for continuous methane concentration monitoring and targeted pollution control strategies is recommended as an effective approach.Background and Objectives: Air pollution is one of the major environmental and health challenges that has been exacerbated by industrial growth and increased human activity, particularly in large and industrial cities. Methane gas, as one of the most potent greenhouse gases, plays a significant role in global warming, climate change, and the deterioration of air quality. The sources of methane emissions include wetlands, livestock farming, agriculture, and landfill sites, with human activities contributing significantly to its increase. Measuring and monitoring air pollution often faces spatial and temporal limitations due to ground-based monitoring stations. In this context, satellite data, due to its wide coverage, cost-effectiveness, and ability to provide high spatial and temporal resolution data, is used as one of the most important sources of information for studying air pollution. This research utilizes data from the TROPOMI sensor on the Sentinel-5P satellite, which enables the measurement of methane concentrations in the atmosphere, as the primary data source. These data serve as the basis for spatial and temporal analysis of methane distribution in the Isfahan metropolitan area, providing an opportunity to closely examine the spatial and temporal patterns of this pollutant on a large scale. Despite the high importance of methane pollution, no comprehensive study has been conducted regarding the spatial and temporal distribution of this pollutant in Isfahan. The aim of this research is to conduct a comprehensive and systematic analysis of methane distribution in the city of Isfahan using satellite data and identify the relationship between atmospheric changes and methane variations to offer effective solutions for air pollution management and environmental quality improvement.<br /><br />Materials and Methods: This study aims to analyze the spatial and temporal distribution of methane concentration in the Isfahan metropolitan area using TROPOMI sensor data from the Sentinel-5P satellite over the period from 2019 to 2023. Satellite data were retrieved, processed, and analyzed using the Google Earth Engine platform. To examine the spatial distribution pattern of methane concentration, the Global Moran’s I index and G-statistic were applied to analyze clusters and determine the data dispersion. Additionally, the Gi-statistic was used to identify areas with the highest (hot spots) and lowest (cold spots) methane concentrations. Furthermore, the relationship between methane concentration and climatic factors such as temperature, air pressure, precipitation, and wind speed was evaluated through the calculation of Pearson’s correlation coefficient. Finally, the temporal trends of methane concentration were analyzed on a monthly, seasonal, and annual scale.<br /><br />Results and Discussion: The results from the analyses indicate an increasing trend in methane concentration in the Isfahan metropolitan area during the study period. This gas experienced the highest concentrations in the colder seasons, especially in industrial and agricultural areas. Spatial analyses revealed significant clusters of high concentrations in the northern regions, particularly in areas 4 and 7, as well as in the eastern areas, particularly in areas 12 and 15. These high methane concentrations were linked to activities such as livestock farming, agriculture, and landfill operations. In contrast, the southern regions, particularly areas 2 and 6, as well as some parts of the western areas, were identified as cold spots with lower concentrations. Furthermore, the assessment of the relationship between climatic parameters showed an inverse correlation between temperature and wind speed with methane concentration changes, while air pressure exhibited a positive and significant relationship with the gas concentration changes.<br /><br />Conclusion: The results of this study, based on high-precision satellite data analysis and advanced spatial measurement techniques, provide valuable information for air pollution management and urban planning. Accordingly, it is recommended that methane emission monitoring and control be prioritized during the colder seasons, with a focus on the identified hot spots. In this regard, optimizing industrial processes, efficiently managing waste in the eastern parts of Isfahan, and controlling methane emissions from northern livestock farms using modern technologies, including bioremediation methods, can play an effective role in reducing this pollutant. Additionally, the use of remote sensing data and advanced predictive models for continuous methane concentration monitoring and targeted pollution control strategies is recommended as an effective approach.https://gisj.sbu.ac.ir/article_105523_02fc3eef3031e72153ecfcda48edc018.pdf