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Original Article

Land degradation intensity mapping using environmental factors and Sentinel-2 derived spectral indices in an arid region in south of Iran




To mitigate land degradation and desertification as an environmntal issue, it is crucial to monitor land degradation intensities, identify influential factors, and implement necessary measures. This study utilized remote sensing data and logistic regression modeling to assess desertification in Larestan County. Multiple indicators were considered in this study, encompassing climate factors (such as rainfall, evapotranspiration, and aridity index), groundwater indicators (including electrical conductivity, chloride content, sodium absorption ratio, and groundwater level decline), soil indicators (such as EC, texture, and organic matter content), land use and land cover (LULC) type, and wind erosion. The logistic regression model was employed to identify the most influential factors contributing to desertification. The findings revealed different risk classes: a small low-risk class in the eastern and southern regions covering 2.4% of the total area, a moderate-risk class in the foothill-plain areas covering 38.3% of the total area. The high-risk class of desertification is mainly concentrated in the central part of the study area, adjacent to regions with moderate risk. It is characterized by relatively large patches, particularly in the southwest of the interior plains, covering approximately 1,980 hectares, which accounts for 7.9% of the total area. The concentration of high-risk desertification in specific areas highlights the urgent need for proactive measures to preserve the environmental balance and sustainability of the study area.