Monitoring the severity of degradation and desertification by remote sensing (case study: Hamoun International Wetland)

Monitoring degradation in arid and semi-arid areas is one of the main concerns for governments, given the growing degradation trend. Meanwhile, detecting the areas subjected to degradation requiring management in the shortest time and at the lowest cost is a necessity, especially in border areas such as Hamoun Wetland, located between Iran and Afghanistan. Albedo and normalized difference vegetation index (NDVI) were calculated using remote sensing technology to monitor the degradation intensity in different periods (August 1999, 2009, 2015, and 2020). Change vector analysis in brightness and greenness indices for 1999 and 2020 was used to determine the changes in intensity. Linear regression was run between albedo and NDVI. Finally, degradation intensity (DI) map was developed to monitor degradation intensity. A confusion matrix was created between the change vector analysis (CVA) and the albedo–NDVI model to evaluate the accuracy of the map obtained from this model for 1,476 pixels of different classes. The linear regression between NDVI and albedo showed a negative correlation between indices (R = −0.849). The results showed an increase for the regions with null, low, and medium degradation intensity, while an expansion was observed for the regions with severe and extreme degradation. The confusion matrix results indicated the high accuracy (0.705) of the degradation intensity model for the study area. These changes were about 52.01% from 1999 to 2009, 7.07% from 2009 to 2015, 56.26% from 1999 to 2015, and 55.15% from 2015 to 2020. Additionally, the average rate of changes in degradation intensity between 1999 and 2020 was 13.11%.

Abstract licence: CC BY

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