Instructor and research associate of urban physical development group. ACECR (academic center for education, culture and research),

Since in most mapping models geometric mean of different criteria are used to determine the desertification intensity, one of the most important issues in desertification studies is understanding the similar areas, which require similar management after determining the desertification intensity map. Two similar classes of desertification intensity may require different management due to differences in the criteria that affect its desertification severity. Therefore, after determining the geomorphological facies as the working units in Sistan plain, we used hierarchical cluster analysis to identify the homogeneous environmental management units (HEMUs) based on indices of MEDALUS model. According to the MEDALUS model, the studied area was divided into two categories namely medium and high desertification classes. Working units (geomorphological facies) are classified into five clusters according to HEMUs analysis based on climate, soil, vegetation, and wind erosion criteria. The first cluster (C11) include six facies with moderate and severe desertification; in all of these units the main effective factor was wind erosion, so they need the same management decisions controlling wind erosion. Two working units (1 and 4) with the same desertification severity were placed in two different clusters due to the main factors affecting each other. The results of the Mann-Whitney test showed that the value of the test statistics was 79. Also, the value of Asymp.Sig was obtained to be 0.018, which is less than 0.025 (two-tailed test), and it can be concluded that the classification of work units in the two models, clustering and desertification, is not equal (P<0.05). So It seems that using cluster analysis to identify the same units, which need the same management decision after preparing the desertification intensity, is necessary.

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%.