Volume 10, Issue 4 (12-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(4): 209-224 |
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yousefi Z, Jahantigh H, Zolfaghari F. Assessment of desertification Severity in Morad Abad - Saravan Plain using Albedo-NDVI model. Journal of Spatial Analysis Environmental Hazards 2023; 10 (4) :209-224
URL: http://jsaeh.khu.ac.ir/article-1-3438-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3438-en.html
1- Higher Education Complex of Saravan
2- Higher Education Complex of Saravan ,zol.farhad@gmail.com
2- Higher Education Complex of Saravan ,
Abstract: (1845 Views)
Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.
Type of Study: Research |
Subject:
Special
Received: 2023/08/9 | Accepted: 2023/11/28 | Published: 2024/05/12
Received: 2023/08/9 | Accepted: 2023/11/28 | Published: 2024/05/12
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