Assessment of desertification Severity in Morad Abad-Saravan Plain using Albedo-NDVI model

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yousefi ziba; Jahantigh Hossein; Zolfaghari Farhad

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Journal Paper

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Journal: Journal of Spatial Analysis Environmental Hazards Year:2024 | Volume:10 | Issue:4 Page(s): 209-224

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Title

Assessment of desertification Severity in Morad Abad-Saravan Plain using Albedo-NDVI model

Author(s)

yousefi ziba | Jahantigh Hossein | Zolfaghari Farhad | Issue Writer Certificate

Keywords

Desertification

Albedo-NDVI model

Morad Abad plain

remote sensing

linear regression.

Abstract

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.

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