DOI: https://doi.org/10.14710/geoplanning.8.2.99-114

Development of Desertification Indicators for Desertification Monitoring from Landsat Images Using Python Programming

*Lamyaa Gamal EL-Deen Taha  -  professor of surveying and photogrammetry, Egypt
Manar A. Basheer  -  National Authority for Remote Sensing and Space Science (NARSS), Egypt
Amany Morsi Mohamed  -  National Authority for Remote Sensing and Space Science (NARSS), Egypt

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Abstract

Nowadays, desertification is one of the most serious environment socioeconomic issues and sand dune advances are a major threat that causes desertification. Wadi El-Rayan is one of the areas facing severe dune migration. Therefore, it's important to monitor desertification and study sand dune migration in this area. Image differencing for the years 2000 (Landsat ETM+) and 2019 (OLI images) and Bi-temporal layer stacking was performed. It was found that image differencing is a superior method to get changes of the study area compared to the visual method (Bi-temporal layer stacking). This research develops a quantitative technique for desertification assessment by developing indicators using Landsat images. Spatial distribution of the movement of sand dunes using some spectral indices (NDVI, BSI, LDI, and LST) was studied and a Python script was developed to calculate these indices. The results show that NDVI and BSI indices are the best indices in the identification and detection of vegetation. It was found that mobile sand dunes on the southern side of the lower Wadi El-Rayan Lake caused filling up of large part of the lower lake. The indices results show that sand movement decreased the size of the lower Wadi El-Rayan Lake and there are reclamation activities in the west of the lower lake. The results show that a good result could be achieved from the developed codes compared to ready-made software (ENVI 5).

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Keywords: Desertification;Climate change;Monitoring; Sand dunes; Spectral indice;NDVI; BSI; LDI; Land degradation

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Section: Original Research
Language : EN
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