Evaluating the Accuracy of Change detection Using Spectral Indices and spectral classification, A Case Study of Fayoum Governorate, Egypt | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 3, Volume 52, Issue 4, July 2024, Page 212-232 PDF (1.2 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2024.286600.1333 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Ahmed EL ashiry  
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| Faculty of Engineering, Beni-Suef University, Beni-Suef, Egypt. | ||||
| Abstract | ||||
| Spectral indices developed to extract features from satellite images are simple and fast methods that reduce processing time compared to traditional satellite image classification. In this paper, the effectiveness of the normalized difference building index (NDBI), the normalized difference vegetation index (NDVI), and the Normalized Difference Water Index (NDWI) were evaluated in land cover mapping and detecting its changes in the period from 2013 to 2023 in Fayoum Governorate, Egypt, using Landsat 8-OLI images. The results of supervised classification showed a decrease in green areas between 2013 and 2023 by 76.03 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.8 square kilometers and an increase in built-up areas by 79.83 square kilometers, at the expense of green lands. On the other hand, the results of using spectral indices showed a decrease in green areas between 2013 and 2023 by 91.12 square kilometers, with a slight decrease in the areas occupied by water amounting to 3.35 square kilometers. Meanwhile, we noticed an increase in built-up areas by 94.47 square kilometers at the expense of green areas. The results showed a general convergence in the changes in the studied classes during the study period, with a great convergence in the change in the area of the water bodies resulted from the classification and from the NDWI and a convergence in the effectiveness of both the NDVI and NDBI. | ||||
| Keywords | ||||
| Satellite images; Land cover; Spectral indices; Changes detection; Supervised classification | ||||
| References | ||||
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