LAND USE MAPPING USING MAXIMUM LIKELIHOOD CLASSIFICATION AND REMOTE SENSING INDICES: CASE STUDY AIN-ABID CONSTANTINE (ALGERIA)

Ammar Lakhdar; Toufik Ferhad; Souad Haouari; Mohamed Baadeche

doi:10.31435/rsglobal_ijitss/30062024/8182

Ammar Lakhdar University Constantine 1, Faculty of Earth Sciences, Geography and Spatial Planning, Constantine, Algeria
Toufik Ferhad University Constantine 1, Faculty of Earth Sciences, Geography and Spatial Planning, Constantine, Algeria
Souad Haouari University Constantine 1, Faculty of Earth Sciences, Geography and Spatial Planning, Constantine, Algeria
Mohamed Baadeche University Constantine 1, Faculty of Earth Sciences, Geography and Spatial Planning, Constantine, Algeria

DOI: https://doi.org/10.31435/rsglobal_ijitss/30062024/8182

Keywords: Land use, Maximum Likelihood Classification, NDVI, NDBI

Abstract

Land use is an essential theme in monitoring environmental phenomena. The supervised maximum likelihood classification algorithm has been shown to provide the best results from remotely sensed data. This work is aimed at the application of a supervised classification (maximum likelihood) based on a priori knowledge of the terrain under study and information extracted from the two remote sensing indices NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) for mapping land use of the area of Ain Abid Constantine (located in eastern Algeria) for the year 2020. The obtained result showed that this city is an agricultural area with a percentage of 68.49% of agricultural land and a low percentage of 2.02% of Buildings.

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