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Research Article Open access CC BY 4.0

Analysis of Land Use-land Cover Mapping and Vegetation Status of Kechemo Watershed, Keyih Tekli District, Central Zone of Tigray, Ethiopia

Haftaye Hadush, Guesh Assefa, Meresa Weldeslasie

Asian Journal of Research in Agriculture and Forestry · pp. 22–31 · Published 8 Jan 2026

10.9734/ajraf/2026/v12i1465

Abstract

Assessing the Land Use and Land Cover (LULC) type of a watershed is crucial for environmental management, urban planning, agriculture, disaster mitigation and sustainable development. In Ethiopia, Climate Action through Landscape Management (CALM) Program for Results has been participating in land degradation reduction activities. And currently, it has expanded its program in to Tigray region, Kechemo watershed. However, the current LULC type and vegetation status of the watershed have not been studied, making it impossible to observe and evaluate changes resulting from the program's intervention. Therefore, this study was aimed to assess and map the current land use type of Kechemo watershed. Sentinel 2 image (2024 and 2025) was used to determine the land use and the vegetation index of the watershed. The land use type was classified with the help of supervised classification method using support vector machine (SVM) algorithm. Besides, the vegetation index of the watershed was determined using NDVI and OSAVI vegetation index. The results show that the watershed consists of cultivated land (66.94%), shrub land (28.96%), bare land (3.46%), and settlements (0.64%). The overall accuracy and kappa coefficient was 90.04% and 0.87 respectively. In addition, the NDVI and OSAVI value is highest in autumn (0.436) and lowest in winter (-0.111) season. Averagely, about 56.3% and 42.2% vegetation index of the watershed is classified under low vegetation class and bare land respectively. Therefore, it is recommended to give more emphasis to restore the vegetation resource of the watershed to improve the ecosystem service of the watershed. 

Land use class vegetation index NDVI OSAVI

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