Forest cover change detection in relation to climate variability and LULC changes using GIS and RS techniques. A case of the Kafa zone, southwest Ethiopia

Ethiopia has lost sizable forest resources due to rapid population growth and subsequent increase in the demand for agricultural land and fuel woods. In this study, GIS and remote sensing techniques were used to detect forest cover changes in relation to climate variability in the Kafa zone, southwest Ethiopia. Landsat Thematic Mapper (TM) images of 1986 and 1990, Enhanced Thematic Mapper plus (ETM+) image of 2010 and Landsat-8 Operational Land Imager (OLI-8) image of 2018 were acquired at a resolution of 30 m to investigate spatial-temporal forest cover and land use changes. A supervised image classification was made using a maximum likelihood method in ERDAS imagine V9.2 to identify the various land use and land cover classes. Both spectral (normalised difference vegetation index – NDVI) and post classification change detection methods were used to determine the forest cover changes. To examine the extent and rate of forest cover changes, post classification comparisons were made using ArcGIS V 10.4.1. A net forest cover change of 1168.65 ha (12%) was detected during the study period from 1986 to 2018. The drop in the NDVI from 0.06–0.64 in 1986 to (–0.08)–0.12 in 2018 indicated a marked forest cover change in the study area. The correlation of NDVI values with climate data indicated the forest was not in a stable condition. The declining of the forest cover was most likely caused by climate variability in the study area.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).