The international community affirms the critical role of forests in climate change mitigation, which includes reducing emissions from degradation and deforestation, carbon stock conservation, sustainable forest management, and increasing carbon stocks in developing countries. It relates to land use and land cover changes. This study aims to review land use and land cover changes (LULC) in two decades, namely 2000–2010 and 2010–2020, and the impact on carbon stocks. Landsat satellite imagery in 2000, 2010, and 2020 are classified into six categories: built-up area, cropland, forest, water body, bareland, and grassland. This classification uses supervised classification. The accuracy kappa coefficient values obtained for the LULC 2000, LULC 2010, and LULC 2020 maps were 89.61%, 83.90%, and 87.10%, respectively. The most dominant systematic LULC change processes were forest degradation in 2000–2020; the transition of forest to cropland (349.20 ha), forest to bareland (171.19 ha), and forest to built-up area (661.68 ha). Loss of using the forest for other uses was followed by a decrease in carbon stock. There was a high decrease in carbon stock in the forest category (11,000 Mg C∙y–1). The results showed a significant change in land use and cover. The decline in the area occurred in the forest category, which decreased from year to year. Meanwhile, the built-up area increases every year. Carbon stocks also decrease from year to year, especially forests as the most significant carbon store, decreasing in the area.

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.