ArticlePloS one2025
Mapping forest cover change and estimating carbon stock using satellite-derived vegetation indices in Alemsaga forest, Ethiopia.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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2 authors.
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Abstract
Deforestation and forest degradation are significant threats, leading to a decline in forest cover change, biomass and carbon storage, a crucial factor in mitigating climate change. Remote sensing techniques using satellite imagery offer a valuable tool for efficiently monitoring forest cover and biomass over different areas. This study aimed to map and quantify the forest cover change, biomass and carbon stored in the Alemsaga forest, Ethiopia. The study employed Landsat satellite images from four different periods (1992, 2003, 2013, and 2022) to track changes in forest cover and construct carbon storage maps for the Alemsaga forest. The findings from this study can be used to develop better forest conservation and management strategies. The study revealed a significant increase in dense forest cover in Alemsaga (35.34%) between 1992 and 2022, now encompassing 48.25% of the total forest area. Notably, satellite-derived vegetation indices (NDVI & DVI) exhibited a strong correlation with ground observations (R2 = 0.80), and statistical analysis confirmed this relation with above-ground carbon levels (R2 = 0.84). This enabled the creation of carbon storage maps, revealing a substantial increase from 159.31 t/ha in 1992 to 323.84 t/ha by 2022. It's important to acknowledge that while NDVI/DVI proved effective, other factors might influence carbon storage. However, the study clearly shows that satellite imaging has the capacity to map forest cover change, biomass and estimating carbon stock accurately, which is an important first step toward a better understanding of how forests contribute to climate change.
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