Abstract
The Bench-Sheko zone, parts of the Eastern Afromontane Biodiversity Hotspot, is characterized by its rich biodiversity. However, recent reductions in vegetation cover have drawn concern, highlighting the critical role of remote sensing in monitoring these alterations is critical. Furthermore, this study evaluates the economic value of the ecosystem services rendered by the diverse types of vegetation cover class in the area. The classification of vegetation types and measuring their ecosystem benefits are crucial for monitoring vegetation and analyzing land cover changes. estimating the value of ecosystem services is vital for environmental impact assessments, cost-benefit analyses, and creating payment schemes for these natural services. For the vegetation cover map, it uses Sentinel-2 satellite data and a Random Forest classifier using Google Earth Engine. Based on a properly chosen reference, ecosystem service assessment approaches include benefit transfer, direct market value, and the social cost of carbon. The results highlight the vegetation classes’ enormous value and the services they offer. The largest value for Supporting Services (2829.3 USD ha⁻1yr⁻1) is found in the Remnant Forest, which makes up 30.98% of the total area. With the highest value for both cultural services (2847.7 USD ha⁻1yr⁻1) and regulatory services (5063.9 USD ha⁻1yr⁻1), the wetlands, which make up 4.35% of the total area, stand out. The total annual value of all ecosystem services provided by all vegetation classes is estimated to be 2.089 billion USD. When paired with methods for tracking and assessing changes in vegetation cover over time, high-resolution satellite images and precise classification algorithms can offer insightful information on the condition of the environment and support informed decision-making. In order to evaluate and convey to society and policymakers the advantages of vegetation cover, the value of ecosystem services is essential.
Publisher
Public Library of Science (PLoS)
Reference84 articles.
1. Monitoring natural and rural ecosystems using the NDVI anomaly: an application to the Umbria Region.;G Massei;PeerJ Prepr,2016
2. Vegetation Cover
3. GLC2000: A new approach to global land cover mapping from earth observation data;E Bartholomé;Int J Remote Sens,2005