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Assessing Forest Quality Using Multi-Source Satellite Remote Sensing Data: A Case Study in Western Beijing's Mountainous Regions
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Author(s): Chen Bo (Beijing Vocational College of Agriculture, China), Shan Miao (Beijing Vocational College of Agriculture, China), Yun Zhao (Beijing Jingxi Forestry Administration Office, China)and Jinyu Li (Beijing Academy of Forestry and Landscape Architecture, China)
Copyright: 2025
Volume: 16
Issue: 1
Pages: 19
Source title:
International Journal of Distributed Systems and Technologies (IJDST)
Editor(s)-in-Chief: Nik Bessis (Edge Hill University, UK)
DOI: 10.4018/IJDST.383047
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Abstract
This study uses Sentinel satellite data to estimate forest quality over a large area, focusing on Beijing. By combining ground survey data with remote sensing, a random forest model predicts forest parameters. The results show a correlation coefficient of 0.60-0.76 and a relative root mean square error of 0.09-0.39. Average tree height and diameter at breast height (DBH) had the highest accuracy (75%-80%), followed by canopy density and plant number density (68%-75%). The spatial agreement between predicted and actual forest quality indicates the model's effectiveness.
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