Assessing Forest Quality Using Multi-Source Satellite Remote Sensing Data: A Case Study in Western Beijing's Mountainous Regions

Chen Bo, Shan Miao, Yun Zhao and Jinyu Li
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Chen Bo: Beijing Vocational College of Agriculture, China
Shan Miao: Beijing Vocational College of Agriculture, China
Yun Zhao: Beijing Jingxi Forestry Administration Office, China
Jinyu Li: Beijing Academy of Forestry and Landscape Architecture, China

International Journal of Distributed Systems and Technologies (IJDST), 2025, vol. 16, issue 1, 1-19

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.

Date: 2025
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