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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chen Bo & Shan Miao & Yun Zhao & Jinyu Li, 2025. "Assessing Forest Quality Using Multi-Source Satellite Remote Sensing Data: A Case Study in Western Beijing's Mountainous Regions," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 16(1), pages 1-19, January.
    • Handle: RePEc:igg:jdst00:v:16:y:2025:i:1:p:1-19
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