Accuracy Assessment of Remote Sensing Forest Height Retrieval for Sustainable Forest Management: A Case Study of Shangri-La

Haoxiang Xu, Xiaoqing Zuo, Yongfa Li (), Xu Yang (), Yuran Zhang and Yunchuan Li
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Haoxiang Xu: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Xiaoqing Zuo: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Yongfa Li: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Xu Yang: School of Architecture and Civil Engineering, Kunming University, Kunming 650500, China
Yuran Zhang: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Yunchuan Li: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

Sustainability, 2025, vol. 17, issue 22, 1-20

Abstract: Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an

Keywords: n/a (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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