Assessment of Three High-Resolution Forest Canopy Height Products in China

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Authors: Yue Cao, Jie Ma, Ran Wang, Chunhua Zhang, Di Jody Zhou, Haoran Man, Dan Lu
Cited by: 1
References: 32

Abstract

Large-scale mapping of forest canopy height (FCH) is crucial for accurately understanding ecosystem succession and forest carbon sinks. Recently, three high-resolution FCH products have been released, including global forest canopy height (GFCH), NNGI_FCH_China (NNGI_FCH), and ETH_GlobalCanopyHeight (ETH_GCH). This study provides a detailed assessment of these FCH products across China from forest area, spatial consistency, and overall accuracy, with additional analyses of forest classification errors and evaluation under a unified forest mask. The assessment is conducted using forest inventory data, the China land cover dataset, and field measurement data. The results show that NNGI_FCH had the smallest relative error of 13.4% and achieved better estimates of forest area in all regions but the north and northeast regions. GFCH had the highest spatial consistency of 70.8% nationwide, followed by NNGI_FCH (69.7%), which performed slightly better than GFCH in the east and northwest regions. ETH_GCH exhibited the lowest spatial consistency of 35.6% and remained below 50% across all regions except the northeast and south regions. ETH_GCH demonstrated the highest overall accuracy across the country, with an R2 and RMSE of 0.56 and 4.14 m, followed by NNGI_FCH (R2 = 0.49, RMSE = 3.38 m) and GFCH (R2 = 0.48, RMSE = 3.38 m). Validation results of ETH_GCH were relatively stable in different regions of China, while those of NNGI_FCH varied more but still outperformed GFCH. This study offers valuable insights by evaluating large-scale FCH products, which will be a key basis for in-depth studies on the utilization and improvement of future FCH mapping.

Fields of Study & Subjects

Remote Sensing and LiDAR Applications Remote Sensing in Agriculture Forest ecology and management

Publication details

Journal / source
Remote Sensing
Publisher
MDPI AG
Volume
18
Issue
7
Pages
1046–1046
Publication date
31 March 2026
Language
EN