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

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.

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Publication Details

Journal
Remote Sensing
Published
2026-03-31
DOI
https://doi.org/10.3390/rs18071046
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Chunhua Zhang, Di Zhou, Yue Cao, Jie Ma et al.
Remote Sensing
Remote Sensing and LiDAR Applications
article

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

Chunhua Zhang, Di Zhou, Yue Cao, Jie Ma, Haoran Man, Dan Lu, Ran Wang
article en

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.

Remote SensingVol. 18(7)
Ludong University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Ludong University
Life in Land
Openalex Percentile: Top 34%
Remote Sensing and LiDAR Applications
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