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.
Authors
- Chunhua Zhang (ORCID: https://orcid.org/0000-0002-4349-2426)
- Di Zhou (ORCID: https://orcid.org/0000-0002-2368-4033)
- Yue Cao (ORCID: https://orcid.org/0000-0002-2989-2044)
- Jie Ma (ORCID: https://orcid.org/0009-0008-5020-151X)
- Haoran Man
- Dan Lu
- Ran Wang
Institutions
- Ludong University (CN)
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
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
- Ludong University