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

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Title: Assessment of Three High-Resolution Forest Canopy Height Products in China.
Authors: Cao, Yue1 (AUTHOR), Ma, Jie1,2 (AUTHOR), Wang, Ran1 (AUTHOR), Zhang, Chunhua1,2 (AUTHOR) zchqs@126.com, Zhou, Di2 (AUTHOR), Man, Haoran1 (AUTHOR), Lu, Dan1 (AUTHOR)
Source: Remote Sensing. Apr2026, Vol. 18 Issue 7, p1046. 23p.
Subjects: Tree height, Forests & forestry, Data quality, Error analysis in mathematics, Ecological models, Cartography, Carbon dioxide sinks, Forest monitoring
Geographic Terms: China
Abstract: Highlights: What are the main findings? Performance of three high-resolution forest canopy height (FCH) products varies significantly across spatial scales and evaluation metrics. Discrepancies in forest definitions among datasets critically influence accuracy assessments and comparability. NNGI_FCH shows relatively balanced performance across the evaluated metrics when integrating forest area, spatial consistency, and overall accuracy. What are the implications of the main findings? Our analysis identifies major sources of divergence among three FCH products and offers practical guidance for selecting the most suitable FCH data for China's heterogeneous forest ecosystems. Our findings support improved forest monitoring and contribute to more reliable ecological modeling and sustainable resource management, with implications extending beyond 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. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Assessment of Three High-Resolution Forest Canopy Height Products in China.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 7, p1046. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Tree+height%22">Tree height</searchLink><br /><searchLink fieldCode="DE" term="%22Forests+%26+forestry%22">Forests & forestry</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Error+analysis+in+mathematics%22">Error analysis in mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+models%22">Ecological models</searchLink><br /><searchLink fieldCode="DE" term="%22Cartography%22">Cartography</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+dioxide+sinks%22">Carbon dioxide sinks</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+monitoring%22">Forest monitoring</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Highlights: What are the main findings? Performance of three high-resolution forest canopy height (FCH) products varies significantly across spatial scales and evaluation metrics. Discrepancies in forest definitions among datasets critically influence accuracy assessments and comparability. NNGI_FCH shows relatively balanced performance across the evaluated metrics when integrating forest area, spatial consistency, and overall accuracy. What are the implications of the main findings? Our analysis identifies major sources of divergence among three FCH products and offers practical guidance for selecting the most suitable FCH data for China's heterogeneous forest ecosystems. Our findings support improved forest monitoring and contribute to more reliable ecological modeling and sustainable resource management, with implications extending beyond 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18071046
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1046
    Subjects:
      – SubjectFull: Tree height
        Type: general
      – SubjectFull: Forests & forestry
        Type: general
      – SubjectFull: Data quality
        Type: general
      – SubjectFull: Error analysis in mathematics
        Type: general
      – SubjectFull: Ecological models
        Type: general
      – SubjectFull: Cartography
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      – SubjectFull: Carbon dioxide sinks
        Type: general
      – SubjectFull: Forest monitoring
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      – SubjectFull: China
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      – TitleFull: Assessment of Three High-Resolution Forest Canopy Height Products in China.
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              M: 04
              Text: Apr2026
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