Comprehensive Assessment of Nine Fine-Resolution Global Forest Cover Products in the Three-North Shelter Forest Program Region

Accurate forest cover maps are essential for forest conservation and sustainable development. Numerous global forest cover products have emerged in recent years; however, most tend to neglect sparsely forested arid and semi-arid areas, such as the Three-North Shelter Forest (TNSF) Program Region in China. Despite their sparse distribution, forests in these areas play a vital role in maintaining global ecological balance and biodiversity. Therefore, a comprehensive evaluation of these products is necessary. In this study, the performance of nine global forest cover products was systematically investigated at a 10–30 m resolution (GlobeLand30, GLC_FCS30D, FROM-GLC30, FROM-GLC10, ESA World Cover, ESRI Land Cover, GFC30, GFC 2020, and GFC) in the TNSF region around 2020. Specifically, a novel and comprehensive validation dataset was first generated by integrating all available open-access validation datasets in the TNSF region after visual interpretation. Second, the consistency and accuracy of nine forest cover products were evaluated, and their discrepancies with government statistical data were analyzed. The results indicate that GFC2020 provides the highest overall accuracy (OA) of 90.49%, followed by ESA World Cover, while GlobeLand30 had the lowest accuracy of 84.78%. Meanwhile, compared with statistical data, all nine products underestimated forest areas, especially in these hyper-arid zones (aridity index < 0.03). Notably, 31.04% of the area is identified as forest by only one product, attributable to differences in forest definitions and remote sensing data among the products. Therefore, this study provides a detailed assessment and analysis of nine global forest cover products from multiple perspectives, offering valuable insights for users in selecting appropriate forest cover products and supporting forest management.

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Publication Details

Journal
Remote Sensing
Published
2025-04-05
DOI
https://doi.org/10.3390/rs17071296
Citations
3
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
2.40

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article

Comprehensive Assessment of Nine Fine-Resolution Global Forest Cover Products in the Three-North Shelter Forest Program Region

Tingting Zhao, C. Wang, Xiao Zhang, Liangyun Liu
3 citations
Remote Sensing
Remote Sensing and LiDAR Applications
2.40
article

Comprehensive Assessment of Nine Fine-Resolution Global Forest Cover Products in the Three-North Shelter Forest Program Region

Tingting Zhao, C. Wang, Xiao Zhang, Liangyun Liu
article en
3 citations

Abstract

Accurate forest cover maps are essential for forest conservation and sustainable development. Numerous global forest cover products have emerged in recent years; however, most tend to neglect sparsely forested arid and semi-arid areas, such as the Three-North Shelter Forest (TNSF) Program Region in China. Despite their sparse distribution, forests in these areas play a vital role in maintaining global ecological balance and biodiversity. Therefore, a comprehensive evaluation of these products is necessary. In this study, the performance of nine global forest cover products was systematically investigated at a 10–30 m resolution (GlobeLand30, GLC_FCS30D, FROM-GLC30, FROM-GLC10, ESA World Cover, ESRI Land Cover, GFC30, GFC 2020, and GFC) in the TNSF region around 2020. Specifically, a novel and comprehensive validation dataset was first generated by integrating all available open-access validation datasets in the TNSF region after visual interpretation. Second, the consistency and accuracy of nine forest cover products were evaluated, and their discrepancies with government statistical data were analyzed. The results indicate that GFC2020 provides the highest overall accuracy (OA) of 90.49%, followed by ESA World Cover, while GlobeLand30 had the lowest accuracy of 84.78%. Meanwhile, compared with statistical data, all nine products underestimated forest areas, especially in these hyper-arid zones (aridity index < 0.03). Notably, 31.04% of the area is identified as forest by only one product, attributable to differences in forest definitions and remote sensing data among the products. Therefore, this study provides a detailed assessment and analysis of nine global forest cover products from multiple perspectives, offering valuable insights for users in selecting appropriate forest cover products and supporting forest management.

Remote SensingVol. 17(7)
Chinese Academy of Sciences (CN), Beijing Institute of Big Data Research (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 13%
Remote Sensing and LiDAR Applications
2.40
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