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Article

Evaluating Forest Aboveground Biomass Products by Incorporating Spatial Representativeness Analysis

by
Yin Wang
1,2,3,
Xiaohui Wang
1,2,4,*,
Ping Ji
1,2,4,
Haikui Li
1,2,4,
Shengrong Wei
1,2,4 and
Daoli Peng
3
1
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2
Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China
3
State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China
4
National Forestry and Grassland Science Data Center, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(16), 2898; https://doi.org/10.3390/rs17162898
Submission received: 16 June 2025 / Revised: 29 July 2025 / Accepted: 19 August 2025 / Published: 20 August 2025
(This article belongs to the Section Forest Remote Sensing)

Abstract

Forest aboveground biomass (AGB) products serve as essential references for research on carbon cycle and climate change. However, significant uncertainties exist regarding forest AGB products and their evaluation methods. This study aims to evaluate AGB products in the context of discrepancies in plot size and product scales, while also investigate the applicability of large-scale AGB products at a regional level. The National Aeronautics and Space Administration (NASA)’s Global Ecosystem Dynamics Investigation (GEDI) and the European Space Agency (ESA)’s Climate Change Initiative (CCI) biomass data were evaluated using sample plots from the National Forest Inventory (NFI). The study was conducted in Jilin Province, located in Northeast China, which is predominantly covered by natural forests. Spatial representativeness evaluation indicators for sample plots were established, followed by a comprehensive representativeness assessment and the selection of sample plots based on the criteria importance through the intercriteria correlation (CRITIC) method. Additionally, the study conducted an overall evaluation of the products, as well as evaluations across different biomass ranges and various forest types. The results indicate that the accuracy metrics demonstrated improved performance when using representative plots compared to all plots, with the R2 increasing by 15.38%. Both products demonstrated optimal accuracy and stability in the 50–150 Mg/ha range. GEDI and CCI biomass data indicated an overall underestimation, with biases of −25.68 Mg/ha and −83.95 Mg/ha, respectively. Specifically, a slight overestimation occurred in the <50 Mg/ha range, while a gradually increasing underestimation was observed in the ≥50 Mg/ha range. This study highlights the advantages of spatial representativeness analysis in mitigating evaluation uncertainties arising from scale mismatches and enhancing the reliability of product evaluation. The accuracy trends of AGB products offer significant insights that could facilitate improvements and enhance their application.
Keywords: forest aboveground biomass; spatial representativeness analysis; the National Aeronautics and Space Administration’s Global Ecosystem Dynamics Investigation; the European Space Agency’s Climate Change Initiative; remote sensing; carbon cycle forest aboveground biomass; spatial representativeness analysis; the National Aeronautics and Space Administration’s Global Ecosystem Dynamics Investigation; the European Space Agency’s Climate Change Initiative; remote sensing; carbon cycle

Share and Cite

MDPI and ACS Style

Wang, Y.; Wang, X.; Ji, P.; Li, H.; Wei, S.; Peng, D. Evaluating Forest Aboveground Biomass Products by Incorporating Spatial Representativeness Analysis. Remote Sens. 2025, 17, 2898. https://doi.org/10.3390/rs17162898

AMA Style

Wang Y, Wang X, Ji P, Li H, Wei S, Peng D. Evaluating Forest Aboveground Biomass Products by Incorporating Spatial Representativeness Analysis. Remote Sensing. 2025; 17(16):2898. https://doi.org/10.3390/rs17162898

Chicago/Turabian Style

Wang, Yin, Xiaohui Wang, Ping Ji, Haikui Li, Shengrong Wei, and Daoli Peng. 2025. "Evaluating Forest Aboveground Biomass Products by Incorporating Spatial Representativeness Analysis" Remote Sensing 17, no. 16: 2898. https://doi.org/10.3390/rs17162898

APA Style

Wang, Y., Wang, X., Ji, P., Li, H., Wei, S., & Peng, D. (2025). Evaluating Forest Aboveground Biomass Products by Incorporating Spatial Representativeness Analysis. Remote Sensing, 17(16), 2898. https://doi.org/10.3390/rs17162898

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