High-Precision Inversion of Forest Aboveground Biomass in Karst Regions Based on Multi-Scale Synergy and Geomorphological Zoning Modeling

Accurate quantification of forest biomass in the karst mountainous region of Southwest China is critical for regional carbon sink accounting. However, remote sensing-based inversion of forest biomass in this region is subject to the dual constraints of scale effects and spatial heterogeneity. Taking two typical karst landforms—plateau karst and peak-cluster depression karst—as case studies, this study developed an inversion framework that integrates multi-scale synergy (plot—small watershed—region) with geomorphological zoning modeling. Specifically, the high-precision forest aboveground biomass (AGB), retrieved by integrating high-resolution satellite imagery (2 m) of small watersheds with field plot data, was used as the scale-conversion bridge. The dominant class variability-weighted method was applied to upscale the spatial resolution from 2 m to 30 m. Subsequently, Landsat-8 OLI imagery, land use/land cover data, and topographic factors were integrated to construct landform-specific neural network models, namely BPANN-GY for plateau karst and BPANN-FC for peak-cluster depression karst, with validation RMSEs of 11.02% and 12.39%, respectively. The results showed that the mean forest AGB in the plateau karst region was 110.66 t·ha−1 in 2013 and 111.46 t·ha−1 in 2024; in the peak-cluster depression karst region, the mean forest AGB was 123.98 t·ha−1 in 2014 and 125.57 t·ha−1 in 2024. Compared with the baseline years (2013/2014), total forest AGB in both regions increased significantly by 2024, up to 11.43% in the plateau karst region and 6.43% in the peak-cluster depression karst region. Spatially, the most marked increase in AGB occurred in the mid-to-high slope zones, while the differences in forest AGB among slope grades gradually narrowed, indicating progressively enhanced forest structural integrity and functional stability under effective land management. This study methodologically validates the scientific soundness and feasibility of using high-precision AGB retrieved from high-resolution satellite imagery as a scale-conversion bridge. The established AGB inversion system provides a methodological framework and data foundation for carbon sink accounting, ecological restoration, and land management in the karst region of Southwest China, and offers a transferable approach for forest AGB inversion in other highly heterogeneous landscapes.

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Publication Details

Journal
Systems
Published
2026-08-28
DOI
https://doi.org/10.3390/systems14091052
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00

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article

High-Precision Inversion of Forest Aboveground Biomass in Karst Regions Based on Multi-Scale Synergy and Geomorphological Zoning Modeling

Libin Liu, Rui Yang, Yinming Guo, Meiping Zhu et al.
Systems
Land Use and Ecosystem Services
article

High-Precision Inversion of Forest Aboveground Biomass in Karst Regions Based on Multi-Scale Synergy and Geomorphological Zoning Modeling

Libin Liu, Rui Yang, Yinming Guo, Meiping Zhu, Yue Xu
article en

Abstract

Accurate quantification of forest biomass in the karst mountainous region of Southwest China is critical for regional carbon sink accounting. However, remote sensing-based inversion of forest biomass in this region is subject to the dual constraints of scale effects and spatial heterogeneity. Taking two typical karst landforms—plateau karst and peak-cluster depression karst—as case studies, this study developed an inversion framework that integrates multi-scale synergy (plot—small watershed—region) with geomorphological zoning modeling. Specifically, the high-precision forest aboveground biomass (AGB), retrieved by integrating high-resolution satellite imagery (2 m) of small watersheds with field plot data, was used as the scale-conversion bridge. The dominant class variability-weighted method was applied to upscale the spatial resolution from 2 m to 30 m. Subsequently, Landsat-8 OLI imagery, land use/land cover data, and topographic factors were integrated to construct landform-specific neural network models, namely BPANN-GY for plateau karst and BPANN-FC for peak-cluster depression karst, with validation RMSEs of 11.02% and 12.39%, respectively. The results showed that the mean forest AGB in the plateau karst region was 110.66 t·ha−1 in 2013 and 111.46 t·ha−1 in 2024; in the peak-cluster depression karst region, the mean forest AGB was 123.98 t·ha−1 in 2014 and 125.57 t·ha−1 in 2024. Compared with the baseline years (2013/2014), total forest AGB in both regions increased significantly by 2024, up to 11.43% in the plateau karst region and 6.43% in the peak-cluster depression karst region. Spatially, the most marked increase in AGB occurred in the mid-to-high slope zones, while the differences in forest AGB among slope grades gradually narrowed, indicating progressively enhanced forest structural integrity and functional stability under effective land management. This study methodologically validates the scientific soundness and feasibility of using high-precision AGB retrieved from high-resolution satellite imagery as a scale-conversion bridge. The established AGB inversion system provides a methodological framework and data foundation for carbon sink accounting, ecological restoration, and land management in the karst region of Southwest China, and offers a transferable approach for forest AGB inversion in other highly heterogeneous landscapes.

SystemsVol. 14(9)
Zhejiang Normal University (CN), Guizhou University of Finance and Economics (CN), Chinese Academy of Sciences (CN), Institute of Geochemistry (CN)
National Natural Science Foundation of China
Life in Land
Openalex Percentile: Top 13%
Land Use and Ecosystem Services
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