Accurate Regional Above-Ground Biomass Mapping: Canopy Height-Constrained Upscaling from In Situ to Satellite Data.
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| Title: | Accurate Regional Above-Ground Biomass Mapping: Canopy Height-Constrained Upscaling from In Situ to Satellite Data. |
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| Authors: | Guo, Qiyu1 (AUTHOR), Jiang, Jinbao1 (AUTHOR), Qiao, Xiaojun1 (AUTHOR) 202418@cumtb.edu.cn, Li, Kangning1 (AUTHOR), Yan, Xuzhe1 (AUTHOR), Zhao, Yinpeng1 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1170. 26p. |
| Subjects: | Biomass estimation, Forest biomass, Remote sensing, Kriging, Tree height, Spatial analysis (Statistics), Data integration |
| Geographic Terms: | Liaoning Sheng (China), China |
| Abstract: | Highlights: This study proposes a forest canopy height-constrained kriging method to effectively bridge in situ observations with satellite remote sensing data, aiming to improve the estimation accuracy of regional forest above-ground biomass (AGB). The research systematically investigates the impact of scale effects on the AGB upscaling process and optimizes the performance of the upscaling model through sensitivity analysis of moving window parameters. The results show that the AGB upscaling results based on UAV data are significantly better than those derived directly from GF-2 satellite imagery, demonstrating the reliability and superiority of the method in balancing detail preservation and regional coverage. What are the main findings? A forest canopy height-constrained kriging method to link in situ and satellite data. Exploring the influence of scale effects on forest AGB upscaling. UAV-AGB upscaling results are more accurate than direct GF-2 estimates. The sensitivity to moving windows in the AGB upscaling process was investigated. What are the implications of the main findings? Provides a scalable methodological framework for multi-scale forest carbon monitoring. Defines the critical impact of scale effects on biomass upscaling accuracy, offering a scientific basis for optimizing regional mapping schemes. Validates the superiority of the "UAV as an intermediate layer" fusion strategy, refining the technical pathway of remote sensing monitoring systems. Supplies direct technical support for precise carbon sink quantification in service of carbon trading. Accurate estimation of forest above-ground biomass (AGB) is essential for quantifying forest carbon stocks and supporting regional carbon accounting. However, regional AGB mapping requires the integration of field observations with satellite data, and the associated scale transformation often causes the loss of spatial detail and reduced estimation consistency. To address this issue, this study proposes a forest canopy height-constrained area-to-area regression kriging (CCAM) method for upscaling UAV-derived AGB and generating a high-precision wall-to-wall AGB map for artificial forests in the sandy lands of northwest Liaoning Province, China. The framework integrates RFE-SVM-based feature selection, XGBoost-based UAV-AGB modeling, and CHM-constrained residual correction within a Regression-then-Kriging (R-K) strategy, while also evaluating the effects of moving-window size, scale transition, and the order of regression and kriging on upscaling performance. The results showed that the reconstructed UAV-AGB model achieved the highest accuracy, with R2 = 0.91 and rRMSE = 0.12, providing a reliable 0.1 m AGB baseline for subsequent upscaling. Among the tested moving-window sizes, the 7 × 7 window was identified as optimal. Under this setting, CCAM achieved R2 = 0.81 and rRMSE = 0.08, substantially outperforming direct GF-2-based estimation (R2 = 0.49, rRMSE = 0.24). The final 2 m regional AGB map further attained a validation accuracy of R2 = 0.79 and rRMSE = 0.18. These results demonstrate that CCAM can effectively preserve fine-scale UAV-derived biomass information during scale transformation and provide a reliable pathway for linking UAV and satellite observations in regional forest AGB mapping. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: This study proposes a forest canopy height-constrained kriging method to effectively bridge in situ observations with satellite remote sensing data, aiming to improve the estimation accuracy of regional forest above-ground biomass (AGB). The research systematically investigates the impact of scale effects on the AGB upscaling process and optimizes the performance of the upscaling model through sensitivity analysis of moving window parameters. The results show that the AGB upscaling results based on UAV data are significantly better than those derived directly from GF-2 satellite imagery, demonstrating the reliability and superiority of the method in balancing detail preservation and regional coverage. What are the main findings? A forest canopy height-constrained kriging method to link in situ and satellite data. Exploring the influence of scale effects on forest AGB upscaling. UAV-AGB upscaling results are more accurate than direct GF-2 estimates. The sensitivity to moving windows in the AGB upscaling process was investigated. What are the implications of the main findings? Provides a scalable methodological framework for multi-scale forest carbon monitoring. Defines the critical impact of scale effects on biomass upscaling accuracy, offering a scientific basis for optimizing regional mapping schemes. Validates the superiority of the "UAV as an intermediate layer" fusion strategy, refining the technical pathway of remote sensing monitoring systems. Supplies direct technical support for precise carbon sink quantification in service of carbon trading. Accurate estimation of forest above-ground biomass (AGB) is essential for quantifying forest carbon stocks and supporting regional carbon accounting. However, regional AGB mapping requires the integration of field observations with satellite data, and the associated scale transformation often causes the loss of spatial detail and reduced estimation consistency. To address this issue, this study proposes a forest canopy height-constrained area-to-area regression kriging (CCAM) method for upscaling UAV-derived AGB and generating a high-precision wall-to-wall AGB map for artificial forests in the sandy lands of northwest Liaoning Province, China. The framework integrates RFE-SVM-based feature selection, XGBoost-based UAV-AGB modeling, and CHM-constrained residual correction within a Regression-then-Kriging (R-K) strategy, while also evaluating the effects of moving-window size, scale transition, and the order of regression and kriging on upscaling performance. The results showed that the reconstructed UAV-AGB model achieved the highest accuracy, with R2 = 0.91 and rRMSE = 0.12, providing a reliable 0.1 m AGB baseline for subsequent upscaling. Among the tested moving-window sizes, the 7 × 7 window was identified as optimal. Under this setting, CCAM achieved R2 = 0.81 and rRMSE = 0.08, substantially outperforming direct GF-2-based estimation (R2 = 0.49, rRMSE = 0.24). The final 2 m regional AGB map further attained a validation accuracy of R2 = 0.79 and rRMSE = 0.18. These results demonstrate that CCAM can effectively preserve fine-scale UAV-derived biomass information during scale transformation and provide a reliable pathway for linking UAV and satellite observations in regional forest AGB mapping. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081170 |
