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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.
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]
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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  Label: Title
  Group: Ti
  Data: Accurate Regional Above-Ground Biomass Mapping: Canopy Height-Constrained Upscaling from In Situ to Satellite Data.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 8, p1170. 26p.
– Name: Subject
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  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Biomass+estimation%22">Biomass estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+biomass%22">Forest biomass</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br /><searchLink fieldCode="DE" term="%22Tree+height%22">Tree height</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+analysis+%28Statistics%29%22">Spatial analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Liaoning+Sheng+%28China%29%22">Liaoning Sheng (China)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18081170
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1170
    Subjects:
      – SubjectFull: Biomass estimation
        Type: general
      – SubjectFull: Forest biomass
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Kriging
        Type: general
      – SubjectFull: Tree height
        Type: general
      – SubjectFull: Spatial analysis (Statistics)
        Type: general
      – SubjectFull: Data integration
        Type: general
      – SubjectFull: Liaoning Sheng (China)
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Accurate Regional Above-Ground Biomass Mapping: Canopy Height-Constrained Upscaling from In Situ to Satellite Data.
        Type: main
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            NameFull: Guo, Qiyu
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            NameFull: Jiang, Jinbao
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            NameFull: Qiao, Xiaojun
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            NameFull: Li, Kangning
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              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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