Abstract
Accurate estimation of forest biomass at property-level finds diverse applications, being particularly important for REDD + projects (Reducing Emissions from Deforestation and forest Degradation). This study presents a straightforward method for improving carbon stock estimates by integrating geolocated field plot data with open-source large-scale maps. We evaluated the performance of simple predictive models using spatial coordinates and global maps as covariates. Our results demonstrate that even without advanced remote sensing data and complex modeling techniques, incorporating spatial information and open-source data can substantially improve carbon stock estimates. Spatial coordinates and global map information significantly enhanced predictions with a 31.9% decrease in MAE for areas near field plots (i.e., predictions inside the REDD + project), and 18.6% decrease in greater distances (i.e., predictions for the broader region outside the REDD + project). Moreover, our approach allows for the generation of high-resolution wall-to-wall carbon stock maps for entire REDD + project areas, even in the absence of high-quality local remote sensing data. We conclude that large-scale maps, when properly calibrated with local field data, are invaluable for improving carbon stock predictions in tropical forests. Our method is widely applicable, providing a practical solution for users interested in enhancing their carbon stock estimates at a local scale.
Data availability
The data that support the findings of this study are available from BRCarbon company, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of BRCarbon company.
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Acknowledgements
This work was supported by BRCarbon company, which provided all the data for the analysis. This work was partially supported by the US National Science Foundation award 2040819 and NASA’s Carbon Monitoring System (CMS) program (grant 80NSSC23K1257) to DV. DV is also grateful for the support provided by the US Department of Agriculture National Institute of Food and Agriculture McIntire–Stennis project 1005163.
Funding
This work was partially supported by the US National Science Foundation award 2040819 and NASA’s Carbon Monitoring System (CMS) program (grant 80NSSC23K1257) to DV. DV is also grateful for the support provided by the US Department of Agriculture National Institute of Food and Agriculture McIntire–Stennis project 1005163.
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L.H. conceptualized, performed analyses, prepared all figures, and wrote the main manuscript. D.V. helped conceptualize, write the main manuscript, and discuss the results. D.A. helped the conceptualization and discussion of results. L.H., D.A., R.K., S.G., A.S., B.A, C.S. and R.N. collected LiDAR and forest inventory data. All authors reviewed the manuscript.
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Haneda, L.E., de Almeida, D.R.A., Kamimura, R.A. et al. Straightforward model-based approach using only field data and open-source maps to improve carbon stock estimates for REDD + projects.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-37201-x
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DOI: https://doi.org/10.1038/s41598-026-37201-x
Keywords
- Carbon modeling
- Carbon mapping
- Spatial covariates
- Geospatial modeling
- Tropical forest
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