Forest Aboveground Biomass Estimation

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11 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration
Aug 12, 2026Pann Thinzar Seint, Bryan Atwood, Subas ChhatkuliForest Aboveground Biomass Estimation Remote Sensing
DAI Labs, K.K.
Multi-Modal, Multi-Temporal Geospatial Foundation Models Improve Global Above-Ground Biomass Estimation
Aug 5, 2026Ghjulia Sialelli, Linus Scheibenreif, Jan Dirk Wegner +1Geospatial Foundation Models Forest Aboveground Biomass Estimation
Photogrammetry and Remote Sensing, ETH Zurich, Zurich, 8049, Switzerland · ETH AI Center, Zurich, 8092, Switzerland · EcoVision Lab, Department of Mathematical Modeling and Machine Learning, University of Zurich, Zurich, 8057, Switzerland
Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation
Jul 13, 2026Ritu Yadav, Andrea Nascetti, Yifang BanUncertainty Quantification Forest Aboveground Biomass Estimation
Ritu Yadav, Andrea Nascetti and Yifang Ban is with Division of Geoinformatics, KTH Royal Institute of Technology, Sweden.
Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation
Jul 6, 2026Nils Griese, Christoph Kleinn, Nils NölkeForest Aboveground Biomass Estimation Remote Sensing
Department of Forest Inventory and Remote Sensing, University of Göttingen, Göttingen, Germany
Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
Jul 4, 2026Luiz Felipe Parente Santiago, Felipe Ferrari, Daniel Rodrigues dos Santos +1Forest Aboveground Biomass Estimation Remote Sensing
Instituto de Computação, Universidade Federal do Amazonas · Instituto de Pesquisas do Exército na Amazônia · Instituto de Computac¸˜ao, Universidade Federal do Amazonas (IComp/UFAM), Manaus-AM, Brazil +3
Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision
Jun 18, 2026Luke J. Zachmann, David D. Diaz, Vincent A. Landau +8Remote Sensing Forest Aboveground Biomass Estimation
Vibrant Planet Public Benefit Corporation, Truckee, CA, USA
Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin
Jun 3, 2026Sayan Mandal, Rocco Sedona, Simon Besnard +4Remote Sensing Remote Sensing Benchmarks
Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52428 Jülich, Germany · School of Engineering and Natural Sciences (SENS), University of Iceland, 102 Reykjav´ık, Iceland · Global Land Monitoring Group, GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels
May 19, 2026Reza M. Asiyabi, Juan Alberto Molina-Valero, The SEOSAW Partnership +2Forest Aboveground Biomass Estimation Multi-Task Learning
School of Geosciences, University of Edinburgh, UK · National Centre for Earth Observation (NCEO), UK · Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha, Czech Republic
Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance
Apr 4, 2026Robin Young, Srinivasan KeshavForest Aboveground Biomass Estimation Neural Processes
Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, Cambridgeshire, UK
Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data
Mar 4, 2026Habib Pourdelan, Zhengkang Xiang, Hugh Stewart +3Forest Aboveground Biomass Estimation Remote Sensing
The University of Melbourne · Private Forestry Service Victoria, Australia · Nicon Rural Services, Australia
Interpolation of GEDI Biomass Estimates with Calibrated Uncertainty Quantification
Jan 23, 2026Robin Young, Srinivasan KeshavUncertainty Quantification Forest Aboveground Biomass Estimation
Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, Cambridgeshire, UK

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Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration

Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli

DAI Labs, K.K.

Forest Aboveground Biomass Estimation Remote Sensing Model Calibration

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Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable. While field inventories provide high localized accuracy, they are spatially sparse; conversely, spaceborne LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) offers broad biomass samples but lacks spatial continuity and systematic underestimation of high-biomass forests. This paper presents an operational framework centered on a single globally trained convolutional neural network (CNN) that is seamlessly adapted to each new landscape through a lightweight empirical field-calibration workflow. The global model combines optical (Sentinel-2), C-band SAR (Sentinel-1), L-band SAR (ALOS-2 PALSAR-2), and terrain (DEM) data. It is trained once against GEDI Level-4A biomass reference data spanning multiple regions and both wet and dry seasons so that it learns the persistent woody-structure rather than a single-date appearance. To avoid retraining for every landscape, the framework applies a small number of local field plots to fit a scale-and-bias correction that aligns the global prediction with ground truth in each region. The pipeline harmonizes sensor data onto a shared 10 m grid, derives vegetation indices and polarimetric ratios, computes per-band normalization stats, and trains the CNN with a hybrid log-domain SmoothL1 with RMSE loss for skewed biomass distribution. On held-out validation the global GEDI-based model achieved R^2 approximately 0.78 and RMSE approximately 22 Mg/ha. A subsequent field calibration combining Random Forest fine-tuning under a 10-fold cross-validation eliminates localized regional biases. This improves local validation performance to R^2 approximately 0.82 and reduces RMSE to approximately 15 Mg/ha, outperforming both the uncalibrated global model and the ESA CCI Biomass product against field plots.