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Spatiotemporal Interpolation of GEDI Biomass with Calibrated Uncertainty

arXiv:2604.03874v1 Announce Type: new Abstract: Monitoring deforestation-driven carbon emissions requires both spatially explicit and temporally continuous estimates of aboveground biomass density (AGBD) with calibrated uncertainty. NASA's Global Ecosystem Dynamics Investigation (GEDI) provides reliable LIDAR-derived AGBD, but its orbital sampling causes irregular spatiotemporal coverage, and occasional operational interruptions, including a 13-month hibernation from March 2023 to April 2024, leave extended gaps in the observational record. Prior work has used machine learning approaches to fill GEDI's spatial gaps using satellite-derived features, but temporal interpolation of biomass through unobserved periods, particularly across active disturbance events, remains largely unaddressed. Moreover, standard ensemble methods for biomass mapping have been shown to produce systematically miscalibrated prediction intervals. To address these gaps, we extend the Attentive Neural Process (ANP

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Robin Young, Srinivasan Keshav · · 1 min read · 25 views
#cs.LG #cs.CE

arXiv:2604.03874v1 Announce Type: new Abstract: Monitoring deforestation-driven carbon emissions requires both spatially explicit and temporally continuous estimates of aboveground biomass density (AGBD) with calibrated uncertainty. NASA's Global Ecosystem Dynamics Investigation (GEDI) provides reliable LIDAR-derived AGBD, but its orbital sampling causes irregular spatiotemporal coverage, and occasional operational interruptions, including a 13-month hibernation from March 2023 to April 2024, leave extended gaps in the observational record. Prior work has used machine learning approaches to fill GEDI's spatial gaps using satellite-derived features, but temporal interpolation of biomass through unobserved periods, particularly across active disturbance events, remains largely unaddressed. Moreover, standard ensemble methods for biomass mapping have been shown to produce systematically miscalibrated prediction intervals. To address these gaps, we extend the Attentive Neural Process (ANP) framework, previously applied to spatial biomass interpolation, to jointly sparse spatiotemporal settings using geospatial foundation model embeddings. We treat space and time symmetrically, empirically validating a form of space-for-time substitution in which observations from nearby locations at other times inform predictions at held-out periods. Our results demonstrate that the ANP produces well-calibrated uncertainty estimates across disturbance regimes, supporting its use in Measurement, Reporting, and Verification (MRV) applications that require reliable uncertainty quantification for forest carbon accounting.

Executive Summary

This article presents an innovative approach to interpolating aboveground biomass density (AGBD) data from NASA's Global Ecosystem Dynamics Investigation (GEDI) using an Attentive Neural Process (ANP) framework. The proposed method addresses the irregular spatiotemporal coverage and operational interruptions of GEDI by treating space and time symmetrically, utilizing geospatial foundation model embeddings. The results demonstrate well-calibrated uncertainty estimates across disturbance regimes, making the ANP suitable for Measurement, Reporting, and Verification (MRV) applications in forest carbon accounting. The method's ability to handle unobserved periods, particularly across active disturbance events, is a significant improvement over previous approaches. The article's findings have important implications for monitoring deforestation-driven carbon emissions and reliable uncertainty quantification in forest carbon accounting.

Key Points

  • ▸ The article proposes an ANP framework for spatiotemporal interpolation of GEDI biomass data with calibrated uncertainty.
  • ▸ The method treats space and time symmetrically, utilizing geospatial foundation model embeddings.
  • ▸ The results demonstrate well-calibrated uncertainty estimates across disturbance regimes.

Merits

Strength in Addressing Gaps in GEDI Data

The proposed method effectively addresses the irregular spatiotemporal coverage and operational interruptions of GEDI, making it suitable for MRV applications.

Improved Uncertainty Quantification

The ANP framework provides well-calibrated uncertainty estimates, which is essential for reliable forest carbon accounting.

Demerits

Dependence on High-Quality Training Data

The method's performance may be sensitive to the quality and availability of training data, which can be a limiting factor in real-world applications.

Complexity and Computational Requirements

The ANP framework may require significant computational resources and expertise to implement, which can be a barrier to adoption in some settings.

Expert Commentary

The article presents a significant advancement in the field of forest biomass estimation, addressing critical gaps in GEDI data and providing well-calibrated uncertainty estimates. The proposed ANP framework is a valuable tool for researchers and practitioners working in this area. However, the method's dependence on high-quality training data and complexity may limit its adoption in some settings. Further research is needed to evaluate the method's performance in real-world applications and to develop more user-friendly and accessible implementations.

Recommendations

  • ✓ Future research should focus on developing more robust and user-friendly implementations of the ANP framework, including the development of high-quality training data and tools for easy adoption.
  • ✓ The article's findings should be further evaluated in real-world applications, including the development of case studies and pilot projects to demonstrate the method's effectiveness and limitations.

Sources

Original: arXiv - cs.LG

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