Calibrating satellite maps with field data for improved predictions of forest biomass

TL;DR

This paper presents a coregionalization model that combines sparse field measurements with satellite data to produce high-resolution, bias-corrected forest biomass maps, enhancing spatial detail and accuracy.

Contribution

The study introduces a novel coregionalization approach that accounts for zero-inflation and heterogeneous errors, improving biomass prediction accuracy from satellite data.

Findings

Finer spatial detail achieved compared to field measurements.

Significant noise filtering and bias correction of satellite maps.

Effective modeling of zero-inflation and heterogeneous errors.

Abstract

Spatially explicit quantification of forest biomass is important for forest-health monitoring and carbon accounting. Direct field measurements of biomass are laborious and expensive, typically limiting their spatial and temporal sampling density and therefore the precision and resolution of the resulting inference. Satellites can provide biomass predictions at a far greater density, but these predictions are often biased relative to field measurements and exhibit heterogeneous errors. We developed and implemented a coregionalization model between sparse field measurements and a predictive satellite map to deliver improved predictions of biomass density at a 1-by-1 km resolution throughout the Pacific states of California, Oregon and Washington. The model accounts for zero-inflation in the field measurements and the heterogeneous errors in the satellite predictions. A stochastic partial differential equation approach to spatial modeling is applied to handle the magnitude of the satellite data. The spatial detail rendered by the model is much finer than would be possible with the field measurements alone, and the model provides substantial noise-filtering and bias-correction to the satellite map.