Replication code and data “Small field plots can cause substantial uncertainty in gridded aboveground biomass products from airborne lidar data”

Emerging satellite radar and lidar platforms are developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than available field plot data underpinning model calibration and validation efforts. Intermediate resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and the map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate pixel-level mean and variance in AGB maps by propagating uncertainty from the lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at 100 m map resolution (1-ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. The raw data are all publicly available forest inventory and lidar data downloadable from the National Ecological Observatory Network (https://www.neonscience.org/) and the Smithsonian Forest Global Earth Observatory (https://forestgeo.si.edu/). Code (written in R) to analyze those data are also included.