Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.