Estimating biomass compartments and surface fuel loads by integrating various satellite products with a data-model fusion approach

Knowledge about the state of the vegetation at fire occurrence is essential for estimating fire behavior and fire emissions. The spatial distribution and the temporal dynamics of the biomass in various vegetation components, surface litter and woody debris are important controls on fire spread and emissions. So far, no large-scale product exists that combines all these requirements. Maps of canopy height and above ground biomass (AGB) from satellite retrievals provide information on the regional variability of forest biomass, but they have a limited use for fire-related applications because they do not provide information on different fuel components such as biomass in the canopy, wood, grass or litter. Fire-targeted products like the global fuelbed database and the North American Wildland Fuel Database (NAWFD) combine land cover maps with representative values or statistical distributions of fuel properties such as biomass values for trees, shrubs, grass, woody debris and litter. However, those maps do not provide information on the spatial variability of fuel loads within one vegetation type (fuelbed). In addition, information on the temporal dynamics of the fuels is missing. Temporal dynamics of fuels can be approximated by satellite-derived time series of vegetation indices or biophysical parameters. 

Here, we aim to develop an approach that combines the spatial information from remotely-sensed AGB and canopy height maps, the annual temporal information from land cover maps with the high temporal information from leaf area index (LAI) time series to retrieve information on the spatial variability and temporal dynamics of fuel loads. Therefore, we developed a data-model fusion approach that uses the 10-daily LAI product from Sentinel-3 OLCI and Proba-V and land cover maps as input. We apply the approach to a spatial resolution of 333 x 333 m across different study regions in the Amazon, southern Africa, Siberia and the United States. In a first step, the temporal dynamics in tree height is computed from long-term changes in mean LAI and the fractional tree cover by taking observation of canopy height from GEDI as reference. Since canopy height is closely related to AGB through allometric relations between tree height and biomass, the estimated tree height is then used to estimate stem biomass and consecutively branches and leaf biomass, which is calibrated against maps of AGB. The Biomass and Allometry Database is used to calibrate model parameters that regulate the relationships between canopy height, leaf and woody biomass. Estimated temporal changes in tree height directly translate into changes in stem, branches and leaf biomass and hence result in a carbon turnover (e.g. leaf fall, transfer of woody biomass). Based on a simple decomposition model we then compute the dynamics of surface litter and woody debris. The estimates of litter, fine and coarse woody debris correspond well with databases of in situ observations. Our fuel data-model fusion approach allows estimating spatial patterns and temporal dynamics of vegetation and surface carbon pools for the analysis of carbon turnover and pools and as input into fire behaviour and emission models.