High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany

Open access publication under review. Visit this Earth Engine app to explore the data interactively. Abstract: Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data. We propose a two-step approach that estimates five canopy fuel variables at the point level, first using measurements from NASA’s GEDI instrument, before predicting high-resolution raster maps. Instead of using field measurements, we generate plot-level estimates for Canopy (Base) Height (CH, CBH), Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass models. To predict plot-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching. Predictions at >1.6 M GEDI points and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution. The agreement with test data (from the same population) was strong for plot-level predictions and moderate for map predictions. A validation with independent estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH) rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs. The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable. Data: This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Univer