4D-FUELS: a LiDAR-to-satellite system for mapping and monitoring forest structure and fuels

Background Geospatial data characterizing forest structure and fuels are needed for planning fuels treatments and monitoring their effects on potential fire behavior and wildlife habitat, yet up-to-date, dynamic, and validated datasets remain scarce. We developed 4D-FUELS (4D Forest strUcturE from LiDAR and Sentinel), a transparent workflow that extracts LiDAR-based training attributes using R statistical software and maps fourteen forest attributes across time at a regional scale from Sentinel-1 and Sentinel-2 imagery in Google Earth Engine at 10-m resolution. We demonstrated the utility of the workflow with a case study covering 4.16 million hectares and multiple forest types in the southwestern US. 2017 LiDAR campaigns were used for training and spatial validation, and a 2019 campaign was used for an independent temporal validation to quantify map accuracy when using 4D-FUELS for map updating. We applied 4D-FUELS to map forest structure before and after a wildfire as an example application. Results The accuracy of forest structure and fuels maps produced by 4D-FUELS was highest for upper-canopy metrics (tree height and canopy cover), intermediate for canopy base height, mean tree diameter, and volume, and lowest for tree density and lower-strata attributes (sub-canopy and surface cover). Spatial and temporal validations suggested that applying models to ecologically similar locations or years not included in model training resulted in only slight increases in map error. Application of the workflow before and after a wildfire revealed coherent, fire-caused declines in tree density, canopy cover, and quadratic mean diameter. Conclusions The 4D-FUELS workflow enables forest structure mapping beyond where and when direct field or airborne lidar-derived measurements exist. 4D-FUELS produces forest structure maps that are similar in accuracy to other satellite-based forest structure mapping datasets in the western US that provide validation statistics, and the 4D-FUELS workflow supports reconstructing historical time series and updating mapping products as new imagery is acquired. Maps of forest structure produced by 4D-FUELS could be used to plan or justify treatment locations, analyze effects of pre-fire forest structure on subsequent fire severity, or identify forest structural features that are important for wildlife habitat. With further, application-specific validations, 4D-FUELS maps of forest structure could be applied to quantify treatment- or wildfire-caused changes in forest structure and monitor treatment effects. We provide the full validation statistics and the Google Earth Engine code to produce forest structure maps for any target year or set of years since 2017 to support use by the wildland fire ecology research and management community. 4D-FUELS provides a practical, repeatable route from airborne lidar point clouds to satellite-based, application-ready maps of fuel vertical continuity, horizontal continuity, and loads across regions and years, with attribute-specific validation metrics to guide interpretation and use.