Generation and Mapping of Fuel Types for Fire Risk Assessment

Fuel mapping is key to fire propagation risk assessment and regeneration potential. Previous studies have mapped fuel types using remote sensing data, mainly at local-regional scales, while at smaller scales fuel mapping has been based on general-purpose global databases. This work aims to develop a methodology for producing fuel maps across European regions to improve wildland fire risk assessment. A methodology to map fuel types on a regional-continental scale is proposed, based on Sentinel-3 images, horizontal vegetation continuity, biogeographic regions, and biomass data. A vegetation map for the Iberian Peninsula and the Balearic Islands was generated with 85% overall accuracy (category errors between 3% and 28%). Two fuel maps were generated: (1) with 45 customized fuel types, and (2) with 19 fuel types adapted to the Fire Behaviour Fuel Types (FBFT) system. The mean biomass values of the final parameterized fuels show similarities with other fuel products, but the biomass values do not present a strong correlation with them (maximum Spearman’s rank correlation: 0.45) because of the divergences in the existing products in terms of considering the forest overstory biomass or not.

Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

Mapping fuels at multiple scales: landscape application of the Fuel Characteristic Classification SystemThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.

Fuel mapping is a complex and often multidisciplinary process, involving remote sensing, ground-based validation, statistical modelling, and knowledge-based systems. The scale and resolution of fuel mapping depend both on objectives and availability of spatial data layers. We demonstrate use of the Fuel Characteristic Classification System (FCCS) for fuel mapping at two scales and resolutions: the conterminous USA (CONUS) at 1 km resolution and the Wenatchee National Forest, in Washington State, at 25 m resolution. We focus on the classification phase of mapping — assigning a unique fuelbed to each mapped cell in a spatial data layer. Using a rule-based method, we mapped 112 fuelbeds onto 7.8 million 1 km cells in the CONUS, and mapped 34 fuelbeds onto 18 million 25 m cells in the Wenatchee National Forest. These latter 34 fuelbeds will be further subdivided based on quantitative spatial data layers representing stand structure and disturbance history. The FCCS maps can be used for both modelling and management at commensurate scales. Dynamic fuel mapping is necessary as we move into the future with rapid climatic and land-use change, and possibly increasing disturbance extent and severity. The rule-based methods described here are well suited for updating with new spatial data, to keep local, regional, and continental scale fuel assessments current and inform both research and management.