Vegetation Fuel Mapping at Regional Scale Using Sentinel-1, Sentinel-2, and DEM Derivatives—The Case of the Region of East Macedonia and Thrace, Greece

Abstract

The sustainability of Mediterranean ecosystems, even if previously shaped by fire, is threatened by the diverse changes observed in the wildfire regime, in addition to the threat to human security and infrastructure losses. During the two previous years, destructive, extreme wildfire events have taken place in southern Europe, raising once again the demand for effective fire management based on updated and reliable information. Fuel-type mapping is a critical input needed for fire behavior modeling and fire management. This work aims to employ and evaluate multi-source earth observation data for accurate fuel type mapping in a regional context in north-eastern Greece. Three random forest classification models were developed based on Sentinel-2 spectral indices, topographic variables, and Sentinel-1 backscattering information. The explicit contribution of each dataset for fuel type mapping was explored using variable importance measures. The synergistic use of passive and active Sentinel data, along with topographic variables, slightly increased the fuel type classification accuracy (OA = 92.76%) compared to the Sentinel-2 spectral (OA = 81.39%) and spectral-topographic (OA = 91.92%) models. The proposed data fusion approach is, therefore, an alternative that should be considered for fuel type classification in a regional context, especially over diverse and heterogeneous landscapes.

Keywords

• fuel mapping
• SAR
• optical data
• remote sensing
• variable importance
• random forest