On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape

Fuel types are one of the most important factors that should be taken into consideration for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is extremely difficult because the description of fuel properties is very complex. In the present study, forest fuel mapping is considered from a remote sensing perspective by the assessment and mapping of general vegetation complexes. The purpose is to delineate forest types by exploring the use of remote sensing QuickBird data. In order to ascertain how well QuickBird data can provide an exhaustive classification of fuel properties, a sample area characterized by mixed vegetation covers and complex topography data was analysed. The selected sample area has an extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing QuickBird data, were used as ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance evaluation based on the comparison of QuickBird-based results with ground-truth data. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 75%.