Keywords
fuel type mapping, satellite, airborne, sub-pixel classification
Start Date
1-7-2006 12:00 AM
Abstract
In the context of fire management, fuel maps are essential information requested at many spatial and temporal scales for managing wildland fire hazard and risk and for understanding ecological relationships between wildland fire and landscape structure. Remote sensing data provide valuable information for the characterization and mapping of fuel types and vegetation properties at different temporal and spatial scales from global, regional to landscape level.Fuel types is 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. In the present study, forest fuel mapping is considered from a remote sensing perspective. The purpose is to delineate forest types by exploring the use of remote sensing data. For this purpose, multisensor and multiscale remote sensing data such as, Landsat-TM and Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were analyzed for a test area of southern Italy that is characterized by mixed vegetation covers and complex topography. Fieldwork fuel types recognitions, performed at the same time as remote sensing data acquisitions, were used as ground-truth dataset to assess the results obtained for the considered test areas.Two different approaches have been adopted for fuel type mapping: the well-established classification techniques performed at the pixel level and spectral mixture analysis performed at the subpixel level.Results from our investigations showed that remote sensing data can provide valuable information for the characterization and mapping of fuel types and vegetation properties at different temporal and spatial scales from global, regional to landscape level.
Multiscale fuel type characterization by using multisensor remote sensing data for the Mediterranean ecosystems of Southern Italy
In the context of fire management, fuel maps are essential information requested at many spatial and temporal scales for managing wildland fire hazard and risk and for understanding ecological relationships between wildland fire and landscape structure. Remote sensing data provide valuable information for the characterization and mapping of fuel types and vegetation properties at different temporal and spatial scales from global, regional to landscape level.Fuel types is 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. In the present study, forest fuel mapping is considered from a remote sensing perspective. The purpose is to delineate forest types by exploring the use of remote sensing data. For this purpose, multisensor and multiscale remote sensing data such as, Landsat-TM and Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were analyzed for a test area of southern Italy that is characterized by mixed vegetation covers and complex topography. Fieldwork fuel types recognitions, performed at the same time as remote sensing data acquisitions, were used as ground-truth dataset to assess the results obtained for the considered test areas.Two different approaches have been adopted for fuel type mapping: the well-established classification techniques performed at the pixel level and spectral mixture analysis performed at the subpixel level.Results from our investigations showed that remote sensing data can provide valuable information for the characterization and mapping of fuel types and vegetation properties at different temporal and spatial scales from global, regional to landscape level.
