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26 October 2007 Mapping forest fuel types by using satellite ASTER data and neural nets
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A reliable mapping of fuel types is very important for computing fire hazard and risk and simulating fire growth and intensity across a landscape. Due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up especially for large areas. The advent of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas 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 ASTER data, were used as ground-truth dataset to assess the results obtained for the considered test area. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types with a classification accuracy higher than 78%.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rosa Coluzzi, Immacolata di Donna, Antonio Lanorte, and Rosa Lasaponara "Mapping forest fuel types by using satellite ASTER data and neural nets", Proc. SPIE 6742, Remote Sensing for Agriculture, Ecosystems, and Hydrology IX, 67420T (26 October 2007);

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