Remotely sensed characterization of forest fuel types by using satellite ASTER data |
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| Affiliation: | 1. CSIRO Water for a Healthy Country Flagship, CSIRO Land and Water, Canberra ACT 2601, Australia;2. Department of Geography and Center for Natural and Technological Hazards, University of Utah, 270 S Central Campus Dr, Rm 270, Salt Lake City, UT 84123, USA;3. Department of Geography and Geology, University of Alcalá, Calle Colegios 2, 28801 Alcalá de Henares, Spain;4. Instituto de Economía, Geografía y Demografía (IEGD), Centro de Ciencias Humanas y Sociales (CCHS), Consejo Superior de Investigaciones Científicas (CSIC), Albasanz 26-28, 28037 Madrid, Spain;5. Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, 250-N, The Barn, One Shields Avenue, Davis, CA 95616-8617, United States;6. Centre for Environmental Risk Management of Bushfires, University of Wollongong, Australia;7. USDA-ARS Hydrology and Remote Sensing Laboratory, Bldg 007 BARC-West, 10300 Baltimore Avenue, Beltsville, MD 20705, USA;8. Ecosystems and Environment Research Centre, School of Environment and Life Sciences, University of Salford, Salford M5 4WT, UK;1. Fenner School of Environment and Society, The Australian National University, Acton, ACT, Australia;2. Bushfire and Natural Hazards Cooperative Research Centre, Melbourne, Australia;3. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, China;4. Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, 139 Veihmeyer Hall, One Shields Avenue, Davis, CA 95616, USA;5. Instituto de Economía, Geografía y Demografía (IEGD), Centro de Ciencias Humanas y Sociales (CCHS), Consejo Superior de Investigaciones Científicas (CSIC), Albasanz 26-28, 28037 Madrid, Spain;6. National Computational Infrastructure, The Australian National University, Acton, ACT, Australia;1. Geo-Environmental Cartography and Remote Sensing Group (CGAT), Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain;2. Integrated Remote Sensing Studio (IRSS), Department of Forest Resources Management, Forest Science Centre, 2424 Main Mall, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;1. Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia;2. Centre for Environmental Risk Management of Bushfires, Centre for Sustainable Ecosystem Solutions, University of Wollongong, Wollongong, NSW, Australia;3. Department of Earth System Science, University of California, Irvine, CA 92697, USA |
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| Abstract: | The characterization of fuel types is very important for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up. The advent of 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. 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 ASTER-based results with ground-truth. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%. |
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| Keywords: | Fire Fuel type ASTER data |
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