Paper
17 March 2003 Fire risk mapping using satellite imagery and ancillary data: towards operationality
Hugo Carrao, Sergio Freire, Mario R. Caetano
Author Affiliations +
Abstract
Forest fires affect significant areas of the Portuguese forest annually, depending upon seasonal moisture and temperature conditions. Although a large percentage of those fires are not originated by natural causes, there is still a need to develop an effective and timely warning system for fire-prevention. The combination of Earth observation (EO) information with ancillary data of natural parameters for daily monitoring of fire risk, which is allowed by Geographic Information Systems (GIS), offers an appropriate response to that need. The PREMFIRE project was funded by the European Spatial Agency (ESA) for the selection and implementation of the most adequate method for production of fire risk maps for Portugal. The project aims at building a wireless system, enabling its use by fire prevention services in the field with real-time or near real-time exchange of data. We present a methodology to produce fire risk maps using satellite imagery and ancillary data. The approach combines a detailed land cover map and other spatial data sets with vegetation greenness maps and meteorological information to produce a fire potential index map. The vegetation greenness is characterized using NDVI 10-day composites derived from NOOA AVHRR imagery. This methodology is being tested in central Portugal yielding encouraging results.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hugo Carrao, Sergio Freire, and Mario R. Caetano "Fire risk mapping using satellite imagery and ancillary data: towards operationality", Proc. SPIE 4879, Remote Sensing for Agriculture, Ecosystems, and Hydrology IV, (17 March 2003); https://doi.org/10.1117/12.462431
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Cited by 5 scholarly publications.
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KEYWORDS
Vegetation

Earth observing sensors

Satellites

Meteorology

Satellite imaging

Geographic information systems

Humidity

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