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Fuel Map

 

The Seasonal Fuel Map product is  computed on the basis of high resolution EO data and object-oriented classification methods. For the current project , RapidEye data (part of GMES data) have been selected to train the classification because of its radiometric quality and spatial resolution (5 bands, 5m-spatial resolution).
The foreseen updating frequency will be twice a year (peak and valley of the fire season); nevertheless, the updating will discussed and agreed on with the users. The target scale for the derived fuel map will be, at least, 1:25.000. The accuracy of this map naturally depends on the accuracy input data available (vegetation maps, fuel ground truth etc.). The seasonal fuel map will be used as input in the daily and the seasonal hazard products. Finally, based on the obtained results, the procedure to jump from the high resolution scale fuel map to a medium resolution fuel map will be defined. The map will be operationally computed operationally over an entire region, and not only in the studied area. The methodological approach involves:
STEP 1: Input Data Collection: DEM, aerial ortho-photography, EO imagery (RapidEye), actual vegetation map and forest map. In addition and if available cadastral data (like SIGPAC data in Spain) is used. Moreover random sampled vegetation (fuel) data (when available) is extremely useful to derived the most realistic fuel map.
STEP 2: Image Orthorectification 1B level. Rapideye imagery will be taken to 3A level imagery by using the RPC provided with the satellite image. DEM and ortho-photo are needed to accomplish this step. The highest resolution sources will be used. E.g. for the Spanish study area a 10m-resolution DEM and 0.5 m resolution ortho-photography are available.
STEP 3: Land Cover Classification. Needed auxiliary data: land cover resolution and cadastral data, if available. For the particular case of Andalusia (Spanish study area), 1:10.000 land cover data compliant with European standards (INSPIRE, OGC) is available (SIOSE Project: http://www.siose.es/siose/)..
STEP 4: Fuel Type Classification; which exploits an object oriented approach. Depending on the users’ requirements we will decide upon which fuel model standard classification the radiometric classes will be assimilated to. Usual fuel classifications: used for this purpose are the Prometeus classification (like in Arroyo et al, Lasaponara el et al) and the NFDRS classification (Deeming and Brown 1975).
Interrelationships between Rapideye fuel objects and MODIS radiometric data will be used to develop an up-scaling methodology that will enable the computation of a regional fuel map of lesser accuracy but more operational, cheap and, thus, likely to be derived on a periodic basis when PREFER project will be over. This procedure will be tested in the Andalusia test area.

 

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