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7th Workshop on Forest Fire Management - EARSeL, European ...

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244<br />

IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT<br />

2 - Materials and methods<br />

2.1 - Study area and satellite data<br />

In the summer of 2007, the year with most destructive fires in recent history<br />

of Greece, a large fire occurred in Parnitha, Greece. This was the study<br />

area for discussing the spectral properties of burnt surfaces at sub-pixel<br />

level using multi-source satellite data. For this purpose, a dataset c<strong>on</strong>sisting<br />

by an IKONOS, ASTER, LANDSAT and MODIS imagery was established<br />

after the fire and c<strong>on</strong>stituted the basic source of informati<strong>on</strong>. The spatial<br />

resoluti<strong>on</strong> ranges from 1 to 1000 meters, while the spectral resoluti<strong>on</strong> of<br />

the sensors covers the visible, near and mid-infrared, as well as the thermal-infrared<br />

part of the electromagnetic spectrum.<br />

a b<br />

c d<br />

Figure 1 - A dataset c<strong>on</strong>sisting by an IKONOS (a), ASTER (b), LANDSAT (c) and MODIS (d)<br />

imagery was established after the fire and c<strong>on</strong>stituted the basic source of informati<strong>on</strong>.<br />

2.2 - Methods<br />

Classical image processing algorithms were applied to correct geometrically,<br />

radiometrically and atmospherically the available satellite images. The<br />

digital radiometric numbers were c<strong>on</strong>verted first to radiance at sensor and<br />

then to surface reflectance to achieve the necessary compatibility for comparing<br />

them. The satellite data were orthorectified using a digital elevati<strong>on</strong><br />

model created by the 20 meters c<strong>on</strong>tour lines extracted from maps of<br />

1:5000.<br />

The very high resoluti<strong>on</strong> imagery of IKONOS satellite was served as the basis<br />

for extracting the percent of cover of burnt areas, bare land and vegetati<strong>on</strong><br />

by applying the maximum likelihood classificati<strong>on</strong> algorithm. Furthermore,

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