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

SOME NOTES ON SPECTRAL PROPERTIES OF BURNT SURFACES
AT SUB-PIXEL LEVEL USING MULTI-SOURCE SATELLITE DATA
Abstract: The aim of our study is to explore the spectral properties of
burned surfaces at sub-pixel level using multi-source resolution satellite
data. For that purpose, a study case was established in one very destructive
wildfire occurred in Parnitha, Greece on July 2007. Satellite data at
multiple spectral and spatial resolutions were acquired shortly after the fire
from MODIS, LANDSAT, ASTER, and IKONOS satellite sensors. The very high
resolution imagery of IKONOS satellite was served as the basis for extracting
the percent of cover of burnt areas, bare land and vegetation by applying
the maximum likelihood classification algorithm. Furthermore, vegetation
indices of the post-fire satellite images were computed. Then the percent
of cover for each type was correlated to surface reflectance values and
vegetation indices for all satellite images, and linear regression models
were fitted to characterize those relationships.
1 - Introduction
N. Koutsias & M. Pleniou
Department of Environmental and Natural Resources Management,
University of Ioannina, G. Seferi 2, Agrinio, Greece,
nkoutsia@cc.uoi.gr; mpleniou@cc.uoi.gr
The application of space-borne sensor data for forest fire mapping is an old
and currently an active research topic in remote sensing studies. Many characteristic
examples of satellite remote sensing studies on burnt land mapping
and monitoring can be found in the literature, while some of them
focus on spectral properties and mapping of burned surfaces at subpixel
level. It is well justified that there are spectral similarities between burnt
surfaces and other land cover categories, such as water bodies, urban areas,
shadows, and mixed land-water and water-vegetation pixels, that result in
spectral confusion. Some of the problems in discriminating and mapping
the burned surfaces are related to non-homogenous pixels of different fractions
of land cover types (Chuvieco and Congalton, 1988; Koutsias and
Karteris, 2000; Koutsias, Mallinis et al., 2009).
243