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

FUEL MOISTURE CONTENT ESTIMATION BASED ON HYPERSPECTRAL DATA
FOR FIRE RISK ASSESSMENT
T.A. Almoustafa, R.P. Armitage & F.M. Danson
Centre for Environmental Systems Research, School of Environment and Life Sciences,
University of Salford, Salford, UK
T.A.Almoustafa@pgr.salford.ac.uk
Abstract: This study aims to investigate whether the relationships between
fuel moisture content (FMC) and vegetation spectral reflectance identified
in previous studies, can be used to improve fire risk assessment for fireprone
upland vegetation in the UK. Two hyperspectral images from the AISA
Eagle and Hawk sensors were collected over a UK test site in 2008. In-situ
field spectra were collected concurrently with the flights, together with
ground data on live FMC and other relevant variables. The results show that
FMC varies temporally and with vegetation type. Reflectance variability
with FMC is expressed most strongly in the SWIR and NIR. The sensitivity
of SWIR and NIR reflectance to FMC variations is much greater when considering
each vegetation type individually. The results also compare first
derivative and broad band vegetation indices (VI) with FMC.
1 - Introduction
Live fuel moisture content (FMC) is controlled by the interaction of plant
physiology and soil moisture conditions, and is spatially and temporally
highly variable (Chuvieco et al., 2002). Empirical and model-based studies
have examined relationships between broad band spectral reflectance and
vegetation FMC, but few studies have used hyperspectral data. This technology
provides fine spectral resolution that facilitates vegetation biophysical
and biochemical information mapping. In addition, there is a lack
of research on the relationship between spectral reflectance and FMC in
temperate environments, in particular upland areas. This research aims to
test the use of airborne hyperspectral data for mapping FMC in semi-natural
upland areas.
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