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

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IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT
We started with a resampling, which, by means of a bilinear interpolation,
transformed the six bands of SWIR subsystem to the same resolution of
VNIR bands (obtaining a file with nine bands all with a 15 m spatial resolution).
The following step was the choice of the most suitable RGB combination
for photo-interpretation (Chirici & Corona, 2005; Epting et al.,
2005; Grégoire & Brivio, 2001; Lentile et al., 2006). After a preliminary
analysis we decided to make use of 8-3-2 (SWIR-NIR-R) bands and a simulated
Natural Colour Composite representation. Whilst the latter simulates
a sham band corresponding to the blue region by means of a weighted
average of green and red values - which permits to remove all doubts for
surfaces with a response similar to the one connected with burned areas in
the infrared region - the former is more sensitive to fire effects.
The first step in the mono-temporal approach consisted in computing index
values in post-fire image pixels by means of band-math functions in ENVI.
Reference formulas are:
1000000 b2 + b3
BAI = ---------------------------------- NBR = --------------- MIRBI = 10·b3-9,8·b4+2,0
(100-b1) 2 + (60-b2) 2 b2-b3
where b1=band 2 (red), b2=band 3 (NIR) b3=band 8 (SWIR) and b4=band
4 (SWIR).
This procedure allowed us to carry out three different pixel-based binary
classifications thanks to independent thresholding of every single computed
index: for BAI and NBR, predefined threshold values were chosen
(Zaffaroni et al., 2007); instead, for MIRBI we computed frequency histograms
both for the whole image and for assessed wildfires and we chose
threshold values so that they could contain almost every actually burned
pixel.
Therefore we assigned a pixel to the burned class when it satisfied the following
conditions:
DN > 40 for BAI index;
-0,3 < DN < 0.1 for NBR index;
-650 < DN < 0 for MIRBI index.
In order to solve the confusion errors peculiar to each index an AND logic
operation of the three resulting classifications was applied: a pixel was
assigned to the class of fire-affected surfaces only if it was flagged as
potentially burned by all the three algorithms, therefore if it satisfied the
condition:
(-0,3 < NBR < 0.1) & (BAI > 40) & (-650 < MIRBI < 0)
A further attempt to optimise the single-image approach consisted in modifying
the threshold values used in this algorithm on the basis of the statistical
index distribution for assessed wildfires.
We slightly modified values to better fit BAI, NBR and MIRBI ranges to the
histograms of assessed burned areas. This choice allowed us to obtain the
condition