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

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IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT
Chuvieco et al. (2008) LAC burned area record resampled to 5 km using
nearest neighbor to match LTDR. The year 1989 was used for training due
to the largest burned area (~12,000 km 2 ). A total of 895 unburned pixels
were selected in between those pixels that had a Global Environment
Monitoring Index (GEMI) (Pinty and Verstraete, 1992) > 0.36. Out of 814
burned pixels, we selected for training 298 under boreal forest vegetation
type, with the following relaxed fix thresholds on reflectance channel 2
(0.83 µm, r 2 ) < 0.125, brightness temperature (~3.75 µm, T 3 ) > 300 K and
Vi3T (Barbosa et al., 1999) < -0.45.
We considered from April to September, when wildfires are likely to occur,
the year before, during and after the fire. Median, mean, maximum (Max)
and minimum (Min) were computed for: reflectance channel 1 (0.63 µm,
r 1 ), r 2 and T 3 and several spectral indexes: Vi3T, GEMI and a new Burned
Boreal Forest Index (BBFI) (1/ρ 2 + T 3 /2).
Bayes Net (naive Bayes) classifier in the machine learning package WEKA
(http://www.cs.waikato.ac.nz/~ml/index.html) was applied to determine
the probability of a pixel to be burned and the probability to be unburned.
In the Bayesian Network modeling a gain ranking filter determined which
variables had the highest significance. The following ones, ranked in order
of significance, were selected: Median_BBFI__after, Max_BBFI__after,
Median_GEMI_after, Max_BBFI_during, Min_Vi3t_after, Min_Vi3t_during,
Min_ρ 2 _after, Min_ρ 2 _during, Max_T 3 _after.
After the training process, the classifier outputs probability density functions
for the selected variables for both classes, burned and unburned.
Following Bayes´s theorem (Bayes 1763), the joint probability density function
for a given class was written as a product of the individual density
functions.
Before running each pixel through the Bayesian Network model, we applied
the following relaxed thresholds to avoid false burned detections:
(Max_T 3 _during > 300) and (Min_ρ 2 _during < 0.1) and (Max_BBFI_during
> 160) and (Max_BBFI_during
Median_BBFI_before) and (Median_BBFI_after >= 158) and
(Median_GEMI_after < Median_GEMI_before) and (Median_GEMI_before >=
0.36).
The Bayesian model provided the probability of a pixel to be burned and
the probability to be unburned. One option was assigning the class to each
pixel with higher probability, but in order to handle data uncertainty and
avoid false detections, we computed the normalized probability ([0, 1]) for
each class (burned/unburned) and calculated the difference between both,
resulting a value in the range [-1, 1]. Pixels with final probabilities lower
than 0 were flagged as unburned. Based on the training dataset, we used
a classification tree J48 in WEKA to determine that a pixel should always
be flagged burned, if the final probability was >0.997. Pixels within the
range [0, 0.997] were flagged as potentially burned and assigned to burned
or unburned based on the neighboring pixels. The results were finally
smoothed by comparing it to the 4 neighbors to avoid false burned detec-