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

Burned areas mapping by multispectral imagery: a case study in Sicily, summer 2000 281
(-0,15 < NBR < 0.09) & (30 < BAI < 150) & (-650 < MIRBI < -150)
to decide whether a pixel is burned or not.
For the multi-temporal approach the first step was a geometric co-registration
which made it possible to compute temporal differences in indices for
every pixel; for this purpose, 40 Ground Control Points present on both
images were selected. The following step was a radiometric normalization
by an empirical method, with the aim to minimise differences between
reflectance values of invariant pixels and consequently obtain index values
as alike as possible for objects having analogous spectral characteristics in
both images.
The values of BAI and NBR indices were derived for pre- and post- fire
images and, thanks to the pre-processing operations applied, the differences
dBAI and dNBR were computed, and then the change detection
(IndexPost-IndexPre). The empiric threshold values to be applied were chosen
following the same procedure used in the previous approach, i.e. applying
an AND logic operation between the two classifications we had
obtained: a pixel belonged to the burned class if it satisfied
(dBAI > 250) & (-2.0 < dNBR < -0.55)
The last stage was a post-classification: firstly sieving and clumping filters
were applied - which respectively permit to remove isolated points and
aggregate contiguous polygons - and then the results of the 6 binary classifications
were converted in vector form.
3 - Results and discussion
Classification algorithms using the thresholding of single indexes are certainly
very quick; they don’t need elaborate procedures but they show heavy
confusion errors due to surfaces having almost the same spectral
reflectance as burned areas in the bands used. In the case study in fact BAI
results included lakes and other water bodies in the burned class; NBR
reduced this kind of misidentification only to coastal areas, but showed
also relevant errors with both urban and rural areas. Finally, MIRBI presented
commission errors with both urban areas and water bodies but was
more accurate with rural areas. This situation is shown in Figure 3 for a portion
of the area.
Multiple thresholding with fixed values significantly reduced the errors met
in previous methods and permitted an accurate mapping of wildfires with
just a slightly greater operative complexity. However, when dealing with old
or greatly heterogeneous fires, spectral sensitivity may range in value
between burned and intact areas - thus leading to contradictory results.