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

102
I - PRE-FIRE PLANNING AND MANAGEMENT
The analysis has been performed for some test areas in the south of Italy.
Using two different approaches: (I) classification performed using a single
date images, and (ii) classification performed using multidate images.
In the case of single date data processing, we observed that as a whole,
SMA results obtained from Quickbird (overall accuracy 77%) and Aster
(overall accuracy 82%) did not show any significant improvements, whereas
results from TM and MODIS have shown that the use of unmixing technique
allows us to improve at around 7% and 12% the accuracy level for
TM (k coefficient from 57% to 64%) and MODIS (k coefficient from 67% to
79%) respectively compared to the MLC and KNN. These results confirmed
the effectiveness of SMA in handling spectral mixture problems, especially
in fragmented ecosystems as those considered for our analysis.
Results from KNN showed improvements around 3% and 5% for Quickbird
and Aster, whereas no significant improvements have been observed for TM
and MODIS data set.
In the case of multidate processing, improvements around 4% were
obtained for the different data set we considered.
2 - Final remarks
In this paper, imagery from Quickbird, Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER), Landsat Temathic Mapper, and
MODIS, have been evaluated in terms of accuracy and utility for mapping
fuel type and load. Different supervised classification techniques were compared.
Results showed that each data set processed using different classification
provided satisfactory accuracy levels. In particular, KNN well performed
for Quickbird (81%) and Aster (87%), whereas SMA provided the
best results for TM (64%) and MODIS (79%). Additional improvement can
be achieved using data fusion approach to merge the spatial and spectral
characteristics of different satellite data set.
References
Lasaponara R., and Lanorte A., 2007. On the capability of satellite VHR
QuickBird data for fuel type characterization in fragmented landscape.
Ecological Modelling (ECOMOD845R1).