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

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IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT
Among the indexes used in this test for Gran Canaria Island the SVI is the
one that yields better results in the application to ASTER data categorization
of pixels affected by a fire, this index presents minor errors of commission
for classified burnt pixels, and the NDVI is the one that presents
minor errors of omission.
INDEX
ACCURACY
KAPPA
COEFFICIENT
SAMPLING
ERROR
CONFIDENCE
INTERVAL (F)
RGB 92,05% 0,8385 26,10% 92,0 ± 0,5117
NDVI 86,39% 0,7180 33,10% 86,4 ± 0,6486
ASTER SAVI 85,59% 0,7014 33,88% 85,6 ± 0,6641
SVI 86,50% 0,7213 32,98% 86,5 ± 0,6463
TVI 86,41% 0,7188 33,99% 85,5 ± 0,6663
RGB 95,72% 0,8813 43,19% 95,7 ± 0,8466
MODIS NBR 77,48% 0,4861 164,59% 77,5 ± 3,2259
BAIM 77,59% 0,5257 159,53% 77,6 ± 3,1268
NDVI SAVI SVI TVI
ASTER Comis. Omis. Comis. Omis Comis. Omis. Comis. Omis.
Burnt 16,14 4,76 16,08 4,65 17,01 5,41 15,98 4,82
Volcanic 4,6 9,7 5,12 16,94 4,66 9,76 4,65 9,68
Unburned 8,65 27,16 10,05 27,51 9,64 25,75 8,84 26,87
NBR BAIM
MODIS Comis. Omis. Comis. Omis
Burnt 0 27,41 0 28,92
Unburned 55,77 0 49,84 0
Table 1 - Results of the SVM algorithm application to Gran Canaria ASTER and MODIS images.
Also, results of sampling error, comission and omission errors and confidence interval.
In relation to the MODIS sensor data study the NBR presents worse percentages
of accuracy (Table 1) compared to the BAIM values. Nevertheless
commission and omission errors for these indexes are too high comparing
to the ASTER ones, probably as an effect of the different spatial resolution
for these two types of satellite data. The sampling errors and confidence
intervals are worse for MODIS indexes than those obtained for ASTER, probably
because of the vegetation composition inside the fire perimeter and
the orographic influence in an insular environment in which the spatial resolution
of 250 and 500 m. of MODIS channels may be not enough.