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

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IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT
high correlation (R 2 =0.845) between observed and simulated GeoCBI and
an almost 1:1 linear fitting (slope ~1 and constant ~0) as well as a very
low dispersion of the values. The total RMSE was also very low (RMSE=
0.14). In addition, the accuracy was quite homogeneous throughout the
range of GeoCBI values observed in the field (1.68 to 3.00). For the validation
of BE estimation, the results of the linear fitting between dNDVI and
both BEref and BEadj (table 3) showed that the correlation coefficients
were considerably higher in the case of BEadj, ranging from 0.45 (shrubs)
to 0.72 (hardwoods). In the case of BE, the correlation was practically inexistent
(table 3) for all vegetation types.
4 - Discussion and conclusions
The simulation model selected for this study had already been successfully
applied in other Mediterranean fires in Spain and Portugal (De Santis et al.,
2009). Validation results showed that the accuracy of the model inversion
was considerably homogeneous in all ranges of GeoCBI values, and the
RMSE = 0.14 was, in fact, slightly lower than the ones obtained in Spain
and Portugal (RMSE = 0.18-0.21). In addition, the model was tested over a
wider range of GeoCBI values. The results confirmed the consistency of the
model, which was independent of specific site characteristics, at least within
Mediterranean/temperate environments. The BE validation confirmed
that the use of GeoCBI as an adjustment factor improved BE estimation
considerably for all vegetation types analyzed. From a broader perspective,
this study must be regarded as a first attempt to improve BE estimation.
The fundamental improvements shown in BE estimation still lies on the
high accuracy of BS estimation. Therefore, there is room for improvement,
especially concerning alternative ways of optimizing the use of BS to adjust
BE estimation.
References
Barbosa, P.M., Cardoso Pereira, J.M., and Gregoire, J.-M., 1998.
Compositing criteria for burned area assessment using multitemporal
low resolution satellite data. Remote Sens. Environ. 65, 38-49.
Crutzen, P. and Andreae, M., 1990. Biomass burning in the tropics: Impact
on atmospheric chemistry and biogeochemical cycles, Science, 250,
1669-1678.
De Santis, A., Chuvieco, E. & Vaughan, P.J., 2009. Short-term assessment
of burn severity using the inversion of PROSPECT and GeoSail models.
Remote Sensing of Environment, 113 (1), 126-136.
De Santis, A. & Chuvieco, E., 2009. GeoCBI: a modified version of the
Composite Burn Index for the initial assessment of the short-term burn
severity from remotely sensed data. Remote Sensing of Environment, 113