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

Multi-criteria fuzzy-based approach for mapping burned areas in southern Italy with ASTER imagery 123
synthetic score by applying a weighted average (WA) operator with weights
derived from results of separability analysis. The WA map is used to extract
burn seeds (WA>0.7 and cluster size >0.5 ha) and the final burned areas
map is obtained by applying a region growing algorithm (WA_RG). All the
maps produced are then filtered with a 3x3 median filter and only polygons
greater that 1 ha are retained as fire affected areas. Validation was performed
on the third data set. WA_RG maps were compared to polygons identified
from visual interpretation to derive the error matrix and the accuracy
measurements (Congalton, 1991).
3 - Results and discussion
Separability analysis showed that NBR is the index that better separates the
burn class from other surfaces and consequently presents the highest weight
(21%) in the final score (WA). CSI (19%) and SAVI (17%) resulted also
important while the lower weight was assigned to MIRBI (13%). Finally BAI
and NIR complete the weight vector with about 15% of importance each.
NBR BAI NIR CSI SAVI MIRBI
Vegetation 1.99 1.63 1.55 1.74 1.87 1.38
Shadow 1.74 0.53 0.42 1.67 1.19 0.20
Soil 1.10 1.42 1.50 1.01 0.92 1.54
AVG 1.61 1.20 1.16 1.47 1.33 1.04
Weight 21% 15% 15% 19% 17% 13%
Table 2 - Single classes and average (AVG) separability score for each SI. Relative importance
and weights to be used in the WA operator were derived from average values.
The parameters of the sigmoid fuzzy functions interpolating the SI histograms
are reported in table. The functions have been constrained to f=0
for values above or below which pixels are not considered burned (see
Stroppiana et al., 2009).
Sigmoid function: * SI - µ −1 ** SI - µ −1
f = 1 + exp [( ---------------)] f = 1 + exp [( ---------------)]
NBR* BAI** NIR* CSI* SAVI* MIRBI**
µµ 0.20 63.90 0.20 1.34 0.17 1.49
σσ 0.05 7.62 0.00 0.13 0.01 0.05
threshold ≤ -0.3 - ≤ 0.1 ≤ 0.55 ≤ 0.05 ≥ 2.0
σ σ
Table 3 - Parameters of the membership functions derived by interpolation of training data.