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

IMPROVEMENT OF DNBR BURNT AREAS DETECTION PROCEDURE
BY PHYSICAL CONSIDERATIONS BASED ON NDVI INDEX
R. Carlà & L. Santurri
National Research Council - Institute of Applied Physics “N. Carrara” (IFAC-CNR),
Sesto Fiorentino (FI), Italy
satellit@ifac.cnr.it; l.santurri@ifac.cnr.it
L. Bonora & C. Conese
National Research Council - Institute of Biometerology (IBIMET-CNR),
Sesto Fiorentino (FI), Italy
l.bonora@ibimet.cnr.it; c.conese@ibimet.cnr.it
Abstract: The detection and mapping of burned areas have been long studied
and several algorithms have been developed, such as that used in the
dNBR (differential Normalized Burn Ratio) method. This work aims at evaluating
the performance of the dNBR as regards to both the positively
detected burnt areas and the false alarms, when applied in a Mediterranean
region. The dependence of the dNBR method efficiency from the adopted
threshold is assessed, and the performances in terms of omission and commission
errors are evaluated in mapping and detection tasks. A new
enhanced version of the dNBR method based on the introduction of four
simple environmental conditional tests is then presented and the related
results are comparatively reported.
1 - Introduction
The Tuscany is a typical italian mediterranean region, affected each year by
many fires of small dimensions. Several experimental tests reported in the
scientific literature demonstrate the usefulness of satellite data for the
observation of vegetated areas affected by fire (Chuvieco & Congalton,
1988; Koutsis and Karteris, 1998; Pereira and Setzer, 1993; Epting et al.,
2005), but up to now very little attention has been paid to the recognition
and analysis over large territories of areas affected by small fires of very
few hectares (Martin et al., 2006).
For the most part, the algorithms aimed at detecting burnt areas from satellite
data are based on multitemporal analysis. Among the others, the
Normalized Burnt Ratio (NBR) index and its differential form (dNBR), that
is the NBR index temporally differenced between before and after the fire
season, have been widely tested on vast territories and have demonstrated
their efficiency on large fire events (Key et al., 1999). In order to obtain a
binary map of burnt / not burnt pixels by the dNBR method, a spatially
invariant or locally adaptive threshold needs to be defined. This work aims
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