The estimation of the danger of fire is a way to quantify
the potential or capacity of a fire to start, spread and cause damage. The
relationships between the Canadian Fire Weather Index (FWI) System components
and the monthly burned area as well as the number of active fire which has
taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua/TERRA
were investigated in 32 Guinean stations between 2003 and 2013. A statistical
analysis based on a multi-linear regression model was used to estimate the
skills of FWI components on the predictability of burned area and active fire.
The goal of the detection algorithm is to identify “fire pixels” that contain
one or more active burning fires at the time of the satellite overpass. This statistical analysis gave performances
explaining between 16 to 79% of the variance for the burned areas and between
29 and 82% of the variance for the number of fires (P<0.0001) at lag 0.
Respectively 16 to 79 % and 29 to 82 % of the variance of the burned areas and
variance for the number of fires (P<0.0001) at lag0 can be explained based
on the same statistical analysis. All the combinations used gave significant
performances to predict the burned areas and active fire on the monthly
timescale in all stations excepted Fria and Yomou where the predictability of
the burned areas was not obvious. We obtained a significant correlation between
the average over all of the stations of burned areas, active fires and FWI
composites with percentage of variance between (75 to 84% and 29 to 77%) for
active fires and burned areas at lag0 respectively. While for burned area peak
(January), the skill of the predictability remains significant only one month
in advance, for the active fires, the model remains skilful 1 to 3 months in
advance. The results show strong performances of the linear model in the sites
with a homogeneous distribution of the vegetation cover like the case of the
regions of Upper Guinea where one finds vast expanses of Savannah. Results also showed that active fires are
more related to fire behavior indices while the burned areas are related to the
fine fuel moisture codes. These outcomes have implications for seasonal
forecasting of active fire events and burned areas based on FWI components, as
significant predictability is found from 1 to 3 months and one month before
respectively. These results also open up perspectives on the possibility of
using forecasting models to project future and past events in order to better
understand the long-term effects of bush fire on ecosystems, biodiversity and
on the pollution of the environment.
Author(s) Details:
Mamadou Baïlo Barry,
Laboratoire De Physique De l'Atmosphère et De l'Océan Siméon Fongang
(LPAO-SF), Université Cheikh Anta Diop, BP: 5085, Dakar, Senegal, Laboratore De
Physique Institut Supérieur des Sciences de l'Education de Guinée (IS SEG), BP:
795, Conakry, Guinea and Université de Labé, Guinea.
Daouda
Badiane,
Laboratore
De Physique Institut Supérieur des Sciences de l'Education de Guinée (IS SEG),
BP: 795, Conakry, Guinea.
Saïdou Moustapha Sall,
Laboratore De Physique Institut Supérieur des Sciences de
l'Education de Guinée (IS SEG), BP: 795, Conakry, Guinea.
Moussa Diakhaté,
Laboratore De Physique Institut Supérieur des Sciences de
l'Education de Guinée (IS SEG), BP: 795, Conakry, Guinea.
Habib Senghor,
Laboratore
De Physique Institut Supérieur des Sciences de l'Education de Guinée (IS SEG),
BP: 795, Conakry, Guinea.
Please see the link here: https://stm.bookpi.org/RAEGES-V1/article/view/14076
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