@Article{FrankeBBCDHOMS:2018:FuLoMa,
author = "Franke, Jonas and Barradas, Ana Carolina Sena and Borges, Marco
Assis and Costa, M{\'a}ximo Menezes and Dias, Paulo Adriano and
Hoffmann, Anja A. and Orozco Filho, Juan Carlos and Melchiori,
Arturo Emiliano and Siegert, Florian",
affiliation = "{Remote Sensing Solutions GmbH} and {Instituto Chico Mendes de
Conserva{\c{c}}{\~a}o da Biodiversidade (ICMBio)} and {Instituto
Chico Mendes de Conserva{\c{c}}{\~a}o da Biodiversidade
(ICMBio)} and {Instituto Chico Mendes de Conserva{\c{c}}{\~a}o
da Biodiversidade (ICMBio)} and {Instituto Chico Mendes de
Conserva{\c{c}}{\~a}o da Biodiversidade (ICMBio)} and {Remote
Sensing Solutions GmbH} and {Instituto Chico Mendes de
Conserva{\c{c}}{\~a}o da Biodiversidade (ICMBio)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Remote Sensing
Solutions GmbH}",
title = "Fuel load mapping in the Brazilian Cerrado in support of
integrated fire management",
journal = "Remote Sensing of Environment",
year = "2018",
volume = "217",
pages = "221--232",
month = "Nov.",
keywords = "Landsat, Prescribed burning, Integrated fire management,
Sentinel-2, Spectral unmixing.",
abstract = "The Brazilian Cerrado is considered to be the most species-rich
savannah region in the world, covering ~2 million km2.
Uncontrolled late season fires promote deforestation, produce
greenhouse gases (~25% of Brazil's land-use related CO2 emissions
between 2003 and 2005) and are a major threat to the conservation
of biodiversity in protected areas. Governmental institutions
therefore implemented early dry season (EDS) prescribed burnings
as part of integrated fire management (IFM) in protected areas of
the Cerrado, with the aim to reduce the area and severity of late
dry season (LDS) fires. The planning and implementation of EDS
prescribed burning is supported by satellite-based geo-information
on fuel conditions, derived from Landsat 8 and Sentinel-2 data.
The Mixture Tuned Matched Filtering algorithm was used to analyse
the data, and the relationship between the resulting matched
fractions (dry vegetation, green vegetation and soil) and in situ
surface fuel samples was assessed. The linear regression of in
situ data versus matched filter scores (MF scores) of dry
vegetation showed an r2 of 0.81 (RMSE\ =\ 0.15) and
in situ data versus MF scores of soil showed an r2 of 0.65
(RMSE\ =\ 0.38). To predict quantitative fuel load,
a multiple linear regression analysis was carried out with MF
scores of NPV and soil as predictors (adjusted
r2\ =\ 0.86; p\ <\ 0.001; standard
error\ =\ 0.075). The fuel load maps were
additionally evaluated by fire managers while planning EDS
prescribed burning campaigns. The fuel load mapping approach has
proven to be an effective tool for integrated fire management by
improving the planning and implementation of prescribed burning,
promoting pyrodiversity, prioritising fire suppression and
evaluating fire management efforts to meet overall conservations
goals. National and state level authorities have successfully
institutionalized the approach and it was incorporated into IFM
policies in Brazil.",
doi = "10.1016/j.rse.2018.08.018",
url = "http://dx.doi.org/10.1016/j.rse.2018.08.018",
issn = "0034-4257",
language = "en",
targetfile = "franke_fuel.pdf",
urlaccessdate = "27 abr. 2024"
}