@Article{FerroMADMSPOA:2024:ReAsBu,
author = "Ferro, Poliana Domingos and Mataveli, Guilherme Augusto Verola and
Arcanjo, Jeferson de Souza and Dutra, D{\'e}bora Joana and
Medeiros, Tha{\'{\i}}s Pereira de and Shimabukuro, Yosio Edemir
and Pess{\^o}a, Ana Carolina Moreira and Oliveira, Gabriel de and
Anderson, Liana Oighenstein",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Centro Nacional de Monitoramento
e Alertas de Desastres Naturais (CEMADEN)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto de Pesquisa Ambiental
da Amaz{\^o}nia (IPAM)} and {University of South Alabama} and
{Centro Nacional de Monitoramento e Alertas de Desastres
Naturais}",
title = "Regional-Scale Assessment of Burn Scar Mapping in Southwestern
Amazonia Using Burned Area Products and CBERS/WFI Data Cubes",
journal = "Fire",
year = "2024",
volume = "7",
number = "3",
pages = "e67",
month = "Mar.",
keywords = "Amazon, burned area, CBERS, data cubes, linear spectral mixture
model, regional assessment.",
abstract = "Fires are one of the main sources of disturbance in fire-sensitive
ecosystems such as the Amazon. Any attempt to characterize their
impacts and establish actions aimed at combating these events
presupposes the correct identification of the affected areas.
However, accurate mapping of burned areas in humid tropical forest
regions remains a challenging task. In this paper, we evaluate the
performance of four operational BA products (MCD64A1, Fire_cci,
GABAM and MapBiomas Fogo) on a regional scale in the southwestern
Amazon and propose a new approach to BA mapping using fraction
images extracted from data cubes of the Brazilian orbital sensors
CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned
areas consisted of applying the Linear Spectral Mixture Model to
the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to
generate shadow fraction images, which were then segmented and
classified using the ISOSEG non-supervised algorithm. Regression
and similarity analyses based on regular grid cells were carried
out to compare the BA mappings. The results showed large
discrepancies between the mappings in terms of total area burned,
land use and land cover affected (forest and non-forest) and
spatial location of the burned area. The global products MCD64A1,
GABAM and Fire_cci tended to underestimate the area burned in the
region, with Fire_cci underestimating BA by 88%, while the
regional product MapBiomas Fogo was the closest to the reference,
underestimating by only 7%. The burned area estimated by the
method proposed in this work (337.5 km2) was 12% higher than the
reference and showed a small difference in relation to the
MapBiomas Fogo product (18% more BA). These differences can be
explained by the different datasets and methods used to detect
burned areas. The adoption of global products in regional studies
can be critical in underestimating the total area burned in
sensitive regions. Our study highlights the need to develop
approaches aimed at improving the accuracy of current global
products, and the development of regional burned area products may
be more suitable for this purpose. Our proposed approach based on
WFI data cubes has shown high potential for generating more
accurate regional burned area maps, which can refine BA estimates
in the Amazon.",
doi = "10.3390/fire7030067",
url = "http://dx.doi.org/10.3390/fire7030067",
issn = "2571-6255",
language = "en",
targetfile = "fire-07-00067-v2.pdf",
urlaccessdate = "11 maio 2024"
}