%0 Book Section
%3 shimabukuro_mapping.pdf
%4 sid.inpe.br/plutao/2022/12.12.17.39.21
%A Shimabukuro, Yosio Edemir,
%A Dutra, Andeise Cerqueira,
%A Arai, Egidio,
%A Duarte, Valdete,
%A Cassol, Henrique Luís Godinho,
%A Pereira, Gabriel,
%A Cardozo, Francielle da Silva,
%@secondarytype PRE LI
%B Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics
%C Basel
%D 2022
%E Fernández-Manso, A.,
%E Quintano, C.,
%F lattes: 8734553235868564 2 ShimabukuroDuArDuCaPeCa:2022:MaBuAr
%I MDPI
%K burned areas detection, shade fraction image, linear spectral mixing model, VIIRS, , PROBA-V, Landsat-8 OL.
%P 115-137
%T Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets
%U https://www.mdpi.com/books/book/6270-advances-in-remote-sensing-of-postfire-environmental-damage-and-recovery-dynamics
%X Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board AutonomyVegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.
%@area SRE
%@electronicmailaddress yosio.shimabukuro@inpe.br
%@electronicmailaddress andeise.dutra@inpe.br
%@electronicmailaddress egidio.arai@inpe.br
%@electronicmailaddress valdete.duarte@inpe.br
%@electronicmailaddress henrique.cassol@inpe.br
%@electronicmailaddress pereira@ufsj.edu.br
%@electronicmailaddress franciellecardozo@ufsj.edu.br
%@group DIOTG-CGCT-INPE-MCTI-GOV-BR
%@group SER-SRE-DIPGR-INPE-MCTI-GOV-BR
%@group DIOTG-CGCT-INPE-MCTI-GOV-BR
%@group DIOTG-CGCT-INPE-MCTI-GOV-BR
%@group DIOTG-CGCT-INPE-MCTI-GOV-BR
%@isbn 9783036556
%@usergroup lattes
%@resumeid 8JMKD3MGP5W/3C9JJCQ
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JGUP
%@resumeid 8JMKD3MGP5W/3C9JJAU
%@nexthigherunit 8JMKD3MGPCW/3F3NU5S
%@nexthigherunit 8JMKD3MGPCW/46KUATE
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Federal de São João Del Rei
%@affiliation Universidade Federal de São João Del Rei
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.3390/books978-3-0365-5668-0
%2 sid.inpe.br/plutao/2022/12.12.17.39.22