%0 Conference Proceedings
%4 sid.inpe.br/marte2/2017/10.27.13.13.34
%2 sid.inpe.br/marte2/2017/10.27.13.13.35
%@isbn 978-85-17-00088-1
%F 59285
%T Global burned area products from remote sensing: an evaluation of fire patch metrics for regional applications in Brazil
%D 2017
%A Nogueira, Joana Messias Pereira,
%A Ruffault, Julien,
%A Chuvieco, Emilio,
%A Mouillot, Florent,
%@electronicmailaddress joananog@yahoo.com.br
%E Gherardi, Douglas Francisco Marcolino,
%E Aragão, Luiz Eduardo Oliveira e Cruz de,
%B Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)
%C Santos
%8 28-31 maio 2017
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 3592-3599
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%X Global burned area (BA) datasets from remote sensing provide fruitful information for carbon emissions and for Dynamic Global Vegetation Models (DGVM) benchmarking. Patch level analysis recently emerged as an additional informative feature of the fire regime. We evaluated a step further the ability of global BA products to accurately represent fire patch features, in the fire-prone Brazilian savannas. We used the pixel-level burned area from LANDSAT, MODIS MCD45A1 and the European Space Agency (ESA) fire Climate Change Initiative (FIRE_CCI) for the period 2002-2009 to identify individual fire patches that we compared by linear regressions. Correlations between patch areas showed R2>0.6 for all comparisons, with a slope of 0.99 between FIRE_CCI and MCD45A1 but a lower slope (0.6 - 0.8) when compared to the LANDSAT data. Shape complexity was less correlated (R2 = 0.5) between global products, and R2=0.2 between global products and the LANDSAT data, due to coarser resolution. For the morphological features of the ellipse fitted over fire patches, R2 reached 0.6 for the ellipses eccentricity and varied from 0.4 to 0.8 for its azimuthal directional angle. We conclude that global BA products underestimate BA due to missing small fires, but also underestimate patch areas. Patch complexity is the least correlated variable, but ellipse features appear to be reliable information to be further used for quality product assessment, global pyrogeography or DGVM benchmarking.
%9 Geoprocessamento e aplicações
%@language en
%3 59285.pdf