@InProceedings{SantosRoLiPePeSe:2019:BuArMa,
author = "Santos, Filippe and Rodrigues, Julia and Libonati, Renata and
Peres, Leonardo and Pereira, Allan and Setzer, Alberto Waingort",
affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade
Federal do Rio de Janeiro (UFRJ)} and {Universidade Federal do Rio
de Janeiro (UFRJ)} and {Universidade Federal do Rio de Janeiro
(UFRJ)} and {Instituto Federal de Ci{\^e}ncia e Tecnologia do Sul
de Minas Gerais} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Burned area mapping in Brazil using NPP-VIIRS imagery and One
Class Support Vector Machine",
year = "2019",
organization = "EGU General Assembly",
keywords = "burned area, VIIRS, SVM, Cerrado.",
abstract = "Remote sensing observations has improved the understanding of
spatial and temporal fire patterns in Brazil in the last decades
based on quantitative metrics such as severity, location,
extension and duration. Nevertheless, large discrepancies and
uncertainties persist in the currently burned area (BA) products
in determining BA extension, location, and occurrence time.
Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was
launched in 2011 to upgrade and to maintain the Earth long-term
monitoring initiated by Advanced Very High Resolution Radiometer
(AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS)
sensors, but to our knowledge, none BA product has been developed
using VIIRS data imagery. Accordingly, we present a BA mapping
algorithm based on VIIRS imagery which includes two-steps.
Firstly, monthly composites of (V, W) burned index are computed
using spectral information of near infrared (NIR) and middle
infrared (MIR) channels. Secondly, multispectral samples extracted
by VIIRS active fires are used for training a One-Class Support
Vector Machine (OC-SVM) classification that uses cumulative
distribution functions criteria. The active fire data were
screened to prevent extraction of unrepresentative BA samples and
combined with burn index (V, W) monthly composites to produce BA
scars. The procedure was applied over Brazilian savanna for 2015,
a biome that has been increasingly affected by deforestation due
to cropland and pasture expansion, consequently rising and
changing the natural fire regime in region. Then, the developed
algorithm was validated by reference scars obtained from Landsat
imagery and compared with other BA product (e.g., MCD64A1).
Results show that VIIRS BA product based on OC-SVM are able to map
smaller areas more accurately than other products, including
burned areas without active fires, due OC-SVM classification
characterizes BA through active fire samples, thus eliminating a
potential source of omission error.",
conference-location = "Vienna, Austria",
conference-year = "07-12 apr.",
language = "en",
targetfile = "EGU2019-17840-2.pdf",
urlaccessdate = "29 mar. 2024"
}