@Article{SantosLPPNROPSS:2020:AsVICa,
author = "Santos, Filippe Lemos Maia and Libonati, Renata and Peres,
Leonardo F. and Pereira, Allan A. and Narcizo, Luiza C. and
Rodrigues, Julia Abrantes and Oom, Duarte and Pereira, Jos{\'e}
M. C. and Schroeder, Wilfrid 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 {Instituto Federal de Ci{\^e}ncia e
Tecnologia do Sul de Minas} and {Universidade Federal do Rio de
Janeiro (UFRJ)} and {Universidade Federal do Rio de Janeiro
(UFRJ)} and {University of Lisbon} and {University of Lisbon} and
NOAA/NESDIS and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Assessing VIIRS capabilities to improve burned area mapping over
the Brazilian Cerrado",
journal = "International Journal of Remote Sensing",
year = "2020",
volume = "41",
number = "21",
pages = "8300--8327",
month = "Nov.",
abstract = "Coarse spatial resolution of remote sensing imagery still hampers
a comprehensive representation of long-term fire patterns at the
regional level, in particular in areas characterized by small and
sparse fire scars. The Visible Infrared Imaging Radiometer Suite
(VIIRS) sensor launched in 2011 upgrades the spatial resolution
(375 m) and gives continuity to the Earth long-term monitoring
initiated by Advanced Very High-Resolution Radiometer (AVHRR) and
Moderate Resolution Imaging Spectroradiometer (MODIS) sensors.
Therefore, aiming to assess VIIRS 375 m imagery capabilities to
improve the accuracy and reliability of fire scars mapping over
the Brazilian Cerrado, we developed a burned area detection
algorithm (VIIRS-SVM) based on machine learning techniques. For
this purpose, the (V, W) burnt index adjusted to VIIRS
near-infrared and middle-infrared channels and the One-Class
Support Vector Machine algorithm were used for burned area
identification. The VIIRS-SVM algorithm was applied over the
Brazilian Cerrado and evaluated against reference scars from 15
Landsat-8 scenes during the fire season of 2015, covering a large
area with substantial variability in terms of fire scars
characteristics. We also performed a comparison with the MCD64A1
collection-6 product over the validation sites. Relying on VIIRS
375 m imagery, the VIIRS-SVM algorithm allows an enhancement of
25% in discrimination of small and medium fire scars (25 to 1000
ha), when compared to the MODIS-derived product. Results have
demonstrated that the enhancement of medium and small fire scars
mapping over the Cerrado is possible using VIIRS sensor
capabilities.",
doi = "10.1080/01431161.2020.1771791",
url = "http://dx.doi.org/10.1080/01431161.2020.1771791",
issn = "0143-1161",
language = "en",
targetfile = "santos_assessing.pdf",
urlaccessdate = "28 abr. 2024"
}