@InProceedings{FerreiraTCSFALVS:2015:PrMe,
author = "Ferreira, George Porto and Toniol, Alana Carla and Cruz, Pedro
Ferraz and Sano, Edson Eyji and Freitas, Daniel Moraes and Aguiar,
Marcelo Cabral and Lopes, Camila Aparecida Lima and Vilela,
Lidiane de F{\'a}tima and Souza, Marcelo Soares",
affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Detec{\c{c}}{\~a}o de altera{\c{c}}{\~o}es recentes na
cobertura vegetal natural da Amaz{\^o}nia Legal por meio de
imagens Landsat-8: proposta metodol{\'o}gica",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "4727--4733",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "INPE has monitored deforestation in the Brazilian Amazon in near
real-time using the Moderate Resolution Imaging Spectroradiometer
(MODIS) data, which has 1-2 repeat pass and 250-m spatial
resolution. As farmers started to clear-cut forests in small
patches more intensively, the use of MODIS sensor is becoming
limited. In order to complement the information provided by the
INPE´s system, we propose a methodological approach to detect
alterations in forestlands from the Legal Amazonia based on two
subsequent Landsat-8 overpasses in time. The study area was a set
of 85 Landsat-8 scenes located mostly in the states of
Rond{\^o}nia, Mato Grosso and Par{\'a} where ongoing
deforestation is significant. The approach is based on the
difference between these two images, which is entirely processed
using the color rendering function available in the public domain
Quantum GIS Desktop 2.4.0. Pixels presenting spectral changes are
highlighted in bright tones and may be related to clear-cutting or
to forest degradation processes. Visual interpretation of RGB
color composites are then conducted only on such highlighted
portions of images, making the whole process of image analysis
much faster. In the time period of August 1 October 3, 2014, we
identified a total of 924,49 km2 of alterations in the study area.
The municipality of Altamira, Para State, presented the highest
area of alteration (90,3 km2). Results showed that the approach
developed in this study is suitable to detect small-size
alterations in the Brazilian Amazon (~ > 6 ha) within the Landsat
patch frame (185 km) in near real-time.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "926",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM4D7H",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4D7H",
targetfile = "p0926.pdf",
type = "Floresta e vegeta{\c{c}}{\~a}o",
urlaccessdate = "27 abr. 2024"
}