@InProceedings{JorgeSantShimMore:2017:PoImLa,
author = "Jorge, Anderson and Santos, Erone Ghizoni dos and Shimabukuro,
Yosio Edemir and Moreira, Maur{\'{\i}}cio Alves",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Potencial das imagens Landsat ? OLI e RapidEye para identificar
{\'a}reas de degrada{\c{c}}{\~a}o florestal em Quer{\^e}ncia e
Canarana ? MT comparadas com imagens LiDAR",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "2107--2114",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "The degradation process happens when a reduction in forests
quality occurs. In this context, the state of Mato Grosso - MT,
Brazil, is known to have the largest forest degraded areas that
increased during the last years. A recent technique to study the
forest degradation is the Light Detection and Ranging LiDAR, which
allows the assessment of forests in a 3D form. The area studied in
this work is located at northern part of Mato Grosso state,
comprising 1006 ha. We used two approaches to identify the
degradation areas in the OLI and RapdEye images. These results
obtained by these approaches were compared with a LiDAR image
result which have a better spatial resolution. The techniques used
to estimate degradation areas were: the Linear Spectral Mixture
Model (LSMM) and the Maximum Likelihood Classification. The
RapidEye image was better identify forest degradation in isolated
and small areas; and on the other hand, the OLI image was better
to depict the sum of degraded areas. Overall, the LSMM showed a
more accurate classification than the Maximum Likelihood
Classification. Forest degradation is better identified with LiDAR
image, but optical images are a possibility when there isnt the
option to use the cloud points 3D.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59808",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSLQ22",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLQ22",
targetfile = "59808.pdf",
type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
urlaccessdate = "27 abr. 2024"
}