@InProceedings{CassolShimBeucArag:2019:SeTiAn,
author = "Cassol, Henrique Luis Godinho and Shimabukuro, Yosio Edemir and
Beuchle, Ren{\'e} and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz
de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Joint Research Centre
(JRC)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Sentinel-1 time-series analysis for detection of forest
degragation by selective loggin",
booktitle = "Anais...",
year = "2019",
editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco
and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
pages = "755--758",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "SAR system, machine-learning, cloudcomputing, segmentation,
regression trees.",
abstract = "Forest degradation by selective logging is considered one of the
main causes of biodiversity loss and CO2 emissions in tropical
regions. However, persistent cloud cover limits the detection of
selective logging using optical satellite systems in the Brazilian
Amazon. We develop a novel approach to detect selective logging
using one-year time-series (TS) from Sentinel-1 RADAR data
(C-band), based on state-of-art cloud computing using Google Earth
Engine. The method consists of two temporal TS reductions. The
first reduces the TS for the median monthly record while the
second one computes annual statistics like mean, standard
deviation, and amplitude. The result is a composite band used for
classifying the annual TS through the application of a
machine-learning algorithm (CART). Classification showed 69%
overall accuracy within five classes; however, the
misclassification of the degradation class was 54%. The
classification accuracy has increased to 79% with the removal of
the regrowth class, with 74% of the degradation correctly
classified.",
conference-location = "Santos",
conference-year = "14-17 abril 2019",
isbn = "978-85-17-00097-3",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3TUTNJ5",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3TUTNJ5",
targetfile = "97297.pdf",
type = "Degrada{\c{c}}{\~a}o de florestas",
urlaccessdate = "27 abr. 2024"
}