@Article{DoblasCaShSaArPe:2020:StSeSa,
author = "Doblas, Juan Prieto and Carneiro, Arian and Shimabukuro, Yosio
Edemir and Sant'Anna, Sidnei Jo{\~a}o Siqueira and Arag{\~a}o,
Luiz Eduardo Oliveira e Cruz de and Pereira, Francisca Rocha de
Souza",
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)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Stabilization of sentinel-1 sar time-series using climate and
forest structure data for early tropical deforestation detection",
journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial
Information Sciences",
year = "2020",
volume = "5",
number = "3",
pages = "89--96",
month = "Aug.",
note = "2020 24th ISPRS Congress on Technical Commission III; Nice,
Virtual; France; 31 August 2020 through 2 September 2020;",
keywords = "Remote Sensing, Time-series Data, SAR, Modelling, Deforestation
Detection, Change Detection.",
abstract = "In this study we analyse the factors of variability of Sentinel-1
C-band radar backscattering over tropical rainforests, and propose
a method to reduce the effects of this variability on
deforestation detection algorithms. To do so, we developed a
random forest regression model that relates Sentinel-1 gamma
nought values with local climatological data and forest structure
information. The model was trained using long time-series of 26
relevant variables, sampled over 6 undisturbed tropical forests
areas. The resulting model explained 71.64% and 73.28% of the SAR
signal variability for VV and VH polarizations, respectively. Once
the best model for every polarization was selected, it was used to
stabilize extracted pixel-level data of forested and
non-deforested areas, which resulted on a 10 to 14% reduction of
time-series variability, in terms of standard deviation. Then a
statistically robust deforestation detection algorithm was applied
to the stabilized time-series. The results show that the proposed
method reduced the rate of false positives on both polarizations,
especially on VV (from 21% to 2%, \α=0.01). Meanwhile, the
omission errors increased on both polarizations (from 27% to 37%
in VV and from 27% to 33% on VV, \α=0.01). The proposed
method yielded slightly better results when compared with an
alternative state-of-the-art approach (spatial normalization).",
doi = "10.5194/isprs-Annals-V-3-2020-89-2020",
url = "http://dx.doi.org/10.5194/isprs-Annals-V-3-2020-89-2020",
issn = "0924-2716",
language = "en",
targetfile = "doblas_stabilization.pdf",
urlaccessdate = "28 mar. 2024"
}