@Article{GuimarãesGaloNarvSilv:2020:CoTeDa,
author = "Guimar{\~a}es, Ulisses Silva and Galo, Maria de Lourdes Bueno
Trindade and Narvaes, Igor da Silva and Silva, Arnaldo de
Queiroz",
affiliation = "{Sistema de Prote{\c{c}}{\~a}o da Amaz{\^o}nia (SIPAM)} and
{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Universidade Federal do
Par{\'a} (UFPA)}",
title = "Cosmo-SkyMed and TerraSAR-X datasets for geomorphological mapping
in the eastern of Marajo Island, Amazon coast",
journal = "Geomorphology",
year = "2020",
volume = "350",
pages = "UNSP 106934",
month = "Feb.",
keywords = "Synthetic aperture radar, Amazon coastal environments, Random
Forest.",
abstract = "The Amazon coast is marked by the high discharge of sediments and
freshwater, macrotidal influence, a wide continental shelf,
extensive flood plains and lowered plateaus which make it unique
as a delta and estuary landscape. Further, the tropical climate
imposes heavy rains and incessant cloudiness that render microwave
systems more suitable for cartography. This study proposed to
recognize and map the Amazon coastal environments through the
X-band Synthetic Aperture Radar, provided by Cosmo-SkyMed (CSK)
and TerraSAR-X (TSX) systems. The SAR datasets consisted of
interferometric and stereo pairs, restricted to single-revisit and
obtained with small interval (1-11 days), under steeper (theta <
35 degrees) and shallow (theta >= 35 degrees) incidence angles,
and during the rainy and dry seasons. From the 4 acquisitions of
X-band SAR data, attributes such as the backscattering
coefficient, coefficient of variation, texture, coherence, and
Digital Surface Model (DSM) were derived, adding each variable in
5 scenarios. These combinations resulted in 20 models, which were
submitted individually to the machine learning (ML) classification
approach by Random Forest (RF). The backscattering and altimetry
described the coastal environments which shared ambiguity and high
dispersion, with the lowest separability for vegetated
environments such as Mangrove, Vegetated Coastal Plateau and
Vegetated Fluvial Marine Terrace. The coherence provided by
interferometry was weak (<0.44), even during the dry season, in
the other hand, the cross-correlation was strong (>0.60), during
the rainy and dry season showing more suitability for
radargrammetry on the Amazon coast. The RF models resulted in
Kappa coefficient between 0.39 to 0.70, indicating that the use of
X-band SAR images at an incidence angle greater than 44 degrees
and obtained in the dry season is more appropriated for the
morphological mapping. The RF models given by TSX achieved the
higher mapping accuracies per scenario of SAR products, in order
of 0.48 to 0.63. Despite this, the best classification was carried
out by 19 RF model with 0.70, provided by CSK in shallow incidence
composed by intensity, texture, coherence and stereo DSM. The CSK
and TSX data allowed to map the Amazon coast precisely, based on
X-band at single polarization, high spatial resolution and
revisit, which has demonstrated the support for detailed
cartography scale (1:50,000) and frequent updating (monthly up to
yearly).",
doi = "10.1016/j.geomorph.2019.106934",
url = "http://dx.doi.org/10.1016/j.geomorph.2019.106934",
issn = "0169-555X",
language = "en",
targetfile = "guimaraes_cosmo.pdf",
urlaccessdate = "21 maio 2024"
}