@Article{CordeiroRoss:2015:MaVeLa,
author = "Cordeiro, Carlos Leandro de Oliveira and Rossetti, Dilce de
F{\'a}tima",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Mapping vegetation in a late Quaternary landform of the Amazonian
wetlands using object-based image analysis and decision tree
classification",
journal = "International Journal of Remote Sensing",
year = "2015",
volume = "36",
number = "13",
pages = "3397--3422",
abstract = "Fan-shaped morphologies related to late Quaternary residual
megafan depositional systems are common features over wide areas
in northern Amazonia. These features were formed by ancient
distributary drainage systems that are in great contrast to
tributary drainage networks that typify the modern Amazon basin.
The surfaces of the Amazonian megafans constitute vegetacional
mosaic wetlands with different campinarana types. A
fine-scale-resolution investigation is required to provide
detailed classification maps for the various campinarana and
surrounding forest types associated with the Amazonian megafans.
This approach remains to be presented, despite its relevance for
analysing the relationship between stages of plant succession and
sedimentary dynamics associated with the evolution of megafans. In
this work, we develop a methodology for classifying vegetation
over a fan-shaped megafan palaeoform from a northern Amazonian
wetland. The approach included object-based image analysis (OBIA)
and data-mining (DM) techniques combining Advanced Spaceborne
Thermal Emission and Reflection Radiometer (ASTER) images,
land-cover fractions derived by the linear spectral mixing model,
synthetic aperture radar (SAR) images, and the digital elevation
model (DEM) acquired during the Shuttle Radar Topography Mission
(SRTM). The DEM, vegetation fraction, and ASTER band 3 were the
most useful parameters for defining the forest classes. The
normalized difference vegetation index (NDVI), ASTER band 1,
vegetation fraction, and the Advanced Land Observing Satellite
(ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR)
transmitting and receiving horizontal polarization (HH) and
transmitting horizontal and receiving vertical polarization (HV)
were all effective in distinguishing the wetland classes
campinarana and Mauritia. Tests of statistical significance
indicated the overall accuracies and kappa coefficients (kappa) of
88% and 0.86 for the final map, respectively. The allocation
disagreement coefficient of 5% and a quantity disagreement value
of 7% further attested the statistical significance of the
classification results. Hence, in addition to water, exposed soil,
and deforestation areas, OBIA and DM were successful for
differentiating a large number of open (forest, wood, shrub, and
grass campinaranas), forest (terra firme, varzea, igapo, and
alluvial), as well as Mauritia wetland classes in the inner and
outer areas of the studied megafan.",
doi = "10.1080/01431161.2015.1060644",
url = "http://dx.doi.org/10.1080/01431161.2015.1060644",
issn = "0143-1161",
language = "en",
urlaccessdate = "27 abr. 2024"
}