@Article{SartoriImaMurNovSil:2011:MaMaSp,
author = "Sartori, L. R. and Imai, N. N and Mura, J. C. and Novo, E. M. L.
D. M and Silva, T. S. F.",
affiliation = "Sao Paulo State University (UNESP), Presidente Prudente 19060-190,
Brazil and Sao Paulo State University (UNESP), Presidente Prudente
19060-190, Brazil and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
Geography Department, University of Victoria, Victoria, BC V8W
3R4, Canada",
title = "Mapping macrophyte species in the amazon floodplain wetlands using
fully polarimetric ALOS/PALSAR data",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
year = "2011",
volume = "49",
number = "12 PART 1",
pages = "4717--4728",
month = "Dec.",
keywords = "Amazon floodplain, classification, macrophyte species, Phased
Array Type L-Band Synthetic Aperture Radar (PALSAR) data,
polarimetric decomposition, radar polarimetry.",
abstract = "The purpose of this paper was to evaluate attributes derived from
fully polarimetric PALSAR data to discriminate and map macrophyte
species in the Amazon floodplain wetlands. Fieldwork was carried
out almost simultaneously to the radar acquisition, and macrophyte
biomass and morphological variables were measured in the field.
Attributes were calculated from the covariance matrix [C] derived
from the single-look complex data. Image attributes and macrophyte
variables were compared and analyzed to investigate the
sensitivity of the attributes for discriminating among species.
Based on these analyses, a rule-based classification was applied
to map macrophyte species. Other classification approaches were
tested and compared to the rule-based method: a classification
based on the Freeman-Durden and Cloude-Pottier decomposition
models, a hybrid classification (Wishart classifier with the input
classes based on the H/a plane), and a statistical-based
classification (supervised classification using Wishart distance
measures). The findings show that attributes derived from fully
polarimetric L-band data have good potential for discriminating
herbaceous plant species based on morphology and that estimation
of plant biomass and productivity could be improved by using these
polarimetric attributes.",
doi = "10.1109/TGRS.2011.2157972",
url = "http://dx.doi.org/10.1109/TGRS.2011.2157972",
issn = "0196-2892",
language = "en",
targetfile = "05995161.pdf",
urlaccessdate = "30 abr. 2024"
}