@Article{CarreirasPereShim:2006:LaMaBr,
author = "Carreiras, Jo{\~a}o M. B. and Pereira, Jos{\'e} M. C. and
Shimabukuro, Yosio Edemir",
affiliation = "Department of Forestry, Instituto Superior de Agronomic, Tapada da
Ajuda, 1349-017 Lisboa, Portugal and Department of Forestry,
Instituto Superior de Agronomic, Tapada da Ajuda, 1349-017 Lisboa,
Portugal and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Land-cover mapping in the Brazilian Amazon using SPOT-4 vegetation
data and machine learning classification methods",
journal = "Photogrammetric Engineering and Remote Sensing",
year = "2006",
volume = "72",
number = "8",
pages = "897--910",
month = "Aug.",
keywords = "VEGETA{\C{C}}{\~A}O, remotely-sensed data, decision-tree
classification, resolution satellite data, spatial-resolution,
accuracy assessment, avhrr data, multispectral data, tropical
regions, eastern amazon, mixing models.",
abstract = "The main objective of this study is to evaluate the feasibility of
deriving a land-cover map of the state of Mato Grosso, Brazil, for
the year 2000, using data from the 1 km SPOT-4 VEGETATION (VGT)
sensor. For this purpose we used a VGT temporal series of 12
monthly composite images, which were further transformed to
physicalmeaningful fraction images of vegetation, soil, and shade.
Classification of fraction images was implemented using several
recent machine learning developments, namely, filtering input
training data and probability bagging in a classification tree
approach. A 10-fold cross validation accuracy assessment indicates
that filtering and probability bagging are effective at increasing
overall and class-specific accuracy. Overall accuracy and mean
probability of class membership were 0.88 and 0.80, respectively.
The map of probability of class membership indicates that the
larger errors are associated with cerrado savanna and
semi-deciduous forest.",
issn = "0099-1112",
language = "en",
targetfile = "Carreiras_etal_PERS2006.pdf",
urlaccessdate = "03 jun. 2024"
}