@Article{CarreirasPereCampShim:2006:AsExAg,
author = "Carreiras, Jo{\~a}o M. B. and Pereira, Jos{\'e} M. C. and
Campagnolo, Manuel L. and Shimabukuro, Yosio Edemir",
affiliation = "Department of Forestry, Instituto Superior de Agronomia, Tapada da
Ajuda, 1349-017 Lisboa, Portugal and Department of Forestry,
Instituto Superior de Agronomia, Tapada da Ajuda, 1349-017 Lisboa,
Portugal and Department of Mathematics, Instituto Superior de
Agronomia, Tapada da Ajuda, 1349-017 Lisboa, Portugal and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Assessing the extent of agriculture/pasture and secondary
succession forest in the Brazilian Legal Amazon using SPOT
VEGETATION data",
journal = "Remote Sensing of Environment",
year = "2006",
volume = "101",
number = "3",
pages = "283--298",
month = "Apr.",
keywords = "VEGETA{\C{C}}{\~A}O, Brazilian Legal Amazon (BLA), agriculture,
pasture, secondary succession forest, SPOT-4, vegetation,
supervised classification land-cover change, biosphere-atmosphere
experiment, NOAA AVHRR data, tropical forest, accuracy assessment,
eastern amazonia, satellite data, prior probabilities, abandoned
pastures.",
abstract = "There has been growing concern about land use/land cover change in
tropical regions, as there is evidence of its influence on the
observed increase in atmospheric carbon dioxide concentration and
consequent climatic changes. Mapping of deforestation by the
Brazil's National Space Research Institute (INPE) in areas of
primary tropical forest using satellite data indicates a value of
587,727 km(2) up to the year 2000. Although most of the efforts
have been concentrated in mapping primary tropical forest
deforestation, there is also evidence of large-scale deforestation
in the cerrado savanna, the second most important biome in the
region. The main purpose of this work was to assess the extent of
agriculture/pasture and secondary succession forest in the
Brazilian Legal Amazon (BLA) in 2000, using a set of multitemporal
images from the 1-km SPOT-4 VEGETATION (VGT) sensor. Additionally,
we discriminated primary tropical forest, cerrado savanna, and
natural/artificial waterbodies. Four classification algorithms
were tested: quadratic discriminant analysis (QDA), simple
classification trees (SCT), probability-bagging classification
trees (PBCT), and k-nearest neighbors (K-NN). The agriculture/
pasture class is a surrogate for those areas cleared of its
original vegetation cover in the past, acting as a source of
carbon. On the contrary, the secondary succession forest class
behaves as a sink of carbon. We used a time series of 12 monthly
composite images of the year 2000, derived from the SPOT-4 VGT
sensor. A set of 19 Landsat scenes was used to select training and
testing data. A 10-fold cross validation procedure rated PBCT as
the best classification algorithm, with an overall sample accuracy
of 0.92. High omission and commission errors occurred in the
secondary succession forest class, due to confusion with
agriculture/pasture and primary tropical forest classes. However,
the PBCT algorithm generated the lower misclassification error in
this class. Besides, this algorithm yields information about class
membership probability, with similar to 80% of the pixels with
class membership probability greater or equal than 0.8. The
estimated total area of agriculture/pasture and secondary
succession forest in 2000 in the BLA was 966 x 103 and 140 x 103
king, respectively. Comparison with an existing land cover map
indicates that agriculture/pasture occurred primarily in areas
previously occupied by primary tropical forest (46%) and cerrado
savanna (33%), and also in transition forest (19%), and other
vegetation types (2%). This further confines the existing evidence
of extensive cerrado savanna conversion. This study also concludes
that SPOT-4 VGT data are adequate for discriminating several major
land cover types in tropical regions. Agriculture/pasture was
mapped with errors of about 5%. Very high classification errors
were associated with secondary succession forest, suggesting that
a different methodology/sensor has to be used to address this
difficult land cover class (namely with the inclusion of ancillary
data). For the other classes, we consider that accurate maps can
be derived from SPOT-4 VGT data with errors lower than 20% for the
cerrado savanna, and errors lower than 10% for the other land
cover classes. These estimates may be useful to evaluate impacts
of land use/land cover change on the carbon and water cycles,
biotic diversity, and soil degradation. (c) 2006 Elsevier Inc. All
rights reserved.",
copyholder = "SID/SCD",
doi = "10.1016/j.rse.2005.12.017",
url = "http://dx.doi.org/10.1016/j.rse.2005.12.017",
issn = "0034-4257",
language = "en",
targetfile = "carreiras - assessing.pdf",
urlaccessdate = "27 abr. 2024"
}