@Article{PicoliCSSCMCEABAA:2018:BiEaOb,
author = "Picoli, Michelle Cristina Ara{\'u}jo and Camara, Gilberto and
Sanches, Ieda Del'Arco and Sim{\~o}es, Rolf Ezequiel de Oliveira
and Carvalho, Alexandre and Maciel, Adeline Marinho and Coutinho,
Alexandre and Esquerdo, Julio and Antunes, Jo{\~a}o and Begotti,
Rodrigo Anzolin and Arvor, Damien and Almeida, Cl{\'a}udio
Aparecido de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto de Pesquisa Economica Aplicada
(IPEA)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and
{Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and
{Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Universite
de Rennes} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Big earth observation time series analysis for monitoring
Brazilian agriculture",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
year = "2018",
volume = "145",
number = "B",
pages = "328--339",
month = "Nov.",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura
sustent{\'a}vel}",
keywords = "Big earth observation data, Land use science, Satellite image time
series, Crop expansion, Brazilian Amazonia biome, Brazilian
Cerrado biome, Tropical deforestation.",
abstract = "This paper presents innovative methods for using satellite image
time series to produce land use and land cover classification over
large areas in Brazil from 2001 to 2016. We used Moderate
Resolution Imaging Spectroradiometer (MODIS) time series data to
classify natural and human-transformed land areas in the state of
Mato Grosso, Brazil's agricultural frontier. Our hypothesis is
that building high-dimensional spaces using all values of the time
series, coupled with advanced statistical learning methods, is a
robust and efficient approach for land cover classification of
large data sets. We used the full depth of satellite image time
series to create large dimensional spaces for statistical
classification. The data consist of MODIS MOD13Q1 time series with
23 samples per year per pixel, and 4 bands (Normalized Difference
Vegetation Index (NDVI), Enhanced Vegetation Index (EVI),
near-infrared (nir) and mid-infrared (mir)). By taking a series of
labelled time series, we fed a 92 dimensional attribute space into
a support vector machine model. Using a 5-fold cross validation,
we obtained an overall accuracy of 94% for discriminating among
nine land cover classes: forest, cerrado, pasture, soybean fallow,
fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and
soybean-sunflower. Producer and user accuracies for all classes
were close to or better than 90%. The results highlight important
trends in agricultural intensification in Mato Grosso. Double crop
systems are now the most common production system in the state,
sparing land from agricultural production. Pasture expansion and
intensification has been less studied than crop expansion,
although it has a stronger impact on deforestation and greenhouse
gas (GHG) emissions. Our results point to a significant increase
in the stocking rate in Mato Grosso and to the possible
abandonment of pasture areas opened in the state's frontier. The
detailed land cover maps contribute to an assessment of the
interplay between production and protection in the Brazilian
Amazon and Cerrado biomes.",
doi = "10.1016/j.isprsjprs.2018.08.007",
url = "http://dx.doi.org/10.1016/j.isprsjprs.2018.08.007",
issn = "0924-2716",
language = "en",
targetfile = "picoli_big.pdf",
urlaccessdate = "27 abr. 2024"
}