@Article{BendiniFSKRSLH:2019:DeAgLa,
author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and
Schwieder, Marcel and K{\"o}rting, Thales Sehn and Rufin,
Philippe and Sanches, Ieda Del'Arco and Leit{\~a}o, Pedro J. and
Hostert, Patrick",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and
{Humboldt-Universit{\"a}t zu Berlin} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Humboldt-Universit{\"a}t zu
Berlin} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Humboldt-Universit{\"a}t zu Berlin} and
{Humboldt-Universit{\"a}t zu Berlin}",
title = "Detailed agricultural land classification in the Brazilian cerrado
based on phenological information from dense satellite image time
series",
journal = "International Journal of Applied Earth Observation and
Geoinformation",
year = "2019",
volume = "82",
pages = "UNSP 101872",
month = "Oct.",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura
sustent{\'a}vel} and {Pr{\^e}mio CAPES Elsevier 2023 - ODS 8:
Trabalho decente e crescimento econ{\^o}mico} and {Pr{\^e}mio
CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
keywords = "Big data, Time-Series mining, Random forest algorithm, Land use
and Land cover mapping (LULC), Multi-Sensor.",
abstract = "The paradox between environmental conservation and economic
development is a challenge for Brazil, where there is a complex
and dynamic agricultural scenario. This reinforces the need for
effective methods for the detailed mapping of agriculture. In this
work, we employed land surface phenological metrics derived from
dense satellite image time series to classify agricultural land in
the Cerrado biome. We used all available Landsat images between
April 2013 and April 2017, applying a weighted ensemble of Radial
Basis Function (RBF) convolution filters as a kernel smoother to
fill data gaps such as cloud cover and Scan Line Corrector
(SLC)-off data. Through this approach, we created a dense Enhanced
Vegetation Index (EVI) data cube with an 8-day temporal resolution
and derived phenometrics for a Random Forest (RF) classification.
We used a hierarchical classification with four levels, from land
cover to crop rotation classes. Most of the classes showed
accuracies higher than 90%. Single crop and Non-commercial crop
classes presented lower accuracies. However, we showed that
phenometrics derived from dense Landsat-like image time series, in
a hierarchical classification scheme, has a great potential for
detailed agricultural mapping. The results are promising and show
that the method is consistent and robust, being applicable to
mapping agricultural land throughout the entire Cerrado.",
doi = "10.1016/j.jag.2019.05.005",
url = "http://dx.doi.org/10.1016/j.jag.2019.05.005",
issn = "0303-2434",
language = "en",
targetfile = "bendini_detailed.pdf",
urlaccessdate = "03 jun. 2024"
}