@Article{BellónBégSeeAlmSim:2017:ReSeAp,
author = "Bell{\'o}n, Beatriz and B{\'e}gu{\'e}, Agn{\`e}s and Seen,
Danny Lo and Almeida, Cl{\'a}udio Aparecido de and Sim{\~o}es,
Margareth",
affiliation = "Cirad, UMR TETIS and Cirad, UMR TETIS and Cirad, UMR TETIS and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Embrapa
Solos}",
title = "A remote sensing approach for regional-scale mapping of
agricultural land-use systems based on NDVI time series",
journal = "Remote Sensing",
year = "2017",
volume = "9",
number = "6",
month = "June",
keywords = "geographic object-based image analysis (GEOBIA), Moderate
Resolution Imaging Spectroradiometer (MODIS), principal components
analysis (PCA), cropping systems, Stratification.",
abstract = "In response to the need for generic remote sensing tools to
support large-scale agricultural monitoring, we present a new
approach for regional-scale mapping of agricultural land-use
systems (ALUS) based on object-based Normalized Difference
Vegetation Index (NDVI) time series analysis. The approach
consists of two main steps. First, to obtain relatively
homogeneous land units in terms of phenological patterns, a
principal component analysis (PCA) is applied to an annual MODIS
NDVI time series, and an automatic segmentation is performed on
the resulting high-order principal component images. Second, the
resulting land units are classified into the crop agriculture
domain or the livestock domain based on their land-cover
characteristics. The crop agriculture domain land units are
further classified into different cropping systems based on the
correspondence of their NDVI temporal profiles with the
phenological patterns associated with the cropping systems of the
study area. A map of the main ALUS of the Brazilian state of
Tocantins was produced for the 20132014 growing season with the
new approach, and a significant coherence was observed between the
spatial distribution of the cropping systems in the final ALUS map
and in a reference map extracted from the official agricultural
statistics of the Brazilian Institute of Geography and Statistics
(IBGE). This study shows the potential of remote sensing
techniques to provide valuable baseline spatial information for
supporting agricultural monitoring and for large-scale land-use
systems analysis.",
doi = "10.3390/rs9060600",
url = "http://dx.doi.org/10.3390/rs9060600",
issn = "2072-4292",
language = "en",
targetfile = "Bellon_remote.pdf",
urlaccessdate = "02 maio 2024"
}