@Article{LucianoPiRoFrSaLeMa:2018:GeSpCl,
author = "Luciano, Ana Cl{\'a}udia dos Santos and Picoli, Michelle Cristina
Ara{\'u}jo and Rocha, Jansle Vieira and Franco, Henrique Coutinho
Junqueira and Sanches, Guilherme Martineli and Leal, Manoel Regis
Lima Verde and Maire, Guerric le",
affiliation = "{Centro Nacional de Pesquisa em Energia e Materiais (CNPEM)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Estadual de Campinas (UNICAMP)} and {Centro Nacional
de Pesquisa em Energia e Materiais (CNPEM)} and {Centro Nacional
de Pesquisa em Energia e Materiais (CNPEM)} and {Centro Nacional
de Pesquisa em Energia e Materiais (CNPEM)} and CIRAD, UMR",
title = "Generalized space-time classifiers for monitoring sugarcane areas
in Brazil",
journal = "Remote Sensing of Environment",
year = "2018",
volume = "215",
pages = "438--451",
month = "Sept.",
keywords = "Object-based classification, Classifier extension, Random Forest,
Sugarcane, Map update, Machine learning.",
abstract = "Spatially and temporally accurate information on crop areas is a
prerequisite for monitoring the multiannual dynamics of crop
production. Satellite images have proven their high potential for
mapping crop areas at large scales, even at the crop-species
level, when a classifier is calibrated on the same image with
reference data corresponding to the same period. For operational
monitoring purposes, however, it is critical to develop
generalized classification methodologies applicable to large
scales and different years. Generalized classifiers were presented
in this study as follows: a) simple cross-year calibration and
application (M1); b) multiyear calibrations (M2); and c) map
updating through change detection with multiyear calibrations
(M3). These three methods were developed in a classical frame of
object-based classifications for a time series of Landsat images
with the Random Forest machine learning algorithm. Therein, we
tested these methods for sugarcane classification in Sao Paulo
state, Brazil, as sugarcane is an economically important crop that
has developed substantially in the past decades. Eight years of
sugarcane reference maps were used to calibrate and validate the
classifiers at four different sites. The cross-year application of
M1 provided a low average accuracy Dice coefficient (DC) of 0.84,
while it was, on average, 0.94 for the classical same-year
calibration. When the classifier was trained on a multiyear
dataset (M2), the accuracies achieved average values of 0.91 in
independent years. The map updating method M3 showed promising
results but was not able to reach the accuracy of visual
interpretation methods for detecting annual sugarcane land use
change. The multiyear classifier M2 was applied to four
contrasting sites and provided reliable results for new sites and
years for sugarcane classification. Calibration of the machine
learning algorithm on a multiyear dataset of standardized and
gap-filled satellite images and reference data proved to give an
accurate and space-time generalized classifier, reducing the time,
cost and resources for mapping sugarcane areas at large scales.",
doi = "10.1016/j.rse.2018.06.017",
url = "http://dx.doi.org/10.1016/j.rse.2018.06.017",
issn = "0034-4257",
language = "en",
targetfile = "luciano-generalized.pdf",
urlaccessdate = "27 abr. 2024"
}