@Article{LucianoPiRoDuLaLeLe:2019:GeSpOB,
author = "Luciano, Ana Cl{\'a}udia dos Santos and Picoli, Michelle Cristina
Ara{\'u}jo and Rocha, Jansle Vieira and Duft, Daniel Garbellini
and Lamparelli, Rubens Augusto Camargo and Leal, Manoel Regis Lima
Verde and Le Maire, Guerric",
affiliation = "{Centro Naconal de Pesquisa em Energia e Materiais (CNPEM)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Estadual de Campinas (UNICAMP)} and {Centro Naconal
de Pesquisa em Energia e Materiais (CNPEM)} and {Universidade
Estadual de Campinas (UNICAMP)} and {Centro Naconal de Pesquisa em
Energia e Materiais (CNPEM)} and CIRAD, UMR Eco\&So",
title = "A generalized space-time OBIA classification scheme to map
sugarcane areas at regional scale, using Landsat images
time-series and the random forest algorithm",
journal = "International Journal of Applied Earth Observation and
Geoinformation",
year = "2019",
volume = "80",
pages = "127--136",
month = "Aug.",
keywords = "Classifier extension, Data mining, Machine learning, Sugarcane
mapping.",
abstract = "The monitoring of sugarcane areas is important for sustainable
planning and management of the sugarcane industry in Brazil. We
developed an operational Object-Based Image Analysis (OBIA)
classification scheme, with generalized space-time classifier, for
mapping sugarcane areas at the regional scale in Sao Paulo State
(SP). Binary random forest (RF) classification models were
calibrated using multi-temporal data from Landsat images, at 10
sites located across SP. Space and time generalization were tested
and compared for three approaches: a local calibration and
application; a cross-site spatial generalization test with the RF
model calibrated on a site and applied on other sites; and a
unique space-time classifier calibrated with all sites together on
years 2009-2014 and applied to the entire SP region on 2015. The
local RF models Dice Coefficient (DC) accuracies at sites 1 to 8
were between 0.83 and 0.92 with an average of 0.89. The cross-site
classification accuracy showed an average DC of 0.85, and the
unique RF model had a DC of 0.89 when compared with a reference
map of 2015. The results demonstrated a good relationship between
sugarcane prediction and the reference map for each municipality
in SP, with R-2 = 0.99 and only 5.8% error for the total sugarcane
area in SP, and compared with the area inventory from the
Brazilian Institute of Geography and Statistics, with R-2 = 0.95
and -1% error for the total sugarcane area in SP. The final unique
RF model allowed monitoring sugarcane plantations at the regional
scale on independent year, with efficiency, low-cost, limited
resources and a precision approximating that of a
photointerpretation.",
doi = "10.1016/j.jag.2019.04.013",
url = "http://dx.doi.org/10.1016/j.jag.2019.04.013",
issn = "0303-2434",
language = "en",
targetfile = "1-s2.0-S0303243418311917-main.pdf",
urlaccessdate = "27 abr. 2024"
}