@Article{SaitoArMoSaEuFi:2016:UsGeAn,
author = "Saito, Nath{\'a}lia Suemi and Arguello, Fernanda Viana Paiva and
Moreira, Maur{\'{\i}}cio Alves and Santos, Alexandre Rosa dos
and Eugenio, Fernando Coelho and Figueiredo, Alvaro Costa",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Univesidade Federal do
Esp{\'{\i}}rito Santo (UFES)} and {Univesidade Federal do
Esp{\'{\i}}rito Santo (UFES)} and {Univesidade Federal do
Esp{\'{\i}}rito Santo (UFES)}",
title = "Uso da geotecnologia para an{\'a}lise temporal da cobertura
florestal",
journal = "Cerne",
year = "2016",
volume = "22",
number = "1",
pages = "11--18",
month = "jan./mar.",
keywords = "Data Mining, GeoDMA, Landscape ecology.",
abstract = "The landscape ecology metrics associated with data mining can be
used to increase the potential of remote sensing data analysis and
applications, being an important tool for decision making. The
present study aimed to use data mining techniques and landscape
ecology metrics to classify and quantify different types of
vegetation using a multitemporal analysis (2001 and 2011), in
S{\~a}o Lu{\'{\i}}s do Paraitinga city, S{\~a}o Paulo, Brazil.
Object-based image analyses and the C4.5 data-mining algorithm
were used for automated classification. Classification accuracies
were assessed using the kappa index of agreement and the recently
proposed allocation and quantity disagreement measures. Four land
use and land cover classes were mapped, including Eucalyptus
plantations, whose area increased from 4.4% to 8.6%. The automatic
classification showed a kappa index of 0.79 and 0.80, quantity
disagreements of 2% e 3.5% and allocation measures of 5.5% and 5%
for 2001 and 2011, respectively. We therefore concluded that the
data mining method and landscape ecology metrics were efficient in
separating vegetation classes.",
doi = "10.1590/01047760201622011935",
url = "http://dx.doi.org/10.1590/01047760201622011935",
issn = "0104-7760",
language = "en",
targetfile = "saito_uso.pdf",
urlaccessdate = "27 abr. 2024"
}