Data/hora: 07/06/2024 08:35:05

Provedor de dados: | 70
País: | Brazil
Título: | Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus
Autores: | Silva,Alessandra F.; Barbosa,Ana Paula; Zimback,Célia R. L.; Landim,Paulo M. B.
Data: | 2013-12-01
Ano: | 2013
Palavras-chave: | Indicator Kriging; Cluster; Maxver; CBERS-2B satellite; Spatial classification.
Resumo: | This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162013000600017
Editor: | Associação Brasileira de Engenharia Agrícola
Relação: | 10.1590/S0100-69162013000600017
Formato: | text/html
Fonte: | Engenharia Agrícola  v.33 n.6 2013
Fonte: | info:eu-repo/semantics/openAccess