English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/49411
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Citado 15 veces en Web of Knowledge®  |  Ver citas en Google académico
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar bibText (RIS)Exportar csv (RIS)
Título

Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery

Autor Alatorre, L. C. ; Sánchez-Andrés, Raquel; Cirujano, Santos; Beguería, Santiago ; Sánchez Carrillo, Salvador
Palabras clave remote sensing
maximum likelihood algorithm
curve ROC
mangrove
sensitivity/specificity
Gulf of California
Fecha de publicación jul-2011
EditorMultidisciplinary Digital Publishing Institute
Citación Alatorre LC, Sánchez-Andrés R, Cirujano S, Beguería S, Sánchez-Carrillo S. Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery. Remote Sensing 3 (8): 1568-1583 (2011)
ResumenIn remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non‑mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.
Descripción 16 Pags., 4 Tabls., 8 Figs.
Versión del editorhttp://dx.doi.org/10.3390/rs3081568
URI http://hdl.handle.net/10261/49411
DOI10.3390/rs3081568
ISSN2072-4292
E-ISSN2072-4292
Aparece en las colecciones: (EEAD) Artículos
(MNCN) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
BegueriaS_RemoteSensing_2011.pdf625,66 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 



NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.