Remote Sens. 2011, 3(8), 1568-1583; doi:10.3390/rs3081568
Article

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

Luis C. Alatorre 1,2,* email, Raquel Sánchez-Andrés 3 email, Santos Cirujano 3 email, Santiago Beguería 4 email and Salvador Sánchez-Carrillo 5 email
1 División Multidisciplinaría de la UACJ en Cuauhtémoc, Universidad Autónoma de Ciudad Juárez (UACJ), Calle Morelos y privada del Roble núm. 101, Fracc. El Roble, C.P. 31579, Cuauhtémoc, Chihuahua, México 2 Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas, Zaragoza 50080, Spain 3 Real Jardín Botánico, Consejo Superior de Investigaciones Científicas, Madrid 28014, Spain 4 Estación Experimental de Aula Dei, Consejo Superior de Investigaciones Científicas, Zaragoza 50059, Spain 5 Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones Científicas, Madrid 28006, Spain
* Author to whom correspondence should be addressed.
Received: 27 April 2011; in revised form: 5 July 2011 / Accepted: 8 July 2011 / Published: 25 July 2011
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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Abstract: In 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.
Keywords: remote sensing; maximum likelihood algorithm; curve ROC; mangrove; sensitivity/specificity; Gulf of California

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Cite This Article

MDPI and ACS Style

Alatorre, L.C.; 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 Sens. 2011, 3, 1568-1583.

AMA Style

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. 2011; 3(8):1568-1583.

Chicago/Turabian Style

Alatorre, Luis C.; Sánchez-Andrés, Raquel; Cirujano, Santos; Beguería, Santiago; Sánchez-Carrillo, Salvador. 2011. "Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery." Remote Sens. 3, no. 8: 1568-1583.

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