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Separating Mangrove Species and Conditions Using Laboratory Hyperspectral Data: A Case Study of a Degraded Mangrove Forest of the Mexican Pacific

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Department of Geography and Geology, Algoma University, 1520 Queen Street East, Sault Ste. Marie, ON P6A 2G4, Canada
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Department of Geography, Nipissing University, 100 College Drive, North Bay, ON P1B 8L7, Canada
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Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614, USA
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Instituto del Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Mazatlán, SIN 82040, Mexico
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Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, A.P. 70-305, Av. Universidad 3000, Ciudad Universitaria, Coyoacán D.F. 04510, México
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Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(12), 11673-11688; https://doi.org/10.3390/rs61211673
Received: 19 August 2014 / Revised: 18 November 2014 / Accepted: 19 November 2014 / Published: 25 November 2014
Given the scale and rate of mangrove loss globally, it is increasingly important to map and monitor mangrove forest health in a timely fashion. This study aims to identify the conditions of mangroves in a coastal lagoon south of the city of Mazatlán, Mexico, using proximal hyperspectral remote sensing techniques. The dominant mangrove species in this area includes the red (Rhizophora mangle), the black (Avicennia germinans) and the white (Laguncularia racemosa) mangrove. Moreover, large patches of poor condition black and red mangrove and healthy dwarf black mangrove are commonly found. Mangrove leaves were collected from this forest representing all of the aforementioned species and conditions. The leaves were then transported to a laboratory for spectral measurements using an ASD FieldSpec® 3 JR spectroradiometer (Analytical Spectral Devices, Inc., USA). R2 plot, principal components analysis and stepwise discriminant analyses were then used to select wavebands deemed most appropriate for further mangrove classification. Specifically, the wavebands at 520, 560, 650, 710, 760, 2100 and 2230 nm were selected, which correspond to chlorophyll absorption, red edge, starch, cellulose, nitrogen and protein regions of the spectrum. The classification and validation indicate that these wavebands are capable of identifying mangrove species and mangrove conditions common to this degraded forest with an overall accuracy and Khat coefficient higher than 90% and 0.9, respectively. Although lower in accuracy, the classifications of the stressed (poor condition and dwarf) mangroves were found to be satisfactory with accuracies higher than 80%. The results of this study indicate that it could be possible to apply laboratory hyperspectral data for classifying mangroves, not only at the species level, but also according to their health conditions. View Full-Text
Keywords: mangrove; degradation; hyperspectral remote sensing; classification; Mexico mangrove; degradation; hyperspectral remote sensing; classification; Mexico
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Zhang, C.; Kovacs, J.M.; Liu, Y.; Flores-Verdugo, F.; Flores-de-Santiago, F. Separating Mangrove Species and Conditions Using Laboratory Hyperspectral Data: A Case Study of a Degraded Mangrove Forest of the Mexican Pacific. Remote Sens. 2014, 6, 11673-11688.

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