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Remote Sens. 2019, 11(3), 367;

Mapping the Mangrove Forest Canopy Using Spectral Unmixing of Very High Spatial Resolution Satellite Images

Université de Nantes, UMR CNRS 6554 Littoral Environnement Télédétection Géomatique, Campus Tertre, 44312 Nantes, France
AMAP, IRD, CNRS, CIRAD, INRA, Université de Montpellier, 34000 Montpellier, France
French Institute of Pondicherry, Pondicherry 605001, India
ECOLAB, Université de Toulouse, CNRS, INPT, UPS, 118 route de Narbonne, 31500 Toulouse, France
UMR Ecologie des Forêts de Guyane (EcoFoG), INRA, CNRS, Cirad, AgroParisTech, Université des Antilles, Université de Guyane, 97310 Kourou, French Guiana, France
Author to whom correspondence should be addressed.
Received: 15 January 2019 / Revised: 8 February 2019 / Accepted: 8 February 2019 / Published: 12 February 2019
(This article belongs to the Special Issue Remote Sensing of Mangroves)
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Despite the low tree diversity and scarcity of the understory vegetation, the high morphological plasticity of mangrove trees induces, at the stand level, a very large variability of forest structures that need to be mapped for assessing the functioning of such complex ecosystems. Fully constrained linear spectral unmixing (FCLSU) of very high spatial resolution (VHSR) multispectral images was tested to fine-scale map mangrove zonations in terms of horizontal variation of forest structure. The study was carried out on three Pleiades-1A satellite images covering French island territories located in the Atlantic, Indian, and Pacific Oceans, namely Guadeloupe, Mayotte, and New Caledonia archipelagos. In each image, FCLSU was trained from the delineation of areas exclusively related to four components including either pure vegetation, soil (ferns included), water, or shadows. It was then applied to the whole mangrove cover imaged for each island and yielded the respective contributions of those four components for each image pixel. On the forest stand scale, the results interestingly indicated a close correlation between FCLSU-derived vegetation fractions and canopy closure estimated from hemispherical photographs (R2 = 0.95) and a weak relation with the Normalized Difference Vegetation Index (R2 = 0.29). Classification of these fractions also offered the opportunity to detect and map horizontal patterns of mangrove structure in a given site. K-means classifications of fraction indeed showed a global view of mangrove structure organization in the three sites, complementary to the outputs obtained from spectral data analysis. Our findings suggest that the pixel intensity decomposition applied to VHSR multispectral satellite images can be a simple but valuable approach for (i) mangrove canopy monitoring and (ii) mangrove forest structure analysis in the perspective of assessing mangrove dynamics and productivity. As with Lidar-based surveys, these potential new mapping capabilities deserve further physically based interpretation of sunlight scattering mechanisms within forest canopy. View Full-Text
Keywords: mangrove; Remote Sensing; forest structure; hemispherical photographs; Guadeloupe; Mayotte; New Caledonia mangrove; Remote Sensing; forest structure; hemispherical photographs; Guadeloupe; Mayotte; New Caledonia

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Taureau, F.; Robin, M.; Proisy, C.; Fromard, F.; Imbert, D.; Debaine, F. Mapping the Mangrove Forest Canopy Using Spectral Unmixing of Very High Spatial Resolution Satellite Images. Remote Sens. 2019, 11, 367.

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