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Remote Sens. 2017, 9(7), 748;

Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

LabEx DRIIHM (Programme “Investissements D’avenir”: ANR-11-LABX-0010), INEE-CNRS 3 Rue Michel-Ange, 75016 Paris, France
GEODE UMR 5602 CNRS, Université Toulouse Jean Jaurès, 5 Allées Antonio Machado, 31058 Toulouse CEDEX 1, France
ONERA, Optics and Associated Techniques Department, 2 Avenue Edouard Belin, 31005 Toulouse CEDEX, France
LETG-Rennes UMR 6554 CNRS, Université Rennes 2, Place du Recteur Henri le Moal, 35043 Rennes CEDEX, France
Author to whom correspondence should be addressed.
Academic Editors: Chein-I Chang, Meiping Song, Junping Zhang, Chao-Cheng Wu and Prasad Thenkabail
Received: 24 May 2017 / Revised: 26 June 2017 / Accepted: 9 July 2017 / Published: 20 July 2017
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)


This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation type spectral signatures have similarities. Consequently, they can be compared to a reference spectral database. To catch those similarities, several similarities criteria (related to distances (Euclidean distance, Manhattan distance, Canberra distance) or spectral shapes (Spectral Angle Mapper) or probabilistic behaviour (Spectral Information Divergence)) and several mathematical transformations of spectral signatures enhancing absorption features (such as the first derivative or the second derivative, the normalized spectral signature, the continuum removal, the continuum removal derivative reflectance, the log transformation) were investigated. Furthermore, those similarity measures were applied on spectral ranges which characterize specific biophysical properties. On the other hand, we suppose that specific biophysical properties/components may help to discriminate between vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges and transformed spectral signatures, as explained above. RLR classifier applied on spectral vegetation indices (training size = 25%) was able to achieve 77.21% overall accuracy in discriminating peatland vegetation types. It was also able to discriminate between 83.95% vegetation types considering specific spectral range [[range-phrase = –]3501350 n m ], first derivative of spectral signatures and training size = 25%. Conversely, similarity criterion was able to achieve 81.70% overall accuracy using the Canberra distance computed on the full spectral range [[range-phrase = –]3502500 n m ]. The results of this study suggest that RLR classifier and similarity criteria are promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture. View Full-Text
Keywords: biodiversity; peatland; vegetation type; classification; hyperspectral; in situ measurements biodiversity; peatland; vegetation type; classification; hyperspectral; in situ measurements

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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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Erudel, T.; Fabre, S.; Houet, T.; Mazier, F.; Briottet, X. Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements. Remote Sens. 2017, 9, 748.

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