Research Papers

Multitemporal classification of TerraSAR-X data for wetland vegetation mapping

[+] Author Affiliations
Julie Betbeder

LETG Rennes COSTEL UMR CNRS 6554 /OSUR, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes Cedex, France

Sébastien Rapinel

LETG Rennes COSTEL UMR CNRS 6554 /OSUR, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes Cedex, France

Thomas Corpetti

LETG Rennes COSTEL UMR CNRS 6554 /OSUR, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes Cedex, France

Eric Pottier

IETR UMR CNRS 6164, Université de Rennes 1, Campus Beaulieu-bât 11D, 263, av du général Leclerc, CS 74205, 35042 Rennes Cedex, France

Samuel Corgne

LETG Rennes COSTEL UMR CNRS 6554 /OSUR, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes Cedex, France

Laurence Hubert-Moy

LETG Rennes COSTEL UMR CNRS 6554 /OSUR, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes Cedex, France

J. Appl. Remote Sens. 8(1), 083648 (Apr 10, 2014). doi:10.1117/1.JRS.8.083648
History: Received January 6, 2014; Revised March 11, 2014; Accepted March 13, 2014
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Abstract.  This paper is concerned with wetland vegetation mapping using multitemporal synthetic aperture radar imagery. Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to precisely map the distribution of vegetation formations considering flood duration. A series of six dual-polarization TerraSAR-X images (HH-VV) was acquired in 2012 during dry and wet seasons. One polarimetric parameter, the Shannon entropy (SE), and two intensity parameters (σ° HH and σ° VV), which vary with wetland flooding status and vegetation roughness, were first extracted. These parameters were then classified using support vector machine techniques based on a specific kernel adapted to the comparison of time-series data, K-nearest neighbors, and decision tree (DT) algorithms. The results show that the vegetation formations can be identified very accurately (kappa index=0.85) from the classification of SE temporal profiles derived from the TerraSAR-X images. They also reveal the importance of the use of polarimetric parameters instead of backscattering coefficients alone (HH or VV) or combined (HH and VV).

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Julie Betbeder ; Sébastien Rapinel ; Thomas Corpetti ; Eric Pottier ; Samuel Corgne, et al.
"Multitemporal classification of TerraSAR-X data for wetland vegetation mapping", J. Appl. Remote Sens. 8(1), 083648 (Apr 10, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083648


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