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Remote Sens. 2014, 6(12), 12187-12216; doi:10.3390/rs61212187

Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach

1
United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
2
Dynamac Corporation c/o, United States Environmental Protection Agency, Cincinnati, OH 45268, USA
3
Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA
4
Institute of General and Experimental Biology, Russian Academy of Sciences, Siberian Branch, Ulan-Ude 670047, Russia
5
Department of Botany and Genetics, Irkutsk State University, Irkutsk 664003, Russia
6
Institute of Geography, Russian Academy of Sciences, Siberian Branch, Irkutsk 664003, Russia
*
Author to whom correspondence should be addressed.
Received: 13 June 2014 / Revised: 18 November 2014 / Accepted: 27 November 2014 / Published: 8 December 2014
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Abstract

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system. View Full-Text
Keywords: Selenga River delta; Lake Baikal; coastal band; NIR2 band; NDVI; grey-level co-occurrence matrix; image texture; Worldview-2 Selenga River delta; Lake Baikal; coastal band; NIR2 band; NDVI; grey-level co-occurrence matrix; image texture; Worldview-2
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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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MDPI and ACS Style

Lane, C.R.; Liu, H.; Autrey, B.C.; Anenkhonov, O.A.; Chepinoga, V.V.; Wu, Q. Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach. Remote Sens. 2014, 6, 12187-12216.

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