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Object-based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques

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Abstract

Accurate and informative vegetation maps are in urgent demand to support the Kissimmee-Okeechobee-Everglades ecosystem restoration project in South Florida. In this study, we evaluated the applicability of fine spatial resolution hyperspectral data collected from the HyMap sensor for both community- and species-level vegetation mapping. Informative and accurate vegetation maps were produced by combining machine learning methods (Support Vector Machines (SVM) and Random Forest (RF)), object-based image analysis techniques, and Minimum Noise Fraction (MNF) data transformation. An overall accuracy of 90% was obtained in discriminating 14 vegetation communities. Classification of a large number of species is also promising. An overall accuracy of 85% was achieved in identifying 55 species using a SVM model. The results indicate that fine spatial resolution hyperspectral data classification using such automated procedure has great potential to replace the manual interpretation of aerial photos for vegetation mapping in heterogeneous wetland ecosystems.

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Acknowledgments

We appreciate the constructive comments and suggestions from two anonymous reviewers to improve this paper. The South Florida Water Management District (SFWMD) provided data for this research.

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Authors and Affiliations

  1. Department of Geosciences, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA

    Caiyun Zhang & Zhixiao Xie

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  1. Caiyun Zhang
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  2. Zhixiao Xie
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Correspondence to Caiyun Zhang.

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Zhang, C., Xie, Z. Object-based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques. Wetlands 33, 233–244 (2013). https://doi.org/10.1007/s13157-012-0373-x

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  • DOI: https://doi.org/10.1007/s13157-012-0373-x

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