资源与生态学报

Journal of Resources and Ecology ›› 2016, Vol. 7 ›› Issue (2): 107-114.DOI: 10.5814/j.issn.1674-764x.2016.02.005

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Remote Sensing Classification of Marsh Wetland with Different Resolution Images

LI Na1, 2, XIE Gaodi1, ZHOU Demin3, ZHANG Changshun1, JIAO Cuicui1, 2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, C A S, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Base of State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China
  • Received:2015-12-28 Revised:2016-02-15 Online:2016-04-12 Published:2016-04-12
  • Contact: * Xie Gaodi,Tel,+86-10-6488-9441,E-mail:xiegd@igsnrr.ac.cn
  • Supported by:

    This study was jointly supported by the National Science and Technology Support Program (No; 2013BAC03B05), Ecological environment evaluation of disaster area(No; O7M73120AM)

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Abstract:

Successful biological monitoring depends on judicious classification. An attempt has been made to provide an overview of important characteristics of marsh wetland. Classification was used to describe ecosystems and land cover patterns. Different spatial resolution images show different landscape characteristics. Several classification images were used to map and monitor wetland ecosystems of Honghe National Nature Reserve (HNNR) at a plant community scale. HNNR is a typical inland wetland and fresh water ecosystem in the North Temperate Zone. SPOT-5 10 m × 10 m, 20 m × 20 m, and 30 m × 30 m images and Landsat -5 Thematic Mapper (TM) images were used to classify based on maximum likelihood classification (MLC) algorithms. In order to validate the precision of the classifications, this study used aerial photography classification maps as training samples because of their high accuracy. The accuracy of the derived classes was assessed with the discrete multivariate technique called KAPPA accuracy. The results indicate: (1) training samples are important to classification results. (2) Image classification accuracy is always affected by areal fraction and aggregation degree as well as by diversities and patch shape. (3) The core zone area is protected better than buffer zone and experimental zone wetland. The experimental zone degrades fast because of irrational development by humans.