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Coastal Land Dynamics and Vulnerability Analyses based on the Brightness-Greenness-Wetness Model

Author

Listed:
  • Xuelian Meng
  • Haitian Zhu

    (Department of Geography and Anthropology, Louisiana State University, USA)

  • Xuelian Meng

    (Coastal Studies Institute, Louisiana State University, USA)

  • Haitian Zhu

    (National Satellite Ocean Application Service (NSOAS), State Oceanic Administration, China)

Abstract

This paper introduces the simplified brightness-greenness-wetness (B-G-W) model for multi-temporal land dynamics and vulnerability analyses in coastal environments and searches for an optimal mapping solution based on Landsat images to support its applications. A comparative study using the machine-learning algorithm of Support Vector Machine (SVM) first evaluated different sets of input bands (DN, KTC transformation, and different indices) and compared pixel- vs. object-based approaches to select an accurate and robust classification approach. The adoption of a recently developed multi-scale analysis method helped to quantify and identify the optimal segmentation scale for object-based classification.

Suggested Citation

  • Xuelian Meng & Haitian Zhu & Xuelian Meng & Haitian Zhu, 2019. "Coastal Land Dynamics and Vulnerability Analyses based on the Brightness-Greenness-Wetness Model," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 22(5), pages 169-181, December.
    • Handle: RePEc:adp:ijesnr:v:22:y:2019:i:5:p:169-181
    DOI: 10.19080/IJESNR.2019.22.556098
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    Keywords

    earth and environment journals; environment journals; open access environment journals; peer reviewed environmental journals; open access; juniper publishers; ournal of Environmental Sciences; juniper publishers journals ; juniper publishers reivew;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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