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

Xuelian Meng, Haitian Zhu, Xuelian Meng and Haitian Zhu
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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

International Journal of Environmental Sciences & Natural Resources, 2019, vol. 22, issue 5, 169-181

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.

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 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:adp:ijesnr:v:22:y:2019:i:5:p:169-181

DOI: 10.19080/IJESNR.2019.22.556098

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