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Remote Sens. 2014, 6(8), 7276-7302; doi:10.3390/rs6087276

Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

Center for Spatial Analysis, University of Oklahoma, 301 David L. Boren Blvd. Suite 3120 Norman, OK 73019, USA
Received: 31 January 2014 / Revised: 23 May 2014 / Accepted: 24 July 2014 / Published: 5 August 2014
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Abstract

Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu’s method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop. View Full-Text
Keywords: object-based image analysis; image segmentation optimized for delineation of tree density; very high resolution imagery; species association labeling; the Z values of Moran’s I object-based image analysis; image segmentation optimized for delineation of tree density; very high resolution imagery; species association labeling; the Z values of Moran’s I
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Liau, Y.-T. Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California. Remote Sens. 2014, 6, 7276-7302.

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