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PubMed · 17147177

[Segmentation-based multi-scale urban green space landscape].

Xiaofang Sun·Jian Lu·Yibin Sun
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Abstract

In this study, three scales urban green space landscapes were generated by multi-resolution segmentation. With 50 and 300 pixels as the object segmentation thresholds, the small- and large-scale landscape object image layers were produced, and the two object image layers were obtained by the nearest neighbor classification method. The result of small-scale landscape classification image was segmented into middle-scale landscape image, and then classified. Green space information was extracted through vector form of object image layers of three scales landscape classification. The landscape indexes diversity, dominance, evenness, fractal dimension, fragmentation, and interior to edge ration were calculated, with the largest values of the former four indexes being 2.2, 0.681, 0.948, and 0.326, and the smallest values being 1.641, 0. 122, 0.707, and 0.113, respectively, indicating that the diversity, evenness and fragmentation decreased, while the dominance increased with increasing landscape scale. The method of multi-resolution segmentation to generate multi-scale landscape could meet the needs of urban green space landscape research.

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BiodiversityChinaCity PlanningConservation of Natural ResourcesEcologyEcosystemEnvironment DesignPoaceaeSatellite CommunicationsTreesEnglish AbstractJournal ArticleResearch Support, Non-U.S. Gov't

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Xiaofang Sun, Jian Lu, Yibin Sun. 2006. [Segmentation-based multi-scale urban green space landscape].. https://pubmed.ncbi.nlm.nih.gov/17147177/

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