Enhancing Landscape Connectivity in Detroit through Multifunctional Green Corridor Modeling and Design

Abstract

Maintaining habitats is important for plants and animals. However, habitats in urban environments are fragmented due to urbanization. The fragmentation is a barrier for wildlife movement because landscape connectivity is decreased in urban environments. Previous studies in conservation ecology paid more attention to natural landscapes than urban environments. Other studies aimed at improving urban landscape connectivity were not practical because of restrictions in fully developed urban environments. This study provides practical conservation methods by taking advantage of existing vacant land to develop green infrastructure and increase landscape connectivity. The city of Detroit is chosen as a case study because of its large potential to redevelop existing vacant land. The paper includes examining structural and functional connectivity by FRAGSTATS and Conefor, selecting core patches in ArcGIS, identifying potential corridors by the least-cost-path and evaluating corridors by gravity model. By comparing data before and after corridor built-up, results show that census tract-level connectivity metrics would be improved by developing proposed corridors. To further link research results with the city of Detroit, multi-functional green infrastructure typologies for vacant land re-development are provided. This paper provides a systematic and scientific method for developing vacant lands and other available lands by green infrastructure network, which benefits both humans and wildlife. By developing green infrastructure network, both social connection and ecology connection will be achieved. Furthermore, this paper connects research with real world situations, providing a founded and practical strategy for other cities having similar vacant lands situation to Detroit to redevelop.Master of Science Master of Landscape ArchitectureNatural Resources and EnvironmentUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/136561/1/Zhang_Zhenzhen_Thesis.pd

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