期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 23, 期 9, 页码 1211-1232出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658810802344101
关键词
geographical information systems; agent based modelling; land use; land cover; policy
类别
资金
- National Science and Engineering Research Council of Canada (NSERC)
- Graham Environmental Sustainability Institute (GESI) at the University of Michigan
- National Science Foundation Biocomplexity in the Environment Programme [BCS-0119804]
We use a GIS-based agent-based model (ABM), named dynamic ecological exurban development (DEED), with spatial data in hypothetical scenarios to evaluate the individual and interacting effects of lot-size zoning and municipal land-acquisition strategies on possible forest-cover outcomes in Scio Township, a municipality in Southeastern Michigan. Agent types, characteristics, behavioural methods, and landscape perceptions (i.e. landscape aesthetics) are empirically informed using survey data, spatial analyses, and a USDA methodology for mapping landscape aesthetic quality. Results from our scenario experiments computationally verified literature that show large lot-size zoning policies lead to greater sprawl, and large lot-size zoning policies can lead to increased forest cover, although we found this effect to be small relative to municipal land acquisition. The return on land acquisition for forest conservation was strongly affected by the location strategy used to select parcels for conservation. Furthermore, the location strategy for forest conservation land acquisition was more effective at increasing aggregate forest levels than the independent zoning policies, the quantity of area acquired for forest conservation, and any combination of the two. The results using an integrated GIS and ABM framework for evaluating land-use development policies on forest cover provide additional insight into how these types of policies may act out over time and what aspects of the policies were more influential towards the goal of maximising forest cover.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据