Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics
Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics
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DOI：
10.1016/j.compenvurbsys.2005.09.002
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发表时间：
2006-11-01
影响因子：
6.8
通讯作者：
Underhill, Karen
中科院分区：
文献类型：
--
作者：
Ji, Wei;Ma, Jia;Underhill, Karen
本研究旨在探讨城市土地变化的空间分析方法，以识别城市土地变化的总体趋势和更微妙的模式。美国大都市堪萨斯城的Landsat图像被用来生成过去三十年的土地覆盖数据的时间序列。基于遥感土地覆盖数据，计算景观指数。遥感数据和景观指标都被用来描述城市无计划扩展的长期趋势和模式。在大都市，县，市一级的土地覆盖变化分析表明，在过去的三十年中，在研究区的建设用地的显着增加，主要是在牺牲非森林植被覆盖。土地覆被变化的空间和时间异质性使得能够确定快速和缓慢蔓延的地区。景观指标进行了分析，跨司法管辖区的水平，以了解建设扩张的林地和非森林植被覆盖的影响。分析结果表明，在大都市一级的非森林植被和林地的地区变得更加分散，由于发展，而大型森林斑块的影响较小。统计数据表明，这种景观效应在县级水平上发生适度，而在城市水平上只能微弱地识别，这表明城市化的景观响应可以在更大的空间单元（例如，与城市相比，大都市区或县）。的建成斑块密度指标的解释有助于确定不同阶段的城市化在两个主要的城市蔓延方向的大都市区。土地消耗指数（LCI）的设计与遥感建成增长的变化，住房和商业建筑的主要驱动因素，提供了一个有效的措施，比较和表征城市蔓延的管辖边界和时间段。(c)2005爱思唯尔有限公司保留所有权利。
This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods. (c) 2005 Elsevier Ltd. All rights reserved.