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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Development of a geospatial model to quantify, describe and map urban growth
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Development of a geospatial model to quantify, describe and map urban growth

机译:开发地理空间模型以量化,描述和绘制城市增长图

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In the United States, there is widespread concern about understanding and curbing urban sprawl, which has been cited for its negative impacts on natural resources, economic health, and community character. There is not, however, a universally accepted definition of urban sprawl. It has been described using quantitative measures, qualitative terms, attitudinal explanations, and landscape patterns. To help local, regional and state land use planners better understand and address the issues attributed to sprawl, researchers at NASA's Northeast Regional Earth Science Applications Center (RESAC) at The University of Connecticut have developed an urban growth model. The model, which is based on land cover derived from remotely sensed satellite imagery, determines the geographic extent, patterns, and classes of urban growth over time. Input data to the urban growth model consist of two dates of satellite-derived land cover data that are converted, based on user-defined reclassification options, to just three classes: developed, non-developed, and water. The model identifies three classes of undeveloped land as well as developed land for both dates based on neighborhood information. These two images are used to create a change map that provides more detail than a traditional change analysis by utilizing the classes of non-developed land and including contextual information. The change map becomes the input for the urban growth analysis where five classes of growth are identified: infill, expansion, isolated, linear branch, and clustered branch. The output urban growth map is a powerful visual and quantitative assessment of the kinds of urban growth that have occurred across a landscape. Urban growth further can be characterized using a temporal sequence of urban growth maps to illustrate urban growth dynamics. Beyond analysis, the ability of remote sensing-based information to show changes to a community's landscape, at different geographic scales and over time, is a new and unique resource for local land use decision makers as they plan the future of their communities. (C) 2003 Elsevier Inc. All rights reserved. [References: 28]
机译:在美国,人们普遍了解并遏制城市扩张,这被认为是对自然资源,经济健康和社区特征的负面影响。但是,还没有公认的城市扩张定义。它已使用定量方法,定性术语,态度解释和景观格局进行了描述。为了帮助地方,区域和州的土地利用规划者更好地理解和解决蔓延引起的问题,康涅狄格大学NASA东北地区地球科学应用中心(RESAC)的研究人员开发了一种城市增长模型。该模型基于从遥感卫星图像获得的土地覆盖率,确定了随时间推移的地理范围,模式和城市增长类别。城市增长模型的输入数据包括两个卫星衍生的土地覆盖数据日期,这些数据根据用户定义的重分类选项转换为三个类别:已开发,未开发和水。该模型根据邻域信息确定了两个日期的未开发土地和已开发土地的类别。这两个图像用于创建变更图,该变更图通过利用未开发土地的类别并包括上下文信息来提供比传统变更分析更多的细节。变更图成为城市增长分析的输入,其中确定了五类增长:填充,扩展,隔离,线性分支和群集分支。输出的城市增长图是对整个景观中各种类型的城市增长的强大视觉和定量评估。可以使用城市增长图的时间序列来说明城市增长动态,从而进一步表征城市增长。除分析外,基于遥感的信息显示不同地域规模和随着时间变化的社区景观变化的能力,对于本地土地使用决策者在规划社区的未来时是一种新的独特资源。 (C)2003 Elsevier Inc.保留所有权利。 [参考:28]

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