دانلود مقاله ISI انگلیسی شماره 17526
عنوان فارسی مقاله

سنجش از راه دور / ادغام GIS برای شناسایی سایت های مسکن کم درآمد بالقوه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
17526 2000 13 صفحه PDF سفارش دهید 6760 کلمه
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عنوان انگلیسی
Remote sensing/GIS integration to identify potential low-income housing sites

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Cities, Volume 17, Issue 2, April 2000, Pages 97–109

کلمات کلیدی
سنجش از راه دور - بانکوک - مسکن کم درآمد
کلمات کلیدی انگلیسی
Remote sensing,GIS,Bangkok,Low income housing
پیش نمایش مقاله
پیش نمایش مقاله سنجش از راه دور / ادغام GIS برای شناسایی سایت های مسکن کم درآمد بالقوه

چکیده انگلیسی

Perhaps the most notable problem in the rapid pace of urbanization in the developing world is the need for housing and the provision of related services. The quality of planning and decision making processes can be substantially improved when suitable data are appropriately and efficiently handled. This study reviews the development of remote sensing and geographic information system (GIS) techniques for urban analysis. It then applies these techniques to evaluate several types of planning related information in a raster based (GIS) to identify potential low income housing sites in the eastern portion of the Bangkok Metropolitan Area. This work demonstrates how satellite imagery can provide both site specific information on land cover for mapping urban residential land use, and also act as a medium to generate a variety of GIS coverages.

مقدمه انگلیسی

Similar to other capital cities in the developing world, Bangkok is experiencing many of the growth related pressures that create enormous needs for land and housing that are not matched by a comparable growth in access to land, urban services, or infrastructure. Related problems include high land prices, land tenure security, and poor housing quality. As a result, most of these urban agglomerations are faced with rapid growth and the spread of informal housing settlements of high density, poor house quality, and increased pollution and related health problems. The quantity and variety of publications on this topic in just the last few years underscores the need to obtain greater amounts of information about these settlements that can be used to find solutions to this urban dilemma (see for example, Rakodi and Withers, 1995, Abelson, 1996, O'Hare et al, 1998 and Sen, 1998). In an effort to address specific aspects of land and housing acquisition, and monitor the growth of these settlements, a number of technologies are increasingly used to predict where these sites occur and to better understand the processes that result in these phenomena. These techniques include digital imaging, remote sensing and photogrammetry, object recognition, environmental modeling, and artificial intelligence. All may be referred to collectively as spatial information technology even though they are not exclusively spatial (Mason et al, 1997). Increasingly these systems are becoming integrated within a GIS environment. There are numerous functions a GIS can perform in its supporting role for informal settlement analysis. Inherent in this process are spatial and physical parameters, such as access to transport linkages, to employment, and to services. The GIS is well suited to deal with these factors in a common decision making environment. However, the practical success of this tool depends upon geo-spatial data collection systems that can provide low-cost data (the degree to which community level information gathering proves to be viable), the ability to integrate collected data into low-cost, easy-to-use GIS databases suitable for decision making at the community level, and the awareness and capacity of participants to support the GIS, eg capacity for maintenance (Mason et al, 1997). Planners must take into account both the socio-economic characteristics of sites as well as the constraints of physical layout, available area, and land suitability in performing their tasks. One of the advantages of GIS for urban planning, especially in rapidly growing areas, is that the combination of digital map and database information allows for great flexibility in assessing alternative scenarios. Unfortunately, compiling an urban GIS takes a major resource commitment in time and funding. One major cost is constructing the database information that is associated with the maps. It is estimated that up to 80 percent of time and costs involved in developing GIS are spent on database acquisition and integration (Ehlers et al, 1990). To meet the demand for current and accurate data, remotely sensed images have become increasingly used as an important data source for land cover analysis. Given the dynamics of informal settlements, their density, and the type and quantity of spatial data required for their management, the type of imagery that is used becomes an important decision. Although conventional image processing techniques based solely on spectral observation are often not sufficiently accurate for urban studies, a solution is to extend the classification procedure using other digital ancillary data accessed through a GIS.

نتیجه گیری انگلیسی

As urban areas in the developing world grow at an unprecedented rate, the need for suitable and affordable housing is paramount. The major difficulty in keeping pace with demand is the rapidly rising cost of land. While most governments are able to obtain land for the public good, it is often difficult to identify suitable housing sites. Although vacant land is rather easy to identify, determining the most appropriate sites for development encompasses many more criteria. The use of satellite imagery and its integration into a GIS can provide a timely and appropriate tool for identifying potential housing sites at medium scales. The thematic maps obtained at relatively low cost and in a short time compare favorably with traditional methods of investigation. Owing to the broad level of land use categories, the relatively large areas that were needed for the project, and limited information on roads and infrastructure, it was possible to derive the associated coverages from the satellite image. Once digitization of the various coverages had been completed, identification of potential sites by GIS analysis was efficiently accomplished.

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