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Integration of remote sensing and ce...

Li, Guiying.

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Integration of remote sensing and census data for land-use and land cover classification and population estimation in Indianapolis, Indiana.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Integration of remote sensing and census data for land-use and land cover classification and population estimation in Indianapolis, Indiana./
作者:	Li, Guiying.
面頁冊數:	104 p.
附註:	Adviser: Qihao Weng.
Contained By:	Dissertation Abstracts International69-03A.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3305420
ISBN:	9780549516538

Integration of remote sensing and census data for land-use and land cover classification and population estimation in Indianapolis, Indiana.
Li, Guiying.

Integration of remote sensing and census data for land-use and land cover classification and population estimation in Indianapolis, Indiana. - 104 p.

Adviser: Qihao Weng.

Thesis (Ph.D.)--Indiana State University, 2008.

Increasing population and economic growth has resulted in rapid urban expansion in the past decades. Timely and accurately mapping urban land-use and land-cover (LULC) and associated population distribution are often required for many applications such as urban management and planning, and environmental monitoring and assessment. Although many techniques have been developed, urban LULC classification is still a challenge based on remote sensing data. This research explored the integration of Landsat ETM+ image and census data for improving urban LULC classification accuracy, focusing on the distinction of different densities of residential use. The housing data were examined for use at three different stages of image classification, that is, at pre-classification for selection of training sample plots, during the classification as an extra channel, and at post-classification by sorting the classified image. The results indicated that the use of housing data was effective in improving overall urban LULC classification accuracy, especially useful for separating residential classes by post-classification sorting.

ISBN: 9780549516538Subjects--Topical Terms:

524010
Geography.

Integration of remote sensing and census data for land-use and land cover classification and population estimation in Indianapolis, Indiana.
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520 $a Increasing population and economic growth has resulted in rapid urban expansion in the past decades. Timely and accurately mapping urban land-use and land-cover (LULC) and associated population distribution are often required for many applications such as urban management and planning, and environmental monitoring and assessment. Although many techniques have been developed, urban LULC classification is still a challenge based on remote sensing data. This research explored the integration of Landsat ETM+ image and census data for improving urban LULC classification accuracy, focusing on the distinction of different densities of residential use. The housing data were examined for use at three different stages of image classification, that is, at pre-classification for selection of training sample plots, during the classification as an extra channel, and at post-classification by sorting the classified image. The results indicated that the use of housing data was effective in improving overall urban LULC classification accuracy, especially useful for separating residential classes by post-classification sorting.
520 $a Much research has been conducted for population estimation with remote sensing data over the past decades. Although many techniques have been used, it is still difficult to select a proper approach to estimate population and to achieve high accuracy. The performance of population estimation models varies with methods used, details of population data set (block, block group, tract, or derived pixel level), and different settings of study areas. This research compared different methods for population estimation based on the same ETM+ image and census data in order to reach some general conclusions. The results indicated that the use of residential classes can provide better estimation performance than use of other variables. The major advantage for using residential classes was that it can be easily transferred to other data sets for population estimation, because other sources of remotely sensed data can also be used to extract residential land classes.
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