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A regression model for predicting pe...

Florida Atlantic University.

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A regression model for predicting percent built-up land cover from remotely sensed imagery of Pucallpa, Peru.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A regression model for predicting percent built-up land cover from remotely sensed imagery of Pucallpa, Peru./
Author:	Sprague, Drake H.
Description:	67 p.
Notes:	Adviser: Maria Garcia Quijano.
Contained By:	Masters Abstracts International46-03.
Subject:	Geography. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1449325
ISBN:	9780549302742

A regression model for predicting percent built-up land cover from remotely sensed imagery of Pucallpa, Peru.
Sprague, Drake H.

A regression model for predicting percent built-up land cover from remotely sensed imagery of Pucallpa, Peru. - 67 p.

Adviser: Maria Garcia Quijano.

Thesis (M.A.)--Florida Atlantic University, 2007.

Accurate information about built-up land cover and population density is essential for sustainable urban growth, especially in lesser developed countries. Unfortunately, this data is often too expensive for planning agencies, prompting use of outdated and unreliable information. As a proxy for estimating population density, a linear regression model is proposed to test the relationship between the percentage of built-up land cover and vegetation in Pucallpa, Peru. Expert knowledge, low-cost moderate-resolution satellite imagery, and high-resolution Google Earth images are used to estimate the percentage of built-up land cover at randomly assigned reference locations. Normalized Difference Vegetation Index (NDVI) data, acquired at each reference point, is the independent variable in a linear regression model constructed to predict the percentage of built-up land cover. The results were successful, with an adjusted R 2 = 0.774 at 95% confidence. Strength and accuracy are further evaluated against zoning maps and population estimates provided by local authorities.

ISBN: 9780549302742Subjects--Topical Terms:

524010
Geography.

A regression model for predicting percent built-up land cover from remotely sensed imagery of Pucallpa, Peru.
LDR:02022nam 2200301 a 45 001 856221
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020 $a 9780549302742
035 $a (UMI)AAI1449325
035 $a AAI1449325
040 $a UMI $c UMI
100 1 $a Sprague, Drake H. $3 1022999
245 1 2 $a A regression model for predicting percent built-up land cover from remotely sensed imagery of Pucallpa, Peru.
300 $a 67 p.
500 $a Adviser: Maria Garcia Quijano.
500 $a Source: Masters Abstracts International, Volume: 46-03, page: 1292.
502 $a Thesis (M.A.)--Florida Atlantic University, 2007.
520 $a Accurate information about built-up land cover and population density is essential for sustainable urban growth, especially in lesser developed countries. Unfortunately, this data is often too expensive for planning agencies, prompting use of outdated and unreliable information. As a proxy for estimating population density, a linear regression model is proposed to test the relationship between the percentage of built-up land cover and vegetation in Pucallpa, Peru. Expert knowledge, low-cost moderate-resolution satellite imagery, and high-resolution Google Earth images are used to estimate the percentage of built-up land cover at randomly assigned reference locations. Normalized Difference Vegetation Index (NDVI) data, acquired at each reference point, is the independent variable in a linear regression model constructed to predict the percentage of built-up land cover. The results were successful, with an adjusted R 2 = 0.774 at 95% confidence. Strength and accuracy are further evaluated against zoning maps and population estimates provided by local authorities.
590 $a School code: 0119.
650 4 $a Geography. $3 524010
650 4 $a History, Latin American. $3 1017580
650 4 $a Remote Sensing. $3 1018559
650 4 $a Urban and Regional Planning. $3 1017841
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690 $a 0366
690 $a 0799
690 $a 0999
710 2 0 $a Florida Atlantic University. $3 1017837
773 0 $t Masters Abstracts International $g 46-03.
790 $a 0119
790 1 0 $a Quijano, Maria Garcia, $e advisor
791 $a M.A.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1449325