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A Regression Model for Predicting Percent Built-up Land Cover from Remotely Sensed Imagery of Pucallpa, Peru

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Date Issued:
2007
Abstract/Description:
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 Unfortunately, this data is often too expensive for planning agencies, prompting use of outdated and unreliable information.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 sate llite 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 R2 = 0.774 at 95% confidence. Strength and accuracy are further evaluated against zoning maps and population estimates provided by local authorities.
Title: A Regression Model for Predicting Percent Built-up Land Cover from Remotely Sensed Imagery of Pucallpa, Peru.
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Name(s): Sprague, Drake H.
Garcia Quijano, Maria, Thesis advisor
Florida Atlantic University, Degree grantor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2007
Date Issued: 2007
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 78 p.
Language(s): English
Abstract/Description: 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 Unfortunately, this data is often too expensive for planning agencies, prompting use of outdated and unreliable information.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 sate llite 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 R2 = 0.774 at 95% confidence. Strength and accuracy are further evaluated against zoning maps and population estimates provided by local authorities.
Identifier: FA00000966 (IID)
Degree granted: Thesis (M.A.)--Florida Atlantic University, 2007.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Dorothy F. Schmidt College of Arts and Letters
Subject(s): Geodynamics
Geographic information systems
Land use, Rural--Government policy--Peru
Vegetation dynamics--Peru--Pucallpa
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00000966
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.