Note
Dorothy F. Schmidt College of Arts and Letters
Member of
Contributors
Publisher
Florida Atlantic University
Date Issued
2007
EDTF Date Created
2007
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of outdated and unreliable information.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.
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of outdated and unreliable information.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.
Language
Type
Form
Extent
78 p.
Subject (Topical)
Identifier
FA00000966
Additional Information
Dorothy F. Schmidt College of Arts and Letters
Thesis (M.A.)--Florida Atlantic University, 2007.
FAU Electronic Theses and Dissertations Collection
Date Backup
2007
Date Created Backup
2007
Date Text
2007
Date Created (EDTF)
2007
Date Issued (EDTF)
2007
Extension
FAU
IID
FA00000966
Organizations
Attributed name: Florida Atlantic University
Person Preferred Name
Sprague, Drake H.
Graduate College
Physical Description
application/pdf
78 p.
Title Plain
A Regression Model for Predicting Percent Built-up Land Cover from Remotely Sensed Imagery of Pucallpa, Peru
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.
http://rightsstatements.org/vocab/InC/1.0/
Origin Information
2007
2007
Florida Atlantic University
Boca Raton, Fla.
Physical Location
Florida Atlantic University Libraries
Place
Boca Raton, Fla.
Sub Location
Digital Library
Title
A Regression Model for Predicting Percent Built-up Land Cover from Remotely Sensed Imagery of Pucallpa, Peru
Other Title Info
A Regression Model for Predicting Percent Built-up Land Cover from Remotely Sensed Imagery of Pucallpa, Peru