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A Regression Model for Predicting Percent Built-up Land Cover from Remotely Sensed Imagery of Pucallpa, Peru
- 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 Unfortunately, this data is often too expensive for planning agencies, prompting use of outdated and unreliable information.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 |
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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 Unfortunately, this data is often too expensive for planning agencies, prompting use of outdated and unreliable information.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 |
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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. |