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Land use/land cover change modeling:...

Myers, Seth J.

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Land use/land cover change modeling: Investigation of calibration methods and the influence of income on physical constraints to development at multiple spatial scales.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Land use/land cover change modeling: Investigation of calibration methods and the influence of income on physical constraints to development at multiple spatial scales./
Author:	Myers, Seth J.
Description:	148 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
Subject:	Ecology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3614773
ISBN:	9781303804151

Land use/land cover change modeling: Investigation of calibration methods and the influence of income on physical constraints to development at multiple spatial scales.
Myers, Seth J.

Land use/land cover change modeling: Investigation of calibration methods and the influence of income on physical constraints to development at multiple spatial scales. - 148 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--State University of New York College of Environmental Science and Forestry, 2013.

This item must not be sold to any third party vendors.

Land use/cover change (LULCC) is a well-documented and globally significant ecological trend. These changes are a significant way that humans influence terrestrial, atmospheric, and aquatic environments. Given rising concerns about both local and global sustainability the degree to which we can understand better the dynamics and drivers of LULCC, the greater will be our ability to manage the landscape for optimal use by all species. LULCC modeling is the tool most often used to assess past and project future change; however, LULCC models often have low predictive accuracy. This may be due to lack of: adequate model methodology and calibration, understanding of the importance of one independent variable over another in explaining the spatial pattern of transition, accounting for spatial autocorrelation in data inputs, accounting for the interaction of independent variables, and accounting for fine scale decision making (at the pixel level) in modeling large scale patterns of transition, such as sprawl. The objectives of the three manuscripts developed for this dissertation were to address these issues. The model employed in the first and second manuscripts is the popular GEOMOD. In the first manuscript, I test the predictive power of a direct search optimization method (DSO), applied to analysis of LULCC in western Connecticut, versus three other methods (median value, principal component weights, and equal weights) of calibration that have been used in previous GEOMOD applications. In the second, I compare DSO results to objective benchmarks using Monte Carlo simulation, and in the third I investigate probability and pattern of development at two spatial scales using generalized linear mixed and generalized linear models. The tests I proposed rendered support for all hypotheses, and offer promising improvements to not only GEOMOD, but to the field of land change modeling in general. My results enhance calibration, validation, analysis, and prediction at several spatial scales and offer a better means of assessing confidence in model projections, something that is desired by the decision making public who are relying on LULCC models to assess possible future landscape development alternatives and their socio-economic-ecological consequences.

ISBN: 9781303804151Subjects--Topical Terms:

516476
Ecology.

Land use/land cover change modeling: Investigation of calibration methods and the influence of income on physical constraints to development at multiple spatial scales.
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245 1 0 $a Land use/land cover change modeling: Investigation of calibration methods and the influence of income on physical constraints to development at multiple spatial scales.
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500 $a Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
500 $a Advisers: Myrna H. Hall; Charles A. Hall.
502 $a Thesis (Ph.D.)--State University of New York College of Environmental Science and Forestry, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a Land use/cover change (LULCC) is a well-documented and globally significant ecological trend. These changes are a significant way that humans influence terrestrial, atmospheric, and aquatic environments. Given rising concerns about both local and global sustainability the degree to which we can understand better the dynamics and drivers of LULCC, the greater will be our ability to manage the landscape for optimal use by all species. LULCC modeling is the tool most often used to assess past and project future change; however, LULCC models often have low predictive accuracy. This may be due to lack of: adequate model methodology and calibration, understanding of the importance of one independent variable over another in explaining the spatial pattern of transition, accounting for spatial autocorrelation in data inputs, accounting for the interaction of independent variables, and accounting for fine scale decision making (at the pixel level) in modeling large scale patterns of transition, such as sprawl. The objectives of the three manuscripts developed for this dissertation were to address these issues. The model employed in the first and second manuscripts is the popular GEOMOD. In the first manuscript, I test the predictive power of a direct search optimization method (DSO), applied to analysis of LULCC in western Connecticut, versus three other methods (median value, principal component weights, and equal weights) of calibration that have been used in previous GEOMOD applications. In the second, I compare DSO results to objective benchmarks using Monte Carlo simulation, and in the third I investigate probability and pattern of development at two spatial scales using generalized linear mixed and generalized linear models. The tests I proposed rendered support for all hypotheses, and offer promising improvements to not only GEOMOD, but to the field of land change modeling in general. My results enhance calibration, validation, analysis, and prediction at several spatial scales and offer a better means of assessing confidence in model projections, something that is desired by the decision making public who are relying on LULCC models to assess possible future landscape development alternatives and their socio-economic-ecological consequences.
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