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Incorporating uncertainty in environmental models informed by imagery

Falk, Matthew Gregory (2010) Incorporating uncertainty in environmental models informed by imagery. PhD by Publication, Queensland University of Technology.

Abstract

In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given the increased data size with models informed by imagery. Measurement error models are another approach to incorporating uncertainty when data is informed by imagery. These models for measurement uncertainty, considered in a Bayesian conditional independence framework, are applied to ecological data generated from imagery. The models are shown to be appropriate and useful in certain situations. Measurement uncertainty is also considered in the context of change detection when two images are not co-registered. An approach for detecting change in two successive images is proposed that is not affected by registration. The procedure uses the Kolmogorov-Smirnov test on homogeneous segments of an image to detect change, with the homogeneous segments determined using a Bayesian mixture model of pixel values. Using the mixture model to segment an image also allows for uncertainty in the composition of an image. This thesis concludes by comparing several different Bayesian image segmentation approaches that allow for uncertainty regarding the allocation of pixels to different ground components. Each segmentation approach is applied to a data set of chlorophyll values and shown to have different benefits and drawbacks depending on the aims of the analysis.

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ID Code: 33235
Item Type: QUT Thesis (PhD by Publication)
Supervisor: Mengersen, Kerrie, Pettitt, Anthony, & Denham, Robert
Keywords: Bayesian melding, Bayesian method, Bayesian Normal mixture model, change detection, discrete hidden Markov random field, finite mixture models, Gamma random field, Geographic Information System, GIS, Kolmogorov-Smirnov test, KS test, Local Morans Ii, MCMC, measurement error, otolith measurements, prior specification, regression kriging, remote sensing, Revised Universal Soil Loss Equation, RUSLE, spatial autocorrelation, spatial sampling, species distribution modelling, stratified sampling, uncertainty, variational Bayes, Voronoi tessellations
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Institution: Queensland University of Technology
Deposited On: 23 Jul 2010 13:13
Last Modified: 22 Feb 2013 11:28

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