Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/7043
Title: High resolution remote sensing for native vegetation assessment and monitoring: an impact assessment approach
Contributor(s): Jenkins, Ross  (author)orcid ; Frazier, Paul  (supervisor); Lamb, David  (supervisor); Wilkes, Janelle  (supervisor)
Conferred Date: 2010
Copyright Date: 2009
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/7043
Abstract: The last decade has seen major advances in remote sensing technology, particularly in high-resolution satellite imagery and airborne laser scanning (ALS). Fundamental differences in data capture mean that new assessment techniques are required, particularly for vegetation structure and multi-temporal analysis. Here, high-resolution remote sensing tools are developed using a longwall mine subsidence impact assessment framework, based primarily on a scrubby forest-woodland setting on the Woronora Plateau, NSW Australia. Linear regression and t-tests were used to compare vegetation structural metrics from field and ALS data, with ANOVA and post-hoc tests used to determine solar energy and moisture controls on vegetation variation at hillslope scale. Landscape stratification was based on insolation and topographic wetness surfaces derived from ALS-based digital elevation models (DEM). Image matching and linear regression was used to test 3D-method orthorectification accuracy for off-nadir QuickBird imagery using different-resolution DEM. High resolution ALS-derived digital elevation models (DEM) allow pixel-accurate orthorectification of off-nadir imagery, a necessary precursor to multi-temporal image analysis.
Publication Type: Thesis Doctoral
Fields of Research (FoR) 2008: 050204 Environmental Impact Assessment
Socio-Economic Objective (SEO) 2008: 960505 Ecosystem Assessment and Management of Forest and Woodlands Environments
Rights Statement: Copyright 2009 - Ross Barrett Jenkins
HERDC Category Description: T2 Thesis - Doctorate by Research
Appears in Collections:Thesis Doctoral

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