Remote sensing of wildland bushfire fuels in the Royal National Park, NSW
thesisposted on 2022-03-28, 11:01 authored by Liam Turner
Field based techniques assessing wildland bushfire fuel characteristics are limited by the spatial extent at which they can be implemented due to cost, labour intensity, and terrain accessibility. Within Australia, research using remote sensing instruments to assess fuel characteristics has utilised low resolution imagery, simultaneously assessing all fuel strata. Within this research, fuel load is used to quantify wildland fuel distribution. Fuel load has been suggested to not account for fuel particle arrangement. This thesis presents two manuscripts: the first investigates the fuel hazard classification of pan-sharpened imagery to improve bushfire fuel assessment resolution, and the second investigates machine learning algorithms and the fusion of LiDAR and high resolution imagery to classify a single fuel stratum, the understory. Results suggest that the use of pan-sharpening to improve bushfire fuel assessment resolution is plausible, and that understory fuels can be classified with moderate accuracy using Support Vector Machine classification of a fusion on imagery and LiDAR metrics.
Table of Contents1 Introduction -- 2 Estimating fuel hazard using optical datasets obtained at different times with pan-sharpening -- 3 Classifying understory fuel hazard using LiDAR and high resolution imagery, integrating fusion ans machine learning -- 4 Conclusion.
NotesTheoretical thesis. Bibliography: pages vi-xiii
Awarding InstitutionMacquarie University
Degree TypeThesis MRes
DegreeMRes, Macquarie University, Faculty of Science and Engineering, Department of Environmental Sciences
Department, Centre or SchoolDepartment of Environmental Sciences
Year of Award2017
Principal SupervisorMichael Chang
RightsCopyright Liam Turner 2016. Copyright disclaimer: http://mq.edu.au/library/copyright
JurisdictionNew South Wales
Extent1 online resource (xvi, 50 pages) colour illustrations
Former Identifiersmq:70248 http://hdl.handle.net/1959.14/1261732
remote sensingLiDARFuel reduction (Wildfire prevention) -- New South Wales -- Royal National ParkWildfiresFire risk assessmentFuel reduction (Wildfire prevention)Wildfires -- New South Wales -- Royal National Parkmachine learningbushfireWildfires -- Environmental aspects -- New South Wales -- Royal National ParkFuel reduction (Wildfire prevention) -- Remote sensingFire risk assessment -- New South Wales -- Royal National ParkWildfires -- Prevention and controlwildland firepan-sharpening