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Improved estimates of vegetation and terrain parameters from waveform LiDAR. / Craig Mahoney

Swansea University Author: Craig Mahoney

Abstract

Light Detection And Ranging (LiDAR) technologies have evolved rapidly over the last decade, contributing to our knowledge of the Earth's surface evolution from local to global scales. A relatively young form of LiDAR is continuous waveform, which has not yet been fully exploited. The current re...

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Published: 2014
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42490
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first_indexed 2018-08-02T18:54:50Z
last_indexed 2018-08-03T10:10:17Z
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spelling 2018-08-02T16:24:29.4314045 v2 42490 2018-08-02 Improved estimates of vegetation and terrain parameters from waveform LiDAR. 549058ce3283da73428ae2b3be74089d NULL Craig Mahoney Craig Mahoney true true 2018-08-02 Light Detection And Ranging (LiDAR) technologies have evolved rapidly over the last decade, contributing to our knowledge of the Earth's surface evolution from local to global scales. A relatively young form of LiDAR is continuous waveform, which has not yet been fully exploited. The current research investigates and develops new methods, highlighting the potential and possible pitfalls of working with continuous waveform LiDAR. The first piece of research investigates the effects of shadowing in LiDAR waveforms in physically observed, large footprint LiDAR waveforms, based on previous works noting shadowing effects in radiative transfer models, and in a controlled environment experiment. For this investigation airborne LiDAR derived digital elevation models were employed in conjunction with spatially corresponding waveform returns to identify possible shadowing effects. It was found that shadows occur more frequently over more severely sloped terrain, affecting the accuracy of waveform derived vegetation parameters. The implications of shadows in waveform data are also discussed. The second piece of research develops and tests two methods, the Slope Screening Model and Independent Slope Model, such to determine ground slope information from LiDAR waveforms. Both methods were validated against discrete return airborne LiDAR data, and British Ordnance Survey data, such to identify winch method is most suited to retrieving slope. The third piece of research utilises the favoured method for slope prediction from the second r(\searc4i topic to correct vegetation height estimates for slope. Two methods (Lox' and modified) are investigated and tested, and validated against airborne LiDAR equivalent results at the regional scale, and against normalised difference vegetation index at the near global scale. Both correction methods produced statistically significant differences in mean global vegetation heights with regards to a control dataset. E-Thesis Remote sensing. 31 12 2014 2014-12-31 COLLEGE NANME Geography COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:29.4314045 2018-08-02T16:24:29.4314045 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Craig Mahoney NULL 1 0042490-02082018162458.pdf 10801720.pdf 2018-08-02T16:24:58.6030000 Output 20121675 application/pdf E-Thesis true 2018-08-02T16:24:58.6030000 false
title Improved estimates of vegetation and terrain parameters from waveform LiDAR.
spellingShingle Improved estimates of vegetation and terrain parameters from waveform LiDAR.
Craig Mahoney
title_short Improved estimates of vegetation and terrain parameters from waveform LiDAR.
title_full Improved estimates of vegetation and terrain parameters from waveform LiDAR.
title_fullStr Improved estimates of vegetation and terrain parameters from waveform LiDAR.
title_full_unstemmed Improved estimates of vegetation and terrain parameters from waveform LiDAR.
title_sort Improved estimates of vegetation and terrain parameters from waveform LiDAR.
author_id_str_mv 549058ce3283da73428ae2b3be74089d
author_id_fullname_str_mv 549058ce3283da73428ae2b3be74089d_***_Craig Mahoney
author Craig Mahoney
author2 Craig Mahoney
format E-Thesis
publishDate 2014
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
description Light Detection And Ranging (LiDAR) technologies have evolved rapidly over the last decade, contributing to our knowledge of the Earth's surface evolution from local to global scales. A relatively young form of LiDAR is continuous waveform, which has not yet been fully exploited. The current research investigates and develops new methods, highlighting the potential and possible pitfalls of working with continuous waveform LiDAR. The first piece of research investigates the effects of shadowing in LiDAR waveforms in physically observed, large footprint LiDAR waveforms, based on previous works noting shadowing effects in radiative transfer models, and in a controlled environment experiment. For this investigation airborne LiDAR derived digital elevation models were employed in conjunction with spatially corresponding waveform returns to identify possible shadowing effects. It was found that shadows occur more frequently over more severely sloped terrain, affecting the accuracy of waveform derived vegetation parameters. The implications of shadows in waveform data are also discussed. The second piece of research develops and tests two methods, the Slope Screening Model and Independent Slope Model, such to determine ground slope information from LiDAR waveforms. Both methods were validated against discrete return airborne LiDAR data, and British Ordnance Survey data, such to identify winch method is most suited to retrieving slope. The third piece of research utilises the favoured method for slope prediction from the second r(\searc4i topic to correct vegetation height estimates for slope. Two methods (Lox' and modified) are investigated and tested, and validated against airborne LiDAR equivalent results at the regional scale, and against normalised difference vegetation index at the near global scale. Both correction methods produced statistically significant differences in mean global vegetation heights with regards to a control dataset.
published_date 2014-12-31T03:53:04Z
_version_ 1763752630679502848
score 11.035349

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