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Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

Craig Mahoney, Chris Hopkinson, Natascha Kljun Orcid Logo, Eva van Gorsel

Remote Sensing, Volume: 9, Issue: 1, Start page: 59

Swansea University Author: Natascha Kljun Orcid Logo

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DOI (Published version): 10.3390/rs9010059

Abstract

Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary withrespect to discrete return airborne equivalents due to their greater sensitivity to reflectance differencesbetween canopy and ground surfaces resulting from differences in footprint size, energy thresholding,noise cha...

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Published in: Remote Sensing
ISSN: 2072-4292
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa31620
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spelling 2017-03-26T22:14:08.7145615 v2 31620 2017-01-12 Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems c96172d106206ba8c504317bb7887587 0000-0001-9650-2184 Natascha Kljun Natascha Kljun true false 2017-01-12 FGSEN Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary withrespect to discrete return airborne equivalents due to their greater sensitivity to reflectance differencesbetween canopy and ground surfaces resulting from differences in footprint size, energy thresholding,noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopyportions of waveforms has successfully circumvented this issue, but not at large scales. This studydevelops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensedvegetation, terrain and environmental attributes are best suited to predicting scaling factors basedon an independent measure of importance. The most important attributes were identified as: soilphosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields productand terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS datafor all available data and an optimized subset, where the latter produced most encouraging results(R2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates ofGF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors.Large-scale active sensor estimates of GF can establish a baseline from which future monitoringinvestigations can be initiated via upcoming Earth Observation missions. Journal Article Remote Sensing 9 1 59 2072-4292 vegetation; remote sensing; forestry; LiDAR 11 1 2017 2017-01-11 10.3390/rs9010059 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2017-03-26T22:14:08.7145615 2017-01-12T10:49:56.1529245 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Craig Mahoney 1 Chris Hopkinson 2 Natascha Kljun 0000-0001-9650-2184 3 Eva van Gorsel 4 0031620-31012017095041.pdf remotesensing0900059.pdf 2017-01-31T09:50:41.4870000 Output 5825856 application/pdf Version of Record true 2017-01-11T00:00:00.0000000 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license. true 0031620-12012017105127.pdf mahoney_etal_2017.pdf 2017-01-12T10:51:27.0570000 Output 5830474 application/pdf Author's Original true 2017-01-12T00:00:00.0000000 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license. false
title Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems
spellingShingle Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems
Natascha Kljun
title_short Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems
title_full Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems
title_fullStr Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems
title_full_unstemmed Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems
title_sort Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems
author_id_str_mv c96172d106206ba8c504317bb7887587
author_id_fullname_str_mv c96172d106206ba8c504317bb7887587_***_Natascha Kljun
author Natascha Kljun
author2 Craig Mahoney
Chris Hopkinson
Natascha Kljun
Eva van Gorsel
format Journal article
container_title Remote Sensing
container_volume 9
container_issue 1
container_start_page 59
publishDate 2017
institution Swansea University
issn 2072-4292
doi_str_mv 10.3390/rs9010059
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 Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary withrespect to discrete return airborne equivalents due to their greater sensitivity to reflectance differencesbetween canopy and ground surfaces resulting from differences in footprint size, energy thresholding,noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopyportions of waveforms has successfully circumvented this issue, but not at large scales. This studydevelops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensedvegetation, terrain and environmental attributes are best suited to predicting scaling factors basedon an independent measure of importance. The most important attributes were identified as: soilphosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields productand terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS datafor all available data and an optimized subset, where the latter produced most encouraging results(R2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates ofGF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors.Large-scale active sensor estimates of GF can establish a baseline from which future monitoringinvestigations can be initiated via upcoming Earth Observation missions.
published_date 2017-01-11T03:38:38Z
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score 11.017797

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