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Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

Mahoney, Craig; Hopkinson, Chris; Kljun, Natascha LU and van Gorsel, Eva (2017) In Remote Sensing 9(1).
Abstract
Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between 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 canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most... (More)
Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between 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 canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for 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 of GF 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 monitoring investigations can be initiated via upcoming Earth Observation missions. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
vegetation, remote sensing, forestry, LiDAR
in
Remote Sensing
volume
9
issue
1
publisher
MDPI AG
external identifiers
  • scopus:85010651080
ISSN
2072-4292
DOI
10.3390/rs9010059
language
English
LU publication?
no
id
9806c2c6-5ee2-45fd-a9d5-2e0f2b0bbc52
date added to LUP
2018-04-16 14:44:07
date last changed
2019-04-23 04:31:22
@article{9806c2c6-5ee2-45fd-a9d5-2e0f2b0bbc52,
  abstract     = {Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between 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 canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for 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 of GF 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 monitoring investigations can be initiated via upcoming Earth Observation missions.},
  articleno    = {59},
  author       = {Mahoney, Craig and Hopkinson, Chris and Kljun, Natascha and van Gorsel, Eva},
  issn         = {2072-4292},
  keyword      = {vegetation,remote sensing,forestry,LiDAR},
  language     = {eng},
  month        = {01},
  number       = {1},
  publisher    = {MDPI AG},
  series       = {Remote Sensing},
  title        = {Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems},
  url          = {http://dx.doi.org/10.3390/rs9010059},
  volume       = {9},
  year         = {2017},
}