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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9806c2c6-5ee2-45fd-a9d5-2e0f2b0bbc52
- author
- Mahoney, Craig
; Hopkinson, Chris
; Kljun, Natascha
LU
and van Gorsel, Eva
- publishing date
- 2017-01-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- vegetation, remote sensing, forestry, LiDAR
- in
- Remote Sensing
- volume
- 9
- issue
- 1
- article number
- 59
- 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
- 2022-04-25 06:53:23
@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.}},
author = {{Mahoney, Craig and Hopkinson, Chris and Kljun, Natascha and van Gorsel, Eva}},
issn = {{2072-4292}},
keywords = {{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}},
doi = {{10.3390/rs9010059}},
volume = {{9}},
year = {{2017}},
}