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Remote Sens. 2017, 9(1), 59; doi:10.3390/rs9010059

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

1
Department of Geography, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
2
Department of Geography, Swansea University, Singleton Park, Swansea SA2 8PP, UK
3
CSIRO Oceans and Atmosphere, Wilf Crane Crescent, Yarralumla ACT 2600, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Lars T. Waser, Randolph H. Wynne and Prasad S. Thenkabail
Received: 19 September 2016 / Revised: 19 December 2016 / Accepted: 22 December 2016 / Published: 11 January 2017
View Full-Text   |   Download PDF [5689 KB, uploaded 11 January 2017]   |  

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. View Full-Text
Keywords: vegetation; remote sensing; forestry; LiDAR vegetation; remote sensing; forestry; LiDAR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mahoney, C.; Hopkinson, C.; Kljun, N.; van Gorsel, E. Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems. Remote Sens. 2017, 9, 59.

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