as snow and wetlands (uncommon across Australia) on
GLAS
canopy height retrievals are unknown as they were unavail-
able for testing. In terms of specific
GLAS
and
ALS
metrics that
are most compatible for model development, it was found that
in the general case,
GLAS
RH
ROS
was most accurately predicted
from
ALS
p95 from an all returns point cloud (
RMSE
= 8.10 m,
R
2
= 0.69, N = 110). The significance of this result is that plot-
and stand-level forest attributes that are frequently modeled
as a function of
ALS
percentiles, can be directly transferred to
GLAS
waveform metrics, which will facilitate the scaling of
attributes from
ALS
extents to
GLAS
continental-scales.
The suggested “optimal”
GLAS
height dataset identified
in this study may be applicable elsewhere across the globe.
However, it is recommended that studies in other geographic
regions follow a similar testing and model development
framework to identify region-specific
GLAS
/
ALS
canopy height
relationships. As new satelliteborne lidar sensors come
on line in the near future (e.g.,
ICESat
2, Global Ecosystem
Dynamics Investigation, and Lidar Surface Topography), and
as airborne lidar datasets become more widely and publicly
available (e.g.,
, such controlled analy-
ses and calibration of canopy height metrics will be necessary
to ensure data consistency and the ability to track biomass
variations in space and time.
Acknowledgments
ICESat
/
GLAS
data were obtained from the National Snow and Ice
Data Center (
NSIDC
),
ALS
data for Robson Creek,
and Watts Creek were obtained through
CSIRO
Marine and
Atmosphere Research, and AusCover
.
au
). AusCover is the remote sensing data products facility of
the Terrestrial Ecosystem Research network (
TERN
,
.
tern.org.au
). Lidar data for Tumbarumba were collected with
support from
NCEO
EO
mission support 2009. Special thanks to
Jorg Hacker and Airborne Research Australia (
ARA
) for carrying
out the airborne campaigns. The authors greatly appreciate
the feedback and suggestions of the anonymous reviewers.
Mahoney acknowledges postdoctoral funding through the
NSERC CREATE
and Campus Alberta Innovates Programs.
References
Abshire, J.B., X.L. Sun, H. Riris, J.M. Sirota, J.F. McGarry, S. Palm,
D.H. Yi, and P. Liiva, 2005. Geoscience Laser Altimeter
System (GLAS) on the ICESat mission: On-orbit measurement
performance,
Geophysical Research Letters
, 32(21).
Allouis, T., S. Durrieu, C. Vega, and P. Couteron, 2013. Stem volume
and above-ground biomass estimation of individual pine trees
from LiDAR data: Contribution of full-waveform signals,
IEEE
Journal of Selected Topics in Applied Earth Observations and
Remote Sensing
, 6(2): 924–934.
ANBG, 2014. Eucalypts at the Australian National Botanic Gardens,
Parks Australia, Canberra, URL:
/
cpbr/cd-keys/euclid3/euclidsample/html/learn.htm
(last date
accessed: 10 March 2016).
Auscover, 2014. Watts Creek site information from Terrestrial
Ecosystem Research Network/Auscover, URL:
auscover.org.au/field
(last date accessed: 10 March 2015).
Baltsavias, E.P., 1999. Airborne laser scanning: Basic relations
and formulas,
ISPRS Journal of Photogrammetry and Remote
Sensing
, 54(2-3):199–214.
Brenner, A., H. Zwally, C. Bentley, B. Csathó, D. Harding, M.
Hofton, J. Minster, L. Roberts, J. Saba, R. Thomas, and D. Yi,
2003.
Geoscience Laser Altimeter System (GLAS) Algorithm
Theoretical Basis Document 4.1: Derivation of Range and Range
Distributions From Laser Pulse Waveform Analysis for Surface
Elevations, Roughness, Slope, and Vegetation Heights
, National
Aeronautics and Space Administration, Goddard Space Flight
Center, Greenbelt, Maryland.
Broich, M., A. Huete, M. Tulbure, X. Ma, Q. Xin, M. Paget, N.
Restrepo-Coupe, K. Davies, R. Devadas, and A. Held, 2014. Land
surface phenological response to decadal climate variability
across Australia using satellite remote sensing,
Biogeosciences
Discussions
, 11(5):7685–7719.
Chen, Q., 2010. Assessment of terrain elevation derived from satellite
laser altimetry over mountainous forest areas using airborne
lidar data,
ISPRS Journal of Photogrammetry and Remote
Sensing
, 65(1):111–122.
Chen, Q., 2010. Retrieving vegetation height of forests and woodlands
over mountainous areas in the Pacific Coast region using satellite
laser altimetry,
Remote Sensing of Environment
, 114(7):1610–
1627.
Clark, M.L., D.B. Clark, and D.A. Roberts, 2004. Small-footprint lidar
estimation of sub-canopy elevation and tree height in a tropical
rain forest landscape,
Remote Sensing of Environment
, 91(1):68–89.
DAFF, 2014. Australia’s Forests at the Australian Government
Department of Agriculture, Fisheries and Forestry, URL:
http://
(last date
accessed: 10 March 2016).
Dessler, A.E., S.P. Palm, and J.D. Spinhirne, 2006. Tropical cloud-
top height distributions revealed by the Ice, Cloud, and Land
Elevation Satellite (ICESat)/Geoscience Laser Altimeter System
(GLAS),
Journal of Geophysical Research-Atmospheres
,
111(D12215):1–11.
Dubayah, R.O., and J.B. Drake, 2000. Lidar remote sensing for forestry,
Journal of Forestry
, 98(6):44–46.
Duong, V.H., R. Lindenbergh, N. Pfeifer, and G. Vosselman, 2008.
Single and two epoch analysis of ICESat full waveform data
over forested areas,
International Journal of Remote Sensing
,
29(5):1453–1473.
Eisenhauer, J.G., 2003. Regression through the origin,
Teaching
Statistics
, 25(3):76-80.
Fatoyinbo, T.E., and M. Simard, 2013. Height and biomass of
mangroves in Africa from ICESat/GLAS and SRTM,
International
Journal of Remote Sensing
, 34(2):668–681.
Hopkinson, C., 2007. The influence of flying altitude, beam
divergence, and pulse repetition frequency on laser pulse return
intensity and canopy frequency distribution,
Canadian Journal
of Remote Sensing
, 33(4):312–324.
Hopkinson, C., and L. Chasmer, 2009. Testing LiDAR models of
fractional cover across multiple forest ecozones,
Remote Sensing
of Environment
, 113(1):275-288.
Hopkinson, C., J. Lovell, L. Chasmer, D. Jupp, N. Kljun, and E. van
Gorsel, 2013. Integrating terrestrial and airborne lidar to calibrate
a 3D canopy model of effective leaf area index,
Remote Sensing
of Environment
, 136:301–314.
Hug, C., P. Krzystek, and W. Fuchs, 2004. Advanced lidar
data processing with Lastools,
International Archives of
Photogrammetry and Remote Sensing
,
Comission II, ISPRS 20
th
Congress
, Istanbul, Turkey, pp. 832–837.
Isenburg, M., 2011. LAStools: Efficient Tools for LiDAR Processing,
URL:
/
(last date accessed: 10
March 2016).
Lefsky, M.A., W.B. Cohen, S.A. Acker, G.G. Parker, T.A. Spies, and D.
Harding, 1999. Lidar remote sensing of the canopy structure and
biophysical properties of Douglas-fir western hemlock forests,
Remote Sensing of Environment
, 70(3):339–361.
Lefsky, M.A., W.B. Cohen, G.G. Parker, and D.J. Harding, 2002. Lidar
remote sensing for ecosystem studies,
Bioscience
, 52(1):19–30.
Lefsky, M.A., D. Harding, W.B. Cohen, G. Parker ,and H.H. Shugart,
1999. Surface lidar remote sensing of basal area and biomass in
deciduous forests of eastern Maryland, USA,
Remote Sensing of
Environment
, 67(1):83–98.
Lefsky, M.A., D.J. Harding, M. Keller, W.B. Cohen, C.C. Carabajal, F.D.
Espirito-Santo, M.O. Hunter, and R. de Oliveira, 2005. Estimates
of forest canopy height and aboveground biomass using ICESat,
Geophysical Research Letters
, 32(L22S02).
Lefsky, M.A., M. Keller, Y. Pang, P.B. de Camargo, and M.O. Hunter,
2007. Revised method for forest canopy height estimation from
Geoscience Laser Altimeter System waveforms,
Journal of
Applied Remote Sensing
, 1(013537).
362
May 2016
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
