PE&RS August 2015 - page 634

vegetation structure. It is recognized that the presented
technique does not account for factors caused by an increased
flying height such as increase in laser footprint size or attenua-
tion of laser power caused by increased atmospheric thickness
(Goodwin et al., 2006), as well as acquisition at oblique view-
ing angles (Lovell et al., 2005) or with a different instrument.
Physical models such as that presented by Disney et al. (2010)
could potentially be used to more accurately model different
acquisition scenarios. However, this would require significant
effort to recreate landscape scale variance in native forest
structure and is therefore beyond the scope of this study.
Conclusions
Land managers are commissioning small-footprint discrete
return airborne laser scanning (ALS ) acquisitions over increas-
ingly large areas which may in turn capture a variety of forest
types. This study examines the sensitivity of pulse density
(sampling frequency) on primary descriptors of vegetation
structure (canopy height, canopy cover, return height coeffi-
cient of variation, and COVVES) across a broad range of forest
types: from sparsely vegetated savannah woodlands to dense
rainforest. ALS was acquired with the same campaign and
capture specifications and ALS instrument which facilitated
comparisons between forest types. Point clouds were thinned
to six different densities (0.05 to 4 pl m-2) using a novel tech-
nique that systematically selected nine subsets of data from
each original plot dataset, this allowed metrics to be comput-
ed in a robust way as well as assessing the reproducibility of
ALS acquisition. Metrics derived from thinned datasets were
compared to a dataset with a pulse density of 10 pl m-2.
Simulated acquisition with a pulse density of ≥0.5 pl m-2
resulted in minimal differences for all metrics across all for-
est types when compared to a dataset with a pulse density of
10 pl m-2. Furthermore, intra-plot variance was significantly
lower at higher pulse densities than for less dense simula-
tions. This result suggests there is minimal gain from acquir-
ing ALS data at a pulse density >0.5 pl m -2 which could result
in potential cost savings for land management agencies. The
primary reason for erroneous estimation at lower pulse densi-
ties was the poor identification of the ground surface, which
propagated to metric estimation, and heterogeneous (e.g.,
clumped) vegetation being inadequately sampled. The analy-
sis presented here will allow land managers to be confident in
specifying lower pulse densities when planning ALS capture
for large area vegetation characterization, even over dense,
very sparse, tall, or vertically complex forests.
Acknowledgments
This research was funded by the Australian Postgraduate
Award, Cooperative Research Centre for Spatial Informa-
tion under Project 2.07 and the Commonwealth Scientific
and Industrial Research Organisation (CSIRO) Postgraduate
Scholarship. The Airborne Laser Scanning data were obtained
through the University of Queensland server and AusCover
(http://www.auscover.org.au). AusCover is the remote sensing
data products facility of the Terrestrial Ecosystem Research
Network (TERN: http://www.tern.org.au). The authors are
also grateful to Professor Nicholas Chrisman for his insightful
comments and guidance in the preparation of this manuscript.
References
Armston, J., M. Disney, P. Lewis, P., Scarth, S. Phinn, R. Lucas, P.
Bunting, P., and N.R. Goodwin, 2013. Direct retrieval of canopy
gap probability using airborne waveform lidar, Remote Sensing
of Environment, 134:24–38, doi:10.1016/j.rse.2013.02.021
Asner, G.P., G.V. Powell, J. Mascaro, D.E. Knapp, J.K. Clark, J.
Jacobson, T. Kennedy-Bowdoin, T. Balaji, A. Aravindh P.
Guayana, V. Eloy, L. Secada, M. Valqui, and R.F.
Hughes, 2010. High-resolution forest carbon stocks and emissions
in the Amazon, Proceedings of the National Academy of
Sciences of the United States of America, 107(38):16738–16742.
doi:10.1073/pnas.1004875107
Baltsavias, E., 1999. Airborne laser scanning: Basic relations and
formulas, ISPRS Journal of Photogrammetry and Remote
Sensing, 54(2-3):199–214. doi:10.1016/S0924-2716(99)00015-5
Bater, C.W., M.A. Wulder, N.C. Coops, R.F. Nelson, T. Hilker, and E.
Næsset, 2011. Stability of Sample-Based Scanning-LiDAR-Derived
Vegetation Metrics for Forest Monitoring, IEEE Transactions on
Geoscience and Remote Sensing, 49(6):2385–2392.
Bolton, D.K., N.C. Coops, and M.A. Wulder, 2013. Measuring forest
structure along productivity gradients in the Canadian boreal
with small-footprint Lidar, Environmental Monitoring and
Assessment, 185(8):6617–34. doi:10.1007/s10661-012-3051-9
Department of Environment, Water, Land and Planning, 2012.
Permanent Growth Plots, URL: URL: http://www.depi.vic.
gov.au/forestry-and-land-use/forest-management/forest-
sustainability/victorian-forest-monitoring-program (last date
accessed: 17 June 2015).
Disney, M.I., V. Kalogirou, P. Lewis, A. Prieto-Blanco, S. Hancock,
and M. Pfeifer, 2010. Simulating the impact of discrete-return
lidar system and survey characteristics over young conifer and
broadleaf forests, Remote Sensing of Environment, 114(7):1546–
1560. doi:10.1016/j.rse.2010.02.009
Drake, J.B., R.O. Dubayah, R.G. Knox, D.B. Clark, J.B. Blair, and C.
Rica, 2002. Sensitivity of large-footprint lidar to canopy structure
and biomass in a neotropical rainforest, Remote Sensing of
Environment, 81:378–392.
Evans, J.S., and A.T. Hudak, 2007. A multiscale curvature algorithm for
classifying discrete return LiDAR in forested environments, IEEE
Transactions on Geoscience and Remote Sensing, 45:1029–1038.
Evans, J.S., A.T. Hudak, R. Faux, and A.M.S. Smith, 2009. Discrete
return lidar in natural resources: Recommendations for project
planning, data processing, and deliverables, Remote Sensing ,
1(4):776–794. doi:10.3390/rs1040776
Gobakken, T., and E. Næsset, 2008. Assessing effects of laser point
density, ground sampling intensity, and field sample plot size on
biophysical stand properties derived from airborne laser scanner
data, Canadian Journal of Forest Research, 38(5):1095–1109.
doi:10.1139/X07-219
Goodwin, N.R., N.C. Coops, and D.S. Culvenor, 2006. Assessment of
forest structure with airborne LiDAR and the effects of platform
altitude, Remote Sensing of Environment, 103(2):140–152.
doi:10.1016/j.rse.2006.03.003
Hayashi, R., A. Weiskittel, and S. Sader, 2014. Assessing the
feasibility of low-density LiDAR for stand inventory attribute
predictions in complex and managed forests of northern Maine,
USA, Forests, 5(2):363–383. doi:10.3390/f5020363
Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones, and A. Jarvis, 2005.
Very high resolution interpolated climate surfaces for global land
areas, International Journal of Climatology 25:1965-1978.
Holmgren, J., and T. Jonsson, 2004. Large scale airborne laser
scanning of forest resources in Sweden, ISPRS Archives of
Photogrammetry, Remote Sensing and Spatial Information
Sicences, XXXVI(8/W2):157–160.
Isenburg, M., 2012. LAStools [computer software], version 130225,
URL: www.lastools.org (last date accessed: 17 June 2015).
Jakubowski, M.K., Q. Guo, and M. Kelly, 2013. Tradeoffs between
lidar pulse density and forest measurement accuracy,
Remote Sensing of Environment, 130:245–253. doi:10.1016/j.
rse.2012.11.024
Kane, V.R., R.J. McGaughey, J.D. Bakker, R.F. Gersonde, J.A. Lutz, and
J.F. Franklin, 2010. Comparisons between field- and LiDAR-
based measures of stand structural complexity, Canadian Journal
of Forest Research, 40(4):761–773. doi:10.1139/X10-024
634 August 2015 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING