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Remote Sens. 2014, 6(10), 10051-10069; doi:10.3390/rs61010051

Slope Estimation from ICESat/GLAS

1
Department of Geography, College of Science, Swansea University, Singleton Park, Swansea, SA28PP, UK
2
Centre for Studies of Carbon Cycle and Climate Interactions (LUCCI), Department of Physical Geography and Ecosystem Science, Lund University, Lund, S22362, Sweden
3
Department of Geography, University of Lethbridge, Lethbridge, Alberta, T1K3M4, Canada
4
Airborne Research Australia, Flinders University, Adelaide, SA5042, Australia
5
Forest Research, Northern Research Station, Roslin, Midlothian, EH25 9SY, UK
6
Oceans and Atmosphere, CSIRO, Canberra, ACT 2601, Australia
*
Author to whom correspondence should be addressed.
Received: 30 June 2014 / Revised: 24 September 2014 / Accepted: 24 September 2014 / Published: 21 October 2014
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Abstract

We present a novel technique to infer ground slope angle from waveform LiDAR, known as the independent slope method (ISM). The technique is applied to large footprint waveforms (\(\sim\) mean diameter) from the Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) to produce a slope dataset of near-global coverage at \(0.5^{\circ} \times 0.5^{\circ}\) resolution. ISM slope estimates are compared against high resolution airborne LiDAR slope measurements for nine sites across three continents. ISM slope estimates compare better with the aircraft data (R\(^{2}=0.87\) and RMSE\(=5.16^{\circ}\)) than the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) inferred slopes (R\(^{2}=0.71\) and RMSE\(=8.69^{\circ}\)) ISM slope estimates are concurrent with GLAS waveforms and can be used to correct biophysical parameters, such as tree height and biomass. They can also be fused with other DEMs, such as SRTM, to improve slope estimates. View Full-Text
Keywords: LiDAR; slope; terrain; waveform; SRTM; biophysical parameter retrieval LiDAR; slope; terrain; waveform; SRTM; biophysical parameter retrieval
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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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MDPI and ACS Style

Mahoney, C.; Kljun, N.; Los, S.O.; Chasmer, L.; Hacker, J.M.; Hopkinson, C.; North, P.R.J.; Rosette, J.A.B.; van Gorsel, E. Slope Estimation from ICESat/GLAS. Remote Sens. 2014, 6, 10051-10069.

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