Next Article in Journal
Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling
Previous Article in Journal
Elevation Change Rates of Glaciers in the Lahaul-Spiti (Western Himalaya, India) during 2000–2012 and 2012–2013
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(12), 1039; doi:10.3390/rs8121039

The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems

Applied Research Laboratories, University of Texas at Austin, Austin, TX 78712, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi, Anu Swatantran and Prasad S. Thenkabail
Received: 13 September 2016 / Revised: 9 December 2016 / Accepted: 14 December 2016 / Published: 21 December 2016
View Full-Text   |   Download PDF [3800 KB, uploaded 21 December 2016]   |  

Abstract

With a planned launch no later than September 2018, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) will provide a global distribution of geodetic elevation measurements for both the terrain surface and relative canopy heights. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 is a LiDAR system sensitive to the photon level. The photon-counting technology has many advantages for space-based altimetry, but also has challenges, particularly with delineating the signal from background noise. As such, a current unknown facing the ecosystem community is the performance of ICESat-2 for terrain and canopy height retrievals. This paper aims to provide the science user community of ICESat-2 land/vegetation data products with a realistic understanding of the performance characteristics and potential uncertainties related to the vertical sampling error, which includes the error in the perceived height value and the measurement precision. Terrain and canopy heights from simulated ICESat-2 data are evaluated against the airborne LiDAR ground truth values to provide a baseline performance uncertainty for multiple ecosystems. Simulation results for wooded savanna and boreal forest result in a mean bias error and error uncertainty (precision) for terrain height retrievals at 0.06 m (0.24 m RMSE) and −0.13 m (0.77 m RMSE). In contrast, results over ecosystems with dense vegetation show terrain errors of 1.93 m (1.66 m RMSE) and 2.52 m (3.18 m RMSE), indicating problems extracting terrain height due to diminished ground returns. Simulated top of canopy heights from ICESat-2 underestimated true top of canopy returns for all types analyzed with errors ranging from 0.28 m (1.39 m RMSE) to 1.25 m (2.63 m RMSE). These results comprise a first step in a comprehensive evaluation of ICESat-2 anticipated performance. Future steps will include solar noise impact analysis and investigation into performance discrepancy between visible and near-infrared wavelengths. View Full-Text
Keywords: LiDAR; ICESat-2; photon-counting; vegetation mapping LiDAR; ICESat-2; photon-counting; vegetation mapping
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Neuenschwander, A.L.; Magruder, L.A. The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems. Remote Sens. 2016, 8, 1039.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top