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Remote Sens. 2016, 8(9), 771; doi:10.3390/rs8090771

Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar

1
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
2
Sigma Space Corporation, 4600 Forbes Blvd, Lanham, MD 20706, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Jie Shan, Juha Hyyppä, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 27 June 2016 / Revised: 6 September 2016 / Accepted: 12 September 2016 / Published: 19 September 2016
(This article belongs to the Special Issue Airborne Laser Scanning)
View Full-Text   |   Download PDF [2182 KB, uploaded 19 September 2016]   |  

Abstract

Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm) lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r2 = 0.69, bias = 0.42 m, RMSE = 4.85 m) and discrete return lidar heights (r2 = 0.94, bias = 1.07 m, RMSE = 2.42 m). Results are consistently better than height accuracies from the histogram method (field data: r2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r2 = 0.78, bias = −0.06 m and RMSE = 4.88 m). Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data. View Full-Text
Keywords: single photon lidar; canopy height; noise removal single photon lidar; canopy height; noise removal
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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

Tang, H.; Swatantran, A.; Barrett, T.; DeCola, P.; Dubayah, R. Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar. Remote Sens. 2016, 8, 771.

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