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Remote Sens. 2019, 11(7), 856;

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Global Environmental Modelling and Earth Observation (GEMEO), Department of Geography, Swansea University, Swansea SA2 8PP, UK
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Forest Research, Northern Research Station, Roslin, Midlothian EH25 9SY, Scotland, UK
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Author to whom correspondence should be addressed.
Received: 18 February 2019 / Revised: 6 April 2019 / Accepted: 7 April 2019 / Published: 9 April 2019
(This article belongs to the Section Forest Remote Sensing)
PDF [12463 KB, uploaded 10 April 2019]


ICESat-2 is the new generation of NASA’s ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean R 2 value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2. View Full-Text
Keywords: ICESat-2; photon counting LiDAR; forest parameters; LiDAR metrics ICESat-2; photon counting LiDAR; forest parameters; LiDAR metrics

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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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Chen, B.; Pang, Y.; Li, Z.; North, P.; Rosette, J.; Sun, G.; Suárez, J.; Bye, I.; Lu, H. Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest. Remote Sens. 2019, 11, 856.

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