Applicability of an automatic surface detection approach to micro-pulse photon-counting lidar altimetry data: implications for canopy height retrieval from future ICESat-2 data

2014 ◽  
Vol 35 (13) ◽  
pp. 5263-5279 ◽  
Author(s):  
Mahsa S. Moussavi ◽  
Waleed Abdalati ◽  
Ted Scambos ◽  
Amy Neuenschwander
2018 ◽  
Vol 26 (10) ◽  
pp. A520 ◽  
Author(s):  
Sheng Nie ◽  
Cheng Wang ◽  
Xiaohuan Xi ◽  
Shezhou Luo ◽  
Guoyuan Li ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2204 ◽  
Author(s):  
Yang Yu ◽  
Bo Liu ◽  
Zhen Chen ◽  
ZhiKang Li

A macro-pulse photon counting Lidar is described in this paper, which was designed to implement long-range and high-speed moving target detection. The ToF extraction method for the macro-pulse photon counting Lidar system is proposed. The performance of the macro pulse method and the traditional pulse accumulation method were compared in theory and simulation experiments. The results showed that the performance of the macro-pulse method was obviously better than that of the pulse accumulation method. At the same time, a laboratory verification platform for long range and high-speed moving targets was built. The experimental results were highly consistent with the theoretical and simulation results. This proved that the macro pulse photon counting Lidar is an effective method to measure long range high-speed moving targets.


1979 ◽  
Vol 51 (2) ◽  
pp. 245-250 ◽  
Author(s):  
E. J. Darland ◽  
J. E. Hornshuh ◽  
C. G. Enke ◽  
G. E. Leroi

1979 ◽  
Vol 51 (2) ◽  
pp. 240-245 ◽  
Author(s):  
E. J. Darland ◽  
G. E. Leroi ◽  
C. G. Enke

Author(s):  
D. Gwenzi ◽  
M. A. Lefsky

Discrete return and waveform lidar have demonstrated a capability to measure vegetation height and the associated structural attributes such as aboveground biomass and carbon storage. Since discrete return lidar (DRL) is mainly suitable for small scale studies and the only existing spaceborne lidar sensor (ICESat-GLAS) has been decommissioned, the current question is what the future holds in terms of large scale lidar remote sensing studies. The earliest planned future spaceborne lidar mission is ICESat-2, which will use a photon counting technique. To pre-validate the capability of this mission for studying three dimensional vegetation structure in savannas, we assessed the potential of the measurement approach to estimate canopy height in a typical savanna landscape. We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA Goddard. MABEL fires laser pulses in the green (532 nm) and near infrared (1064 nm) bands at a nominal repetition rate of 10 kHz and records the travel time of individual photons that are reflected back to the sensor. The photons’ time of arrival and the instrument’s GPS positions and Inertial Measurement Unit (IMU) orientation are used to calculate the distance the light travelled and hence the elevation of the surface below. A few transects flown over the Tejon ranch conservancy in Kern County, California, USA were used for this work. For each transect we extracted the data from one near infrared channel that had the highest number of photons. We segmented each transect into 50 m, 25 m and 10 m long blocks and aggregated the photons in each block into a histogram based on their elevation values. We then used an expansion window algorithm to identify cut off points where the cumulative density of photons from the highest elevation resembles the canopy top and likewise where such cumulative density from the lowest elevation resembles mean ground elevation. These cut off points were compared to DRL derived canopy and mean ground elevations. The correlation between MABEL and DRL derived metrics ranged from <i>R</i><sup>2</sup> = 0.70, RMSE = 7.9 m to <i>R</i><sup>2</sup> = 0.83, RMSE = 2.9 m. Overall, the results were better when analysis was done at smaller block sizes, mainly due to the large variability of terrain relief associated with increased block size. However, the increase in accuracy was more dramatic when block size was reduced from 50 m to 25 m than it was from 25 m to 10 m. Our work has demonstrated the capability of photon counting lidar to estimate canopy height in savannas at MABEL's signal and noise levels. However, analysis of the Advanced Topography Laser Altimeter System (ATLAS) sensor on ICESat-2 indicate that signal photons will be substantially lower than those of MABEL while sensor noise will vary as a function of solar illumination, altitude and declination, as well as the topographic and reflectance properties of surfaces. Therefore, there are reasons to believe that the actual data from ICESat-2 will give poorer results due to a lower sampling rate and use of only the green wavelength. Further analysis using simulated ATLAS data are required before more definitive results are possible, and these analyses are ongoing.


2018 ◽  
Vol 10 (12) ◽  
pp. 1962 ◽  
Author(s):  
Xiaoxiao Zhu ◽  
Sheng Nie ◽  
Cheng Wang ◽  
Xiaohuan Xi ◽  
Zhenyue Hu

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.


2018 ◽  
Vol 208 ◽  
pp. 154-170 ◽  
Author(s):  
S.C. Popescu ◽  
T. Zhou ◽  
R. Nelson ◽  
A. Neuenschwander ◽  
R. Sheridan ◽  
...  

2002 ◽  
Vol 12 (3) ◽  
pp. 145-148
Author(s):  
C. Jorel ◽  
P. Feautrier ◽  
J.-C. Villégier ◽  
A. Benoit

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