# Evaluation of SPL100 Single Photon Lidar Data

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Sources

#### 2.1.1. Airborne Lidar

#### 2.1.2. GNSS Data

#### 2.2. Methods

#### 2.2.1. Positional Accuracy

^{®}to manually determine the closest point cloud position corresponding to each GPS site. The differences in the horizontal and vertical positions were recorded for every survey location for both the SPL and LML data sets.

#### 2.2.2. Positional Precision

^{2}in surface area, were chosen to include a variety of surface materials and slopes. Many of these samples came from rooftops of commercial and residential homes, but other flat regions such as sidewalks, tennis courts, and parking lots were also used. The same sample regions were used to analyze both the SPL and LML data sets.

^{®}was used to determine the standard deviation of the lidar points with respect to a best fit plane (i.e., the measure of dispersion), the incidence angle at which the plane was observed, and the mean intensity of the returns from the plane. The best fit plane for each planar region is defined by the centroid of the points and the direction of a vector normal to the surface of a plane that intersects the centroid and minimizes the sum of the squared orthogonal distances between the points and the plane (i.e., the sum of the squared point-to-plane residuals). The centroid location is simply the mean of each of the point coordinate components. The normal vector and the standard deviation of the planar fit were computed from a singular value decomposition of the centroid-removed point coordinates. The smallest singular vector is normal to the best fit plane; the smallest singular value is the square root of the sum of the squared point-to-plane residuals, from which the standard deviation is easily computed [17]. A sample planar region of points, the best fit plane, and the normal vector to the best fit plane are illustrated in Figure 2.

#### 2.2.3. DEM Comparison

#### 2.2.4. Canopy Penetration and Multiple Returns

^{®}. An important part of this analysis was determining how many of the emitted pulses reached the ground. To accomplish this, the cloth simulation filtering (CSF) algorithm [19] was used to classify ground points. The CSF algorithm creates a surface model by inverting the point cloud and simulating a cloth draped over the terrain. It has been made available in a variety of programming languages, including Python and MATLAB

^{®}, as well as a plugin for the open-source program CloudCompare. The CSF algorithm was more effective at creating a suitable classification for these small sample areas than using TerraScan. The following CSF parameters were used: 0.1 for cloth resolution, 500 iterations maximum, and 0.1 classification threshold. Multiple returns were identified by a return number greater than 1 in the LAS point records and grouped according to time (multiple returns originate from the same emitted laser pulse and thus are stamped with the same time).

## 3. Results and Discussion

#### 3.1. Positional Accuracy

#### 3.2. Positional Precision

#### 3.3. DEM Comparison

#### 3.4. Canopy Penetration and Multiple Returns

^{2}under the treetops while the SPL100 data had about 28 points/m

^{2}. The reason the SPL100 is capable of generating higher ground densities under tree canopies, as compared to the Titan, is due to the higher number of lidar measurements (i.e., emitted pulses) rather than superior penetration performance. This is reinforced by the work in [10], where SPL100 and LML data acquisitions were planned to achieve similar overall point density (20 points/m

^{2}), and the LML point density under vegetation surpassed that of the SPL100. In general, an LML sensor with an equivalent ground sampling rate as the SPL100 will surpass the SPL100 in under-canopy point density.

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Degnan, J.J. Scanning, Multibeam, Single Photon Lidars for Rapid, Large Scale, High Resolution, Topographic and Bathymetric Mapping. Remote Sens.
**2016**, 8, 958. [Google Scholar] [CrossRef][Green Version] - Stoker, J.M.; Abdullah, Q.A.; Nayegandhi, A.; Winehouse, J. Evaluation of Single Photon and Geiger Mode Lidar for the 3D Elevation Program. Remote Sens.
**2016**, 8, 767. [Google Scholar] [CrossRef][Green Version] - Swatantran, A.; Tang, H.; Barrett, T.; DeCola, P.; Dubayah, R. Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar. Sci. Rep.
**2016**, 6, 28277. [Google Scholar] [CrossRef] [PubMed][Green Version] - Degnan, J.; Machan, R.; Leventhal, E.; Lawrence, D.; Jodor, G.; Field, C. Inflight performance of a second-generation photon-counting 3D imaging lidar. In Laser Radar Technology and Applications XIII; Turner, M.D., Kamerman, G.W., Eds.; International Society for Optics and Photonics, SPIE: Bellingham, WA, USA, 2008; Volume 6950, pp. 32–40. [Google Scholar] [CrossRef]
- Ullrich, A.; Pfennigbauer, M. Linear LIDAR versus Geiger-mode LIDAR: Impact on data properties and data quality. In Laser Radar Technology and Applications XXI; Turner, M.D., Kamerman, G.W., Eds.; International Society for Optics and Photonics, SPIE: Bellingham, WA, USA, 2016; Volume 9832, pp. 29–45. [Google Scholar] [CrossRef]
- Ullrich, A.; Pfennigbauer, M. Noisy lidar point clouds: Impact on information extraction in high-precision lidar surveying. In Laser Radar Technology and Applications XXIII; Turner, M.D., Kamerman, G.W., Eds.; International Society for Optics and Photonics, SPIE: Bellingham, WA, USA, 2018; Volume 10636, pp. 133–138. [Google Scholar] [CrossRef]
- Jutzi, B. Less Photons for more LiDAR? A Review from Multi-Photon-Detection to Single-Photon-Detection. In Proceedings of the 56th Photogrammetric Week (PhoWo 2017), Stuttgart, Germany, 11–15 September 2017; p. 6 S. [Google Scholar]
- Degnan, J.J.; Field, C.T. Moderate to high altitude, single photon sensitive, 3D imaging lidars. In Advanced Photon Counting Techniques VIII; Itzler, M.A., Campbell, J.C., Eds.; International Society for Optics and Photonics, SPIE: Bellingham, WA, USA, 2014; Volume 9114, pp. 56–66. [Google Scholar] [CrossRef]
- Heidemann, H.K. Lidar Base Specification; Technical report; U.S. Geological Survey: Reston, VA, USA, 2012. [CrossRef][Green Version]
- Mandlburger, G.; Lehner, H.; Pfeifer, N. A Comparison of Single Photon and Full Waveform Lidar. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.
**2019**, IV-2/W5, 397–404. [Google Scholar] [CrossRef][Green Version] - Wästlund, A.; Holmgren, J.; Lindberg, E.; Olsson, H. Forest Variable Estimation Using a High Altitude Single Photon Lidar System. Remote Sens.
**2018**, 10, 1422. [Google Scholar] [CrossRef][Green Version] - Fernandez-Diaz, J.C.; Carter, W.E.; Glennie, C.; Shrestha, R.L.; Pan, Z.; Ekhtari, N.; Singhania, A.; Hauser, D.; Sartori, M. Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar. Remote Sens.
**2016**, 8, 936. [Google Scholar] [CrossRef][Green Version] - Leica SPL100 Single Photon LidAR Sensor. Available online: https://leica-geosystems.com/en-us/products/airborne-systems/topographic-lidar-sensors/leica-spl100 (accessed on 12 January 2020).
- Titan. Available online: https://www.teledyneoptech.com/en/products/airborne-survey/titan/ (accessed on 12 January 2020).
- Mader, G.L.; Weston, N.D.; Morisson, M.L.; Milbert, D.G. The On-Line Positioning User Service (OPUS). Prof. Surv.
**2003**, 23, 26–30. [Google Scholar] - Soler, T. CORS and OPUS for Engineers: Tools for Surveying and Mapping Applications; American Society of Civil Engineers: Reston, VA, USA, 2011. [Google Scholar]
- Shakarji, C. Least-squares fitting algorithms of the NIST algorithm testing system. J. Res. Natl. Inst. Stand. Technol.
**1998**, 103, 633. [Google Scholar] [CrossRef] [PubMed] - Axelsson, P. DEM Generation from Laser Scanner Data Using Adaptive TIN Models. Int. Arch. Photogramm. Remote Sens.
**2000**, XXXIII, 110–117. [Google Scholar] - Zhang, W.; Qi, J.; Wan, P.; Wang, H.; Xie, D.; Wang, X.; Yan, G. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens.
**2016**, 8, 501. [Google Scholar] [CrossRef] - Kim, M.; Park, S.; Danielson, J.; Irwin, J.; Stensaas, G.; Stoker, J.; Nimetz, J. General external uncertainty models of three-plane intersection point for 3D absolute accuracy assessment of lidar point cloud. Remote Sens.
**2019**, 11, 2737. [Google Scholar] [CrossRef][Green Version] - Wujanz, D.; Burger, M.; Mettenleiter, M.; Neitzel, F. An intensity-based stochastic model for terrestrial laser scanners. ISPRS J. Photogramm. Remote Sens.
**2017**, 125, 146–155. [Google Scholar] [CrossRef] - Baltsavias, E. Airborne laser scanning: Basic relations and formulas. ISPRS J. Photogramm. Remote Sens.
**1999**, 54, 199–214. [Google Scholar] [CrossRef] - Hartzell, P.; Dang, Z.; Pan, Z.; Glennie, C. Radiometric Evaluation of an Airborne Single Photon Lidar Sensor. IEEE Geosci. Remote Sens. Lett.
**2018**, 15, 1466–1470. [Google Scholar] [CrossRef] - Hartzell, P.J.; Gadomski, P.J.; Glennie, C.L.; Finnegan, D.C.; Deems, J.S. Rigorous error propagation for terrestrial laser scanning with application to snow volume uncertainty. J. Glaciol.
**2015**, 61, 1147–1158. [Google Scholar] [CrossRef][Green Version] - O’Banion, M.S.; Olsen, M.J.; Parrish, C.E.; Bailey, M. Interactive Visualization of 3D Coordinate Uncertainties in Terrestrial Laser-Scanning Point Clouds Using OpenGL Shader Language. J. Surv. Eng.
**2019**, 145, 04018012. [Google Scholar] [CrossRef] - Pan, Z. Personal communication, 2019.

**Figure 1.**University of Houston campus survey area and GNSS observation sites. Tick labels are Universal Transverse Mercator (UTM) Zone 15N.

**Figure 2.**Illustration of a sample region of planar points (black dots), the planar surface best fit to the points, the vector normal to the best fit plane (red vector), and the laser path vector from the plane to the airborne sensor at the time of observation (blue vector). The incidence angle at which the plane was observed is computed as the angle between the normal and laser path vectors.

**Figure 3.**Variation of planar standard deviation with mean intensity for the (

**A**) SPL100 and (

**B**) Titan 532 nm channel. A least squares fit of a power function in the form of Equation (1) is shown for both.

**Figure 4.**Variation of planar standard deviation with planar incidence angle for the (

**A**) SPL100 and (

**B**) Titan 532 nm channel. Best fit line shown. The data points are colored by the mean intensity of each planar surface.

**Figure 5.**Difference of DEMs (SPL100–Titan) using HxMap software in 2017 (

**left**side) and after using updated HxMap software in 2019 (

**right**side).

**Figure 6.**Histogram of the DEM differences (SPL100–Titan) before and after reprocessing with a more recent version of HxMap.

**Figure 7.**Tree canopy examples colored by return number (the lidar sensors have the ability to detect and record more than one return energy event for each emitted laser pulse). First returns are red, second returns are blue, third and fourth returns are green. Titan data is shown in the left column (panels (

**A**) and (

**C**)) and SPL100 data in the right column (panels (

**B**) and (

**D**)).

**Figure 8.**Histograms of range distance between first and second returns for the tree canopy examples shown in Figure 7. Titan data is shown in the left column (panels (

**A**) and (

**C**)) and SPL100 data in the right column (panels (

**B**) and (

**D**)).

**Figure 9.**Tree canopy examples colored by return number for the unfiltered SPL100 point clouds (left column, panels (

**A**) and (

**C**)). First return is red, second return is blue, third and fourth returns are green. Histograms of range distance between first and second returns (right column, panels (

**B**) and (

**D**)). The canopy examples are the same as in Figure 7.

Leica SPL100 | Optech Titan | |
---|---|---|

Size (Volume) * | 0.417 m${}^{3}$ | 0.430 m${}^{3}$ |

Weight * | 106 kg | 116 kg |

Power | 600 W/28 VDC | 800 W/28 VDC |

Laser | 100 channels: all 532 nm | 3 channels: 532, 1064, and 1550 nm |

Max. Effective Pulse Rate | 6.0 MHz | 900 KHz |

Scan Angle (Field of View) | Fixed: 20${}^{\circ}$, 30${}^{\circ}$, 40${}^{\circ}$, or 60${}^{\circ}$ | Adjustable: 0–60${}^{\circ}$ |

Multiple Returns | Up to 10 per channel | Up to 4 per channel |

Operating AGL ** | 2000–4500 m | 300–2000 m |

$\Delta $ Easting GNSS | $\Delta $ Northing GNSS | $\Delta $ NAVD88 Height GNSS | |
---|---|---|---|

Mean | 0.45 cm | 12.58 cm * | −13.27 cm * |

Standard Dev. | 17.83 cm | 15.60 cm | 7.63 cm |

$\Delta $ Easting GNSS | $\Delta $ Northing GNSS | $\Delta $ NAVD88 Height GNSS | |
---|---|---|---|

Mean | −2.92 cm | −1.27 cm | 11.67 cm * |

Standard Dev. | 11.00 cm | 10.68 cm | 7.88 cm |

Avg. Number of Returns | % Pulses With Multiple Returns | % Pulses Reaching the Ground | Avg. Vertical Separation from Return 1 to 2 | |
---|---|---|---|---|

Titan | 17,000 | 87% | 71% | 4.3 m |

SPL100 | 36,000 | 22% | 43% | 9.9 m |

Standard Dev. Under Canopy | Standard Dev. Open Terrain | |
---|---|---|

Titan | 2.7 cm | 1.5 cm |

SPL100 | 3.6 cm | 3.0 cm |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Brown, R.; Hartzell, P.; Glennie, C. Evaluation of SPL100 Single Photon Lidar Data. *Remote Sens.* **2020**, *12*, 722.
https://doi.org/10.3390/rs12040722

**AMA Style**

Brown R, Hartzell P, Glennie C. Evaluation of SPL100 Single Photon Lidar Data. *Remote Sensing*. 2020; 12(4):722.
https://doi.org/10.3390/rs12040722

**Chicago/Turabian Style**

Brown, Rebecca, Preston Hartzell, and Craig Glennie. 2020. "Evaluation of SPL100 Single Photon Lidar Data" *Remote Sensing* 12, no. 4: 722.
https://doi.org/10.3390/rs12040722