# Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Background

#### 2.1. Discrete Return and Full Waveform Systems

#### 2.2. Vertical Target Discrimination Distance

## 3. Enhancing Capabilities of DR Technology for 3D Vegetation Mapping

**Figure 1.**Examples of four-return records from vegetation;

**(a)**ALTM 3100; minimum pulse return separation distance of 2.14 m;

**(b)**ALTM Gemini: minimum pulse separation distance of 1.54 m.

#### 3.1. Objectives and Methodology

^{2}; the laser pulse repetition frequency (equal to data collection rate) was kept at the maximum of 200 kHz for all datasets. This study is based on the data collected over three areas of vegetation: a cornfield with an average height 2.2–2.8 m and two nearby mixed forested areas with an average height of 6–7 m on one side of the cornfield and 20–22 m on the other side. For the purpose of this study these datasets will be referred in this paper as low-, medium- and high-canopy vegetation data. All datasets were collected by the same ALTM-Orion system during the same data collection mission.

- Empirical evaluation of the minimum vertical target discrimination distance for ALTM-Orion data using statistical analysis of the field data collected over selected types of vegetation targets.
- Analysis of the capabilities of ALTM-Orion to represent the vertical structure of vegetation targets: number and distribution of multiple returns, typical vertical target discrimination values, correlation of number of multiple returns with vegetation height, signal penetration to the ground.
- Comparison of the results of 1–2 to a similar analysis based on ALTM-Gemini data.
- Investigate the potential of return signal waveform modeling for DR data.

#### 3.2. Results and Discussion

**Figure 2.**ALTM-Orion data: (

**a**) A four-return record for one emitted laser pulse over 6 m high mixed forest; (

**b**) A three-return record for one emitted laser pulse over 2.6 m high cornfield.

**Table 1.**ALTM-Orion: Average and minimal multiple return separation distances for the cornfield data; the last column shows the average vegetation height in the selected samples.

Sample | Avg ∆R1,2 (m) | Avg ∆R2,3 (m) | Avg ∆R3,4 (m) | Avg height (m) |
---|---|---|---|---|

1 | 1.36 | 1.06 | n/a | 2.31 |

2 | 1.25 | 0.99 | n/a | 1.92 |

3 | 1.34 | 1.00 | n/a | 2.12 |

Sample | Min ∆R1,2 (m) | Min ∆R2,3 (m) | Min ∆R3,4 (m) | Avg height (m) |

1 | 0.67 | 0.69 | n/a | 2.31 |

2 | 0.64 | 0.65 | n/a | 1.92 |

3 | 0.66 | 0.67 | n/a | 2.12 |

**Table 2.**ALTM-Orion: Average and minimum multiple return separation distances for the forest data; the last column shows the average vegetation height in the selected samples.

Sample | Avg ∆R1,2 (m) | Avg ∆R2,3 (m) | Avg ∆R3,4 (m) | Avg height (m) |
---|---|---|---|---|

1 | 2.54 | 2.10 | 1.91 | 6.0 |

2 | 3.64 | 3.92 | 4.26 | 22.5 |

3 | 3.44 | 3.50 | 3.69 | 20.0 |

Sample | Min ∆R1,2 (m) | Min ∆R2,3 (m) | Min ∆R3,4 (m) | Avg height (m) |

1 | 0.68 | 0.71 | 0.73 | 6.0 |

2 | 0.70 | 0.70 | 0.64 | 22.5 |

3 | 0.65 | 0.61 | 0.68 | 20.0 |

Sample | Pulse Return | % of Total |
---|---|---|

Sample 1 Average height 6 m | 1 | 55.88 |

2 | 33.03 | |

3 | 9.38 | |

4 | 1.71 | |

Sample 2 Average height 22.5 m | 1 | 43.22 |

2 | 31.6 | |

3 | 17.71 | |

4 | 7.47 | |

Sample 3 Average height 20 m | 1 | 44.8 |

2 | 31.91 | |

3 | 16.79 | |

4 | 6.51 |

^{2}for both datasets.

**Figure 4.**Vegetation canopy of 15–17 m average height represented by multiple return data collected by two ALTM models operating at the same operational settings.

**(a)**ALTM-Gemini;

**(b)**ALTM-Orion.

^{2}. The range measurement resolution of this sensor, determined by the system hardware design, depends on the sequential number of the multiple returns and varies within 1.5–2.5 m [32]. Based on the distribution of the multiple returns in this case, one can conclude that for a tall vegetation canopy of 22–27 m, all four discrete returns are recorded, including the last one from the ground beneath the vegetation canopy; the percentage of third and fourth returns is substantial. However, for low-canopy vegetation of 6–7 m, the system cannot detect all four returns because of the 2.5 m minimal vertical target discrimination limit. The percentages of third and fourth returns for these samples are negligible as the system cannot resolve any two targets in vertical domain within a distance less than 2.5 m for the last two returns.

Pulse Return | Sample | % of Total | Sample | % of Total |
---|---|---|---|---|

1 | Sample 1 Average height 6–7 m | 84.5 | Sample 4 Average height 22–27 m | 43.5 |

2 | 15.3 | 35.3 | ||

3 | 0.2 | 16.9 | ||

4 | 0.0 | 4.3 | ||

1 | Sample 2 Average height 6–7 m | 85.7 | Sample 5 Average height 22–27 m | 39.8 |

2 | 14.1 | 31.9 | ||

3 | 0.2 | 21.4 | ||

4 | 0.0 | 6.9 | ||

1 | Sample 3 Average height 6–7 m | 83.6 | Sample 6 Average height 22–27 m | 42.1 |

2 | 15.8 | 34.0 | ||

3 | 0.6 | 18.7 | ||

4 | 0.0 | 5.2 |

**Figure 5.**Evolution of minimal vertical target discrimination capabilities for ALTM series of airborne lidar sensors.

## 4. Waveform Modeling for DR Data

_{i}in our analysis we used only a simple three-parametric Gaussian for all partial return for all vegetation types:

_{i}, was proportional to the recorded intensity values of each discrete return i, while the entire reflected laser pulse energy (blue Gaussian curve) represents the sum of all four DR components (1).

_{i}parameter to determine the center position of each peak. The Gaussian pulse width, δ

_{i}, was the fitting parameter, which was first estimated based on the system hardware parameters (laser pulse width, receiver bandwidth) and after that adjusted through the modeling described below. This step in the described methodology is based on the fact that the echoes reflected from different type of targets represent the convolution of the functions of the emitted laser pulse waveform, system receiver bandwidth and scattering properties of the targets [24,26]. In our simplified approach, we assumed that each one of the partial returns including ground and canopy returns could be approximated by simple three-parametric symmetrical Gaussian (2) function. Then the pulse widths, amplitudes and positions of the modeled Gaussians for each discrete return would fully determine the waveform of the modeled signal profile over the vertical extent of the target.

_{r}, which connected to the transmitted optical power P

_{t}and can be modeled through the lidar equation [33]. Considering partial signal returns P

_{i}, the intensity (peak power) of each Gaussian pulse was modeled using the lidar equation in the form derived by Jelalian [34]:

- P
_{i}is the received signal power for i-return - P
_{t}is the transmitted laser pulse power - D
_{r}is the diameter of the lidar receiver aperture - Q is the optical efficiency of the lidar system
- $\underset{.}{\vartheta}$ is the laser beam divergence
- T
_{atm}is the atmospheric transmittance factor - R
_{i}is the range from the sensor to i-target - σ
_{i}is the effective backscattering cross-section of i-target

_{i}are described by the backscattering cross-section σ

_{i}, which is proportional to the i-target reflectance ρ

_{i}and the i-fraction of the total received power P

_{r}in each return:

_{i}is the area of the target illuminated by the i-fraction of the laser footprint, which created the discrete return f

_{i}and k

_{i}is the fitting parameter, characterizing scattering properties of i-target, which could be derived using redundant measurements. An approach similar in some aspects to this one was applied to the analysis of full waveform data by Wagner and co-authors [24,35,36].

_{r}through the lidar equation for a single laser shot. Since P

_{r}was assumed to be a superposition of all four (or three) partial returns P

_{i}with a simple Gaussian waveform, and knowing the intensities for each discrete return, it was possible to model the amplitudes a

_{i}and pulse widths δ

_{i}of each discrete return for the selected laser shots so that the sum of the return pulse energies of all partial returns P

_{i}would represent the pulse energy of the total return P

_{r}. Figure 7 illustrates the results of the Gaussian waveform modeling for the samples of multiple returns of the vegetation data presented earlier in Figure 2.

## 4. Conclusions

## Acknowledgements

## References

- Aldred, A.; Bonner, M. Application of Airborne Lasers to Forest Surveys; Information Report PI‑X-51; Petawawa National Forestry Centre, Canadian Forestry Service: Petawawa, ON, Canada, 1987; p. 62. [Google Scholar]
- Nelson, R.; Krabill, W.; Maclean, G. Determining forest canopy characteristics using airborne laser data. Remote Sens. Environ.
**1984**, 15, 201–212. [Google Scholar] [CrossRef] - Dubayah, R.O.; Drake, J.B. Lidar remote sensing for forestry. J. Forestry
**2000**, 98, 44–46. [Google Scholar] - Nelson, R.; Parker, G.; Hom, M. A portable airborne laser system for forest inventory. Photogramm. Eng. Remote Sensing
**2003**, 69, 267–273. [Google Scholar] [CrossRef] - Roberts, S.D.; Dean, T.J.; Evans, D.L.; McCombs, J.W.; Harrington, R.L.; Glass, P.A. Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions. Forest Ecol. Manage.
**2005**, 213, 54–70. [Google Scholar] [CrossRef] - Hudak, A.T.; Evans, J.S.; Smith, A.M.S. Review: LiDAR utility for natural resource managers. Remote Sens.
**2009**, 1, 934–951. [Google Scholar] [CrossRef] - Ussyshkin, V.; Theriault, L. Advances in Airborne Lidar Technology for Forestry and Other 3D Mapping Applications. In Proceedings of the Regional ISPRS Conference: Latin American Remote Sensing Week (LARS), Santiago, Chile, 4–7 October 2010.
- Hopkinson, C.; Sitar, M.; Chasmer, L.; Treitz, P. Mapping snowpack depth beneath forest canopies using airborne lidar. Photogramm. Eng. Remote Sensing
**2004**, 70, 323–330. [Google Scholar] [CrossRef] - Alharthy, A.; Bethel, J. Heuristic Filtering and 3D Feature Extraction from Lidar Data. In Proceedings of ISPRS Commission III, Symposium 2002 “Photogrammetric Computer Vision”, Graz, Austria, 9–13 September 2002.
- Bates, P.D.; Pappenberger, F.; Romanowicz, R. Uncertainty and risk in flood inundation modeling. In Flood Forecasting; Beven, K., Hall, J., Eds.; Wiley & Co.: New York, NY, USA, 1999. [Google Scholar]
- Raber, G.T.; Jensen, J.R.; Schill, S.R.; Schuckman, K. Creation of digital terrain models using an adaptive Lidar vegetation point removal process. Photogramm. Eng. Remote Sensing
**2002**, 68, 1307–1316. [Google Scholar] - Renslow, M.; Greenfield, P.; Guay, T. Evaluation of Multi-Return LIDAR for Forestry Applications; RSAC-2060/4810-LSP-0001-RPT1; US Department of Agriculture Forest Service-Engineering: Salt Lake City, UT, USA, 2000. Available online: http://www.ndep.gov/USDAFS_LIDAR.pdf (accessed on 19 November 2010).
- Ussyshkin, V.; Sitar, M. Advantages of Airborne Lidar Technology for Power Line Asset Management. In Proceedings of 5th Annual CIGRÉ Canada Conference: Innovation and Renewal-Building the New Power System, Vancouver, BC, Canada, 17–19 October 2010. [CDROM].
- Chauve, A.; Mallet, C.; Bretar, F.; Durrieu, S.; Pierrot-Deseilligny, M.; Puech, W. Processing Full-Waveform Lidar Data: Modelling Raw Signals. In Proceedings of ISPRS Workshop on Laser Scanning 2007, Espoo, Finland, 12 September 2007; Volume 36, Part 3/W52. pp. 102–107.
- Korpela, I.; Ørka, H.O.; Maltamo, M.; Tokola, T.; Hyyppä, J. Tree species classification using airborne LiDAR—Effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fennica
**2010**, 44, 319–339. [Google Scholar] [CrossRef] - Lim, K.; Treitz, P.; Wulder, M.; St-Onge, B.; Flood, M. LiDAR remote sensing of forest structure. Progr. Phys. Geogr.
**2003**, 27, 88–106. [Google Scholar] [CrossRef] - Parrish, C.E.; Scarpace, F.L. Investigation of Full Waveform Lidar Data for Detection and Recognition of Vertical Objects. In Proceedings of ASPRS 2007 Annual Conference, Tampa, FL, USA, 7–11 May 2007.
- Chauve, A.; Vega, C.; Bretar, F.; Durrieu, S.; Allouis, T.; Pierrot-Deseilligny, M.; Puech, W. Processing full-waveform lidar data in an alpine coniferous forest: assessing terrain and tree height quality. Int. J. Remote Sens.
**2009**, 30, 5211–5228. [Google Scholar] [CrossRef] - Magruder, L.A.; Neuenschwander, A.L.; Marmillion, S.P.; Tweddale, S.A. Obstruction detection comparison of small-footprint full-waveform and discrete return lidar. Proc. SPIE
**2010**, 7684, 768410. [Google Scholar] [CrossRef] - Chauve, A.; Bretar, F.; Pierrot-Deseilligny, M.; Puech, W. Full Analyze: A Research Tool for Handling, Processing and Analyzing Full-Waveform Lidar Data. In Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12–17 July 2009.
- Ussyshkn, V.; Theriault, L. ALTM-Orion: Bridging Conventional Lidar and Full Waveform Digitizer Technology. In ISPRS TC VII Symposium “100 Years ISPRS”, Vienna, Austria, 5–7 July 2010; Volume 38, Part7B. pp. 606–611, [CDROM].
- Bretar, F.; Chauve, A.; Mallet, C.; Jutzi, B. Managing Full Waveform Lidar Data: A Challenging Task for the Forthcoming Years. In Proceeding sof XXIst ISPRS Congress, Beijing, China, 3–11 July 2008; Volume 37, Part B1. pp. 415–420.
- Neuenschwander, A.L.; Magruder, L.A.; Tyler, M. Landcover classification of small-footprint, full-waveform lidar data. J. Appl. Remote Sens.
**2009**, 3, 033544. [Google Scholar] [CrossRef] - Wagner, W.; Hollaus, M.; Briese, C.; Ducic, V. 3D vegetation mapping using small-footprint full‑waveform airborne laser scanners. Int. J. Remote Sens.
**2008**, 29, 1433–1452. [Google Scholar] [CrossRef] - Petrie, G. Current Developments in Airborne Laser Scanning Technologies. In Proceedings of IX International Scientific & Technical Conference—From Imagery to Map: Digital Photogrammetric Technologies, Attica, Greece, 5–8 October 2009.
- Jutzi, B.; Stilla, U. Characteristics of the Measurement Unit of a Full-Waveform Laser System. In Symposium of ISPRS Commission I: From Sensors to Imagery, Paris, France, May 2006; Volume 36. Part 1/A [CDROM].
- Reitberger, J.; Krzystek, P.; Stilla, U. Analysis of Fullwaveform LiDAR Data for Tree Species Classification. In Proceedings of ISPRS Symposium of Commission III “Photogrammetric Computer Vision and Image Analysis”, Bonn, Germany, 20–22 September 2006; Volume 36, pp. 228–233.
- Jutzi, B.; Stilla, U. Range determination with waveform recording laser systems using a Wiener Filter. ISPRS J. Photogramm. Remote Sens.
**2006**, 61, 95–107. [Google Scholar] [CrossRef] - Hofton, M.A.; Blair, J.B. Laser altimeter return pulse correlation: A method for detecting surface topographic change. J. Geodyn.
**2002**, 34, 477–489. [Google Scholar] [CrossRef] - Hussein, M.; Tripp, J.; Hill, B. An ultra compact laser terrain mapper for deployment onboard unmanned aerial vehicles. Proc. SPIE
**2009**, 7307, 73070B. [Google Scholar] - Ussyshkin, V.; Theriault, L. Precise Mapping: ALTM Orion Establishes a New Standard in Airborne Lidar Performance. In Proceedings of ASPRS Annual Conference, San Diego, CA, USA, 26–30 April 2010.
- Ussyshkin, V.; Theriault, L. Empirical Evaluation of the Resolution of Range Measurements in ALTM-Gemini; Internal Optech Document; Optech: Vaughan, ON, Canada, 2010. [Google Scholar]
- Measures, R.M. Laser Remote Sensing, Fundamentals and Applications; Wiley Interscience: New York, NY, USA, 1984. [Google Scholar]
- Jelalian, A.V. Laser Radar Systems; Artech House: Boston, MA, USA, 1992. [Google Scholar]
- Wagner, W.; Ullrich, A.; Ducic, V.; Melzer, T.; Studnicka, N. Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J. Photogramm. Remote Sens.
**2006**, 60, 100–112. [Google Scholar] [CrossRef] - Wagner, W.; Ullrich, A.; Melzer, T.; Briese, C.; Kraus, K. From Single-Pulse to Full-Waveform Airborne Laser Scanners: Potential and Practical Challenges. In Proceedings of the International Society for Photogrammetry and Remote Sensing 20th Congress Commission 3, Istanbul, Turkey, 12–23 July 2004; Volume 35, Part B/3. pp. 6–12.
- Schaer, P.; Skaloud, J.; Landtwing, S.; Legat, K. Accuracy Estimation for Laser Point Cloud Including Scanning Geometry. In Proceedings of The 5th International Symposium on Mobile Mapping Technology, Padua, Italy, 29–31 May 2007.
- Jutzi, B.; Gross, H. Normalization of Lidar Intensity Data Based on Range and Surface Incidence Angle. In Proceedings of Laserscanning 09, Paris, France, 1–2 September 2009; Volume 38, Part 3/W8. pp. 213–218.

© 2011 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 license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Ussyshkin, V.; Theriault, L.
Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping. *Remote Sens.* **2011**, *3*, 416-434.
https://doi.org/10.3390/rs3030416

**AMA Style**

Ussyshkin V, Theriault L.
Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping. *Remote Sensing*. 2011; 3(3):416-434.
https://doi.org/10.3390/rs3030416

**Chicago/Turabian Style**

Ussyshkin, Valerie, and Livia Theriault.
2011. "Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping" *Remote Sensing* 3, no. 3: 416-434.
https://doi.org/10.3390/rs3030416