A Comparison of Two Open Source LiDAR Surface Classification Algorithms
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. LiDAR Data and Acquisition
2.3. Point Classification with MCC and BCAL
Algorithm | BCAL Parameters | MCC Parameters | ||
---|---|---|---|---|
Window | Threshold | Scale | Curvature | |
BCAL | 7 m | 0.10 m | – | – |
MCC | – | – | 1.0 | 0.05 |
Combo | 7 m | 0.10 m | 1.0 | 0.05 |
2.4. Ground Reference Data
Cover Type | N | Mean Slope | Max. Slope | Min. Slope | Mean Cover | Max. Cover | Min. Cover |
---|---|---|---|---|---|---|---|
Meadow | 19 | 8.2 | 16.9 | 4.2 | 82% | 100% | 14% |
Shrub | 20 | 7.9 | 15.3 | 3.5 | 96% | 100% | 84% |
Bare Ground | 10 | 6.8 | 11.8 | 2.6 | 24% | 100% | 0% |
Ceanothus | 3 | 8.9 | 12.5 | 6.1 | 96% | 100% | 91% |
Aspen | 9 | 6.5 | 9.5 | 3.9 | 66% | 84% | 39% |
Conifer | 10 | 12.4 | 17.9 | 7.4 | 79% | 100% | 48% |
3. Analysis and Results
3.1. 1.0 m Resolution
3.2. 0.5 m Resolution
4. Conclusions
Cover Type | BCAL | MCC | ||||
---|---|---|---|---|---|---|
Mean Error | Std. Dev. Error | Rank | Mean Error | Std. Dev. Error | Rank | |
Aspen | −0.151 | 0.116 | 1 | −0.149 | 0.122 | 1 |
Ceonothus | −0.012 | 0.125 | 1 | 0.342 | 0.229 | 2 |
Conifer | −0.160 | 0.190 | 1 | −0.085 | 0.280 | 2 |
Forb | −0.058 | 0.116 | 1 | −0.061 | 0.116 | 1 |
Rock | −0.270 | 0.429 | 1 | −0.248 | 0.395 | 1 |
Shrub | −0.052 | 0.263 | 1 | −0.046 | 0.265 | 1 |
Overall | −0.106 | 0.252 | 1 | −0.079 | 0.268 | 1 |
Acknowledgments
References
- Martinuzzi, S.; Vierling, L.A.; Gould, W.A.; Falkowski, M.J.; Evans, J.S.; Hudak, A.T.; Vierling, K.T. Mapping snags and understory shrubs for a lidar-based assessment of wildlife habitat suitability. Remote Sens. Environ. 2009, 113, 2533–2546. [Google Scholar] [CrossRef]
- Falkowski, M.J.; Evans, J.S.; Martinuzzi, S.; Gessler, P.E.; Hudak, A.T. Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA. Remote Sens. Environ. 2009, 113, 946–956. [Google Scholar] [CrossRef]
- Essery, R.; Bunting, P.; Hardy, J.; Link, T.; Marks, D.; Melloh, R.; Pomeroy, J.; Rowlands, A.; Rutter, N. Radiative transfer modeling of a coniferous canopy characterized by airborne remote sensing. J. Hydrometeorol. 2008, 9, 228–241. [Google Scholar] [CrossRef]
- Asner, G.P. Tropical forest carbon assessment: Integrating satellite and airborne mapping approaches. Environ. Res. Lett. 2009, 4, 1–11. [Google Scholar] [CrossRef]
- Abermann, J.; Fischer, A.; Lambrecht, A.; Geist, T. On the potential of very high-resolution repeat DEMs in glacial and periglacial environments. The Cryosphere 2010, 4, 53–65. [Google Scholar] [CrossRef]
- Marks, K.; Bates, P. Integration of high-resolution topographic data with floodplain flow models. Hydrol. Process. 2000, 14, 2109–2112. [Google Scholar] [CrossRef]
- Hodgson, M.E.; Jensen, J.; Raber, G.; Tullis, J.; Davis, B.A.; Thompson, G.; Schuckman, K. An evaluation of lidar-derived elevation and terrain slope in leaf-off conditions. Photogramm. Eng. Remote Sensing 2005, 71, 817–823. [Google Scholar] [CrossRef]
- Hollaus, M.; Wagner, W.; Eberhofer, C.; Karel, W. Accuracy of large-scale canopy heights derived from lidar data under operational constraints in a complex alpine environment. ISPRS J. Photogram. Remote Sens. 2006, 60, 323–338. [Google Scholar] [CrossRef]
- Baltsavias, E.P. Airborne laser scanning: Existing systems and firms and other resources. ISPRS J. Photogram. Remote Sens. 1999, 54, 165–198. [Google Scholar] [CrossRef]
- Hodgson, M.E.; Jenson, J.R.; Schmidt, L.; Schill, S.; Davis, B. An evaluation of lidar- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs. Remote Sens. Environ. 2003, 84, 295–308. [Google Scholar] [CrossRef]
- Smith, S.L.; Holland, D.A.; Longley, P.A. The importance of understanding error in lidar digital elevation models. In Proceedings of XXth ISPRS Congress: Commission IV “Geo-Imagery Bridging Continents”, Istanbul, Turkey, 12–23 July 2004; Volume 35, pp. 996–1001.
- Reutebuch, S.E.; McGaughey, R.J.; Andersen, H.-E.; Carson, W.W. Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Can. J. Remote Sens. 2003, 29, 527–535. [Google Scholar] [CrossRef]
- Hodgson, M.E.; Bresnahan, E. Accuracy of airborne lidar-derived elevation: Empirical assessment and error budget. Photogram. Eng. Remote Sensing 2004, 70, 331–339. [Google Scholar] [CrossRef]
- Anderson, E.S.; Thompson, J.A.; Austin, R.E. Lidar density and linear interpolator effects on elevation estimates. Int. J. Remote Sens. 2005, 26, 3889–3900. [Google Scholar] [CrossRef]
- Hyyppä, H.; Yu, Z.; Hyyppa, J.; Kaartinen, H.; Kaasalainen, S.; Honkavaara, E.; Ronnholm, P. Factors affecting the quality of DTM generation in forested areas. In Proceedings of the ISPRS Workshop Laser Scanning 2005, Enschede, The Netherlands, 12–14 September 2005; Volume 36 Part 3/W19, pp. 97–102.
- Su, J.; Bork, E. Influence of vegetation, slope, and lidar sampling angle on DEM accuracy. Photogram. Eng. Remote Sensing 2006, 72, 1265–1274. [Google Scholar] [CrossRef]
- Bates, C.W.; Coops, N.C. Evaluating error associated with lidar-derived DEM interpolation. Comput. Geosci. 2008, 35, 289–300. [Google Scholar]
- Shan, J.; Toth, C.K. Topographic Laser Ranging and Scanning: Principles and Processing; CRC Press: Boca Raton, FL, USA, 2009. [Google Scholar]
- Spaete, L.P.; Glenn, N.F.; Derryberry, D.P.; Sanki, T.T.; Mitchell, J.; Hardegree, S.P. The effects of slope and vegetation cover type on the accuracy of a small-footprint airborne lidar derived digital elevation model. Remote Sens. Lett. 2011, in press. [Google Scholar] [CrossRef]
- Guo, Q.; Li, W.; Yu, H.; Alvarez, O. Effects of topographic variability and lidar sampling density on several DEM interpolation methods. Photogram. Eng. Remote Sensing 2010, 76, 1–12. [Google Scholar] [CrossRef]
- Evans, J.S.; Hudak, A.T.; Faux, R.; Smith, A.M.S. Discrete return lidar in natural resources: Recommendations for project planning, data processing, and deliverables. Remote Sens. 2009, 1, 776–794. [Google Scholar] [CrossRef]
- Jensen, J.R. Active and passive microwave remote sensing. In Remote Sensing of the Environment: An Earth Resource Perspective; Prentice Hall: Upper Saddle River, NJ, USA, 2007; pp. 335–354. [Google Scholar]
- Evans, J.S.; Hudak, A.T. A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1029–1038. [Google Scholar] [CrossRef]
- Streutker, D.; Glenn, N. lidar measurement of sagebrush steppe vegetation heights. Remote Sens. Environ. 2006, 102, 135–145. [Google Scholar] [CrossRef]
- Hudak, A.T.; Crookston, N.L.; Evans, J.S.; Falkowski, M.J.; Smith, A.M.S.; Gessler, P.E.; Morgan, P. Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data. Can. J. Remote Sens. 2006, 32, 126–138. [Google Scholar] [CrossRef]
- Hudak, A.T.; Crookston, N.L.; Evans, J.S.; Hall, D.E.; Falkowski, M.J. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from lidar data. Remote Sens. Environ. 2008, 112, 2232–2245. [Google Scholar] [CrossRef]
- Jensen, J.L.R.; Humes, K.S.; Vierling, L.A.; Hudak, A.T. Discrete return lidar-based prediction of leaf area index in two conifer forests. Remote Sens. Environ. 2008, 112, 3947–3957. [Google Scholar] [CrossRef]
- Falkowski, M.J.; Hudak, A.T.; Crookston, N.L.; Gessler, P.E.; Uebler, E.; Smith, A.M.S. Landscape-scale parameterization of a tree-level forest growth model: A k-nearest neighbor imputation approach incorporating lidar data. Can. J. For. Res. 2010, 40, 184–199. [Google Scholar] [CrossRef]
- Glenn, N.F.; Spaete, L.; Sankey, T.; Derryberry, D.R.; Hardegree, S. Lidar-derived shrub height and crown area: development of methods and the lack of influence from sloped terrain. J. Arid Environ. 2011, in press. [Google Scholar] [CrossRef]
- Sankey, T.T.; Bond, P. LiDAR-based classification of sagebrush community types. Rangeland Ecol. Manage. 2011, 64, 92–98. [Google Scholar] [CrossRef]
© 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
Tinkham, W.T.; Huang, H.; Smith, A.M.S.; Shrestha, R.; Falkowski, M.J.; Hudak, A.T.; Link, T.E.; Glenn, N.F.; Marks, D.G. A Comparison of Two Open Source LiDAR Surface Classification Algorithms. Remote Sens. 2011, 3, 638-649. https://doi.org/10.3390/rs3030638
Tinkham WT, Huang H, Smith AMS, Shrestha R, Falkowski MJ, Hudak AT, Link TE, Glenn NF, Marks DG. A Comparison of Two Open Source LiDAR Surface Classification Algorithms. Remote Sensing. 2011; 3(3):638-649. https://doi.org/10.3390/rs3030638
Chicago/Turabian StyleTinkham, Wade T., Hongyu Huang, Alistair M. S. Smith, Rupesh Shrestha, Michael J. Falkowski, Andrew T. Hudak, Timothy E. Link, Nancy F. Glenn, and Danny G Marks. 2011. "A Comparison of Two Open Source LiDAR Surface Classification Algorithms" Remote Sensing 3, no. 3: 638-649. https://doi.org/10.3390/rs3030638