Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour
D. Panagiotidis, P. Surový, K. Kuželka
https://doi.org/10.17221/92/2015-JFSCitation:Panagiotidis D., Surový P., Kuželka K. (2016): Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour. J. For. Sci., 62: 357-365.With the advantage of Structure from Motion technique, we reconstructed three-dimensional structures from two-dimensional image sequences in a circular plot with a radius of 6 m. The main objective of this research was to clarify the potential of using a low cost hand-held camera for evaluation of the stem accuracy reconstruction, through the comparison of data from two different point clouds. The first cloud comprises data collected with a digital camera that are compared with those collected by direct measurement of the FARO® Focus3D S120 laser scanner. Photos were taken in a circular plot of pine trees using the stop-and-go method. We estimated the Euclidean distance for corresponding points for both clouds and we found out that most of the points with error less than 11 cm are concentrated mainly on the ground. Regression analysis showed a significant relationship between height above ground and error, the error is more pronounced for points located higher on the stems. As expected, no dependence was found between the error of the points and the diameter at breast height of their respective stems.
Keywords:hand-held digital camera; point cloud; stem optimization; image segmentation; photogrammetry
References:Akca Devrim, Freeman Mark, Sargent Isabel, Gruen Armin (2010): Quality assessment of 3D building data. The Photogrammetric Record, 25, 339-355 https://doi.org/10.1111/j.1477-9730.2010.00598.x Bay Herbert, Ess Andreas, Tuytelaars Tinne, Van Gool Luc (2008): Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110, 346-359 https://doi.org/10.1016/j.cviu.2007.09.014
Brolly G., Kiraly G. (2009): Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silvatica & Lignaria Hungarica, 5: 119–130. Dandois Jonathan P., Ellis Erle C. (2010): Remote Sensing of Vegetation Structure Using Computer Vision. Remote Sensing, 2, 1157-1176 https://doi.org/10.3390/rs2041157 Dassot Mathieu, Constant Thiéry, Fournier Meriem (2011): The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Annals of Forest Science, 68, 959-974 https://doi.org/10.1007/s13595-011-0102-2
Dick A.R., Kershaw J.A., MacLean D.A. (2010): Spatial tree mapping using photography. Northern Journal of Applied Forestry, 27: 68–74. Fitzgibbon A., Pilu M., Fisher R.B. (): Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 476-480 https://doi.org/10.1109/34.765658 Fritz A., Kattenborn T., Koch B. (2013): UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS – TREE STEM MAPPING IN OPEN STANDS IN COMPARISON TO TERRESTRIAL LASER SCANNER POINT CLOUDS. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W2, 141-146 https://doi.org/10.5194/isprsarchives-XL-1-W2-141-2013
Furukawa Y., Ponce J. (2007): Accurate, dense and robust multi-view stereopsis. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Mineapolis, June 18–23, 2007: 1–8. Harwin Steve, Lucieer Arko (2012): Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery. Remote Sensing, 4, 1573-1599 https://doi.org/10.3390/rs4061573 Hopkinson Chris, Chasmer Laura, Young-Pow Colin, Treitz Paul (2004): Assessing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research, 34, 573-583 https://doi.org/10.1139/x03-225 Huang Huabing, Li Zhan, Gong Peng, Cheng Xiao, Clinton Nick, Cao Chunxiang, Ni Wenjian, Wang Lei (2011): Automated Methods for Measuring DBH and Tree Heights with a Commercial Scanning Lidar. Photogrammetric Engineering & Remote Sensing, 77, 219-227 https://doi.org/10.14358/PERS.77.3.219
Juujärvi J., Heikkonen J., Brandt S.S., Lampinen J. (1998): Digital image based tree measurement for forest inventory. In: Casasent D.P. (ed.): Proceedings of the 17th SPIE Conference on Intelligent Robots and Computer Vision: Algorithms, Techniques, and Active Vision, Boston, Oct 6, 1998: 114–123. Lovell J.L., Jupp D.L.B., Newnham G.J., Culvenor D.S. (2011): Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 46-55 https://doi.org/10.1016/j.isprsjprs.2010.08.006
Lowe D.G. (2004): Method and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image. US Patent 6,711,293. Mar 23, 2004. Xinlian Liang , Kankare Ville, Xiaowei Yu , Hyyppa Juha, Holopainen Markus (2014): Automated Stem Curve Measurement Using Terrestrial Laser Scanning. IEEE Transactions on Geoscience and Remote Sensing, 52, 1739-1748 https://doi.org/10.1109/TGRS.2013.2253783 Liang Xinlian, Litkey Paula, Hyyppa Juha, Kaartinen Harri, Vastaranta Mikko, Holopainen Markus (2012): Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning. IEEE Transactions on Geoscience and Remote Sensing, 50, 661-670 https://doi.org/10.1109/TGRS.2011.2161613
Lucieer A., Robinson S., Turner D. (2011): Unmanned aerial vehicle (UAV) remote sensing for hyperspatial terrain mapping of Antarctic moss beds based on structure from motion (SfM) point clouds. In: Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, Apr 10–15, 2011: 11–15. Maas H.‐G., Bienert A., Scheller S., Keane E. (2008): Automatic forest inventory parameter determination from terrestrial laser scanner data. International Journal of Remote Sensing, 29, 1579-1593 https://doi.org/10.1080/01431160701736406
Melkas T., Vastaranta M., Holopainen M. (2008): Accuracy and efficiency of the laser-camera. In: Hill R., Rosette J., Suárez J. (eds): Proceedings of SilviLaser 2008: 8th International Conference on LiDAR Applications in Forest Assessment and Inventory, Edinburgh, Sept 17–19, 2008: 315–324.
Mémoli F., Sapiro G. (2004): Comparing point clouds. In: Proceedings of the Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, Nice, July 8–10, 2004: 32–40. Moskal L. Monika, Zheng Guang (2012): Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest. Remote Sensing, 4, 1-20 https://doi.org/10.3390/rs4010001 Murphy Glen E., Acuna Mauricio A., Dumbrell Ian (2010): Tree value and log product yield determination in radiata pine ( Pinus radiata ) plantations in Australia: comparisons of terrestrial laser scanning with a forest inventory system and manual measurements. Canadian Journal of Forest Research, 40, 2223-2233 https://doi.org/10.1139/X10-171
Neitzel F., Klonowski J. (2011): Mobile 3D mapping with a low-cost UAV system. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-1/C22: 1–6. Rosnell Tomi, Honkavaara Eija (2012): Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera. Sensors, 12, 453-480 https://doi.org/10.3390/s120100453 Strahler Alan H, Jupp David L.B, Woodcock Curtis E, Schaaf Crystal B, Yao Tian, Zhao Feng, Yang Xiaoyuan, Lovell Jenny, Culvenor Darius, Newnham Glenn, Ni-Miester Wenge, Boykin-Morris William (): Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna ® ). Canadian Journal of Remote Sensing, 34, S426-S440 https://doi.org/10.5589/m08-046 Surový Peter, Yoshimoto Atsushi, Panagiotidis Dimitrios (2016): Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry. Remote Sensing, 8, 123- https://doi.org/10.3390/rs8020123 Van der Zande D., Hoet W., Jonckheere I., van Aardt J., Coppin P. (2006): Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agricultural and Forest Meteorology, 141, 147–160
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