J. For. Sci., 2016, 62(8):357-365 | DOI: 10.17221/92/2015-JFS
Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviourOriginal Paper
- Department of Forest Management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
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
Published: August 31, 2016 Show citation
| ACS | AIP | APA | ASA | Harvard | Chicago | Chicago Notes | IEEE | ISO690 | MLA | NLM | Turabian | Vancouver |
References
- Akca D., Freeman M., Sargent I., Gruen A. (2010): Quality assessment of 3D building data. The Photogrammetric Record, 25: 339-355.
Go to original source... - Bay H., Ess A., Tuytelaars T., Van Gool L. (2008): Speeded Up Robust Features (SURF). Computer Vision and Image Understanding, 110: 346-359.
Go to original source... - Brolly G., Kiraly G. (2009): Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silvatica & Lignaria Hungarica, 5: 119-130.
Go to original source... - Dandois J.P., Elis E.C. (2010): Remote sensing of vegetation structure using computer vision. Remote Sensing, 2: 1157-1176.
Go to original source... - Dassot M., Constant T., Fournier M. (2011): The use of terrestrial LiDAR technology in forest science: Application fields, benefits and challenges. Annals of Forest Science, 68: 959-974.
Go to original source... - Dick A.R., Kershaw J.A., MacLean D.A. (2010): Spatial tree mapping using photography. Northern Journal of Applied Forestry, 27: 68-74.
Go to original source... - Fitzgibbon A., Pilu M., Fisher R.B. (1999): Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21: 476-480.
Go to original source... - 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. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/ W2: 141-146.
Go to original source... - 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.
Go to original source... - Harwin S., Lucieer A. (2012): Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sensing, 6: 1573-1599.
Go to original source... - Hopkinson C., Chasmer L., Young Pow C., Treitz P. (2004): Assessing forest metrics with ground-based scanning LiDAR. Canadian Journal of Forest Research, 34: 573-583.
Go to original source... - Huang H., Li Z., Gong P., Cheng X., Clinton N., Cao C., Ni W., Wang L. (2011): Automated methods for measuring DBH and tree heights with a commercial scanning LiDAR. Photogrammetric Engineering and Remote Sensing, 77: 219-227.
Go to original source... - 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.
Go to original source... - 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 Photogrammetry and Remote Sensing, 66: 46-55.
Go to original source... - 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.
- Liang X., Kankare V., Yu X., Hyyppa J., Holopainen M. (2014): Automated stem curve measurement using terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 52: 1739-1748.
Go to original source... - Liang X., Litkey P., Hyyppa J., Kaartinen H., Vastaranta M., Holopainen M. (2012): Automatic stem mapping using single-scan terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 50: 661-670.
Go to original source... - 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.
Go to original source... - 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.
Go to original source... - Moskal L.M., Zheng G. (2012): Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sensing, 4: 1-20.
Go to original source... - Murphy G.E., Acuna M.A., Dumbrell I. (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.
Go to original source... - 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.
Go to original source... - Rosnell T., Honkavaara E. (2012): Point cloud generation from aerial image data acquired by a quadcopter type micro unmanned aerial vehicle and a digital still camera. Sensors, 12: 453-480.
Go to original source...
Go to PubMed... - Strahler A.H., Jupp D.L.B., Woodcock C.E., Schaaf C.B., Yao T., Zhao F., Yang X., Lovell J., Culvenor D., Newnham G. (2008): Retrieval of forest structural parameters using a ground-based LiDAR instrument (Echidna®). Canadian Journal of Remote Sensing, 34: 426-440.
Go to original source... - Surový P., Yoshimoto A., Panagiotidis D. (2016): Accuracy of reconstruction of the tree stem surface using terrestrial close-range photogrammetry. Remote Sensing, 8: 123.
Go to original source... - 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.
Go to original source...
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.

