A Comparison of Point Clouds Derived from Stereo Imagery and Airborne Laser Scanning for the Area-Based Estimation of Forest Inventory Attributes in Boreal Ontario

REFERENCES

• Baltsavias, P. 1999. “A comparison between photogrammetry and laser scanning.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 54: pp. 83–94.
   Web of Science ®Google Scholar
• Bohlin, J., Wallerman, J., and Fransson, J.E. S. 2012. “Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM.” Scandinavian Journal of Forest Research, Vol. 27(No. 7): pp. 692–699.
   Web of Science ®Google Scholar
• Bollandsås, O.M., Maltamo, M., Gobakken, T., and Næsset, E. 2013. “Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest.” Forestry, Vol. 86: pp. 493–501. doi:10.1093/forestry/cpt020
   Web of Science ®Google Scholar
• Ekstrom, B., Breau, J., Thompson, E., Woolnough, N., and Quist, L. 2007. “Forest management plan for the Hearst Forest for the 10-year period from April 1, 2007 to March 31, 2017.” Hearst District, Northeast Region, Hearst Forest Management Inc. 516 p. [online] Available from http://www.hearstforest.com/english/PDF/HearstForest2007FMP.pdf [cited May 1, 2014].
   Google Scholar
• Frazer, G.W., Hobart, G., White, J.C., and Wulder, M.A. 2011. Predictive Modeling of Forest Inventory Attributes Using Airborne Lidar and Ground-Reference Measurements Derived from Hinton Wood Products’ Permanent Growth Sample (PGS) program. Final Report., Victoria, B.C., Canada: Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre and Hinton Wood Products, Edmonton, Alta.
   Google Scholar
• Gehrke, S., Morin, K., Downey, M., Boehrer, N., and Fuchs, T. 2010. “Semi-global matching: an alternative to lidar for dsm generation?” International Archives of Photogrammetry & Remote Sensing, Calgary, AB, Vol. 38, Part B1.
   Google Scholar
• Gehrke, S., Uebbing, R., Downey, M., and Morin, K. 2011. “Creating and using very high density point clouds derived from ADS imagery.” Paper presented at Proceedings of the American Society of Photogrammetry and Remote Sensing 2011 Annual Conference, Milwaukee, Wisconsin, May 1–5, 2011.
   Google Scholar
• Gehrke, S., Downey, M., Uebbing, R., Welter, J., and LaRocque, W. 2012. “A multi-sensor approach to semi-global matching.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia.
   Google Scholar
• Gougeon, F.A. 2010. The ITC Suite Manual : A Semi-automatic Individual Tree Crown (ITC) Approach to Forest Inventories. 92 p. Victoria B.B. Canada: Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada.
   Google Scholar
• Hilker, T., Frazer, G.W., Coops, N.C., Wulder, M.A., Newnham, G.J., Stewart, J.D., van Leeuwen, M., and Culvenor, D.S. 2013. “Prediction of wood fibre attributes from lidar–derived forest canopy indicators.” Forest Science, Vol. 59(No. 2): pp. 231–242.
   Web of Science ®Google Scholar
• Hirschmüller, H. 2008. “Stereo processing by semiglobal matching and mutual information.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30(No. 2): pp. 328–341.
   PubMed Web of Science ®Google Scholar
• Holmgren, J. 2004. “Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning.” Scandinavian Journal of Forest Research, Vol. 19(No. 6): pp. 543–553.
   Web of Science ®Google Scholar
• Holmgren, J., Persson, Å., and Söderman, U. 2008. “Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images.” In Special Issue of International Journal of Remote Sensing—”3D Remote Sensing in Forestry”, Vol. 29(No. 5): pp. 1537–1552.
   Google Scholar
• Holopainen, M., Vastaranta, M., Rasinmäki, J., Kalliovirta, J., Mäkine, A., Haapanen, R., Melkas, T., Yu, X., and Hyyppä, J. 2010. “Uncertainty in timber assortment estimates predicted from forest inventory data.” European Journal of Forest Research, Vol. 129: pp. 1131–1142. doi 10.1007/s10342-010-0401-4.
   Web of Science ®Google Scholar
• Järnstedt, J., Pekkarinen, A., Tuominen, S., Ginzler, C., Holopainen, M., and Viitala, R. 2012. “Forest variable estimation using a high-resolution digital surface model.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 4: pp.78–84.
   Web of Science ®Google Scholar
• Kraus, K., and Pfeifer, N. 1998. “Determination of terrain models in wooded areas with airborne laser scanner data.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 53: pp. 193–203.
   Web of Science ®Google Scholar
• Lacroix, S., and Charette, F. 2013. “Better planning with LiDAR-enhanced forest inventory.” FPInnovations Advantage Report. Vol 14(No. 1): pp. 1–8.
   Google Scholar
• Liaw, A., and Wiener, M. 2002. “Classification and regression by randomforest.” R News, Vol. 2(No. 3): pp. 18–22.
   Google Scholar
• Littell, R.C., Freund, R.J., and Spector, P.C. 1991. SAS® System for Linear Models, Third Edition. Cary, NC: SAS Institute Inc.
   Google Scholar
• Luther, J.E., Skinner, R., Fournier, R.A., van Lier, O.R., Bowers, W.W., Coté, J.-F., Hopkinson, C., and Moulton, T. 2013. “Predicting quantity and quality attributed of balsam fir and black spruce using airborne laser scanner data.” Forestry, Vol. 87(No. 2): pp. 313–326.
   Web of Science ®Google Scholar
• Næsset, E. 2002. “Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data.” Remote Sensing of Environment, Vol. 80(No. 1): pp. 88–99.
   Web of Science ®Google Scholar
• Næsset, E. 2004. “Accuracy of forest inventory using airborne laser–scanning: evaluating the first Nordic full–scale operational project.” Scandinavian Journal of Forest Research, Vol. 19(No. 6): pp. 554–557.
   Web of Science ®Google Scholar
• Næsset, E., Gobakken, T., Holmgren, J., Hyyppä, H., Hyyppä, J., Maltamo, M., Nilsson, M., Olsson, H., Persson, Å., and Söderman, U. 2004. “Laser scanning of forest resources: The Nordic experience.” Scandinavian Journal of Forest Research, Vol. 19(No. 6): pp. 482–499.
   Web of Science ®Google Scholar
• Nurminen, K, Karjalainen, M., Yu, X., Hyyppä, J., and Honkavaara, E. 2013. “Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 83: pp. 104–115.
   Web of Science ®Google Scholar
• Ontario Ministry of Natural Resources (OMNR). 2012a. Ontario Forest Resources Inventory Calibration Plot Specifications - Revised. Ontario Ministry of Natural Resources Internal publication. 41 pp.
   Google Scholar
• Ontario Ministry of Natural Resources (OMNR). 2012b. Ontario Forest Resources Inventory Photo Interpretation Specifications - Revised. Ontario Ministry of Natural Resources Internal publication. 87 pp.
   Google Scholar
• Parton, J., Vasiliauskas, S., Lucking, G., and Watt, W.R. 2006. Standard Forest Units for Northeastern Ontario Boreal Forests. Technical Note TN-021. Porcupine, Ontario, Canada: Ontario Ministry of Natural Resources, Northeast Science & Information.
   Google Scholar
• Penner, M., Pitt, D.G., and Woods, M. 2013. “Parametric vs. nonparametric lidar models for operational forest inventory in boreal Ontario.” Canadian Journal of Remote Sensing, Vol. 39(No. 5): pp. 426–443.
   Web of Science ®Google Scholar
• Pitt, D.G., and Pineau, J.. 2009. “Forest inventory research at the Canadian Wood Fibre Centre: Notes from a research coordination workshop.” Forestry Chronicle, Vol. 85(No. 6) pp. 859–869.
   Web of Science ®Google Scholar
• R Development Core Team. 2012. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available from http://www.R-project.org/ [Cited Dec. 6, 2013].
   Google Scholar
• Sharma, M., and Parton, J. 2007. Height-diameter equations for boreal tree species in Ontario using a mixed-effects modeling approach.” Forest Ecology and Management, Vol. 249(No. 3): pp. 187–196. doi: 10.1016/j.foreco.2007.05.006.
   Web of Science ®Google Scholar
• St-Onge, B., Jumelet, J., Cobello, M., and Véga, C. 2004. “Measuring individual tree height using a combination of stereophotogrammetry and lidar.” Canadian Journal of Forest Research, Vol. 34(No. 10): pp. 2122–2130.
   Web of Science ®Google Scholar
• Straub, C., Stepper, C., Seitz, R., and Waser, L.T. 2013. “Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests.” Canadian Journal of Forest Research, Vol. 43(No. 8): pp. 731–741.
   Web of Science ®Google Scholar
• Treitz, P., Lim, K., Woods, M., Pitt, D., Nesbitt, D., and Etheridge, D. 2012. “LiDAR Sampling density for forest resource inventories in Ontario, Canada.” Remote Sensing, Vol. 4(No. 4): pp. 830–848. doi:10.3390/rs4040830.
   Web of Science ®Google Scholar
• van Ewijk, K.Y., Treitz, P.M., and Scott, N.A. 2011. “Characterizing forest succession in central Ontario using lidar-derived indices.” Photogrammetric Engineering and Remote Sensing, Vol. 77(No. 3): pp. 261–269.
   Web of Science ®Google Scholar
• Vastaranta, M., Wulder, M.A., White, J.C., Pekkarinen, A., Tuominen, S., Ginzler, C., Kankare, V., Holopainen, M., Hyyppä, J., and Hyyppä, H. 2013. “Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update.” Canadian Journal of Remote Sensing, Vol. 39(No. 5): pp. 382–395.
   Web of Science ®Google Scholar
• White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D.G., and Woods, M. 2013a. A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach. Information Report FI-X-010. 50 p. Victoria, BC, Canada: Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Pacific Forestry Centre. . Available from http://cfs.nrcan.gc.ca/publications?id=34887 [cited May 30, 2014].
   Google Scholar
• White, J., Wulder, M.A., Vastaranta, M., Coops, N.C., Pitt, D., and Woods, M. 2013b. “The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning.” Forests, Vol. 4: pp. 518–536. doi:10.3390/f4030518.
   Web of Science ®Google Scholar
• Woods, M., Pitt, D.G., Penner, M., Lim, K., Nesbitt, D., Etheridge, D., and Treitz, P. 2011. “Operational implementation of a lidar inventory in Boreal Ontario.” Forestry Chronicle, Vol. 87(No. 4): pp. 512–528.
   Web of Science ®Google Scholar
• Yang, Y., Monserud, R.A., and Huang, S. 2004. “An evaluation of diagnostic tests and their role in validating forest biometric models.” Canadian Journal of Forest Research, Vol. 34(No. 3): pp. 619–629.
   Web of Science ®Google Scholar
• Zakrzewski, W.T. 1999. “A mathematically tractable stem profile model for jack pine in Ontario.” Northern Journal of Applied Forestry, Vol. 16(No. 3): pp. 138–143.
   Google Scholar
• Zakrzewski, W.T. and Penner, M. 2013. A Comparison of Tree Stem Taper Models for Use in Ontario. OMNR Forest Research Report No.176. 26p. Sault Ste. Marie, ON, Canada: Ontario Forest Research Institute, Ministry of Natural Resources.
   Google Scholar
• Zhang, W., Baoxin, H., Linhai, J., Woods, M.E., and Courville, P. 2008. “Automatic forest species classification using combined LIDAR data and optical imagery.” Paper presented at Proceedings of the International Geoscience and Remote Sensing Symposium, Boston, MA, July 7–11, 2008. ISBN: 978-1-4244-2807-6.
   Google Scholar