Watts, A.C.; Ambrosia, V.G.; Hinkley, E.A. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sens. 2012, 4, 1671-1692.
Turner, D.; Lucieer, A.; Watson, C. An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote Sens. 2012, 4, 1392-1410.
Koh, L.P.; Wich, S.A. Dawn of drone ecology: Low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 2012, 5, 121-132.
Haala, N. Comeback of digital image matching. Photogram. Week 2009, 9, 289-301.
Pierrot-Deseilligny, M.; Clery, I. ́ Evolutions ŕecentes en photogramḿetrie et mod́elisation 3d par photo des milieux naturels. Collect. EDYTEM 2011, 12, 51-64.
Snavely, N.; Seitz, S.M.; Szeliski, R. Modeling the world from internet photo collections. Int. J. Comput. Vis. 2008, 80, 189-210.
Baltsavias, E.; Gruen, A.; Eisenbeiss, H.; Zhang, L.; Waser, L.T. High-quality image matching and automated generation of 3D tree models. Int. J. Remote Sens. 2008, 29, 1243-1259.
Dandois, J.P.; Ellis, E.C. Remote sensing of vegetation structure using computer vision. Remote Sens. 2010, 2, 1157-1176.
Corona, P.; Fattorini, L. Area-based lidar-assisted estimation of forest standing volume. Can. J. For. Res. 2008, 38, 2911-2916.
Steinmann, K.; Mandallaz, D.; Ginzler, C.; Lanz, A. Small area estimations of proportion of forest and timber volume combining lidar data and stereo aerial images with terrestrial data. Scand. J. For. Res. 2013, 28, 373-385.
Næsset, E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 2002, 80, 88-99.
Næsset, E.; Gobakken, T.; Holmgren, J.; Hyypp̈a, H.; Hyypp̈a, J.; Maltamo, M.; Nilsson, M.; Olsson, H.; Persson, A.; S̈oderman, U. Laser scanning of forest resources: The Nordic experience. Scand. J. For. Res. 2004, 19, 482-499.
Wehr, A.; Lohr, U. Airborne laser scanning-An introduction and overview. J. Photogram. Remote Sens. 1999, 54, 68-82.
Maltamo, M.; Eerik̈ainen, K.; Packaĺen, P.; Hyypp̈a, H. Estimation of stem volume using laser scanning-based canopy height metrics. Forestry 2006, 79, 217-229.
Miura, N.; Jones, S.D. Characterizing forest ecological structure using pulse types and heights of airborne laser scanning. Remote Sens. Environ. 2010, 114, 1069-1076.
Jaskierniak, D.; Lane, P.N.J.; Robinson, A.; Lucieer, A. Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions. Remote Sens. Environ. 2011, 115, 573-585.
Zhao, K.G.; Popescu, S.; Meng, X.L.; Pang, Y.; Agca, M. Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sens. Environ. 2011, 115, 1978-1996.
Lindberg, E.; Hollaus, M. Comparison of methods for estimation of stem volume, stem number and basal area from airborne laser scanning data in a hemi-boreal forest. Remote Sens. 2012, 4, 1004-1023.
Baltsavias, E.P. A comparison between photogrammetry and laser scanning. ISPRS J. Photogram. Remote Sens. 1999, 54, 83-94.
Lim, K.; Treitz, P.;Wulder, M.; St-Onge, B.; Flood, M. LiDAR remote sensing of forest structure. Progr. Phys. Geogr. 2003, 27, 88-106.
Hyypp̈a, J.; Hyypp̈a, H.; Leckie, D.; Gougeon, F.; Yu, X.; Maltamo, M. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int. J. Remote Sens. 2008, 29, 1339-1366.
V́ega, C.; St-Onge, B. Mapping site index and age by linking a time series of canopy height models with growth curves. For. Ecol. Manag. 2009, 257, 951-959.
V́ega, C.; St-Onge, B. Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models. Remote Sens. Environ. 2008, 112, 1784-1794.
Huang, H.; Gong, P.; Cheng, X.; Clinton, N.; Li, Z. Improving measurement of forest structural parameters by co-registering of high resolution aerial imagery and low density LiDAR data. Sensors 2009, 9, 1541-1558.
Bohlin, J.; Wallerman, J.; Fransson, J.E.S. Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scand. J. For. Res. 2012, 27, 692-699.
Mora, B.; Wulder, M.A.; Hobart, G.W.; White, J.C.; Bater, C.W.; Gougeon, F.A.; Varhola, A.; Coops, N.C. Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots. Int. J. Remote Sens. 2013, 34, 4406-4424.
Nurminen, K.; Karjalainen, M.; Yu, X.; Hyypp̈a, J.; Honkavaara, E. Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables. ISPRS J. Photogram. Remote Sens. 2013, 83, 104-115.
White, J.; Wulder, M.; Vastaranta, M.; Coops, N.; Pitt, D.; Woods, M. The utility of image-based point clouds for forest inventory: A comparison with airborne laser scanning. Forests 2013, 4, 518-536.
Eisenbeiss, H. UAV Photogrammetry. Ph.D. Thesis, ETH, Zurich, Switzerland, 2009.
St-Onge, B.; Vega, C.; Fournier, R.A.; Hu, Y. Mapping canopy height using a combination of digital stereo-photogrammetry and lidar. Int. J. Remote Sens. 2008, 29, 3343-3364.
Dandois, J.P.; Ellis, E.C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 2013, 136, 259-276.
Tao,W.; Lei, Y.; Mooney, P. Dense Point Cloud Extraction from UAV Captured Images in Forest Area. In Proceedings of the 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), Fuzhou, China, 29 June-1 July 2011; pp. 389-392.
Wallace, L.; Lucieer, A.; Watson, C.; Turner, D. Development of a UAV-LiDAR system with application to forest inventory. Remote Sens. 2012, 4, 1519-1543.
Jaakkola, A.; Hyypp̈a, J.; Kukko, A.; Yu, X.; Kaartinen, H.; Lehtom̈aki, M.; Lin, Y. A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J. Photogram. Remote Sens. 2010, 65, 514-522.
Rondeux, J. La Mesure des Arbres et des Peuplements Forestiers; Presses Agronomiques de Gembloux: Gembloux, Belgium, 1999.
Kitahara, F.; Mizoue, N.; Yoshida, S. Effects of training for inexperienced surveyors on data quality of tree diameter and height measurements. Silv. Fenn. 2010, 44, 657-667.
Description of the Gatewing X100. Available online: http://uas.trimble.com/X100 (accessed on 4 September 2013)
Laliberte, A.S.;Winters, C.; Rango, A. UAS remote sensing missions for rangeland applications. Geocarto Int. 2011, 26, 141-156.
Dunford, R.; Michel, K.; Gagnage, M.; Piegay, H.; Tremelo, M. Potential and constraints of unmanned aerial vehicle technology for the characterization of Mediterranean riparian forest. Int. J. Remote Sens. 2009, 30, 4915-4935.
Wolf, P.; Dewitt, B. Elements of Photogrammetry: With Applications in GIS, 3rd ed.; McGraw-Hill: New York, NY, USA, 2000.
Aber, J.; Marzolff, I.; Ries, J. Small-Format Aerial Photography: Principles, Techniques and Applications; Elsevier Science: Amsterdam, The Netherlands,2010.
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. Photogram. Eng. Remote Sens. 2005, 71, 817.
Suarez, J.; Ontiveros, C.; Smith, S.; Snape, S. Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry. Comput. Geosci. 2005, 31, 253-262.
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.
Aguilar, F.J.; Mills, J.P.; Delgado, J.; Aguilar, M.A.; Negreiros, J.G.; Perez, J.L. Modelling vertical error in LiDAR-derived digital elevation models. ISPRS J. Photogram. Remote Sens. 2010, 65, 103-110.
Zhang, Y.; Xiong, J.; Hao, L. Photogrammetric processing of low-altitude images acquired by unpiloted aerial vehicles. Photogram. Record 2011, 26, 190-211.
L̈abe, T.; F̈orstner, W. Geometric Stability of Low-Cost Digital Consumer Cameras. In Proceedings of the 20th ISPRS Congress, Istanbul, Turkey, 12-23 July 2004; pp. 528-535.
Presentation of the Photogrammetric Suite MICMAC. Available online: http://www.micmac.ign.fr/ (accessed on 4 September 2013)
Lowe, D. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2004, 60, 91-110.
Pierrot-Deseilligny, M.; Clery, I. Apero, an Open Source Bundle Adjustment Software for Automatic Calibration and Orientation of Set of Images. In Proceedings of the ISPRS Symposium, 3D-ARCH 2011, Trento, Italy, 24 March 2011.
Triggs, B.; McLauchlan, P.; Hartley, R.; Fitzgibbon, A. Bundle Adjustment-A Modern Synthesis. In Vision Algorithms: Theory and Practice; Springer-Verlag: Berlin Heidelberg, Germany, 2000; pp. 298-372.
Pierrot-Deseilligny, M.; Paparoditis, N. A multiresolution and optimization-based image matching approach: An application to surface reconstruction from SPOT5-HRS stereo imagery. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 2006, 36, 73-77.
Roy, S.; Cox, I.J. A Maximum-Flow Formulation of the n-Camera Stereo Correspondence Problem. In Proceedings of the Sixth International Conference on Computer Vision, Bombay, India, 4-7 January 1998; pp. 492-499.
CloudCompare (Version 2.3) (GPL Software); EDF R&D and Telecom ParisTech: Paris, France, 2011. Accessed online: http://www.danielgm.net/cc/ (accessed on 4 September 2013).
Besl, P.J.; McKay, N.D. A method for registration of 3-D shapes. IEEE Trans. Patt. Anal. Mach. Intell. 1992, 14, 239-256.
Kasser, M.; Egels, Y. Digital Photogrammetry; Taylor & Francis: London, UK, 2002.
J̈arnstedt, J.; Pekkarinen, A.; Tuominen, S.; Ginzler, C.; Holopainen, M.; Viitala, R. Forest variable estimation using a high-resolution digital surface model. ISPRS J. Photogram. Remote Sens. 2012, 74, 78-84.
Kraus, K.; Karel, W.; Briese, C.; Mandlburger, G. Local accuracy measures for digital terrain models. Photogram. Record 2006, 21, 342-354.
Lin, S.Y.; Muller, J.P.; Mills, J.P.; Miller, P.E. An assessment of surface matching for the automated co-registration of MOLA, HRSC and HiRISE DTMs. Earth Planet. Sci. Lett. 2010, 294, 520-533.
R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2011.
Gruen, A. Development and status of image matching in photogrammetry. Photogram. Record 2012, 27, 36-57.
St-Onge, B.; Jumelet, J.; Cobello, M.; V́ega, C. Measuring individual tree height using a combination of stereophotogrammetry and lidar. Can. J. For. Res. 2004, 34, 2122-2130.
