Assessing 3D point clouds from aerial photographs for species-specific forest inventories

References

• Agisoft LLC. 2014. Agisoft photoscan user manual. St. Petersburg. Available from: http://www.agisoft.com/pdf/photoscan-pro_1_1_en.pdf
   Google Scholar
• Bohlin J, Olsson H, Olofsson K, Wallerman J. 2006. Tree species discrimination by aid of template matching applied to digital air photos. In: International workshop on 3D remote sensing in forestry; Vienna: BOKU. Available from http://www.rali.boku.ac.at/fileadmin/data/H03000/H85000/H85700/workshops/3drsforestry/presentations/7.4-olsson.pdf
   Google Scholar
• Bohlin J, Wallerman J, Fransson JES. 2012a. Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scand J For Res. 27:692–699. doi: 10.1080/02827581.2012.686625
   Web of Science ®Google Scholar
• Bohlin J, Wallerman J, Fransson JES. 2012b. Species-specific forest variable estimation using non-parametric modelling of multi-spectral photogrammetric point cloud data. In: International archives of the photogrammetry, XXII ISPRS Congress; Melbourne. Available from: http://pub.epsilon.slu.se/10635/8/bohlin_et_al_130710.pdf
   Google Scholar
• Braastad H. 1966. Volume tables for birch. Meddr norske SkogforsVes. 21:23–78. Norwegian with English summary.
   Google Scholar
• Brantseg A. 1967. Volume functions and tables for scots pine. South Norway. Meddr norske SkogforsVes. 22:689–739 . Norwegian with English summary.
   Google Scholar
• Dalponte M, Ørka HO, Ene LT, Gobakken T, Næsset E. 2014. Tree crown delineation and tree species classification in boreal forests using Hyperspectral and ALS Data. Remote Sens Environ. 140:306–317. doi: 10.1016/j.rse.2013.09.006
   Web of Science ®Google Scholar
• Dalponte M, Orka HO, Gobakken T, Gianelle D, Næsset E. 2013. Tree species classification in boreal forests with hyperspectral data. IEEE Trans Geosci Remote Sens. 51:2632–2645. doi: 10.1109/TGRS.2012.2216272
   Web of Science ®Google Scholar
• Donoghue D, Watt P, Cox N, Wilson J . 2007. Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sens Environ. 110:509–522. doi: 10.1016/j.rse.2007.02.032
   Web of Science ®Google Scholar
• Eid T, Næsset E. 1998. Determination of stand volume in practical forest inventories based on field measurements and photo-interpretation: the Norwegian experience. Scand J For Res. 13:246–254. doi: 10.1080/02827589809382982
   Web of Science ®Google Scholar
• Erikson M. 2004. Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures. Remote Sens Environ. 91:469–477. doi: 10.1016/j.rse.2004.04.006
   Web of Science ®Google Scholar
• Fitje A, Vestjordet E. 1977. Stand height curves and new tariff tables for Norway spruce. Meddr norske SkogforsVes. 34:23–62. Norwegian with English summary.
   Google Scholar
• Franklin SE, Hall RJ, Moskal LM, Maudie AJ, Lavigne MB. 2000. Incorporating texture into classification of forest species composition from airborne multispectral images. Int J Remote Sens. 21:61–79. doi: 10.1080/014311600210993
   Web of Science ®Google Scholar
• Gobakken T, Bollandsås OM, Næsset E. 2014. Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data. Scand J For Res. 30:1–37.
   Web of Science ®Google Scholar
• Gobakken T, Næsset E. 2008. Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Can J For Res. 38:1095–1109. doi: 10.1139/X07-219
   Web of Science ®Google Scholar
• Haara A, Haarala M. 2002. Tree species classification using semi-automatic delineation of trees on aerial images. Scand J For Res. 17:556–565. doi: 10.1080/02827580260417215
   Web of Science ®Google Scholar
• Hansen EH, Gobakken T, Bollandsås OM, Zahabu E, Næsset E. 2015. Modeling aboveground biomass in dense tropical submontane rainforest using airborne laser scanner data. Remote Sens. 7:788–807. doi: 10.3390/rs70100788
   Web of Science ®Google Scholar
• Haralick RM, Shanmugam K, Dinstein IH. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3. 6:610–621. doi: 10.1109/TSMC.1973.4309314
   Web of Science ®Google Scholar
• Henningsen A, Toomet O. 2011. Maxlik: a package for maximum likelihood estimation in R. Comput Stat. 26:443–458. doi: 10.1007/s00180-010-0217-1
   Web of Science ®Google Scholar
• Hijazi RH, Jernigan RW. 2007. Modelling compositional data using Dirichlet regression models. J Appl Probab Stat. 4:77–91.
   Google Scholar
• Holmgren J, Persson Å, Söderman U. 2008. Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. Int J Remote Sens. 29:1537–1552. doi: 10.1080/01431160701736471
   Web of Science ®Google Scholar
• Honkavaara E, Arbiol R, Markelin L, Martinez L, Cramer M, Bovet S, Chandelier L, Ilves R, Klonus S, Marshal P, et al. 2009. Digital airborne photogrammetry—a new tool for quantitative remote sensing? A state-of-the-art review on radiometric aspects of digital photogrammetric images. Remote Sens. 1:577. doi: 10.3390/rs1030577
   Web of Science ®Google Scholar
• Järnstedt J, Pekkarinen A, Tuominen S, Ginzler C, Holopainen M, Viitala R. 2012. Forest variable estimation using a high-resolution digital surface model. ISPRS J Photogramm Remote Sens. 74:78–84. doi: 10.1016/j.isprsjprs.2012.08.006
   Web of Science ®Google Scholar
• Korhonen L, Korhonen KT, Stenberg P, Maltamo M, Rautiainen M. 2007. Local models for forest canopy cover with beta regression. Silva Fenn. 41:671–685. doi: 10.14214/sf.275
   Web of Science ®Google Scholar
• Korpela I, Heikkinen V, Honkavaara E, Rohrbach F, Tokola T. 2011. Variation and directional anisotropy of reflectance at the crown scale—implications for tree species classification in digital aerial images. Remote Sens Environ. 115:2062–2074. doi: 10.1016/j.rse.2011.04.008
   Web of Science ®Google Scholar
• Korpela I, Mehtätalo L, Markelin L, Seppänen A, Kangas A. 2014. Tree species identification in aerial image data using directional reflectance signatures. Silva Fenn. 48:1087. doi: 10.14214/sf.1087
   Web of Science ®Google Scholar
• Leckie D, Gougeon F, Hill D, Quinn R, Armstrong L, Shreenan R. 2003. Combined high-density LiDAR and multispectral imagery for individual tree crown analysis. Can J Remote Sens. 29:633–649. doi: 10.5589/m03-024
   Web of Science ®Google Scholar
• Lumley T, Miller A. 2009. Leaps: regression subset selection. Available from: http://cran.r-project.org/web/packages/leaps/leaps.pdf
   Google Scholar
• Maier MJ. 2014. Dirichletreg : Dirichlet regression for compositional data in R Dirichletreg: Dirichlet regression for compositional data in R. Version 0.4.0; Vienna: WU Vienna University of Economics and Business, Department of Statistics and Mathematics.
   Google Scholar
• Maltamo M, Næsset E, Vauhkonen J. 2014a. Forestry applications of airborne laser scanning. Springer.
   Google Scholar
• Maltamo M, Ørka HO, Bollandsås OM, Gobakken T, Næsset E. 2014b. Using pre-classification to improve the accuracy of species-specific forest attribute estimates from airborne laser scanner data and aerial images. Scand J For Res. 30:1–22. doi: 10.1080/02827581.2014.986520
   Web of Science ®Google Scholar
• Maltamo M, Packalén P, Kangas J, Uuttera J, Heikkila J. 2011. Airborne laser scanning based stand level management inventory in Finland. In: Proceedings of SilviLaser 2011, 11th International Conference on LiDAR Applications for Assessing (pp. 16–19). University of Tasmania.
   Google Scholar
• Mikhail EM, Bethel JS, McGlone JC. 2001. Introduction to modern photogrammetry. New York, NY: Wiley.
   Google Scholar
• Miller RG. 1981. Simultaneous statistical inference. (2nd ed.). New York, NY: Springer-Verlag.
   Google Scholar
• Mora B, Wulder MA, White JC. 2010. Identifying leading species using tree crown metrics derived from very high spatial resolution imagery in a boreal forest environment. Can J Remote Sens. 36:332–344. doi: 10.5589/m10-052
   Web of Science ®Google Scholar
• Nilsson M. 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ. 56:1–7. doi: 10.1016/0034-4257(95)00224-3
   Web of Science ®Google Scholar
• Niska H, Skön J-P, Packalén P, Tokola T, Maltamo M, Kolehmainen M. 2010. Neural networks for the prediction of species-specific plot volumes using airborne laser scanning and aerial photographs. IEEE Trans Geosci Remote Sens. 48:1076–1085. doi: 10.1109/TGRS.2009.2029864
   Web of Science ®Google Scholar
• Nurminen K, Karjalainen M, Yu X, Hyyppä J, Honkavaara E. 2013. Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables. ISPRS J Photogramm Remote Sens. 83:104–115. doi: 10.1016/j.isprsjprs.2013.06.005
   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 Sens Environ. 80:88–99. doi: 10.1016/S0034-4257(01)00290-5
   Web of Science ®Google Scholar
• Næsset E. 2004a. Accuracy of forest inventory using airborne laser scanning: evaluating the first Nordic full-scale operational project. Scand J For Res. 19:554–557. doi: 10.1080/02827580410019544
   Web of Science ®Google Scholar
• Næsset E. 2004b. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand J For Res. 19:164–179. doi: 10.1080/02827580310019257
   Web of Science ®Google Scholar
• Næsset E. 2005. Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data. Remote Sens Environ. 98:356–370. doi: 10.1016/j.rse.2005.07.012
   Web of Science ®Google Scholar
• Næsset E. 2014. Area-based inventory in Norway – from innovation to operational reality. In: Maltamo M, Næsset E, Vauhkonen J, editors. Forestry applications of airborne laser scanning. Dordrecht: Springer. p. 215–240.
   Google Scholar
• Ørka HO, Dalponte M, Gobakken T, Næsset E, Ene LT. 2013. Characterizing forest species composition using multiple remote sensing data sources and inventory approaches. Scand J For Res. 28:677–688. doi: 10.1080/02827581.2013.793386
   Web of Science ®Google Scholar
• Ørka HO, Gobakken T, Næsset E, Ene L, Lien V. 2012. Simultaneously acquired airborne laser scanning and multispectral imagery for individual tree species identification. Can J Remote Sens. 38:125–138. doi: 10.5589/m12-021
   Web of Science ®Google Scholar
• Ørka HO, Næsset E, Bollandsås OM. 2010. Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning. Remote Sens Environ. 114:1445–1461. doi: 10.1016/j.rse.2010.01.024
   Web of Science ®Google Scholar
• Packalén P, Maltamo M. 2007. The k-msn method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sens Environ. 109:328–341. doi: 10.1016/j.rse.2007.01.005
   Web of Science ®Google Scholar
• Packalén P, Suvanto A, Maltamo M. 2009. A two stage method to estimate species-specific growing stock. Photogramm Eng Remote Sens. 75:1451–1460. doi: 10.14358/PERS.75.12.1451
   Web of Science ®Google Scholar
• Puliti S, Ørka HO, Gobakken T, Næsset E. 2015. Inventory of small forest areas using an unmanned aerial system. Remote Sens. 7:9632. doi: 10.3390/rs70809632
   Web of Science ®Google Scholar
• Rahlf J, Breidenbach J, Solberg S, Næsset E, Astrup R. 2014. Comparison of four types of 3d data for timber volume estimation. Remote Sens Environ. 155:325–333. doi: 10.1016/j.rse.2014.08.036
   Web of Science ®Google Scholar
• R Core Team. 2015. A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna. Available from: http://www.R-project.org
   Google Scholar
• Smithson M, Verkuilen J. 2006. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol Methods. 11:54–71. doi: 10.1037/1082-989X.11.1.54
   PubMed Web of Science ®Google Scholar
• Snowdon P. 1991. A ratio estimator for bias correction in logarithmic regressions. Can J For Res. 21:720–724. doi: 10.1139/x91-101
   Web of Science ®Google Scholar
• Statistics Norway. 2016. Commercial roundwood removals, Q1 2016, preliminary figures; cited [2016 Apr 20 ]. Available from: http://www.ssb.no/en/jord-skog-jakt-og-fiskeri/statistikker/skogav/kvartal
   Google Scholar
• Straub C, Stepper C, Seitz R, Waser LT. 2013. Potential of ultracamx stereo images for estimating timber volume and basal area at the plot level in mixed European forests. Can J For Res. 43:731–741. doi: 10.1139/cjfr-2013-0125
   Web of Science ®Google Scholar
• Terrasolid. 2012. Terrascan user’s guide, Terrasolid Ltd., Helsinki. Available from: https://www.terrasolid.com/download/tscan.pdf
   Google Scholar
• Vastaranta M, Wulder MA, White JC, Pekkarinen A, Tuominen S, Ginzler C, Kankare V, Holopainen M, Hyyppa J, Hyyppa H. 2013. Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update. Can J Remote Sens. 39:382–395.
   Web of Science ®Google Scholar
• Vauhkonen J, Ørka HO, Holmgren J, Dalponte M, Koch B. 2014. Tree species recognition based on airborne laser scanning and complementary data sources. In: Maltamo M, Næsset E, Vauhkonen J, editors. Forestry applications of airborne laser scanning – concepts and case studies. Dordrecht: Springer. p. 135–156.
   Google Scholar
• Vestjordet E. 1967. Functions and tables for volume of standing trees. Norway Spruce. Meddr norske SkogforsVes. 22:539–574. Norwegian with English summary.
   Google Scholar
• Vestjordet E. 1968. Volum Av Nyttbart Virke Hos Gran Og Furu Basert På Relativ Høyde Og Diameter I Brysthøyde Eller Ved 2,5 M Fra Stubbeavskjær [Merchantable volume of Norway spruce and scots pine based on relative height and diameter at breast height or 2.5 M above stump level]. Meddr norske SkogforsVes. 25:411–459. Norwegian with English summary.
   Google Scholar
• Vihervaara P, Mononen L, Auvinen A-P, Virkkala R, Lü Y, Pippuri I, Packalen P, Valbuena R. 2015. How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale. Landsc Ecol. 30:501–516. doi: 10.1007/s10980-014-0137-5
   Web of Science ®Google Scholar
• Warner TA, McGraw JB, Landenberger R. 2006. Segmentation and classification of high resolution imagery for mapping individual species in a closed canopy, deciduous forest. Sci China Ser E. 49:128–139. doi: 10.1007/s11431-006-8114-0
   Google Scholar
• Waser LT, Ginzler C, Kuechler M, Baltsavias E, Hurni L. 2011. Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from airborne digital sensor (Ads40) and Rc30 data. Remote Sens Environ. 115:76–85. doi: 10.1016/j.rse.2010.08.006
   Web of Science ®Google Scholar
• Wing BM, Ritchie MW, Boston K, Cohen WB, Gitelman A, Olsen MJ. 2012. Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sens Environ. 124:730–741. doi: 10.1016/j.rse.2012.06.024
   Web of Science ®Google Scholar
• Wu C. 2004. Normalized spectral mixture analysis for monitoring urban composition using etm+imagery. Remote Sens Environ. 93:480–492. doi: 10.1016/j.rse.2004.08.003
   Web of Science ®Google Scholar
• Yu B, Member S, Ostland IM, Gong P, Pu R. 1999. Penalized discriminant analysis of in situ hyperspectral data for conifer species recognition. IEEE Trans Geosci Remote Sens. 35:2569–2577. doi: 10.1109/36.789651
   Web of Science ®Google Scholar
• Zvoleff A. 2015. Package ‘GLCM’. version 1.2. Available from: http://cran.r-project.org/web/packages/glcm/glcm.pdf
   Google Scholar