References
- Axelsson P. 1999. Processing of laser scanner data—algorithms and applications. ISPRS J Photogramm Remote Sens. 54:138–147.
- Axelsson P. 2000. DEM generation from laser scanner data using adaptive TIN models. Int Arch Photogramm Remote Sens. 33:110–117.
- Borges JG, Nordström EM, Garcia-Gonzalo J, Hujala T, Trasobares A, editors. 2014. Computer-based tools for supporting forest management. The experience and the expertise world-wide. Umeå: Swedish University of Agricultural Sciences, Department of Forest Resource Management.
- Breidenbach J, Næsset E, Lien V, Gobakken T, Solberg S. 2010. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens Environ. 114:911–924.
- Duvemo K, Lämås T. 2006. The influence of forest data quality on planning processes in forestry. Scand J For Res. 21:327–339.
- Fahlvik N, Wikström P, Elfving B. 2014. Evaluation of growth models used in the Swedish Forest Planning System Heureka. Silva Fenn. 48:2.
- Gobakken T, Næsset E. 2004. Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scand J For Res. 19:529–542.
- Gobakken T, Næsset E. 2005. Weibull and percentile models for lidar-based estimation of basal area distribution. Scand J For Res. 20:490–502.
- Gordon SN, Floris A, Boerboom L, Lämås T, Eriksson LO, Nieuwenhuis M, Garcia J, Rodriguez L. 2013. Studying the use of forest management decision support systems: an initial synthesis of lessons learned from case studies compiled using a semantic wiki. Scand J For Res. doi:10.1080/02827581.2013.856463
- Hogg RV, Tanis EA. 2010. Probability and statistical inference. Upper Saddle (NJ): Pearson.
- Holmström H, Kallur H, Ståhl G. 2003. Cost-plus-loss analyses of forest inventory strategies based on kNN assigned reference sample plot data. Silva Fenn. 37:381–398.
- Kangas AS. 2010. Value of forest information. Eur J Forest Res. 129:863–874.
- Levin DA, Peres Y, Wilmer EL. 2009. Markov chains and mixing times. Providence (RI): American Mathematical Society; p. 48.
- Maltamo M, Næsset E, Bollandsås OM, Gobakken T, Packalén P. 2009. Non-parametric prediction of diameter distributions using airborne laser scanner data. Scand J For Res. 24:541–553.
- McRoberts RE, Cohen WB, Næsset E, Stehman SV, Tomppo EO. 2010. Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scand J For Res. 25:340–367.
- 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.
- Næsset E, Gobakken T, Holmgren J, Hyyppä H, Hyyppä J, Maltamo M, Nilsson M, Olsson H, Persson Å, Söderman U. 2004. Laser scanning of forest resources: the Nordic experience. Scand J For Res. 19:482–499.
- 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.
- Reynolds MR, Burk TE, Huang WC. 1988. Goodness-of-FiT tests and model selection procedures for diameter distribution models. For Sci. 34:373–399.
- Solberg S, Næsset E, Bollandsas OM. 2006. Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogramm Eng Remote Sens. 72:1369–1378.
- Wikström P, Edenius L, Elfving B, Eriksson LO, Lämås T, Sonesson J, Öhman K, Wallerman J, Waller C, Klintebäck F. 2011. The Heureka forestry decision support system: an overview. Math Comput For Natural-Resour Sci. 3:87–94.