Aurenhammer F (1987) Power diagrams: properties, algorithms and applications. SIAM J Comput 16(1):78–96

Article
Google Scholar

Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models, Proc Int Arch Photogramm Remote Sens, Amsterdam, the Netherlands, Jul. 16–22, 2000, vol. XXXIII, part B4, pp 110–117

Google Scholar

Bettinger P, Graetz D, Boston K, Sessions J, Chung W (2002) Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems. Silva Fenn 36(2):561–584

Article
Google Scholar

Bettinger P, Tang M (2015) Tree-level harvest optimization for structure-based forest management based on the species mingling index. Forests 6:1121–1144

Article
Google Scholar

Calama R, Cañadas N, Montero G (2003) Inter-regional variability in site index models for even-aged stands of stone pine (*Pinus pinea* L.) in Spain. Ann For Sci 60(3):259–269

Article
Google Scholar

Calama R, Gordo FJ, Mutke S, Montero G (2008) An empirical ecological-type model for predicting stone pine (*Pinus pinea* L.) cone production in the northern plateau (Spain). For Ecol Manag 255:660–673

Article
Google Scholar

Calama R, Montero G (2004) Interregional non-linear height-diameter model with random coefficients for stone pine in Spain. Can J For Res 34:150–163

Article
Google Scholar

Calama R, Montero G (2005) Multilevel linear mixed model for tree diameter increment in stone pine (*Pinus pinea* L.): a calibrating approach. Silva Fenn 39(1):37–54

Article
Google Scholar

Calama R, Montero G (2006) Stand and tree-level variability on stem form and tree volume in *Pinus pinea* L.: a multilevel random components approach. Forest Syst 15(1):24–41

Google Scholar

Calama R, Mutke S, Tomé J, Gordo J, Montero G, Tomé M (2011) Modelling spatial and temporal variability in a zero-inflated variable: the case of stone pine (*Pinus pinea* L.) cone production. Ecol Model 222:606–618

Article
Google Scholar

Falkowski MJ, Smith AMS, Gessler PE, Hudak AT, Vierling LA, Evans JS (2008) The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Can J Remote Sens 34:338–350

Article
Google Scholar

Heinonen T, Kurttila M, Pukkala T (2007) Possibilities to aggregate raster cells through spatial optimization in forest planning. Silva Fenn 41(1):89–103

Article
Google Scholar

Heinonen T, Mäkinen A, Rasinmäki J, Pukkala T (2018) Aggregating microsegments into harvest blocks by using spatial optimization and proximity objectives. Can J For Res 48:1–10

Article
CAS
Google Scholar

Heinonen T, Pukkala T (2004) A comparison of one- and two- compartment neighbourhoods in heuristic search with spatial forest management goals. Silva Fenn 38:319–332

Article
Google Scholar

Heinonen T, Pukkala T (2007) The use of cellular automaton approach in forest planning. Can J For Res 37:2188–2200

Article
Google Scholar

Hoganson HM, Rose DW (1984) A simulation approach for optimal timber management scheduling. For Sci 30(1):220–238

Google Scholar

Hyyppä J, Hyyppä H, Leckie D, Gougeon F, Yu X, Maltamo M (2008) Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int J Remote Sens 29:1339–1366

Article
Google Scholar

Hyyppä J, Kelle O, Lehikoinen M, Inkinen M (2001) A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans Geo Rem Sens 39:969–975

Article
Google Scholar

Koch B, Heyder U, Weinacker H (2006) Detection of individual tree crowns in airborne lidar data. Photogramm Eng Remote Sens 72:357–363

Article
Google Scholar

Kulakowski D, Seidl R, Holeksa J, Kuuluvainen T, Nagel TA, Panayotov M, Svoboda M, Thorn S, Vacchiano G, Whitlock C, Wohlgemuth T, Bebi P (2017) A walk on the wild side: disturbance dynamics and the conservation and management of European mountain forest ecosystems. For Ecol Manag 388:120–131

Article
Google Scholar

Kurttila M, Pukkala T, Loikkanen J (2002) The performance of alternative spatial objective types in forest planning calculations: a case for flying squirrel and moose. For Ecol Manag 166:245–260

Article
Google Scholar

Kuuluvainen T (2016) Conceptual models of forest dynamics in environmental education and management: keep it as simple as possible, but no simpler. For Ecosyst 3:18. https://doi.org/10.1186/s40663-016-0075-6

Article
Google Scholar

Lähivaara T, Seppänen A, Kaipio JP, Vauhkonen J, Korhonen L, Tokola T, Maltamo M (2014) Bayesian approach to tree detection based on airborne laser scanning data. IEEE Trans Geosci Remote Sens 52(5):2690–2699

Article
Google Scholar

Lindberg E, Holmgren J, Olofsson K, Olsson H, Wallerman J (2010) Estimation of tree lists from airborne laser scanning by combining single tree and area-based methods. Int J Remote Sens 31:1175–1192

Article
Google Scholar

Lu F, Eriksson LO (2000) Formation of harvest units with genetic algorithms. For Ecol Manag 130:57–67

Article
Google Scholar

Magnussen S, Næsset E, Gobakken T (2013) Prediction of tree-size distributions and inventory variables from cumulants of canopy height distributions. Forestry 86:583–595

Article
Google Scholar

Maltamo M, Næsset E, Vauhkonen J (2014) Forestry applications of airborne laser scanning: concepts and case studies. Managing Forest Ecosystems 27, Springer, Dordrecht, Netherlands.

Book
Google Scholar

Martín-Fernández S, García-Abril A (2005) Optimisation of spatial allocation of forestry activities within a forest stand. Comput Electron Agric 49(1):159–174

Article
Google Scholar

Mathey AH, Krcmar E, Tait D, Vertinsky I, Innes J (2007) Forest planning using co-evolutionary cellular automata. For Ecol Manag 239:45–56

Article
Google Scholar

Means JE, Acker SA, Fitt BJ, Renslow M, Emerson L, Hendrix CJ (2000) Predicting forest stand characteristics with airborne scanning LiDAR. Photogramm Eng Remote Sens 66:1367–1372

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

Article
Google Scholar

Nilsson M, Nordkvist K, Jonzén J, Lindgren N, Axensten P, Wallerman J, Egberth M, Larsson S, Nilsson L, Eriksson J, Olsson H (2017) A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the national forest inventory. Remote Sens Environ 194:447–454

Article
Google Scholar

Öhman K (2000) Creating continuous areas of old forest in long-term forest planning. Can J For Res 30(11):1817–1823

Article
Google Scholar

Öhman K (2002) Spatial optimization in forest planning. In: Pukkala T (ed) Multi-objective forest planning. Managing Forest Ecosystems 6, Springer, Dordrecht, pp 153–192

Chapter
Google Scholar

Packalen P, Heinonen T, Pukkala T, Vauhkonen J, Maltamo M (2011) Dynamic treatment units in Eucalyptus plantation. For Sci 57:416–426

Google Scholar

Pasalodos-Tato M, Pukkala T, Calama R, Cañellas I, Sánches-González M (2016) Optimal management of *Pinus pinea* stands when cone and timber production are considered. Eur J For Res 135:607–619

Article
Google Scholar

Pascual A, Pukkala T, de Miguel S, Pesonen A, Packalen P (2019) Influence of size and shape of forest inventory units on the layout of harvest blocks in numerical forest planning. Eur J For Res 138(1):111–123

Article
Google Scholar

Pukkala T, Heinonen T, Kurttila M (2008) An application of the reduced cost approach to spatial forest planning. For Sci 55(1):13–22

Google Scholar

Pukkala T, Lähde E, Laiho O (2015) Which trees should be removed in thinning treatments? For Ecosyst 2(1):1–12. https://doi.org/10.1186/s40663-015-0056-1

Article
Google Scholar

Pukkala T, Miina J (1998) Tree-selection algorithms for optimizing thinning using a distance-dependent growth model. Can J For Res 28:693–702

Article
Google Scholar

Pukkala T, Packalén P, Heinonen T (2014) Dynamic treatment units in forest management planning. In: Borges JG, Diaz-Balteiro L, McDill ME, Rodriguez LCE (eds) Managing Forest Ecosystems 33. Springer, Dordrecht, pp 373–392

Google Scholar

R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna https://www.R-project.org/. Accessed 11 Sept 2019

Google Scholar

Strange N, Meilby H, Bogetoft P (2001) Land use optimization using self-organizing algorithms. Nat Resour Model 14:541–574

Article
Google Scholar

Strange N, Meilby H, Jellesmark Thorsen B (2002) Optimization of land use in afforestation areas using evolutionary self-organization. For Sci 48(3):543–555

Google Scholar

Vauhkonen J (2010) Estimating single-tree attributes by airborne laser scanning: methods based on computational geometry of the 3-D point data. Dissertationes Forestales 104. Dissertation, University of Eastern Finland.

Google Scholar

Vauhkonen J, Ene L, Gupta S, Heinzel J, Holmgren J, Pitkänen J, Solberg S, Wang Y, Weinacker H, Hauglin KM, Lien V, Packalén P, Gobakken T, Koch B, Næsset E, Tokola T, Maltamo M (2011) Comparative testing of single-tree detection algorithms under different types of forest. Forestry 85(1):27–40

Article
Google Scholar

Vauhkonen J, Pukkala T (2016) Selecting trees to be harvested based on the relative value growth of the remaining trees. Eur J For Res 135(3):581–592

Article
Google Scholar

Von Neumann J (1966) Theory of self-reproducing automata. Ed Burks AW. Urbana, University of Illinois Press, Urbana and London, p 388

Google Scholar

Weintraub A, Murray AT (2006) Review of combinatorial problems induced by spatial forest harvesting planning. Discret Appl Math 154(5):867–879

Article
Google Scholar

Wing BM, Boston K, Ritchie MW (2019) A technique for implementing group selection treatments with multiple objectives using an airborne lidar-derived stem map in a heuristic environment. For Sci 65(2):211–222

Google Scholar

Wolfram S (2002) A new kind of science. Wolfram Media, Champaign. ISBN 1-57955-008-8, p 1280

Google Scholar