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