Have a personal or library account? Click to login
Forest modelling and visualisation – state of the art and perspectives Cover

Forest modelling and visualisation – state of the art and perspectives

Open Access
|Nov 2019

References

  1. Aber, J. D., Federer, C. A., 1992: A generalized, lumped-parameter model of photosynthesis, evapotranspi-ration and net primary production in temperate and boreal forest ecosystems. Oecologia, 92:463–474.10.1007/BF00317837
  2. Adelard, L., Boyer, H., Garde, F., Gatina, J.-C., 2000: A detailed weather data generator for building simulations. Energy and Buildings, 31:75–88.10.1016/S0378-7788(99)00009-2
  3. Aertsen, W., Kint, V., Muys, B., Van Orshoven, J., 2012: Effects of scale and scaling in predictive modelling of forest site productivity. Environmental Modelling & Software, 31:19–27.10.1016/j.envsoft.2011.11.012
  4. Allister, K. M., Harding, L. A., Vernon Cole, C., Parton, W. J., 1993: CENTURY Soil Organic Matter Model Environment. Available at: https://www2.nrel.colostate.edu/projects/century/MANUAL/html_manual/man96.html (accessed 12.8.18).
  5. Aschoff, T., Thies, M., Winterhalder, D., Kretschmer, U., Spiecker, H., 2004: Automatisierte Ableitung von forstlichen Inventurparametern aus terrestrischen Laserscannerdaten. 24. Wissenscgaftlich-Technische ahrestagung der DGPF 2004, Halle/saale, p. 341–348.
  6. Assmann, E., Franz, F., 1963: Vorläufige Fichten-Ertragstafel für Bayern. Institut für Ertragskunde der Forst-lichen Forschungsanstalt, München, 104 p.
  7. Auger, P., Lett, C., 2003: Integrative biology: linking levels of organization. Comptes Rendus Biologies, 326:517–522.10.1016/S1631-0691(03)00115-X
  8. Biber, P., Borges, J. G., Moshammer, R., Barreiro, S., Botequim, B., Brodrechtová, Y. et al., 2015: How Sensitive Are Ecosystem Services in European Forest Landscapes to Silvicultural Treatment? Forests, 6:1666–1695.10.3390/f6051666
  9. Bienert, A., Scheller, S. T., 2008: Verfahren zur auto-matischen Bestimmung von Forstinventurparametern aus terrestrischen Laserscannerpunktwolken. 28. Wissenschaftlisch-Technische Jahrestagung der DGPF, p. 110–120.
  10. Blaschke, T., Tiede, D., Heurich, M., 2004: 3D-landscape metrics to modelling forest structure and diversity based on laser-scanning data. In: Thies, M., Koch, B., Spiecker, H.,Weinacker, H. (eds.): Laser Scanners for Forest and Landscape Assessment. Proceedings of the ISPRS Working Group VIII/2. Freiburg, Germany, October 3–6, 2004. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVI, Part 8/W2, p. 129–132.
  11. Bohn, F. J., Frank, K., Huth, A., 2014.: Of climate and its resulting tree growth: Simulating the productivity of temperate forests. Ecological Modelling, 278:9–17.10.1016/j.ecolmodel.2014.01.021
  12. Botkin, D. B., Janak, J. F., Wallis, J. R., 1972: Some ecological consequences of a computer model of forest growth. Journal of Ecology, 60:849–872.10.2307/2258570
  13. Brandtberg, T., 1999: Automatic Individual Tree-Based Analysis of High Spatial Resolution Remotely Sensed Data, Acta Universitatis Agriculturae Sueciae, 16 p.
  14. Brandtberg, T., 2002: Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets and Systems, 132:371–387.10.1016/S0165-0114(02)00049-0
  15. Bruce, D., Mars, de, D. J., Reukema, D. C., 1977: Douglas-fir managed yield simulator: DFIT User’s Guide, USDA, Forest Serv. Gen. Techn. Report PNW-57, PNW Forest and Range Experimental Station, Portland, OR., 2 p.
  16. Brunner, A., 1998: A light model for spatially explicit forest stand models. Forest Ecology and Management, 107:19–46.10.1016/S0378-1127(97)00325-3
  17. Buckley, D. J., Ulbricht, C., Berry, J., 1998: The Virtual Forest: Advanced 3-D Visualization Techniques for Forest Managament and Research. ESRI, Proceedings GIS’98, 15 p.
  18. Bugmann, H. K. M., 1994. On the ecology of mountainous forests in a changing climate: a simulation study (Doctoral Thesis). ETH Zurich.
  19. Bugmann, H., 1996: A simplified forest model to study species composition along climate gradients, Ecology, 77:2055–2074.10.2307/2265700
  20. Bugmann, H., Grote, R., Lasch, P., Lindner, M., Sukkow, F., 1997: A New Forest Gap Model to Study the Effects of Environmental Change on Forest Structure and Functioning. Impacts of Global Change on Tree Physiology and Forest Ecosystems, p. 255–261.10.1007/978-94-015-8949-9_33
  21. Bugmann, H., Lindner, M., Lasch, P., Flechsig, M., Ebert, B., Cramer, W., 2000: Scaling issues in forest succession modelling. Climatic Change, 44:265–289.10.1023/A:1005603011956
  22. Buongiorno, J., 2001: Generalization of Faustmanns formula for stochastic forest growth and prices with Markov decision process models. Forest Science, 47:466–474.10.1093/forestscience/47.4.466
  23. Burkhart, H. E., Tomé, M., 2012: Modeling Forest trees and planter stands. Springer, 457 p.10.1007/978-90-481-3170-9
  24. Campbell, G. S., 1986: Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution. Agricultural and Forest Meteorology, 36:317–321.10.1016/0168-1923(86)90010-9
  25. Campbell, G. S., 1990: Derivation of an angle density function for canopies with ellipsoidal leaf angle distribution. Agricultural and Forest Meteorology, 49:173–176.10.1016/0168-1923(90)90030-A
  26. Černý, M., Bukša, I., 2005: Field-Map – Advanced measurement technology for forest management, nature conservation and landscaping. In: Conference proceedings, International anniversary scientific conference devoted to the 75th anniversary of Ukrainian forestry research institute founding, 30 – 31 March 2005, Kharkov, Ukraine, p. 84–85.
  27. Clark, M. L., Clark, D. B., Roberts, D. A., 2004: Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment, 91:68–89.10.1016/j.rse.2004.02.008
  28. Clemence, B. S. E., 1997: A brief assessment of a weather data generator (CLIMGEN) at Southern African sites. Short Communication. Water SA, 23:271–274.
  29. Clutter, J. L., 1963: Compatible growth and yield models for loblolly pine. Forest Science, 9:354–371.
  30. Collalti, A., Perugini, L., Santini, M., Chiti, T., Nolè, A., Matteucci, G., Valentini, R., 2014. A process-based model to simulate growth in forests with complex structure: Evaluation and use of 3D-CMCC Forest Ecosystem Model in a deciduous forest in Central Italy. Ecological Modelling, 272:362–378.10.1016/j.ecolmodel.2013.09.016
  31. Cornea, 2017: Webpage of CORNEA CAVE system at KAUST Visualization Core Lab (King Abdullah University of Science and Technology). Available at: http://kvl.kaust.edu.sa/Pages/cornea.aspx 2017, [accessed March 8, 2017].
  32. Cruz-Neira, C., Sandin, D. J., DeFanti, T. A., 1993: Surround-screen projection-based virtual reality: the design and implementation of the CAVE. In: Proceedings of the 20th annual conference on Computer graphics and interactive techniques. ACM SIGGRAPH, p. 135–142.10.1145/166117.166134
  33. Cruz-Neira, C., Sandin, D. J., DeFanti, T. A., Kenyon, R. V., Hart, J. C., 1992: The CAVE: audio visual experience automatic virtual environment. Communications of the ACM, 35:64–72.10.1145/129888.129892
  34. Cyberith, 2017: CYBERITH GmbH webpage–Cyberith Virtualizer product description. Available at: http://cyberith.com/ 2017, [accessed March 8, 2017].
  35. de Willigen, P., 1991. Nitrogen turnover in the soil-crop system; comparison of fourteen simulation models. Fertilizer Research, 27:141–149.10.1007/978-94-011-3434-7_1
  36. Dealle, K., Rudemo, M., 1997: Automatic estimation of individual tree positions from aerial photos. Canadian Journal of Forest Research, 27:1728–1736.10.1139/x97-130
  37. Deckmyn, G., Verbeeck, H., Op de Beeck, M., Vans-teenkiste, D., Steppe, K., Ceulemans, R., 2008. ANA-FORE: A stand-scale process-based forest model that includes wood tissue development and labile carbon storage in trees. Ecological Modelling, 215:345–368.10.1016/j.ecolmodel.2008.04.007
  38. DeFanti, T. A., Acevedo, D., Ainsworth, R. A., Brown, M. D., Cutchin, S., Dawe, G.et al., 2011: The future of the CAVE. Central European Journal of Engineering, 1:16–37.10.2478/s13531-010-0002-5
  39. DeFanti, T. A., Dawe, G., Sandin, D. J., Schulze, J. P., Otto, P., Girado, J. et al., 2009: The StarCAVE, a third-generation CAVE and virtual reality OptIPortal. Future Generation Computer Systems, 25:169–178.10.1016/j.future.2008.07.015
  40. Dieckmann, U., Law, R., Metz, J. A. J., 2000: The geometry of ecological interactions: Simplifying spatial complexity, Cambridge University Press, Cambridge, 564 p.10.1017/CBO9780511525537
  41. Donatelli, M., Bellocchi, G., Habyarimana, E., Bregaglio, S., Confalonieri, R., Baruth, B., 2009: CLIMA: a weather generator framework. In: 18th World IMACS / MODSIM Congress, Cairns, Australia, 13–17 July 2009, Avaiable at: http://mssanz.org.au/modsim09.
  42. Dubrovský, M., 1997: Creating daily weather series with use of the weather generator. Environmetrics, 8:409–424.10.1002/(SICI)1099-095X(199709/10)8:5<;409::AID-ENV261>3.0.CO;2-0
  43. Dufour-Kowalski S., Courbaud B., Dreyfus P., Meredieu C., de Coligny F., 2012. Capsis: an open software framework and community for forest growth modelling. Annals of Forest Science, 69:221–233.10.1007/s13595-011-0140-9
  44. Dufrêne, E., Davi, H., François, C., Maire, G. le, Dantec, V.L., Granier, A., 2005. Modelling carbon and water cycles in a beech forest. Ecological Modelling, 185:407–436.10.1016/j.ecolmodel.2005.01.004
  45. Eckersten, H., Jansson, P.-E., 1991. Modelling water flow, nitrogen uptake and production for wheat. Fertilizer Research, 27:313–329.10.1007/978-94-011-3434-7_16
  46. Ek, A. R., Monserud, R. A., 1974: Trials with program FOREST: Growth and reproduction simulation for mixed species even- or uneven-aged forest stands. In: Fries, J. (Hrsg.): Growth models for tree and stand simulation. Royal College of Forestry, Stockholm, Sweden, Research Notes, 30:56–73.
  47. Fabrika, M., 2005: Simulátor biodynamiky lesa SIBYLA. Koncepcia, konštrukcia a programové riešenie. Habilitačná práca, Technická univerzita vo Zvolene, 238 p.
  48. Fabrika, M., Ďurský, J., 2005: Algorithms and software solution of thinning models for SIBYLA growth simulator. Journal of Forest Science, 51:431–445.10.17221/4577-JFS
  49. Fabrika, M., Ďurský, J., 2006: Implementing Tree Growth Models in Slovakia. In: Hasenauer, H. (Ed.), Sustainable Forest Management: Growth Models for Europe. Springer Berlin Heidelberg, Berlin, Heidelberg, p. 315–341.10.1007/3-540-31304-4_19
  50. Fabrika, M., Pretzsch, H., 2013: Forest Ecosystem Analysis and Modelling. Technical University in Zvolen, 619 p.
  51. Fan, Y., Roupsard, O., Bernoux, M., Le Maire, G., Panferov, O., Kotowska, M.M., Knohl, A., 2015. A sub-canopy structure for simulating oil palm in the Community Land Model (CLM-Palm): phenology, allocation and yield. Geoscientific Model Development, 8:3785–3800.10.5194/gmd-8-3785-2015
  52. Febretti, A., Nishimoto, A., Thigpen, T., Talandis, J., Long, L., Pirtle, J. D. et al., 2013: CAVE2: A Hybrid Reality Environment for Immersive Simulation and Information Analysis. In: IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, p. 864903-864903-12.10.1117/12.2005484
  53. Fernandes, K. J., Raja, V., Eyre, J., 2003: Immersive learning system for manufacturing industries. Computers in Industry, 51:31–40.10.1016/S0166-3615(03)00027-7
  54. Fox, T. R., Allen, L., Wynne, R. H., Blinn, Ch. E., 2008: Precision Silviculture in the 21st Century: Linking GIS and Remote Sensing to Develop Site Specific Silvicultural Regimes in Southern Pine Plantations, In: Bettinger, P., Merry, K., Frei, S., Drake, J., Nibbelink, Heinstall, (eds.): Proceedings of the 6th Southern Forestry and Natural Resources GIS Conference. Warner School of Forestry and Natural Resources, University of Georgia, Athens.
  55. Franc, A., Gourlet-Fleury, S., Picard, N., 2000: Une Introduction á la Modélisation des Forêts Hétérogènes. ENGREF, Nancy, France.
  56. Franz, F., 1968: das EDV-Programm STAOET – zur Herleitung mehrgliedriger Standort-Leistungstafeln. Manuskriptdruck, München unveröff.
  57. Gadow, von K., 1987: Untersuchungen zur Konstruktion von Wuchsmodellen für schnellwüchsige Plan-tagenbaumarten. Forstliche Forschungsber. Mün-chen, No. 77, 147 p.
  58. Geng S., Auburn, J., Brandstetter, E., Li, B., 1988: A Program to Simulate Meteorological Variables. Documentation for SIMMETEO. (Agronomy Report No. 204). University of California, Davis Crop Extension, Davis, California.
  59. Geng, S., Penning De Vries, F. W. T., Supit, I., 1986: A simple method for generating daily rainfall data. Agricultural and Forest Meteorology, 36:363–376.10.1016/0168-1923(86)90014-6
  60. Gitelson, A. A., Kaufman, Y. J., Stark, R., Rundquist, D., 2002: Novel algorithm for remote estimation of vegetation fraction. Remote Sensing of Environment, 80: 76–87.10.1016/S0034-4257(01)00289-9
  61. Gougeon, F. A., 1995: A crown following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing, 21:274–284.10.1080/07038992.1995.10874622
  62. Grote, R., 1998. Integrating dynamic morphological properties into forest growth modelling. Forest Ecology and Management, 111:193–210.10.1016/S0378-1127(98)00328-4
  63. Grote, R., Kiese, R., Grünwald, T., Ourcival, J.-M., Granier, A., 2011. Modelling forest carbon balances considering tree mortality and removal. Agricultural and Forest Meteorology, 151:179–190.10.1016/j.agrformet.2010.10.002
  64. Grote, R., Pretzsch, H., 2002. A Model for Individual Tree Development Based on Physiological Processes. Plant Biology, 4:167–180.10.1055/s-2002-25743
  65. Grote, R., Pretzsch, H., 2002: A model for individual tree development based on physiological processes. Plant Biology, 4:167–180.10.1055/s-2002-25743
  66. Grote, R., Reiter, I.M., 2004. Competition-dependent modelling of foliage biomass in forest stands. Trees, 18.10.1007/s00468-004-0352-9
  67. Guillemot, J., Francois, C., Hmimina, G., Dufrêne, E., Martin-StPaul, N. K. et al., 2016: Environmental control of carbon allocation matters for modelling forest growth. New Phytologist, 214:180–193.10.1111/nph.14320
  68. Halaj, et al., 1987: Rastové tabuľky hlavných drevín ČSSR. Bratislava, Príroda, 361 p.
  69. Hamilton, G., Christie, J. M., 1973: Construction and application of stand yield tables. British For. Com. Res. and Developm. Paper, London, No. 96, 14 p.
  70. Hansen, J. W., Mavromatis, T., 2001: Correcting low-frequency variability bias in stochastic weather generators. Agricultural and Forest Meteorology, 109:297–310.10.1016/S0168-1923(01)00271-4
  71. Harding, D. J., Lefsky, M. A., Parker, G. G., Blair, J. B., 2001: Laser altimetry canopy height profiles methods and validation for closed-canopy, broadleaf forests. Remote Sensing of Environment, 76:283–297.10.1016/S0034-4257(00)00210-8
  72. Hauhs, M., Kastner-Maersch, A., Rost-Siebert, K., 1995: A model relating forest growth to ecosystem-scale budgets of energy and nutrients. Ecological Modelling, 83:229–243.10.1016/0304-3800(95)00101-Z
  73. Hayhoe, H. N., 2000: Improvements of stochastic weather data generators for diverse climates. Climate Research, 14:75–87.10.3354/cr014075
  74. Heurich, M., Schneider, T., Kennel, E., 2003: Laser Scanning for Identification of Forest Structures in the Bavarian Forest National Park. In: Hyyppä, Naesset, Olsson, Pahlen, Reese (eds.): Proceedings of the Scandlaser Scientific Workshop on Airborne Laser Scanning of Forests., p. 97–106.
  75. Heurich, M., Perssson, A., Holmgren, J., Kennel, E., 2004: Detecting and measuring individual trees with laser scanning in mixed mountain forest of Central Europe using an algorithm developed for Swedish boreal forest conditions. International Archives Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI:307–312.
  76. Hidy, D., Barcza, Z., Marjanović, H., Ostrogović Sever, M.Z., Dobor, L., Gelybó, G. et al., 2016. Terrestrial Ecosystem Process Model Biome-BGCMuSo: Summary of improvements and new modeling possibilities. Geoscientific Model Development Discussions, p. 1–60.10.5194/gmd-2016-93
  77. Holdridge, L. R., 1947: Determination of World Plant Formations from Simple Climatic Data. Science, 105: 367–369.10.1126/science.105.2727.367
  78. Holmgren, J., Persson, Å., 2004: Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment, 90:415–423.10.1016/S0034-4257(03)00140-8
  79. Hopkinson, C., Chasmer, L., Young-Pow, C., Treitz, P., 2004: Assesing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research, 34:573–583.10.1139/x03-225
  80. Houllier, F., 1995: A propos des modèles de la dynamique des peuplements hétérogènes structures, processus démographiques et mécanismes de regulation. Revue d‘Écologie, 50: 273–282.10.3406/revec.1995.2177
  81. Hurtt, G. C., Moorcroft, P. R., Pacala, S. W., 2013: Ecosystem Demography Model: Scaling Vegetation Dynamics Across South America.
  82. Huth, R, Mládek, R., Metelka, L., Sedlák, P., Huthová, Z., Kliegerová, S. et al., 2003: On the integrability of limited-area numerical weather prediction model ALADIN over extended time periods. Studia Geophysica et Geodaetica, 47:863–873.10.1023/A:1026351004242
  83. Jansson, P.-E., Karlberg, L., 2004: Coupled heat and mass transfer model for soil-plant-atmosphere systems. Royal Institute of Technology, Department of Civil and Environmental Engineering.
  84. Jonard, M., André, F., 2018: Heterofor [Capsis] [WWW Document]. URL http://capsis.cirad.fr/capsis/help_en/heterofor (accessed 12.8.18).
  85. Jones, P. G., Thornton, P. K., 2000: MarkSim: software to generate daily weather data for Latin America and Africa. Agronomy Journal, 92:445–453.10.2134/agronj2000.923445x
  86. Kahn, M., 1994: Modellierung der Höhenentwicklung ausgewählter Baumarten in Abhängigkeit vom Stan-dort. Forstliche Forschungsber. München, Vol. 141, 221 p.
  87. Karjalainen, E., Tyrväinen, L., 2002: Visualization in forst lanscape preference reaearch: a Finnish perspective. Lanscape and Urban Planning, 59:13–28.10.1016/S0169-2046(01)00244-4
  88. Keenan, T., Niinemets, Ü., Sabate, S., Gracia, C., Peñuelas, J., 2009. Process based inventory of isoprenoid emissions from European forests: model comparisons, current knowledge and uncertainties. Atmospheric Chemistry and Physics, 9: 4053–4076.10.5194/acp-9-4053-2009
  89. Kimmins, H., Blanco, J. A., Seely, B., Welham, C., Scoullar, K., 2010: Forecasting Forest Futures – A Hybrid Modelling Approach to the Assessment of Sustainability of Forest Ecosystems and Their Values. New York, Earthscan, 304 p.
  90. King, A. W., 1991: Translating models across scales in the landscape. In: Turner, M. G., Gardner, R. H. (eds.). Quantitative methods in landscape ecology: the analysis and interpretation of landscape heterogeneity, New York, Springer, Vol. 82, p. 470–517.10.1007/978-1-4757-4244-2_19
  91. Klemmt, H. J., Tauber, R., 2008: Automatisierte Ermittlung forstinventurrelevanter Parameter aus 3D-Laserscanning-Daten sowie aus 2D-DendroScandaten – Eine vergleichende Feldstudie. In: DVFFA – Sektion Ertragskunde, Jahrestagung 2008, Trippstadt, 5.–8. Mai 2008, p. 169–179.
  92. Kniemeyer, O., 2008: Design and Implementation of a Graph Grammar Based Language for Functional-Structural Plant Modelling. Dissertation. Fakultät für Mathematik, Naturwissenschaften und Informatik der Brandenburgischen Technischen Universität Cottbus, 432 p.
  93. Koreň, M., Mokroš, M., Bucha, T., 2017: Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods. International Journal of Applied Earth Observation and Geoinformation, 63:122–128.10.1016/j.jag.2017.07.015
  94. Kramer, K., Buiteveld, J., Forstreuter, M., Geburek, T., Leonardi, S., Menozzi, P. et al., 2008: Bridging the gap between ecophysiological and genetic knowledge to assess the adaptive potential of European beech. Ecological Modelling, 216:333–353.10.1016/j.ecolmodel.2008.05.004
  95. Kramer, K., van der Werf, B., Schelhaas, M. J., 2015: Bring in the genes: genetic-ecophysiological modeling of the adaptive response of trees to environmental change. With application to the annual cycle. Frontiers in Plant Science, 5:742.10.3389/fpls.2014.00742429223325628628
  96. Kramer, K., van der Werf, D. C., 2010: Equilibrium and non-equilibrium concepts in forest genetic modelling: population- and individually-based approaches. Forest Systems, 19:100–112.10.5424/fs/201019S-9312
  97. Kurth, W., 1994: Growth Grammar Interpreter GROGRA 2.4: A software tool for 3-dimensional interpretation of stochastic, sensitive growth grammars in the context of plant modelling. Intoduction and Reference Manual. Berichte des Forsungszentrums Waldökosysteme der Universität Göttingen, Ser. B, Vol. 38, 192 p.
  98. Kurth, W., 1999: Die Simulation der Baumarchitektur mit Wachstumsgrammatiken. Wissenschaftlicher Verlag Berlin, 327 p.
  99. Landsberg, J., Sands, P., 2011: Physiological Ecology of Forest Production, Principles, Processes and Models, Volume 4 in the Terrestrial Ecology Series. Elsevier Inc., 331 p.10.1016/B978-0-12-374460-9.00001-9
  100. Landsberg, J. J., Waring, R. H., 1997: A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management, 95:209–228.10.1016/S0378-1127(97)00026-1
  101. Lembcke, G., Knapp, E., Dittmar, O., 1975: Die neue DDR-Kiefernertragstafel 1975. Beiträge für die Forstwirtschaft, 15:55–64.
  102. Lexer, M. J., Hönninger, K., 2001: A modified 3D-patch model for spatially explicit simulation of vegetation composition in heterogeneous landscapes. Forest Ecology and Management, 144:43–65.10.1016/S0378-1127(00)00386-8
  103. Liang, X., Kankare, V., Hyyppä, J., Wang, Y., Kukko, A., Haggrén, H. et al., 2016. Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 115:63–77.10.1016/j.isprsjprs.2016.01.006
  104. Lim, K., Treitz, P., Wulder, M., St-Onge, B., Flood, M., 2003: LIDAR remote sensing of forest structure. Progress in Physical Geography, 27:88–106.10.1191/0309133303pp360ra
  105. Lischke, H., 2001: New developments in forest modeling: convergence between applied and theoretical approaches. Natural Ressource Modeling, 14:71–102.10.1111/j.1939-7445.2001.tb00051.x
  106. Lischke, H., Löffler, Th. J., Thornton, P. E., Zimmer-mann, N. E., 2006: Model up-scaling in landscape research. In: Kienast et al. (eds): A Changing World. Challenges for Landscape Research, p. 259–282.10.1007/978-1-4020-4436-6_16
  107. Lischke, H., Zimmermann, N. E., Bolliger, J., Ricke-busch, S., Löffler, T. J., 2006: TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecological Modelling, 199:409–420.10.1016/j.ecolmodel.2005.11.046
  108. Liu, J. G., Ashton, P.S., 1998: FORMOSAIC: An Individual Based, Spatially Explicit Model for Simulating Forest. In: Dynamics in Landscape Mosaics, Ecological Modelling, p. 106–177.10.1016/S0304-3800(97)00191-9
  109. Loustau, D., 2010. Forests, Carbon Cycle and Climate Change. Editions Quae.10.35690/978-2-7592-0385-7
  110. Loustau, D., Bosc, A., Colin, A., Ogee, J., Davi, H., Francois, C. et al., 2005: Modeling climate change effects on the potential production of French plains forests at the sub-regional level. Tree Physiology, 25:813–823.10.1093/treephys/25.7.813
  111. Magnussen, S., Boudewyn, P., 1998: Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Canadian Journal of Forest Research, 28:1016–1031.10.1139/x98-078
  112. McCaskill, M. R., 1990: TAMSIM—a program for preparing meteorological records for weather driven models. Tropical Agronomy Technical Memorandum, No. 65.
  113. McGaughey, R. J., 1997: Visualizing forest stand dynamics using the stand visualization system. In: Seattle, W. A., Bethesda, D: Proceedings of the 1997, ACSM/ASPRS Annual Convention and Exposition; April 7–10, 1997. American Society for Photogrammetry and Remote Sensing, 4:248–257.
  114. Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y., Moorcroft, P. R., 2009: Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. Journal of Geophysical Research-Biogeosciences, 114 p.10.1029/2008JG000812
  115. Merganič, J., Sterba, H., 2006: Characterisation of diameter distribution using the Weibull function: method of moments. European Journal of Forest Research, 125:427–439.10.1007/s10342-006-0138-2
  116. Merrill, S., 2009: KAUST: Visualization beyond the CAVE. Available at: http://techcrunch.com/2009/09/22/kaust-visualization-beyond-the-cave/ September 22, 2009, [accessed March 8, 2017].
  117. Mikita, T., Janata, P., Surový, P., 2016. Forest stand inventory based on combined aerial and terrestrial close-range photogrammetry. Forests, 7: 1–14.10.3390/f7080165
  118. Mohan, M., Silva, A. C., Klauberg, C., Jat, P., Catts, G., Cardil, A. et al., 2017: Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest. For.10.3390/f8090340
  119. Mokroš, M., Liang, X., Surový, P., Valent, P., Černňava, J., Chudý, F. et al., 2018a: Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters. ISPRS International Journal of Geo-Information, 7:1–13.10.3390/ijgi7030093
  120. Mokroš, M., Výbošťok, J., Tomaštík, J., Grznárová, A., Valent, P., Slávik, M., Merganič J., 2018b: High Precision Individual Tree Diameter and Perimeter Estimation from Close-Range Photogrammetry. Forests, 9:1–12.10.3390/f9110696
  121. Moser, J. W., 1974: A system of equations for the components of forest growth. In: Fries, J. (Hrsg.): Growth models for tree and stand simulation. Royal College of Forestry, Stockholm, Sweden, Research Notes, No. 30, 397 p.
  122. Munro, D. D., 1974: Forest growth-models: A prognosis. In: Fries, J. (ed.): Growth models for tree and stand simulation. Royal College of orestry Res Notes, 30, Stockholm, p. 7–21.
  123. Nagel, J., 1996: Anwendungsprogramm zur Bestandesbewertung und zur Prognose der Bestandesentwicklung. Forst und Holz, 3:76–78.
  124. Nagel, J., Biging, G. S., 1995: Schätzung der Parameter der Weibullfunktion zur Generierung von Durchmesserverteilungen. Allgemeine Forst- und Jagdzeitung, 166:185–189.
  125. Naudts, K., Ryder, J., McGrath, M. J., Otto, J., Chen, Y., Valade, A. et al., 2015: A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes. Geoscientific Model Development, 8:2035–2065.10.5194/gmd-8-2035-2015
  126. Oculus, 2017: Oculus Rift – Virtual Reality Headset for 3D Gaming | Oculus VR® webpage–product description. Available at: http://oculus.com/ 2017, [accessed March 8, 2017].
  127. Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Levis, S. et al., 2013: Technical Description of version 4.5 of the Community Land Model (CLM), 434 p.
  128. Orland, B., (ed.), 1992: Data Visualization Techniques in Environmental Managament. Special Issue, Landscape Urban Planning, 21:237–319.10.1016/0169-2046(92)90030-4
  129. Orland, B., 1997: Final Report: SmartForest. Part II. Forest visual modeling for forest pest management and planning. USDA Forest Service, FPM-FHTET, State and Private Forestry, Washington, DC.
  130. Parton, W. J., Schimel, D. S., Cole, C. V., Ojima, D. S., 1987: Analysis of Factors Controlling Soil Organic Matter Levels in Great Plains Grasslands 1. Soil Science Society of America Journal, 51:1173–1179.10.2136/sssaj1987.03615995005100050015x
  131. Persson, Å., Holmgren, J., Söderman, U., 2002: Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering & Remote Sensing, 68:925–932.
  132. Perttunen, J., Sievänen, R., Nikinmaa, E., 1998: LIGNUM: a model combining the structure and the functioning of trees. Ecological Modelling, 108:189–198.10.1016/S0304-3800(98)00028-3
  133. Pfeifer, N., Gorte, B., Winterhalder, D., 2004: Automatic reconstruction of single trees from terrestrial laser scanner data, ISPRS – International Archie-ves of Photogrammetry, Remote Sensing and Spatial informatik Sciebce. Vol. XXXV, Part B: 114–119.
  134. Pfreundt, J., 1988: Modellierung der räumlichen Verteilung von Strahlung, Photosynthesekapazität und Produktion in einem Fichtebestand und ihre Bezie-hung zur Bestandesstruktur. Dissertation, Universität Göttingen, 163 p.
  135. Polhemus, 2017: POLHEMUS innovation to motionTM webpage–Electromagnetic motion tracking systems. Available at: http://polhemus.com/ 2017, [accessed March 8, 2017].
  136. Pommerening, A., 1999: Methoden zur Reproduktion und Forstschreibung einzelner konzentrischer Proberkreise von Betriebs- und Landeswaldinventuren. In DVFF – Sektion Ertragskunde, Volpriehausen.
  137. Pommerening, A., Biber, P., Stoyan, D., Pretzsch, H., 2000: Neue Methoden zur Analyse und Charakterisierung von Bestandesstrukturen. Photogrammetric Engineering & Remote Sensing, 119 p.10.1007/BF02769127
  138. Popescu, S.,Wynne, R. H., Nelson, R. F., 2002: Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37:71–95.10.1016/S0168-1699(02)00121-7
  139. Porte, A., Bartelink, H. H., 2002: Modelling mixed forest growth a review of models for forest management. Ecological Modelling, 150:141–188.10.1016/S0304-3800(01)00476-8
  140. Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A., Solomon, A. M., 1992: A global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography, 19:117–143.10.2307/2845499
  141. Pretzsch, H., 1997: Analysis and modeling of spatial stand structures. Methodological considerations based on mixed beech-larch stands in Lower Saxony, Forest Ecology Management, 97:237–253.10.1016/S0378-1127(97)00069-8
  142. Pretzsch, H., 2001: Modellierung des Waldwachstums. Parey Buchverlag Berlin, 341 p.
  143. Pretzsch, H., 2009: Forest Dynamics, Growth and Yield. From Measurement to Model. Springer, 664 p.10.1007/978-3-540-88307-4
  144. Pretzsch, H., Biber, P., Ďurský, J., 2002: The single tree-based stand simulator SILVA: construction, application and evaluation, Forest Ecology and Management, 162:3–21.10.1016/S0378-1127(02)00047-6
  145. Pretzsch, H., Grote, R., Reineking, B., Rötzer, T. H., Seifert, S. T., 2007: Models for forest ecosystem management: a European perspective. Annals of Botany, 101:1065–1087.10.1093/aob/mcm246271027817954471
  146. Prusinkiewicz, P., Lindenmayer, A., 1990: The Algorithmic Beauty of Platns. Springer-Verlag, New York, 228 p.10.1007/978-1-4613-8476-2
  147. Puliti, S., Gobakken, T., Ørka, H.O., Næsset, E., 2017. Assessing 3D point clouds from aerial photographs for species-specific forest inventories. Scandinavian Journal of Educational Research, 32:68–79.10.1080/02827581.2016.1186727
  148. Rastetter, E. B., King, A. W., Cosby, B. J., Hornberger, G. M., Oneill, R. V., Hobbie, J. E., 1992: Aggregating Fine-Scale Ecological Knowledge to Model Coarser-Scale Attributes of Ecosystems. Ecological Applications, 2:55–70.10.2307/194188927759192
  149. Richardson, C.W., Wright, D. A., 1984: WGEN: a model for generating daily weather variables. U.S. Department of Agriculture, Agricultural Research Service, ARS-8, Washington, D.C, USA.
  150. Rötzer, T., Leuchner, M., Nunn, A. J., 2010: Simulating stand climate, phenology, and photosynthesis of a forest stand with a process-based growth model. International Journal of Biometeorology, 54:449–464.10.1007/s00484-009-0298-020084520
  151. Rötzer, T., Seifert, T., Gayler, S., Priesack, E., Pretzsch, H., 2012. Effects of Stress and Defence Allocation on Tree Growth: Simulation Results at the Individual and Stand Level. In: Matyssek, R., Schnyder, H., Oßwald, W., Ernst, D., Munch, J. C., Pretzsch, Hans (eds.), Growth and Defence in Plants: Resource Allocation at Multiple Scales, Ecological Studies. Springer Berlin Heidelberg, Berlin, Heidelberg, p. 401–432.10.1007/978-3-642-30645-7_18
  152. Rötzer, T., Seifert, T., Pretzsch, H., 2009: Modelling above and below ground carbon dynamics in a mixed beech and spruce stand influenced by climate. European Journal of Forest Ressearch, 128:171–182.10.1007/s10342-008-0213-y
  153. Running, S., Hunt, E., 1993. Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOMEBCG, and an Application for Global-Scale Models. Scaling Physiological Processes: Leaf to Globe: A volume in Physiological Ecology, p. 141–158.10.1016/B978-0-12-233440-5.50014-2
  154. Scheller, R., Hua, D., Bolstad, P., A. Birdsey, R., Mladenoff, D., 2011: The effects of forest harvest intensity in combination with wind disturbance on carbon dynamics in Lake States Mesic Forests, 222:144–153.10.1016/j.ecolmodel.2010.09.009
  155. Schmidt, A., 1971: Wachstum und Ertrag der Kiefer auf wirtshaftlich wichtigen Standorteinheiten der Oberpfalz. Forstliche Forschungsber. München, Bd. 1, 178 p.
  156. Seidl, R., Baier, P., Rammer, W., Schopf, A., Lexer, M. J., 2007: Modelling tree mortality by bark beetle infestation in Norway spruce forests. Ecological Modelling, 206:383–399.10.1016/j.ecolmodel.2007.04.002
  157. Seidl, R., Lexer, M. J., Jäger, D., Hönninger, K., 2005: Evaluating the accuracy and generality of a hybrid patch model. Tree Physiology, 25:939–951.10.1093/treephys/25.7.93915870060
  158. Seidl, R., Rammer, W., Bellos, P., Hochbichler, E., Lexer, M. J., 2009: Testing generalized allometries in allocation modeling within an individual-based simulation framework. Trees, 24:139–150.10.1007/s00468-009-0387-z
  159. Seidl, R., Rammer, W., Scheller, R. M., Spies, T. A., 2012: An individual-based process model to simulate landscape-scale forest ecosystem dynamics. Ecological Modelling, 231:87–100.10.1016/j.ecolmodel.2012.02.015
  160. Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G. et al., 2017: Forest disturbances under climate change. Nature Climate Change, 7:395.10.1038/nclimate3303557264128861124
  161. Semenov, M. A., Brooks, R. J., Barrow, E. M., Richardson, C. W., 1998: Comparison of WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Resourses, 10:95–107.10.3354/cr010095
  162. Shinozaki, K., Yoda, K., Hozumi, K., Kira, T., 1964: A Quantitative analysis of plant form-the pipe model theory: i. Basic analyses. Japanese Journal of Ecology, 14:97–105.
  163. Shugart, H. H., 1984: A Theory of Forest Dynamics. The Ecological Implications of Forest Succesion Models. Springer-Verlag New York, Berlin, Heidelberg, Tokio, 278 p.
  164. Shugart, H. H., West, D. C., 1977: Development of an Appalachian deciduous forest succesion model and its application to assessment of the impact of the chestnut blight. Journal of Environmental Management, 5:161–179.
  165. Sievänen, R., Perttunen, J., Nikinmaa, E., Kaitaniemi, P., 2008: Toward extension of a single tree functional–structural model of Scots pine to stand level: effect of the canopy of randomly distributed, identical trees on development of tree structure. Functional Plant Biology, 35:964–975.10.1071/FP0807732688846
  166. Simonse, M., Aachhoff, T., Spiecker, H., Thies, M., 2003: Automatic Determinantion of Forest inventory parameters using terrestrial laser scanning, Institute for Growth, Freiburg, ScandLaser scientific Workshop on Airborne Laser Scanning, p. 1–7.
  167. Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W. et al., 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9:161–185.10.1046/j.1365-2486.2003.00569.x
  168. Sloboda, B., 1976: Mathematische und stochastische Modelle zur Beschreibung der Statik und Dynamik von Bäumen und Beständen – insbesondere das bestandesspezifische Wachstum als stochasticher Prozeß. Habil.-Schrift, Univ. Freiburg, 310 p.
  169. Smith, B., Prentice, I. C., Sykes, M. T., 2001: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology and Biogeography, 10:621–637.10.1046/j.1466-822X.2001.t01-1-00256.x
  170. Smith, B., Wårlind, D., Arneth, A., Hickler, T., Lead-ley, P., Siltberg, J., Zaehle, S., 2014: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences, 11:2027–2054.10.5194/bg-11-2027-2014
  171. Sodtke, R., Schmidt, M., Fabrika, M., Nagel, J., Ďurský, J., Pretzsch, H., 2004: Anwendung und Einsatz von Einzelbaummodellen als Komponenten von entscheidungsunterstützenden Systemen für die strategische Forstbetriebsplannung. Forstarchiv, 75:51–64.
  172. Sterba, H., 1995: PROGNAUS – ein absandsunabhängiger Wachstumssimulator für ungleichaltrige Mischbestände. DVFF – Sektion Ertragskunde, Joachimstahl, p. 173–183.
  173. Surový, P., Ribeiro, N., Oliveira, A. C., Scheer, Ľ., 2004: Discrimination of vegetation from the background in high resolution colour remote sensed imagery. Journal of Forest Science, 50:161–170.10.17221/4611-JFS
  174. Suzuki, T., 1971: Forest transition as a stochastic process. Mitt. der Forstlichen Bundesversuchsanstalt Wien, 91:137–150.
  175. Svensson, M., Jansson, P.-E., Kleja, D. B., 2008. Modelling Soil C Sequestration in Spruce Forest Ecosystems along a Swedish Transect Based on Current Conditions. Biogeochemistry, 89:95–119.10.1007/s10533-007-9134-y
  176. Thornton, P., Running, S. W., Hunt, E. R., 2005: Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1.
  177. Urban, D. L., 2005: Modeling ecological processes across scales. Ecology, 86:1996–2006.10.1890/04-0918
  178. Van Oijen, M., Rougier, J., Smith, R., 2005: Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiology, 25:915–927.10.1093/treephys/25.7.91515870058
  179. Vanclay, J. K., 1994: Modelling forest growth and yield (Application to mixed tropical forests). CAB International, Wallingford, UK, 312 p.
  180. Vicon Bonita, 2014: Vicon Motion Systems Ltd - Optical motion capture systems – webpage. Avaiable at: http://www.vicon.com/system/bonita 2014, [accessed November 9, 2014].
  181. Virtuix, 2017: Virtuix OmniTM webpage—product description. Available at: http://virtuix.com/ 2017, [accessed March 8, 2017].
  182. Virtusphere, 2017: ©Virtusphere, Inc. webpage. Virtusphere product description. Available at: http://www.virtusphere.com/index.html 2017, [accessed March 8, 2017].
  183. VRAC, 2008: Webpage of C-6 CAVE system at Virtual Reality Application Center (Iowa State University). Available at: http://www.vrac.iastate.edu/c6.php 2008, [accessed March 8, 2017].
  184. Vuokila, Y., 1966: Functions for variable density yield tables of pine based on temporary sample plots. Communicationes Instituti Forestalis Fenniae, 60: 86.
  185. Warnant, P., FrançOis, L., Strivay, D., GéRard, J.-C., 1994. CARAIB: A global model of terrestrial biological productivity. Global Biogeochemical Cycles 8: 255–270.10.1029/94GB00850
  186. Weiskittel, A. R., Hann, D. W., Kershaw, Jr., J. A., Van-clay, J. K., 2011: Forest Growth and Yield Modeling. Wiley-Blackwell, 415 p.10.1002/9781119998518
  187. Woodward, F. I., Smith, T. M., 1994: Predictions and Measurements of the Maximum Photosynthetic Rate at the Global Scale, In: Schulze, E. D., Caldwell, M. M. (eds.): Ecological Studies 100, Springer-Verlag, New York, p. 491–509.10.1007/978-3-642-79354-7_23
  188. Wykoff, W. R., Crookston, N. L., Stage, A. R., 1982: User’s Guide to the stand prognosis model. U. S. For. Serv., Gen. Techn. Rep. INT-133, Ogden, Utah, 112 p.10.5962/bhl.title.109367
DOI: https://doi.org/10.2478/forj-2019-0018 | Journal eISSN: 2454-0358 | Journal ISSN: 2454-034X
Language: English
Page range: 147 - 165
Published on: Nov 20, 2019
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Marek Fabrika, Peter Valent, Katarína Merganičová, published by National Forest Centre and Czech University of Life Sciences in Prague, Faculty of Forestry and Wood Sciences
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.