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A narrative of virtual and augmented reality in the forest sector


With the rapid development in data acquisition and presentation, there is a growing interest in virtual forests and computer visualization tools. Forest owners have become more aware about their property and are interested in applying different forest management methods and silvicultural techniques. The tools are also applicable in assessment of the changes to the landscape as a result of natural and anthropogenic disturbances. Virtual reality offers a good opportunity to test and compare different management options before implementing decisions which can lead to irreversible consequences. Advances in spatial and temporal data collection enable new and practical solutions for analysis and visualization of long-term natural processes with new forestry applications. In the near future, forest owners and managers will have the possibility to make management decisions without the direct need to exit the office. Furthermore, the learning process is more enthralling and also more profound through augmented reality, helping to foster better working practices even before starting a job in the forest sector.

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Forest modelling and visualisation – state of the art and perspectives

Model Development, 8:2035–2065. Oculus, 2017: Oculus Rift – Virtual Reality Headset for 3D Gaming | Oculus VR ® webpage–product description. Available at: 2017, [accessed March 8, 2017]. 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. Orland, B., (ed.), 1992: Data Visualization Techniques in Environmental Managament. Special Issue, Landscape Urban Planning, 21:237–319. Orland, B., 1997: Final Report

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Application of Chemometric Analysis to the Study of Snow at the Sudety Mountains, Poland

Appennines. Atmos Res. 2002;61:311-334. DOI: 10.1016/S0169-8095(01)00139-9. [18] Stanimirova I, Walczak B, Massart DL. Multiple factor analysis in environmental chemistry. Anal Chim Acta. 2005;545:1-12. DOI: 10.1016/j.aca.2005.04.054. [19] Mellinger M. Chemometr Intell Lab Syst. 1987;2(29):29-36. DOI: 10.1016/0169-7439(87)80083-7. [20] Kohonen T. Self-organizing Maps. Berlin: Springer; 2001. . [21] Jin H, Shum WH, Leung KS, Wong ML. Expanding self-organizing map for data visualization and cluster analysis. Inf

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