Self-learning growth simulator for modelling forest stand dynamics in changing conditions

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dc.contributor.author Pukkala, Timo
dc.contributor.author Vauhkonen, Jari
dc.contributor.author Korhonen, Kari T.
dc.contributor.author Packalen, Tuula
dc.date.accessioned 2021-08-19T12:41:01Z
dc.date.available 2021-08-19T12:41:01Z
dc.date.issued 2021-07
dc.identifier.citation Pukkala , T , Vauhkonen , J , Korhonen , K T & Packalen , T 2021 , ' Self-learning growth simulator for modelling forest stand dynamics in changing conditions ' , Forestry , vol. 94 , no. 3 , pp. 333-346 . https://doi.org/10.1093/forestry/cpab008
dc.identifier.other PURE: 167718663
dc.identifier.other PURE UUID: 21e0c5cb-7780-4c11-9367-a558d99a72a3
dc.identifier.other WOS: 000680830000001
dc.identifier.uri http://hdl.handle.net/10138/333316
dc.description.abstract Finnish forest structures vary from even-aged planted forests to two- and multi-storied mixed stands. Also, the range of silvicultural systems in use has increased because thinning from above and continuous cover management are gaining popularity. The data currently available for modelling stand dynamics are insufficient to allow the development of unbiased and reliable models for the simulation of all possible transitions between various current and future stand conditions. Therefore, the models should allow temporal and regional calibration along the accumulation of new information on forest development. If the calibration process is automated, the simulators that use these models constitute a self-Learning system that adapts to the properties of new data on stand dynamics. The current study first developed such a model set for stand dynamics that is technically suitable for simulating the stand development in all stand structures, silvicultural systems and their transitions. The model set consists of individual-tree models for diameter increment and survival and a stand-Level model for ingrowth. The models were based on the permanent sample plots of the 10th and 11th national forest inventories of Finland. Second, a system for calibrating the models based on additional data was presented. This optimization-based system allows different types and degrees of calibration, depending on the intended use of the models and the amount of data available for calibration. The calibration method was demonstrated with two external datasets where a set of sample plots had been measured two times at varying measurement intervals. en
dc.format.extent 14
dc.language.iso eng
dc.relation.ispartof Forestry
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject TIMBER VOLUME
dc.subject TREE HEIGHT
dc.subject LAND
dc.subject OPTIMIZATION
dc.subject MANAGEMENT
dc.subject IMPACTS
dc.subject FINLAND
dc.subject 4112 Forestry
dc.title Self-learning growth simulator for modelling forest stand dynamics in changing conditions en
dc.type Article
dc.contributor.organization Department of Forest Sciences
dc.contributor.organization Forest Ecology and Management
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1093/forestry/cpab008
dc.relation.issn 0015-752X
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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