Predicting individual tree growth using stand-level simulation, diameter distribution, and Bayesian calibration

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dc.contributor.author Tian, Xianglin
dc.contributor.author Sun, Shuaichao
dc.contributor.author Mola-Yudego, Blas
dc.contributor.author Cao, Tianjian
dc.date.accessioned 2021-06-10T21:44:26Z
dc.date.available 2021-12-18T03:45:57Z
dc.date.issued 2020-06-11
dc.identifier.citation Tian , X , Sun , S , Mola-Yudego , B & Cao , T 2020 , ' Predicting individual tree growth using stand-level simulation, diameter distribution, and Bayesian calibration ' , Annals of Forest Science , vol. 77 , no. 2 , 57 . https://doi.org/10.1007/s13595-020-00970-0
dc.identifier.other PURE: 140533523
dc.identifier.other PURE UUID: cc9c5c2b-53c0-4e4b-9c6d-c17ce4c064d3
dc.identifier.other WOS: 000542231900001
dc.identifier.uri http://hdl.handle.net/10138/330870
dc.description.abstract Key message We propose a methodology to develop a preliminary version of a growth model when tree-level growth data are unavailable. This modelling approach predicts individual tree growth using only one-time observations from temporary plots. A virtual dataset was generated by linking the whole stand and diameter distribution models. The individual tree model was parameterized using Bayesian calibration and a likelihood of diameter distributions. Context A key component of tree-level growth and yield prediction is the diameter increment model that requires at least two different points in time with individual tree measurements. In some cases, however, sufficient inventory data from remeasured permanent or semitemporary plots are unavailable or difficult to access. Aims The purpose of this study was to propose a three-stage approach for modelling individual tree diameter growth based on temporary plots. Methods The first stage is to predict stand dynamics at 5-year intervals based on stand-level resource inventory data. The second stage is to simulate diameter distribution at 5-year intervals using a Weibull function based on tree-level research inventory data. The final stage is to improve the reliability of individual tree diameter estimates by updating parameters with Bayesian calibration based on a likelihood of diameter distributions. Results The virtual-data-based diameter increment model provided logical patterns and reliable performances in both tree- and stand-level predictions. Although it underestimated the growth of suppressed trees compared with tree cores and remeasurements, this bias was negligible when aggregating tree-level simulations into stand-level growth predictions. Conclusion Our virtual-data-based modelling approach only requires one-time observations from temporary plots, but provide reliable predictions of stand- and tree-level growth when validated with tree cores and whole-stand models. This preliminary model can be updated in a Bayesian framework when growth data from tree cores or remeasurements were obtained. en
dc.format.extent 17
dc.language.iso eng
dc.relation.ispartof Annals of Forest Science
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject Diameter increment
dc.subject Growth and yield
dc.subject Sparse data
dc.subject Data aggregation
dc.subject Parametric uncertainty
dc.subject HEIGHT GROWTH
dc.subject MODELS
dc.subject INCREMENT
dc.subject DISAGGREGATION
dc.subject VALIDATION
dc.subject PARAMETERS
dc.subject INDEX
dc.subject 4112 Forestry
dc.title Predicting individual tree growth using stand-level simulation, diameter distribution, and Bayesian calibration en
dc.type Article
dc.contributor.organization Department of Forest Sciences
dc.contributor.organization Forest Modelling Group
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1007/s13595-020-00970-0
dc.relation.issn 1286-4560
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion

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