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Primerjava različnih regresijskih modelov za napovedovanje debelinskega priraščanja jelke
Andrej Ficko, Vasilije Trifković, 2021

Abstract: V prispevku na primeru jelke predstavljamo sedem regresijskih modelov za modeliranje priraščanja dreves s podatki periodičnih meritev na stalnih vzorčnih ploskvah. Poleg polinomske regresije, modela z dodanim šumom in mešanega linearnega modela, predstavljamo regresijo z naravnimi zlepki in tri modele z omejenimi odvisnimi spremenljivkami: truncated regression, tobit regression in grouped data regression. Modele lahko uporabimo, kadar se zaradi načina merjenja in zaokroževanja podatkov ter hierarhičnosti podatkov srečamo z rezanimi ali krnjenimi slučajnostnimi spremenljivkami, nezveznostjo odvisne spremenljivke in pristransko oceno prirastka. Pri pojasnitvi debelinskega priraščanja 21.013 jelk na 4.405 ploskvah v obdobju 1990–2014 v raznomernih gozdovih v dinarskih jelovo-bukovjih so vsi modeli pokazali podoben vpliv prsnega premera, sestojne temeljnice, temeljnice debelejših dreves, raznomernosti, nagiba, nadmorske višine in le manjše razlike v regresijskih koeficientih in merah prileganja. Največje povprečne napovedi prirastka daje tobit model, mešani model pa se najbolj prilega podatkom. V primerjavi z drugimi modeli model z zlepki kaže na počasnejše zmanjševanje prirastka zelo debelih jelk po kulminaciji prirastka.
Keywords: prirastek, multipla regresija, statistične metode, tobit model, krnjenje, mešani modeli, jelka, modeli z omejenimi odvisnimi spremenljivkami, stalne vzorčne ploskve
DiRROS - Published: 01.12.2021; Views: 1228; Downloads: 861
.pdf Fulltext (2,97 MB)

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Carbon flux and environmental parameters data from an eddy covariance tower in a mid-succession ecosystem developed on abandoned karst grassland in Slovenia (2012-2019)
Koffi Dodji Noumonvi, Klemen Eler, Dominik Vodnik, Primož Simončič, Mitja Ferlan, 2021

Abstract: This data set was used to estimate carbon fluxes by comparing eddy covariance tower (Long = 13.916701, Lat = 45.543491) measurements with vegetation indices based estimates.
Keywords: eddy covariance, GPP, NEE, empirical model, LUE model, vegetation photosynthesis model, vegetation indices
DiRROS - Published: 21.02.2022; Views: 202; Downloads: 163
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Artificial neural networks as an alternative method to nonlinear mixed-effects models for tree height predictions
Jernej Jevšenak, Mitja Skudnik, 2022

Abstract: Tree heights are one of the most important aspects of forest mensuration, but data are often unavailable due to costly and time-consuming field measurements. Therefore, various types of models have been developed for the imputation of tree heights for unmeasured trees, with mixed-effects models being one of the most commonly applied approaches. The disadvantage here is the need of sufficient sample size per tree species for each plot, which is often not met, especially in mixed forests. To avoid this limitation, we used principal component analysis (PCA) for the grouping of similar plots based on the most relevant site descriptors. Next, we compared mixed-effects models with height-diameter models based on artificial neural networks (ANN). In terms of root mean square error (RMSE), mixed-effects models provided the most accurate tree height predictions at the plot level, especially for tree species with a smaller number of tree height measurements. When plots were grouped using the PCA and the number of observations per category increased, ANN predictions improved and became more accurate than those provided by mixed-effects models. The performance of ANN also increased when the competition index was included as an additional explanatory variable. Our results show that in the pursuit of the most accurate modelling approach for tree height predictions, ANN should be seriously considered, especially when the number of tree measurements and their distribution is sufficient.
Keywords: height-diameter models, national forest inventory, permanent sample plot, mixed forests, model comparison, principal component analysis
DiRROS - Published: 08.06.2022; Views: 78; Downloads: 13

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