Total tree height predictions via parametric and artificial neural network modeling approaches

Abstract

Height-diameter relationships are of critical importance in tree and stand volume estimation. Stand description, site quality determination and appropriate forest management decisions originate from reliable stem height predictions. In this work, the predictive performances of height-diameter models developed for Taurus cedar (Cedrus libani A. Rich.) plantations in the Western Mediterranean Region of Turkey were investigated. Parametric modeling methods such as fixed-effects, calibrated fixed-effects, and calibrated mixed-effects were evaluated. Furthermore, in an effort to come up with more reliable stem-height prediction models, artificial neural networks were employed using two different modeling algorithms: the Levenberg-Marquardt and the resilient back-propagation. Considering the prediction behavior of each respective modeling strategy, while using a new validation data set, the mixed-effects model with calibration using 3 trees for each plot appeared to be a reliable alternative to other standard modeling approaches based on evaluation statistics regarding the predictions of tree heights. Regarding the results for the remaining models, the resilient propagation algorithm provided more accurate predictions of tree stem height and thus it is proposed as a reliable alternative to pre-existing modeling methodologies.

Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors

“…In most studies using ANN models, the findings have been discussed only with the consideration of the goodness-of-fit statistics (Ashraf et al 2013, Karatepe et al 2022. In forestry modeling, the principles of forest growth and yield are extremely important (Vanclay & Skovsgaard 1997).…”

“…Because forest inventory data are spatially or temporally correlated (Fox et al 2001), the statistical assumptions are usually violated, and this leads to biased estimates (West et al 1984). To deal with the statistical problems, the autoregressive modeling technique (Hirigoyen et al 2021) and mixed-effect modeling technique (Karatepe et al 2022) can be used. However, some issues associated with these modeling techniques have remained in question (Wang et al 2008).…”

“…Most researchers have paid attention to goodness-of-fit statistics in evaluating the performance of ANN models (Ashraf et al 2013, Karatepe et al 2022). However, growth and yield predictions for forests should also be evaluated based on the principles of forest growth and yield (Weiskittel et al 2011, Seki & Sakici 2022.…”

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

“…However, if the algorithm is not properly treated, it may be trapped into local minima or can be overfitted. The resilient backpropagation algorithm [45][46][47] can be considered as a variant of the traditional backpropagation algorithm. It is robust in the training phase of the network, and its efficiency to overcome problems of the traditional backpropagation algorithm, such as being slow at converging, taking effort at parameter tuning, and getting stuck in local minima, is considered significant.…”

“…Due to its efficiency in overcoming problems of the traditional backpropagation algorithm, which can be slow at converging, require effort at parameter tuning, and get stuck in local minima, the resilient back-propagation artificial neural network (RPNN) supervised learning algorithm is considered as a powerful algorithm with desired properties [45][46][47]. As has been introduced and described by Riedmiller and Braun [45], the innovation of this algorithm that boosts its learning strength in aiming to overcome local minima is that it performs a direct adaptation of weight step based on local gradient information.…”