Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Abstract

The diameters and heights of trees are two of the most important components in a forest inventory. In some circumstances, the heights of trees need to be estimated due to the time and cost involved in measuring them in the field. Artificial intelligence models have many advantages in modeling nonlinear height–diameter relationships of trees, which sometimes make them more useful than empirical models in estimating the heights of trees. In the present study, the heights of trees in uneven-aged and mixed stands in the high elevation forests of northern Iran were estimated using an artificial neural network (ANN) model, an adaptive neuro-fuzzy inference system (ANFIS) model, and empirical models. A systematic sampling method with a 150 × 200 m network (0.1 ha area) was employed. The diameters and heights of 516 trees were measured to support the modeling effort. Using 10 nonlinear empirical models, the ANN model, and the ANFIS model, the relationship between height as a dependent variable and diameter as an independent variable was analyzed. The results show, according to R2, relative root mean square error (RMSE), and other model evaluation criteria, that there is a greater consistency between predicted height and observed height when using artificial intelligence models (R2 = 0.78; RMSE (%) = 18.49) than when using regression analysis (R2 = 0.68; RMSE (%) = 17.69). Thus, it can be said that these models may be better than empirical models for predicting the heights of common, commercially-important trees in the study area.

Forest Land Cover Mapping at a Regional Scale Using Multi-Temporal Sentinel-2 Imagery and RF Models

“…They can be grouped into supervised or unsupervised approaches [23,24], the most common method is supervised classification using machine learning algorithms [5]. Nowadays, artificial intelligence is being widely used in forestry for a variety of purposes such as forest phytopathology [25,26], forestry inventories [27,28] and forest risks [29,30]. The specific machine learning algorithms used to perform supervised classification for species mapping purposes differs among studies.…”

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

“…Ercanli (2020a) also reported better performance with DLA models compared with ANNs in pure pine stands. Bayat et al (2020) used the ANNs and adaptive neurofuzzy inference system (ANFIS) to provide better estimation of tree heights in uneven-aged, mixed stands in Iran compared with regression analysis. Similar observation was reported by Vieira et al (2018) for eucalyptus species.…”

“…ANNs are a subunit of artificial intelligence (AI) whose functionality mimics that of the human brain (Strobl and Forte 2007). ANNs have been consistently used in forestry with significant success for modelling tree height (Özçelik et al 2013;Vieira et al 2018;Bayat et al 2020;Ercanli 2020a;Hamidi et al 2021), tree taper (Nunes and Görgene 2016), site productivity (Aertsen et al 2010), tree biomass and volume (Miguel et al 2016;Özçelik et al 2017), basal area increment (Ashraf et al 2013) and mortality and regeneration (Hamidi et al 2021). These researchers reported reasonable predictions of tree dendrometric variables with ANNs compared with ordinary least square and mixed-effect models.…”

Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression

“…However, there are very few published works comparing the results offered by traditional linear least squares regression with those provided by machine learning methods in relation to modeling tree height-diameter allometry. Most of these studies test some formulation of artificial neural networks (ANN) [38][39][40] or even Deep Learning (DL) approaches [30,41], which generally showed greater precision than traditional linear regression methods.…”