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

Mahmoud Bayat,Pete Bettinger,Sahar Heidari,Meghdad Jourgholami,Azad Henareh Khalyani,Seyedeh Kosar Hamidi

doi:10.3390/f11030324

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.

Highlights

Hyrcanian forests, the only commercial forests in Iran, are considered complete biological communities
The purpose of this paper is to examine the quality of results produced by estimating total tree height from DBH in Hyrcanian forests, using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models, so that the advantages and disadvantages of these methods can be compared against more traditional empirical models
The coefficients of the empirical models are noted in Table 5; the modeling results indicate that the variation in the independent variable (DBH) described is about 64%–69% of the variation in total tree height

Summary

Introduction

Hyrcanian forests, the only commercial forests in Iran, are considered complete biological communities. They have the highest degree of self-regulation and self-renewal among all natural ecosystems in Iran. The accuracy of such models is very important for the preparation of accurate volume tables and for the development of growth prediction models [8]. For this reason, forest managers need to be informed of the connection between the diameters and heights of trees, and be able to accurately estimate the heights of trees using diameter at breast height (DBH). Oriental beech (Fagus orientalis Lipsky), commonhornbeam (Carpinus betulus L.), Caucasian alder

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Methods

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Discussion

Conclusion