Modelling Tree Height-Diameter Relationship in a Mixed Forest Plantation
I. B. Chenge, B. Iorver, D. H. Japheth
Asian Journal of Research in Agriculture and Forestry · pp. 154–163 · Published 21 Jul 2023
10.9734/ajraf/2023/v9i3218Abstract
The diameters and heights of the trees are two of the most important measurements in a forest inventory for biomass estimation and sustainable management. Measuring tree height in a forest stand is time consuming and costly, it is necessary to develop models that accurately estimate tree heights from easily measured variables (tree diameter). This study aims to develop models for estimating tree height in a forest plantation located in North-central, Nigeria. The systematic sampling method was used to twenty-one 0.09 ha sample plots in study area. Data on tree height and diameter were collected. Artificial neural network (ANN) model, support vector regression (SVR) model, and four empirical nonlinear models were tested for estimating tree height. The models were evaluated using the Coefficient of Determination, Residual standard Error, Mean Bias and Akaike’s Information Criterion. The results showed that the SVR model best predicted tree heights in the study area than the ANN and empirical nonlinear models. The SVR model explained about 94% variance associating with the dependent variable. The SVR model can be conveniently used for predicting the height of trees in the study area.
References (33)
- 1 A Flexible Growth Function for Empirical Use [DOI]
- 2 Improved allometric models to estimate the aboveground biomass of tropical trees [DOI]
- 3 Analysis of Growth Equations [DOI]
- 4 neuralnet: Training of Neural Networks [DOI]
- 5 Tree height integrated into pantropical forest biomass estimates [DOI]
- 6 Measuring tree height: a quantitative comparison of two common field methods in a moist tropical forest [DOI]
- 7 Tree height and tropical forest biomass estimation [DOI]
- 8 Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models [DOI]
- 9 Biometry for Forestry and Environmental Data [DOI]
- 10 Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin [DOI]
- 11 Height – Diameter allometry in South Africa’s indigenous high forests: Assessing generic models performance and function forms [DOI]
- 12 Empirical decision model learning [DOI]
- 13 Prognoses of diameter and height of trees of eucalyptus using artificial intelligence [DOI]
- 14 Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data [DOI]
- 15 Statistical Problems in Dynamics of Exploited Fisheries Populations
- 16 An Evaluation of Sampling Methods and Model Forms for Estimating Height-Diameter Relationships in Loblolly Pine Plantations [DOI]
- 17 Height-Diameter Equations for Thirteen Midwestern Bottomland Hardwood Species [DOI]
- 18 Height-diameter allometry and above ground biomass in tropical montane forests: Insights from the Albertine Rift in Africa [DOI]
- 19 Allometric relationships for volume and biomass for stone pine (Pinus pinea L.) in Italian coastal stands [DOI]
- 20 Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm [DOI]
- 21 Predicting Tree Height-Diameter Relationship from Relative Competition Levels Using Quantile Regression Models for Chinese Fir (Cunninghamia lanceolata) in Fujian Province, China [DOI]
- 22 Height–diameter relationship of trees in Omo strict nature forest reserve, Nigeria [DOI]
- 23 An assessment of height–diameter growth variation in an unmanaged Fagus orientalis-dominated forest [DOI]
- 24 Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models [DOI]
- 25 Artificial intelligence as an alternative modelling strategy for reliable height-diameter predictions of mixed-oaks species [DOI]
- 26 A Height–Diameter Curve for Longleaf Pine Plantations in the Gulf Coastal Plain [DOI]
- 27 Modelo generalizado no-lineal altura-diámetro con efectos mixtos para siete especies de Pinus en Durango, México [DOI]
- 28 Diverse scaling relationships of tree height and diameter in five tree species [DOI]
- 29 Developing and Evaluating Tree Height-Diameter Models at Three Geographic Scales for Black Spruce in Ontario [DOI]
- 30 Modelling silvicultural and economic alternatives for Scots pine (Pinus sylvestris L.) plantations in north-western Spain [DOI]
- 31 Mixed-effects modeling for tree height prediction models of Oriental beech in the Hyrcanian forests [DOI]
- 32 Predictive Ability of Mixed-Effects Height–Diameter Models Fit Using One Species but Calibrated for Another Species [DOI]
- 33 Tree height prediction models for two forest reserves in Nigeria using mixed-effects approach [DOI]
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