The use of tree crown variables in over-bark diameter and volume prediction models
Linear and nonlinear crown variable functions for 173 Brutian pine (Pinus brutia Ten.) trees were incorporated into a well-known compatible volume and taper equation to evaluate their effect in model prediction accuracy. In addition, the same crown variables were also incorporated into three neural...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Italian Society of Silviculture and Forest Ecology (SISEF)
2014-06-01
|
| Series: | iForest - Biogeosciences and Forestry |
| Subjects: | |
| Online Access: | https://iforest.sisef.org/contents/?id=ifor0878-007 |
| _version_ | 1818026181837979648 |
|---|---|
| author | Özçelik R Diamantopoulou Maria J Brooks John R |
| author_facet | Özçelik R Diamantopoulou Maria J Brooks John R |
| author_sort | Özçelik R |
| collection | DOAJ |
| description | Linear and nonlinear crown variable functions for 173 Brutian pine (Pinus brutia Ten.) trees were incorporated into a well-known compatible volume and taper equation to evaluate their effect in model prediction accuracy. In addition, the same crown variables were also incorporated into three neural network (NN) types (Back-Propagation, Levenberg-Marquardt and Generalized Regression Neural Networks) to investigate their applicability in over-bark diameter and stem volume predictions. The inclusion of crown ratio and crown ratio with crown length variables resulted in a significant reduction of model sum of squared error, for all models. The incorporation of the crown variables to these models significantly improved model performance. According to results, non-linear regression models were less accurate than the three types of neural network models tested for both over-bark diameter and stem volume predictions in terms of standard error of the estimate and fit index. Specifically, the generated Levenberg-Marquardt Neural Network models outperformed the other models in terms of prediction accuracy. Therefore, this type of neural network model is worth consideration in over-bark diameter and volume prediction modeling, which are some of the most challenging tasks in forest resources management. |
| first_indexed | 2024-12-10T04:27:56Z |
| format | Article |
| id | doaj.art-217e0444342e4707b79c5e8172ac2556 |
| institution | Directory Open Access Journal |
| issn | 1971-7458 1971-7458 |
| language | English |
| last_indexed | 2024-12-10T04:27:56Z |
| publishDate | 2014-06-01 |
| publisher | Italian Society of Silviculture and Forest Ecology (SISEF) |
| record_format | Article |
| series | iForest - Biogeosciences and Forestry |
| spelling | doaj.art-217e0444342e4707b79c5e8172ac25562022-12-22T02:02:14ZengItalian Society of Silviculture and Forest Ecology (SISEF)iForest - Biogeosciences and Forestry1971-74581971-74582014-06-017113213910.3832/ifor0878-007878The use of tree crown variables in over-bark diameter and volume prediction modelsÖzçelik R0Diamantopoulou Maria J1Brooks John R2Faculty of Forestry, Süleyman Demirel University, East Campus, TR-32260, Isparta (Turkey)Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki (Greece)Division of Forestry and Natural Resources, West Virginia University, 322 Percival Hall, 26506-6125 Morgantown (WV - USA)Linear and nonlinear crown variable functions for 173 Brutian pine (Pinus brutia Ten.) trees were incorporated into a well-known compatible volume and taper equation to evaluate their effect in model prediction accuracy. In addition, the same crown variables were also incorporated into three neural network (NN) types (Back-Propagation, Levenberg-Marquardt and Generalized Regression Neural Networks) to investigate their applicability in over-bark diameter and stem volume predictions. The inclusion of crown ratio and crown ratio with crown length variables resulted in a significant reduction of model sum of squared error, for all models. The incorporation of the crown variables to these models significantly improved model performance. According to results, non-linear regression models were less accurate than the three types of neural network models tested for both over-bark diameter and stem volume predictions in terms of standard error of the estimate and fit index. Specifically, the generated Levenberg-Marquardt Neural Network models outperformed the other models in terms of prediction accuracy. Therefore, this type of neural network model is worth consideration in over-bark diameter and volume prediction modeling, which are some of the most challenging tasks in forest resources management.https://iforest.sisef.org/contents/?id=ifor0878-007Crown VariablesTaperBack-Propagation ANNsLevenberg-Marquardt ANNsGeneralized Regression Neural Networks |
| spellingShingle | Özçelik R Diamantopoulou Maria J Brooks John R The use of tree crown variables in over-bark diameter and volume prediction models iForest - Biogeosciences and Forestry Crown Variables Taper Back-Propagation ANNs Levenberg-Marquardt ANNs Generalized Regression Neural Networks |
| title | The use of tree crown variables in over-bark diameter and volume prediction models |
| title_full | The use of tree crown variables in over-bark diameter and volume prediction models |
| title_fullStr | The use of tree crown variables in over-bark diameter and volume prediction models |
| title_full_unstemmed | The use of tree crown variables in over-bark diameter and volume prediction models |
| title_short | The use of tree crown variables in over-bark diameter and volume prediction models |
| title_sort | use of tree crown variables in over bark diameter and volume prediction models |
| topic | Crown Variables Taper Back-Propagation ANNs Levenberg-Marquardt ANNs Generalized Regression Neural Networks |
| url | https://iforest.sisef.org/contents/?id=ifor0878-007 |
| work_keys_str_mv | AT ozcelikr theuseoftreecrownvariablesinoverbarkdiameterandvolumepredictionmodels AT diamantopouloumariaj theuseoftreecrownvariablesinoverbarkdiameterandvolumepredictionmodels AT brooksjohnr theuseoftreecrownvariablesinoverbarkdiameterandvolumepredictionmodels AT ozcelikr useoftreecrownvariablesinoverbarkdiameterandvolumepredictionmodels AT diamantopouloumariaj useoftreecrownvariablesinoverbarkdiameterandvolumepredictionmodels AT brooksjohnr useoftreecrownvariablesinoverbarkdiameterandvolumepredictionmodels |