Total tree height predictions via parametric and artificial neural network modeling approaches
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...
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| Format: | Article |
| Language: | English |
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Italian Society of Silviculture and Forest Ecology (SISEF)
2022-04-01
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| Series: | iForest - Biogeosciences and Forestry |
| Subjects: | |
| Online Access: | https://iforest.sisef.org/contents/?id=ifor3990-015 |
| _version_ | 1811310956019449856 |
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| author | Karatepe Y Diamantopoulou MJ Özçelik R Sürücü Z |
| author_facet | Karatepe Y Diamantopoulou MJ Özçelik R Sürücü Z |
| author_sort | Karatepe Y |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T10:09:27Z |
| format | Article |
| id | doaj.art-2e09890f2e0f4dfcaebff78c5075c167 |
| institution | Directory Open Access Journal |
| issn | 1971-7458 |
| language | English |
| last_indexed | 2024-04-13T10:09:27Z |
| publishDate | 2022-04-01 |
| publisher | Italian Society of Silviculture and Forest Ecology (SISEF) |
| record_format | Article |
| series | iForest - Biogeosciences and Forestry |
| spelling | doaj.art-2e09890f2e0f4dfcaebff78c5075c1672022-12-22T02:50:58ZengItalian Society of Silviculture and Forest Ecology (SISEF)iForest - Biogeosciences and Forestry1971-74582022-04-011519510510.3832/ifor3990-0153990Total tree height predictions via parametric and artificial neural network modeling approachesKaratepe Y0Diamantopoulou MJ1Özçelik R2Sürücü Z3Faculty of Forestry, Isparta University of Applied Sciences, East Campus, 32260 Isparta - TurkeyFaculty of Agriculture, Forestry and Natural Environment, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki - GreeceFaculty of Forestry, Isparta University of Applied Sciences, East Campus, 32260 Isparta - TurkeyMinistry of Agriculture and Forestry, VI Regional Directorate, 27002 Burdur - TurkeyHeight-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.https://iforest.sisef.org/contents/?id=ifor3990-015Tree Height Model PredictionGeneralized ModelsMixed-Effects ModelsLevenberg-Marquardt AlgorithmResilient Propagation |
| spellingShingle | Karatepe Y Diamantopoulou MJ Özçelik R Sürücü Z Total tree height predictions via parametric and artificial neural network modeling approaches iForest - Biogeosciences and Forestry Tree Height Model Prediction Generalized Models Mixed-Effects Models Levenberg-Marquardt Algorithm Resilient Propagation |
| title | Total tree height predictions via parametric and artificial neural network modeling approaches |
| title_full | Total tree height predictions via parametric and artificial neural network modeling approaches |
| title_fullStr | Total tree height predictions via parametric and artificial neural network modeling approaches |
| title_full_unstemmed | Total tree height predictions via parametric and artificial neural network modeling approaches |
| title_short | Total tree height predictions via parametric and artificial neural network modeling approaches |
| title_sort | total tree height predictions via parametric and artificial neural network modeling approaches |
| topic | Tree Height Model Prediction Generalized Models Mixed-Effects Models Levenberg-Marquardt Algorithm Resilient Propagation |
| url | https://iforest.sisef.org/contents/?id=ifor3990-015 |
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