Estimation of tree height with machine learning techniques in coppice-originated pure sessile oak (<i>Quercus petraea</i> (Matt.) Liebl.) stands-Reference-Cited by-同舟云学术

Estimation of tree height with machine learning techniques in coppice-originated pure sessile oak (Quercus petraea (Matt.) Liebl.) stands

Published:2023-01-23 Issue:1-2 Volume:38 Page:87-96
ISSN:0282-7581
Container-title:Scandinavian Journal of Forest Research
language:en
Short-container-title:Scandinavian Journal of Forest Research

Author:

Sahin Abbas¹,Aylak Ozdemir Gafura²^ORCID,Oral Okan³,Aylak Batin Latif⁴,Ince Murat⁵,Ozdemir Emrah²

Affiliation:

1. Marmara Forestry Research Institute, İstanbul, Turkey

2. Faculty of Forestry, Forest Yield and Biometry Department, Istanbul University- Cerrahpaşa, İstanbul, Turkey

3. Department of Mechatronics Engineering, Akdeniz University, Antalya, Turkey

4. Department of Industrial Engineering, Turkish-German University, İstanbul, Turkey

5. Vocational School of Technical Science, Isparta University of Applied Sciences, Isparta, Turkey

Publisher

Informa UK Limited

Subject

Forestry

Link

https://www.tandfonline.com/doi/pdf/10.1080/02827581.2023.2168044

Reference60 articles.

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3. Atalay İ. 2010. Uygulamalı klimatoloji. meta basım matbaacılık hizmetleri, bornova-İzmir. In Turkish.

4. Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

5. Ten-year estimation of Oriental beech (Fagus orientalis Lipsky) volume increment in natural forests: a comparison of an artificial neural networks model, multiple linear regression and actual increment

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