Determination of Relationships Between Stand Variables and Parameters of Weibull Function for Fagus orientalis Libsky Stands

Year 2022, Volume: 22 Issue: 1, 68 - 77, 31.03.2022

Mehmet Seki

https://doi.org/10.17475/kastorman.1095907

Cited By: 3

Abstract

Aim of study: Information about the diameter distribution of a stand is a key resource to determine planning strategies, silvicultural treatment options and product variety. In this study, the diameter distributions of Fagus orientalis Libsky stands located in Karabük region were researched, and relationship between parameters of Weibull function and stand variables was investigated.
Area of study: Data used in this study were obtained from pure Oriental beech (Fagus orientalis Libsky) stands located in the Karabük region, north-central Turkey.
Material and methods: For this study, sixty-two sample plots from pure Fagus orientalis Lipsky stands located in Karabük region were taken. Maximum likelihood method was used to estimate parameters of the two-parameter Weibull probability density function. The parameters estimated were then expressed as linear functions of stand variables such as mean diameter, basal area, minimum and maximum diameters etc.
Main results: The regression model using arithmetic mean diameter as an independent variable and the model using maximum diameter of the stand as an independent variable were found superior for estimation of scale and shape parameters, respectively.
Highlights: While the Weibull distributions determined by both methods give close results, the method of determining the distribution parameters with the developed regression models seems to be superior in terms of examining the diameter distribution changes according to different stand structure simulations.

Keywords

Beech, Diameter Distribution, Karabük, Probability Density, Two Parameter Weibull

Fagus orientalis Libsky Meşcere Özellikleri İle Weibull Parametreleri Arasındaki İlişkilerin Belirlenmesi

Abstract

Çalışmanın amacı: Bir meşcerenin çap dağılımına ilişkin bilgiler, planlama stratejilerini, silvikültürel müdahale seçeneklerini ve ürün çeşitliliğini belirlemek için önemli bir kaynaktır. Bu çalışmada Karabük bölgesindeki Fagus orientalis Libsky meşcerelerinin çap dağılımları incelenmiş ve Weibull fonksiyonunun parametre değerleri ile meşcere özellikleri arasındaki ilişkiler araştırılmıştır.
Çalışma alanı: Bu çalışmada kullanılan veriler, Türkiye'nin kuzey-orta kesiminde Karabük bölgesinde yer alan saf Doğu kayını (Fagus orientalis Lipsky) meşcerelerinden elde edilmiştir.
Materyal ve yöntem: Bu çalışma için Karabük bölgesinde yer alan saf Fagus orientalis Lipsky meşcerelerinden altmış iki adet örnek alan alınmıştır. Maksimum olabilirlik yöntemi ile tahmin edilen Weibull olasılık yoğunluk fonksiyonu parametreleri daha sonra orta çap, göğüs yüzeyi, minimum ve maksimum çaplar gibi meşcere özelliklerinin doğrusal fonksiyonları olarak modellenmiştir.
Temel sonuçlar: Bağımsız değişken olarak aritmetik orta çapı kullanan regresyon modeli ve bağımsız değişken olarak meşcerenin maksimum çap değerini kullanan model sırasıyla ölçek ve şekil parametrelerinin tahmininde üstün bulunmuştur.
Araştırma vurguları: Her iki yöntemle belirlenen Weibull dağılımları birbirine yakın sonuçlar verirken, geliştirilen regresyon modelleri ile dağılım parametrelerinin belirlenmesi, farklı meşcere yapısı simülasyonlarına göre çap dağılım değişimlerinin incelenmesi açısından daha üstün görünmektedir.

Keywords

Kayın, Çap Dağılımı, Karabük, Olasılık Yoğunluk, Iki Parametreli Weibull

References

• Araújo, L. A., Oliveira, R. M., Dobner, M., e Silva, C. S. J. & Gomide, L. R. (2021). Appropriate search techniques to estimate Weibull function parameters in a Pinus spp. plantation. Journal of Forestry Research, 32, 2423–2435.
• Bailey, R. L. & Dell, T. R. (1973). Quantifying diameter distributions with the Weibull function. Forest Science, 19(2), 97-104.
• Bolat, F. & Ercanli, İ. (2017). Modeling diameter distributions by using Weibull function in forests located Kestel-Bursa. Kastamonu University Journal of Forestry Faculty, 17(1), 107-115.
• Burkhart, H. E. & Tomé, M. (2012). Modeling forest trees and stands. Dordrecht: Springer.
• Cao, Q. V. (2004). Predicting parameters of a Weibull function for modeling diameter distribution. Forest Science, 50(5), 682-685.
• Carretero, A. C. & Álvarez, E. T. (2013). Modelling diameter distribution of Quercus suber L. stands in “Los Alcornocales” Natural Park (Cádiz-Malaga, Spain) by using the two-parameter Weibull function. Forest Systems, 22(1), 15-24.
• Carus, S. (1996.) Aynı yaşlı doğu kayını (Fagus orientalis Lipsky). meşcerelerinde çap dağılımının bonitet ve yaşa göre değişimi. İstanbul Üniversitesi Orman Fakültesi Dergisi, 46, 171-182.
• Ciceu, A., Pitar, D. & Badea, O. (2021). Modeling the diameter distribution of mixed uneven-aged stands in the south western Carpathians in Romania. Forests, 12(7), 958.
• Curtis, R. O., Clendenan, G. & DeMars, D. J. (1981). A new stand simulator for coast douglas-fir: DFSIM users guide. Portland: USDA Forest Service General Technical Report, PNW-128.
• Diamantopoulou, M.J., Özçelik, R., Crecente-Campo, F. & Eler, Ü. (2015). Estimation of Weibull function parameters for modelling tree diameter distribution using least squares and artificial neural networks methods. Biosystems Engineering, 133, 33-45.
• Eng, H. (1986). Weibull diameter distribution models for managed stands of douglas-fir in Washington and Oregon. Oregon State University: Thesis of Master of Science.
• Ercanli, İ. & Yavuz, H. (2017). The probability density functions to diameter distributions for Oriental spruce and Scots pine mixed stands. Kastamonu University Journal of Forestry Faculty, 10(1), 68-83.
• Ercanli, İ., Bolat, F., Kahriman, A. (2013). Comparing parameter recovery methods for diameter distribution models of Oriental spruce (Picea orientalis (L.) Link.) and Scotch pine (Pinus sylvestris L.) mixed stands located Trabzon and Giresun Forest Regional Directorate. International Caucasian Forestry Symposium, 23-26 October 2013, Artvin, Turkey, 119-126.
• Gadow, V. K. & Hui, G. (1999). Modelling forest development. Dordrecht: Kluwer Academic Publishers.
• GDF. (2020). Forestry statistics 2020. Ankara (Turkey): General Directorate of Forestry Publications.
• GDM. (2020). Official stats 2020. Ankara (Turkey): General Directorate of Meteorology.
• Gorgoso, J. J., Álvarez González, J. G., Rojo, A. & Grandas-Arias, J. A. (2007). Modelling diameter distributions of Betula alba L. stands in northwest Spain with the two-parameter Weibull function. Investigación Agraria: Sistemas y Recursos Forestales, 16(2), 113-123.
• Lei, Y. (2008). Evaluation of three methods for estimating the Weibull distribution parameters of Chinese pine (Pinus tabulaeformis). Journal of Forest Science, 54(12), 566-571.
• Liu, C., Beaulieu, J., Pregent, G. & Zhang, S.Y. (2009). Applications and comparison of six methods for predicting parameters of the Weibull function in unthinned Picea glauca plantations. Scandinavian Journal of Forest Research, 24(1), 67-75.
• Loetsch, F., Zöhrer, F. & Haller, K. E. (1973). Forest inventory. München: BLV Verlagsgesellschaft.
• Maltamo, M., Puumalainen, J. & Päivinen, R. (1995). Comparison of beta and Weibull functions for modelling basal area diameter distribution in stands of Pinus sylvestris and Picea abies. Scandinavian Journal of Forest Research, 10(1-4), 284-295.
• Nanang, D. M. (1998). Suitability of the Normal, Log-normal and Weibull distributions for fitting diameter distributions of neem plantations in Northern Ghana. Forest Ecology and Management, 103(1), 1-7.
• Nokoe, S. & Okojie, J. A. (1984). Relationship of stand attributes of some plantation mahoganies with estimated Weibull parameters. Ecological Modelling, 24(3-4), 231-240.
• Özdemir, G. A. (2016). Duglas (Pseudotsuga menziesii (Mirb.) Franco) meşcerelerinin çap dağılımlarının modellenmesi. Journal of the Faculty of Forestry Istanbul University, 66(2), 548-558.
• Poudel, K. P. & Cao, Q. V. (2013). Evaluation of methods to predict Weibull parameters for characterizing diameter distributions. Forest Science, 59(2), 243-252.
• R Studio Team. (2020). R Studio: Integrated Development for R. Boston: R Studio, Inc., M.A.URL http://www.rstudio.com.
• Sakici, O. E. (2021). A comparison of diameter distribution models for uneven-aged Kazdağı Fir stands in Kastamonu Region of Turkey. Global Conference on Engineering Research (GLOBCER’21), 578-590, 2-5 June 2021, Turkey.
• Sakici, O. E. & Gulsunar, M. (2012). Diameter distribution of Bornmullerian fir in mixed stands. Kastamonu University Journal of Forestry Faculty, 12(3), 263-270.
• Sakici, O. E. & Dal, E. (2021). Kastamonu yöresi sarıçam meşcereleri için çap dağılımlarının modellenmesi ve çeşitli meşcere özellikleri ile ilişkilerinin belirlenmesi. Bartın Orman Fakültesi Dergisi, 23(3), 1026-1041.
• Sakici, O. E., Seki, M., Saglam, F. & Akyildiz, M. H. (2016). Modeling diameter distributions of black pine stands in Taşköprü Region. International Forestry Symposium, 7-10 December 2016, Kastamonu, Turkey, 521-535.
• Schmidt, L. N., Sanquetta, M. N. I., McTague, J. P., da Silva, G. F., Fraga Filho, C. V., Sanquetta, C. R. & Soares Scolforo, J. R. (2020). On the use of the Weibull distribution in modeling and describing diameter distributions of clonal eucalypt stands. Canadian Journal of Forest Research, 50(10), 1050-1063.
• Schreuder, H. T. & Swank, W. T. (1974). Coniferous stands characterized with the Weibull distribution. Canadian Journal of Forest Research, 4(4), 518-523.
• Sghaier, T., Canellas, I., Calama, R. & Sanchez-Gonzalez, M. (2016). Modelling diameter distribution of Tetraclinis articulata in Tunisia using normal and Weibull distributions with parameters depending on stand variables. iForest-Biogeosciences and Forestry, 9(5), 702.
• Sivrikaya, F. & Karakaş, R. (2020). Modeling diameter distributions in Önsen natural stone pine (Pinus pinea L.) stands. Turkish Journal of Forestry, 21(4), 364-372.
• Stankova, T. V. & Zlatanov, T. M. (2010). Modeling diameter distribution of Austrian black pine (Pinus nigra Arn.) plantations: a comparison of the Weibull frequency distribution function and percentile-based projection methods. European Journal of Forest Research, 129(6), 1169-1179.
• Vanclay, J. K. (1994). Modelling forest growth and yield: applications to mixed tropical forests. Wallingford: CAB international.
• Wang, S., Dai, L., Liu, G., Yuan, J., Zhang, H. & Wang, Q. (2006). Modeling diameter distribution of the broadleaved-Korean pine mixed forest on Changbai Mountains of China. Science in China Series E: Technological Sciences, 49(1), 177-188.
• Yavuz, H., Gül, A. U., Mısır, N., Özçelik, R. & Sakıcı, O. E. (2002). Meşcerelerde çap dağılımının düzenlenmesi ve bu dağılımlara ilişkin parametreler ile çeşitli meşcere öğeleri arasındaki ilişkilerin belirlenmesi, Orman Amenajmanında Kavramsal Açılımlar ve Yeni Hedefler Sempozyumu, 18-19 Nisan, İstanbul, 203-21.
• Zhang, X., Lei, Y. & Cao, Q. V. (2010). Compatibility of stand basal area predictions based on forecast combination. Forest Science, 56(6), 552-557.