Trabzon orman bölge müdürlüğü doğu ladini-sarıçam karışık meşcereleri için karışık etkili doğrusal olmayan regresyon denklemleri ile doğu ladini çap-boy modellerinin geliştirilmesi

Tek ağaçların boy ölçümü zor ve zaman alıcı olduğundan; birçok ormancılık uygulamasında tüm ağaçların boyları ölçülememektedir. Ölçülemeyen ağaç boyları, meşceredeki ağaçların göğüs çapları ile boyları arasındaki istatistiksel ilişkileri modelleyen regresyon denklemleri ile tahmin edilmektedir. Bu çalışmada, Trabzon Orman Bölge Müdürlüğü sınırları içerisinde yer alan Doğu ladini (Picea orientalis (L.) Link)-Sarıçam (Pinus sylvestris L.) karışık meşcerelerinde gelişim gösteren doğu ladini ağaçlarının çap-boy denklemlerinin oluşturulmasında, Doğrusal Olmayan Karışık Etkili Regresyon Modelleri geliştirilmesi amaçlanmıştır. Tek ağaçların çap-boy ilişkisini modellemek üzere, Schnute (1981), Huang vd (2009), Wykoff vd. (1982)’in Karışık Etkili Model yapıları oluşturulmuş ve başarı durumlarına göre karşılaştırılmıştır. En başarılı model olarak belirlenen Schnute (1981)’ın modeline ilişkin Akaike (AIC) Bilgi Ölçütü değeri; 15242 ve Bayesian (BIC) Bilgi Ölçütü değeri ise; 15257 olarak hesaplanmış olup, Schnute (1981)’ın modeli ile tek ağaçların boy gelişimindeki değişimin %95’ni (R2 =0.95) tahmin edilmektedir. Önemli derecede açıklayıcıca sahip olan ve çap-boy verilerindeki seri korelasyon problemini taşımayan Karışık Etkili Doğrusal Olmayan Schnute (1981)’ın modeli, Doğu ladini ağaçlarının boyunun tahmininde güvenilir bir şekilde kullanılabilecektir.

The construction of diameter-height models based on nonlinear mixed effect regression equations for oriental spruce located in mixed oriental spruce-scotch pine stands at Trabzon forest district

Because of hard-working and time-consuming measurements for tree height, heights for all tree in sample plots can’t be measure in many forest applications. These unmeasured tree heights can be predicted by height-diameter models relating tree height and diameters. In modeling the relationships between tree height and diameter, autocorrelation or serially correlations can be occurred in nested sampling systems for tree data measuring forest ecosystems. In this study, it is proposed to predict Nonlinear Mixed Effect Regression Models to alleviate these autocorrelation problems for constructing stand height-diameter curves in Oriental spruce (Picea orientalis (L.) Link) trees growing admixtures with scotch pine (Pinus sylvestris L.). The Schnute (1981), Huang et al. (2009), Wykoff et. al. (1982) nonlinear mixed effect model structure were predicted and compared for model height-diameter relationships in this study. In comparing these models, The Schnute (1981)’s nonlinear mixed effect model produced the best prediction results based on accuracy statistics. The Akaike (AIC) and Bayesian (BIC) Information Criterion were calculated as 15242 and 15257 and Schnute (1981) model predicted the % 95 percent of variability for height (R2 =0.95). Schnute (1981) model with significant explanatory at variability of height and without serial correlation in diameter and height data will be use reliably to obtain predictions for oriental spruce tree heights.

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