Doğal karaçam meşcereleri için yöresel karışık etkili çap-boy modellerinin geliştirilmesi

Abstract

Bu çalışmada, Isparta-Eğirdir Yöresi doğal karaçam (Pinus nigra J. F. Arnold.) meşcereleri için doğrusal karışık etkili çap-boy modelleri geliştirilmiştir. 2021-2022 yılları arasında 135 örnek alandan elde edilen toplam 4271 adet örnek ağaç verisi, modelin parametre tahmini ve kalibrasyon testleri için kullanılmıştır. Model geliştirme grubunda çap ortalaması 34,35 cm, boy ortalaması 15,40 m olarak belirlenirken; test grubunda bu değerler sırasıyla 31,26 cm ve 14,07 m olarak hesaplanmıştır. Farklı kalibrasyon alternatifleri, tahmin performansını artırmak amacıyla 1 ila 10 ağaç kullanılarak test edilmiştir. Sonuçlar, özellikle 5 ve 10 ağaç kalibrasyonu ile modelin tahmin doğruluğunun arttığını göstermiştir. Sabit etkili modele kıyasla karışık etkili modelin hata kareler ortalamasının karekökü (RMSE) değeri %32 oranında daha düşük bulunmuştur. Çalışma, önerilen modelin orman envanter çalışmalarında karaçam meşcerelerinin ağaç boyu tahmininde etkili bir şekilde kullanılabileceğini ortaya koymaktadır.

Keywords

• Boy tahmini
• Tahmin performansı
• Doğrusal regresyon
• Kalibrasyon

Development of regional mixed-effects height–diameter models for natural black pine stands

Abstract

In this study, linear mixed-effects height-diameter models were developed for natural black pine (Pinus nigra J. F. Arnold.) stands in the Isparta-Eğirdir region. Data collected from 135 sample plots between 2021 and 2022, consisting of 4,271 sample trees, were used for parameter estimation and calibration testing. The mean diameter and height in the model development group were 34.35 cm and 15.40 m, respectively, while in the test group, these values were 31.26 cm and 14.07 m. Various calibration alternatives, utilizing 1 to 10 trees, were tested to improve prediction performance. The results showed significant improvements in prediction accuracy of the models, especially with calibrations involving 5 or 10 trees. Compared to the fixed-effects model, the mixed-effects model achieved a 32% reduction in the root mean square error (RMSE). The findings suggest that the proposed model is highly suitable for forest inventory studies to predict tree heights in black pine stands.

Keywords

• Height prediction
• Prediction performance
• Linear regression
• Calibration

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