Hakkı Yavuz

Stem taper functions for Abies nordmanniana subsp. bornmulleriana in Turkey

Abstract In this study, 31 stem taper functions from three model groups (simple polynomial, segmented and variable-form stem taper models) were analysed for Bornmullerian fir (Abies nordmanniana subsp. bornmulleriana Mattf.). Diameter at breast height, total height and diameters outside bark at intervals of 1 m from the stump to the top of the tree were measured on 115 sample trees. Taper functions were fitted with data from 85 trees (76% of total data set) using a non-linear least squares method. Data from the other 30 trees (24% of total data set) were used as the validation data. Four statistical criteria (per cent variation explained, bias, absolute bias and mean squared error) were used to determine model accuracy. Box plots of the residual distributions were also produced to compare statistical accuracy by relative height class. Minimum residuals were obtained with the variable-form stem taper models at all stem parts. When the models were compared within their respective groups, the most accurate simple polynomial, segmented and variable-form models were Demaerschalk-II (1972), Farrar (1987) and Kozak (1988), respectively. Based on the statistical and graphic analysis, the model by Kozak (1988) was recommended for A. nordmanniana subsp. bornmulleriana in the Black Sea region of Turkey. The Demaerschalk-II (1972) model could also be used for practical purposes.

Tree-bark volume prediction via machine learning: A case study based on black alder’s tree-bark production

Abstract Tree-bark volume estimation is a multi-faceted problem and at the same time of vital importance in the area of forest resources management. This importance relies on the fact that it constitutes a key variable for accurately assessing timber quantities, while at the same time its use has been spread as a soil-covering product or as a soil fertilizer or as a substitutional medicinal product. Consequently, due to its substantial economic impact, the accurate prediction of the tree-bark volume is of utmost importance. In this study, we propose three bark volume prediction models for black alder trees (Alnus glutinosa (L.) Gaertn subsp. barbata (C.A. Mey.) Yalt.) each targeting a different creation source of the black alder forest. Hence, we used data from naturally regenerated, plantation and coppice stand types. 1334 stem analysis data were collected for three different stand types. Two different modeling techniques were used, the weighted nonlinear regression and the e-support vector regression techniques. These two modeling approaches were selected due to the fact that the need to handle regression analysis problems (noise in the data, high variability and/or non-normal distributions) is essential. The state-of-the-art approach suggests the usage of machine learning techniques in an effort to build reliable and robust models able to deal with complex environmental problems. An overall illustration of the precision obtained by the constructed models was conducted by statistical criteria such as the root mean square error, the correlation coefficient, the Furnival’s index of fit and the Akaike’s information criterion. Although the estimation and prediction errors of the two different modeling techniques seem to be close in pure numbers, the e-support vector regression models gave the most accurate results for all stand types as compared to the nonlinear regression. Based on the results obtained from this study, the constructed e-support vector regression models for modeling tree-bark volume showed a great ability to generalize, and thus worth considering as an alternative to regression modeling that enables increasing our ability for successful forest management.

Doğu Karadeniz Göknarı-Doğu Ladini Karışık Meşcerelerinde Çeşitli Yarışma Endekslerinin Tek Ağaçların Çap Artımındaki Etkilerinin İncelenmesi

The aim of this study is to investigate statistical relations between each of 12 competition indices (both distance-dependent and distance-independent) and diameter growth of trees. Also we investigated the relations between each of 12 competition indices together with diameter at breast height and diameter growth. For that reason, six sampling plots (ranging from 1000 to 2000 m2 in size) were taken from in mixed stands of Caucasion fir and Oriental spruce located within the Forest Management Districts of Torul and Artvin. Our results from those sampling plots were in agreement with general growth models. Regression analysis between each of competition indexes and diameter growth resulted in coefficients of determination (R2) values ranging from 30 to 64%. The distance-dependent competition indices gave stronger correlations with diameter growth than the distance-independent competition indexes. Coefficients of determination were even higher when competition indexes and dbh were used together as independent variables (R2 = 0.31 - 0.82).