Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Analysis of the changes in forest ecosystem functions,structure and composition in the Black Sea region of Turkey

We used geographical information system to analyze changes in forest ecosystem functions, structure and composition in a typical department of forest management area consisting of four forest management planning units in Turkey. To assess these effects over a 25 year period we compiled data from three forest management plans that were made in 1986, 2001 and 2011. Temporal changes in forest ecosystem functions were estimated based on the three pillars of forest sustainability： economics, ecology and socio-culture. We assessed a few indicators such as land-use and forest cover, forest types,tree species, development stage, stand age classes, crown closure, growing stock and its increment, and timber biomass. The results of the case study suggested a shift in forest values away from economic values toward ecological and socio-cultural values over last two planning periods. Forest ecosystem structure improved, due mainly to increasing forest area, decreasing non-forest areas（especially in settlement and agricultural areas）, forestation on forest openings, rehabilitation of degraded forests, conversion of even-aged forests to uneven-aged forests and conversion of coppice forests to high forests with greater growing stock increments. There were also favorable changes in forest management planning approaches.