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Spatial interpolation methods are widely used to estimate some ecological and environmental parameters that are difficult to measure. One of these parameters is forest site index, which is a demonstration of forest productivity. The aim of this study was to estimate forest site index in a beech forest ecosystem in Turkey. In this context, soil characteristics, stand parameters, and topographic features were measured in 70 temporary sample plots of beech forest stands. Forest site index of beech forest stands was predicted using different modeling techniques such as multiple regression analysis (MLR), multilayer perceptron (MLP), radial basis function (RBF), multiple regression kriging (MLRK), multilayer perceptron kriging (MLPK), and radial basis function kriging (RBFK). The results showed that the RBFK (R 2  = 0.98) and MLRK (R 2  = 0.96) outperformed the others to predict forest site index in the study area. The greatest improvement occurred when krigged residual used with MLR, which increase from 0.23 to 0.96. Thus, MLRK method significantly improved the prediction accuracy for site index. The models combined with krigged residuals were more successful than those used without krigged residuals. The results of this study suggest that the combined methods may help obtaining improved site index maps for forest management.

The objective of this study is to estimate the leaf area index (LAI) of a forest ecosystem using two different satellite images, WorldView-2 and Aster. For this purpose, 108 sample plots were taken from pure Crimean pine forest stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. Each sample plot was imaged with hemispherical photographs with a fish-eye camera to determine the LAI. These photographs were analyzed with the help of Hemisfer Hemiview software program, and thus, the LAI of each sample plot was estimated. Furthermore, multiple regression analysis method was used to model the statistical relationships between the LAI values and band spectral reflection values and some vegetation indices (Vis) obtained from satellite images. The results show that the high-resolution WorldView-2 satellite image is better than the medium-resolution Aster satellite image in predicting the LAI. It was also seen that the results obtained by using the VIs are better than the bands when the LAI value is predicted with satellite images.