Abstract
BackgroundLeaf 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.MethodsOne 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.ResultsThe 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 (R2adj. = 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 R2 (0.6392)) compared to the other studied techniques.ConclusionThe 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.
Highlights
Leaf Area Index (LAI) is an important parameter used in monitoring and modeling of forest ecosystems
The correlation coefficients for the relationships between parameters of number of trees in stands, the stand density, the basal area, the stand age, and the quadratic mean diameter with the LAI values were significant at the 0.01 level (Table 2)
Radj2 between 0.4864 and 0.6392 in all fitting techniques. These goodness-of-fit statistics show that the neural network architecture based on the Radial Basis Function (RBF) 4–19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer showed better fitting ability with sum of squared errors (SSE) (12.1040), Mean Squared Error (MSE) (0.1223), Root Mean Square Error (RMSE) (0.3497), Akaike’s information criterion (AIC) (0.1040), Bayesian information criterion (BIC) (− 77.7310) and R2 (0.6392) than the other studied artificial neural network (ANN) prediction techniques
Summary
Leaf Area Index (LAI) is an important parameter used in monitoring and modeling of forest ecosystems. Leaf Area Index (LAI) is one of the most important parameters of tree foliage, mediating many physical and biological processes in forest ecosystems (Sidabras and Augustaitis 2015). Processes such as evapotranspiration, transpiration, and photosynthesis are a function of LAI (Wulder et al 1998). It is a critical parameter needed in developing environmental and ecological models such as growth and yield models, soil-water balance models, energy balance models, and climate change models (Peng et al 2002; Wang et al 2011; Mason et al 2012). Hemispherical photography is a valuable indirect method for estimating LAI (Jonckheere et al 2004; Sidabras and Augustaitis 2015) beside it’s used for evaluating canopy features (Hale and Edwards 2002)
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