Application Of ANN In Forest Stand Density Control Chart And Forest Asset Assessment

Chinese fir is the main fast-growing economic timber species in South China, especially in northern part of Fujian province, where cultivation has already become dimensions. In recent years, foresters have attached greater importance to the operation and management of Chinese fir plantation. Based on the law of stand density effect, stand density control chart refers to a forest management chart formulated with the mathematical model of establishing the quantitative relationship between the stem density and each test stand density according to the theoretical principle of stand density effect. Stand density control chart is an important tool of forest management, and the reasonable suitable stand density control chart can provide a correct guidance of quantitative thinning and harvest plantations, as well as reference data to estimate for afforestation, resources survery, assessment updating levels and classification of business types.The artificial neural network (ANN) is one of modern intelligent algorithm, with such characteristics as no need to consider the internal structure of mathematical model, no assumption precondition, and no need of man-made determination of the weighting factor etc. Neural network has the advantage in nonlinear modeling, which can approach any nonlinear mapping, also can get the relationship between input and output according to the existing data input and output. Thus given an input, it can give the output according to the functional relation. Compared with the traditional modeling method, it is faster with higher accuracy, especially not reliant on the existing mathematical model. Because many forestry models are nonlinear, the application of neural network in the forestry has gained more attention with a increasingly widespread scope of application.This paper will creatively applies the ANN to establish the equal tree height and diameter liner models and carries out accuracy comparison with those traditional models. As a result, the accuracy of ANN model is higher than traditional one, which has an important significance for the future formulation of stand density control chart. Immune evolutionary algorithm will be used in the process of solving the model parameters. Immune evolutionary algorithm is a new optimization algorithm inspired by biological immune mechanism based on the deep understanding of the existing evolutionary algorithm.At last, this paper will also apply the stand density model established with artificial neural network in asset assessment of forest resources and the estimation of growth and harvest. On the basis of ensuring realization of sustainable utilization of forest resources, it can achieve the optimal target and maximal cut, which explores a new method for the for the asset assessment of forest resources.