Research On The Theory Of Forest Stock Volume Estimation Based On SVM

Forest volume is a basic quantity character of the forest ecological system,it is the resultof long-term production of forest ecosystems in the process of accumulation andmetabolism.It is one of the basic index to reflect the total size of the forest resources,and alsoan important basis to reflect the richness of forest resources and to judge the pros and cons ofthe forest ecological environment. Traditional forest stock volume estimation method has apoor generalization ability with a not high accuracy.Rough set theory with its advantage ofprocessing incomplete noisy data has been widely used in feature extraction. SVM is based onstructural risk minimization principle,nevertheless,it has a good generalization ability in finitesamples.In this paper, the rough set combined with support vector machine applied to forestvolume prediction, the simulation experiments of the area of Simao, Yunnan, verify theeffectiveness of rough set theory and support vector machine regression method, and makes acomparison of Polynomial method and neural network method.The main contents of this paper can be summarized as follows:(1)This paper expounds several basic contents of rough set, based on the methoddeveloped by ROSETTA software and programming.Then extracted out of the129plots ofremote sensing from the area of Simao, Yunnan and GIS features factors.(2)Using the method of polynomial function, neural networks and support vectormachines, established the model of forest volume estimation separately,and made acomparison of the line fitting results of this three kinds of calculation methods of estimationmodel, verified the correctness and effectiveness of the support vector machine (SVM)method.