Construction Of Masson Pine Yield Prediction Expert System On The Basis Of Component-GIS

This paper, taking Visual Studio 2008 as the development environment and C# as the programming language, based on ArcGiS Engine 9.3 component development platform, combined with the growth of Pinus massoniana yield forecast function, aim to establish Pinus massoniana yield prediction expert system which can give spatial information and forecast stand growth. This system, with the GIS function of the vector data acquisition, map browsing and special map making, on the basis of stand age, site index and density, can predict stand yield.The Component-GIS is a product of the development of geographic information system and its most distinctive feature is that it encapsulates a large number of controls for developers which can be used to build application programs with GIS style. Component-GIS can provide friendly visible operating interface of geographic information for this system. And it can also provide basic information including sub-compartment area and spatial location for the prediction module.The yield estimating model of the growth of Masson Pine is a complete system for predicting future harvest and a comprehensive evaluation of growth and silvi-cultural benefits. This paper relates to a number of Masson Pine harvest estimating model, including site index model, dominant average height model, average DBH model, average height model, natural sparse model, thinning model and volume model. Each system does not exist independently, but collaboratively, and some can even reasoning from each other. Depending on the geographic information system module which established by Component-GIS, collecting sub-compartment information, combining the yield estimate model, the prediction function can be realized.Taking the Masson Pine young-age forest of Qingyan Town of Huaxi District in Guiyang as an example, setting 2or 3 times of thinning, predicting stand growth, amount of thinning and final felling, yield harvest of 143 forest sub-compartments have been estimated. The desired results show that all the predicting factors including thinning and final felling yield are reasonable and accurate.