The Research On Pine Wilt Disease Risk Assessment In YunNan Province On 3S Technology

Bursaphelenchus xylophilus is an important eelworm for external plant quarantine in China and it is a kind of withering diseases to coniferous genera and is regarded as important plant quarantine object in the China and abroad. It is very difficult to diagnose for its quick occurring and spreading and many other disease symptoms so it is called"pine cancer". The disease has the characters that the new disease regions initially appear to be concentrated distribution relatively and later it disperses in a large region discretionarily that add some new regions. In 2004, Pine wilt diseasefirstly occurred in Wanding development area which belongs to Ruili city of Dehong state in Yunnan province and it was alert for controlling the disease in Yunnan province. Up to now, there are about 63 species (including varieties) which belong to 20 genera of 6 families of coniferous trees in Yunnan province. Among them, P. yunnanensis, P. kesiya Royle ex Gordon var. langbianensis and P. densata which belong to coniferous genera and hosts of Bursaphelenchus xylophilus has the large area distribution. In order to protect ecological environment and develop healthy forestry, it is a very important task to eliminate or lessen Pine wilt disease and do some research of risk analysis evaluation.Traditional Pest Risk Assessmen(tPRA)which is suited to risk analysis evaluation of large scale area is an index system to establish quantification score based on qualitative description. The risk analysis evaluation index system for Pine wilt disease fit to successive precise space analyzing and calculating by GIS which is brought forward in this chapter. By analyzing the influence of the factors such as hosts, pathogens, intermediary insects, environment, and human being disturbance to Bursaphelenchus xylophilus disease, the system that analyzes the danger of harmful biology more precisely from small scale areas can reveal space difference, distribution pattern, cumulative or intercrossed effects of many space factors. The index system has 5 first-grade indexes, 10 second-grade indexes, 27 third-grade indexes,and 69 fourth-grade indexes.The original datum of risk analysis evaluation for Pine wilt disease are the representative datum of administrative district and are based on the observed datum of meteorological station. It is the essence that it regards geographic regions as the smallest scale space units with uniform quality and then analyzes the risk grades of these units. The method is only suited to schematic analysis of large scale area and cannot reveal precisely the risk difference of each kind of forest under different small climate and different influence of human being in small scale area. The method is to establish space model with 100m*100m space grid unit as the smallest media to express biological factors, communication factors and human being action factors. GIS space analysis and space calculation to do precise analysis with 100m*100m space scale are made in the chapter. Map layers such as forest structure, meteorological factors distribution, environmental factors distribution, and human being action factors distribution are established within 100m*100m space scale by the method of investigating, collecting, making models and simulating.Presently, many research works on basic biology and ecology have expounded the main influence factors of Pine wilt disease about its occurring and spreading, but there is only a few of research results about quantitative description of each factor action or contribution to spread and prevail. The method is brought out in this chapter that how to make successive multiple regression models and use them to analyze the influence of space factors by the method based on the scores that is made by experts or half-quantification description of biological and ecological laws. The precise assessment of adaptability of the disease and intermediary insects, influence of human being action factors and communication factors which are based on meteorological factors,and computer simulation of successive space influence of these factors are shown in the chapter.There are many factors to influence occurring, developing, spreading and prevailing of Bursaphelenchus xylophilus disease. These factors have very complex space distribution structure. They interact and overlap each other in the space and form a very complex space pattern and model. In order to reveal non- uniformity of factors distribution at small scale area and different influence or function of objects in different distance space, a kind of cumulative function influence models based on space location and space distance is brought forward in this chapter. The model can reveal computer simulation of accumulative influence of above factors'space pattern and can explicitly reveal the basic law of cross and cumulative risk influence that epidemic zones, habitat, communication, large or medium enterprises and building engineering to host situated on different space patterns. The long-term meteorological datum is the base of describing meteorological pattern and law and it is indispensable to the research of PRA models. When PRA models are put into practice, the datum of real-time and dynamical meteorology and other factors should be needed, so traditional datum collecting method based on large area land cannot be used. MODIS satellite remote sensing datum can get physical information of the land every day. The information can be used to quickly extract and reflect meteorological datum with successive distribution and change in the land and the datum of forest health changing. The reflections of temperature, humidity, forest leaf coverage index and crown density of pine forest are carried out in this chapter and then it gives a new idea and method to collect the datum of successive meteorological factors and forest healthy factors in the practical application.After accomplishing computer simulation of influence or function of each grade factors, according to the scores made by experts, multiple factors influence analysis models based on each grade indexes are established. 100m*100m space grids are used to reflect these factors and by the support of GIS system, risk probability distribution map of each relative factor is calculated to accomplish by overlapping. These visual expressions are dealt with and at last successive and precise risk analysis map of Pine wilt disease in Yunnan province can be accomplished. The results show that 38.7% lands of Yunnan are at high risk areas which Pine wilt disease will occur and 34.51% coniferous forests are at high risk areas. In each county, the pine forest situated in different ecological pattern has different risk value. This is relative to the distance of the forest in disease zones, communication roads and inhabitant zones and also it is relative to microclimate around hills. The risk analysis model is verified that it has high accuracy by calculating the datum of two zones which suffer from Bursaphelenchus xylophilus disease. Due to the precise risk value of this model can be calculated to every grid (approximately 1ha), the risk forecasting can be detailed to each land, so it is very useful in the practical application.