Remote Sensing Monitoring And Predicting Of Bursaphelenchus Xylophilus In Sanming Of Fujian Province

Pine nematode (Bursaphelenchus xylophilus) has severely affected to the forest health, ecological security and social security of China. Masson pine is one of the main forest species of Fujian province, yet it has been caused a great loss because of pine nematode in recent years. Therefore, it is important to monitor and forecast the pine nematode in the forest.In this thesis, author chooses Sanming City in Fujian province as the investigation area, and the pine forest as the investigating object, and selected the ALOS images of October 2009, to firstly carry out remote sensing image merge, at the same time, we combined with small zoning map extracted from Inventory Database of Sanming City and based on the spectrum value in remote sensing images of different forest types, to classify the different type of forestry using knowledge-rules-based method and extract the pine forest based on above. By comparing the differences of spectral curve between health and sick pines, we identified the best band for monitoring pine disease. This paper studied the relationship between Spectra and transpiration rate, to provide the basis for early detection of Bursaphelenchus xylophilus. And studies on analysis of relationship between different damaged degrees of pine forest and 9 ecological factors was carried out, the studies showed the damaged degrees were highly related to the average diameter of Masson pine, the forest slope and the soil conditions, it was demonstrated that firstly the damage rate increased as the diameter reduced; secondly the extent of damage increased as the forest slope augmented, and thirdly it reduced when the soil fertility was better. Based on this above, we established BP artificial neural network model for prediction.