Remote Sensing Monitoring of Pine Wilt Disease Based on Time-Series Remote Sensing Index

Under the strong influence of climate change and human activities, frequency intensity disturbance events in forest ecosystem both show significant increasing trends. Pine wood nematode (Bursapherenchus xylophilus, PWN) is one major alien invasive species China, which has rapidly infected spread. In recent years, its tendency been to spread from south north, causing serious losses Pinus non-Pinus coniferous forests. It urgent carry out remote sensing monitoring prediction pine wilt disease (PWD). Taking Anhui Province as study area, we applied ground survey, satellite-borne optical imagery environmental factor statistics, relying on Google Earth Engine (GEE) platform build a new vegetation index NDFI based time-series Landsat images extract information used random classification algorithm model PWD infection stage. The results that proposed differentiation threshold method can accurately range, with overall accuracy 87.75%. reaches 81.67%, kappa coefficient 0.622. High temperature low humidity are conducive survival PWN, aggravates occurrence PWD. background global warming, degree gradually increased, transferred southwest middle northeast. Our at regional scale be realized by using long multi-source data, grasp timely manner, crucial for effective control