Research On Dynamic Changes Of Pine Wilt Disease Based On Hyperspectral

Pine wilt disease is a devastating disease of conifers. Although great progress has been made in the current study of its pathogenesis, its monitoring and early warning system is still in exploration stage. Hyper-spectral remote sensing technology is an advanced spatial information technology, and has a unique advantage in the forest pest monitoring. In this study, the black pine (P.thunbergii Parl) and the masson pine (P.massoniana Lamb) were selected as research object, to measure hyper-spectral reflectance, water content, chlorophyll content, soluble sugar, MDA and the population of nematodes in different stages during the period from nematode inoculation to death. The results were as follows:1) Mutiple physio-biochemical changes of the host plants were caused by the pine wood nematode (PWN) infection:the stem-leaf tissue water content dropped gradually as the time post PWN infection increased; The chlorophyll content rised early, but with the needle lossing water and browning, chlorophyll content dropped significantly; malondialdehyde (MDA) and soluble sugar content increased and then decreased in different infected stages; the number of nematodes in susceptible pines increased gradually as the infection aggravated.2) The original hyper-spectral reflectance of infected black pines and masson pines were significantly different from the healthy ones. It showed that green light reflection peak area gradually weakened until it disappeared, near-infrared region of the wide flat reflex was gradually replaced by a straight line, all reflection peak and the absorption valley were gradually weakened until they disappeared.3) Chlorophyll content in infected pines were significantly related to spectral index NDVI(810,450), the chlorophyll content could be obtained with quantitative inversion through the models: CHL=[1.26*NDⅥ(810,450)-0.95]*10; CHL=-15.25*[NDⅥ(810,450)]2+28.08*NDⅥ(810,450)-11.93.4) Through the ratio of 675nm and 760nm spectral index DIR(760,675), we could determine threshold of the chlorophyll content in sick pines and judge them whether infected by PWN or not.5) Studying the variation in infected pines chlorophyll content, we could use the NDVI(810,450) spectral index models: gbday(d)=-5129*[NDⅥ(810,450)]2+7847*[NDVI(810,450)]-2925; gbday(d)=-668.5*NDⅥ(810,450)+614.6 to determine the time of initial infecton on pines quantitatively so as to establish the early monitoring and warning system of pine wilt disease.