中文摘要：

为了探索运用遥感技术监测玉米粘虫灾情的方法,该文通过分析粘虫发生前期、中期和后期的多时相环境减灾卫星CCD影像和野外定位观测的叶片生物量数据,计算并比较了多种植被指数与叶片生物量的相关关系,最终构建了基于重归一化植被指数（renormalized difference vegetation index,RDVI）多时相的叶片生物量定量模型,并采用野外另一组样本对监测结果进行精度验证。结果表明,玉米叶片生物量遥感监测模型的决定系数为0.7376,均方根误差为43.26g/m2。根据叶片生物量与粘虫灾害严重度的关系,进行玉米粘虫灾情严重度及空间分布监测,结果与当地农业部门实际调查结果基本一致。因此,运用多时相HJ-CCD遥感影像可以实现玉米粘虫灾情程度及空间分布的有效监测,为农业部门客观评价玉米粘虫灾害损失提供了方法支持。

英文摘要：

Insect infestation is one of the major biological disasters in crop production. To identify an insect-damaged area and to obtain its spatial distribution are important for agricultural disaster monitoring. These data are usually obtained through field investigation, collation, and summary. As an alternative, the remote sensing of insect infestation has advantages of large range, time savings, labor savings, and high speed. In summer of 2012, an outbreak of oriental armyworm （Mythimna seperata Walker） occurred in a vast area of northeast China. In order to examine the potential of remote sensing technique in monitoring such a migratory, fulminant, and devastating agricultural pest, several data processing and analysis procedures were carried out to assess the spatial distribution of oriental armyworm and its severity level, as follows. 1 ） Cornfield acreage was extracted in the study area using a decision tree classifier based on NDVI and single-band reflectance that was derived from multi-temporal HJ-1A/1B CCD images over the growing season of maize. 2） Based on field measurements, the pest severity level was associated with leaf biomass from several ground agronomic parameters; the aim was to find a certain remote sensing variable and its quantitative model with the ground agronomic parameter to monitor the oriental armyworm disaster severity level. 3） The relationship between vegetation indices that were derived from three temporal HJ-CCD satellite images on three different phases and agronomic parameters were established based on numerical analysis. 4） Using the relationship between agronomic parameters and oriental armyworm disaster severity level, it is possible to use remote sensing data to obtain the spatial distribution of oriental armyworm. The results showed that the leaf biomass was significantly correlated with oriental armyworm severity level （R2=0.905, n=51）. Therefore, it is feasible to use leaf biomass as a surrogate of the hazard grade of oriental armyworm. The dynamical variation