基于GF-1/WFV数据的冬小麦条锈病遥感监测  

Winter wheat stripe rust remote sensing monitoring based on GF-1/WFV data

作　　者：王利民[1] 刘佳[1] 杨福刚[1] 杨玲波[1] 姚保民 高建孟 

机构地区：[1]中国农业科学院农业资源与农业区划研究所,北京100081

出　　处：《农业工程学报》2017年第20期153-160,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划"粮食作物生长监测诊断与精确栽培技术"课题"作物生长与生产力卫星遥感监测预测"(2016YFD0300603)

摘　　要：条锈病是冬小麦常见病害,利用遥感影像对条锈病病害区域进行准确监测具有重要意义。该文利用GF-1/WFV影像,结合条锈病地面光谱数据分析,采用冬小麦条锈病遥感监测指数(wheat stripe rust index,WSRI)对河南西华县冬小麦条锈病发病范围进行了估测。首先,利用冬小麦NDVI加权指数(weighted NDVI index,WNDVI)获取冬小麦种植区域。其次,利用影像4个波段反射率之和提取不同冬小麦品种的分布范围,值较高的为条锈病高抗品种(郑麦系列),较低的则是条锈病易感品种(矮壮系列)。再次,构建冬小麦条锈病指数(wheat stripe rust index,WSRI),结合地面实地调查的条锈病分布数据,通过设定合理的WSRI指数划分阈值,提取条锈病染病区域并进行精度验证。结果表明,研究区内小麦条锈病空间分布识别的总体精度在84.0%以上,具有区域监测应用的潜力。该方法简单,可操作性强,表明宽波段GF-1影像结合WSRI指数的技术,是一种比较可行的小麦条锈病遥感监测方案。Stripe rust is a common disease of winter wheat, and accurate monitoring of stripe rust disease has great significance. By using the GF-1/WFV images on April 18, 2017 and combined with the analysis on stripe rust ground spectral data, this paper conducted estimation on the scope of winter wheat stripe rust in Xihua County of Henan Province with wheat stripe rust index(WSRI). The main contents of this study included identifying winter wheat area, identifying distribution of winter wheat varieties, calculating winter wheat stripe rust monitoring index, identifying distribution of disease, and verifying accuracy. Identification of winter wheat area was achieved by using weighted normalized differential vegetation index(WNDVI), and computation of WNDVI used images of 7 time phases, with the time scope from October, 2016 to April, 2017, one image each month. The distribution of winter wheat varieties was identified by dividing the thresholds of spectral brightness index(SBI). SBI is the sum of reflectances of 4 wave bands of WFV images. The areas with high thresholds were taken as the distribution areas of high stripe rust resistant varieties(Zhengmai series) and the areas with low thresholds were taken as the susceptible varieties of stripe rust(Aizhuang series). The acquisition of the threshold took the sample points of the ground observation as its basis. The identification accuracies of the variety distribution of different SBI points were tested respectively, and the node with the highest accuracy was taken as the threshold. By using observed spectrum of the ground observation, WSRI of the infected areas was calculated based on the average value of the reflectance of winter wheat observed with the same wave band as GF-1/WFV. The WSRI value of the winter wheat of the normal sample points was 0, and all the values of the infected sample points were larger than 0. The WSRI value was increasing with the increase of the infection degree of the disease, which was consistent with the actual observation results. It indic

关 键 词：遥感 监测 作物 GF-1/WFV 小麦条锈病 WSRI 识别 

分 类 号：S252.9[农业科学—农业机械化工程]