联合多源多时相卫星影像和支持向量机的小麦白粉病监测方法(英文)  

Monitoring Wheat Powdery Mildew(Blumeria graminis f.sp.tritici)Using Multisource and Multitemporal Satellite Images and Support Vector Machine Classifier

作　　者：赵晋陵[1] 杜世州[2] 黄林生[1] ZHAO Jinling;DU Shizhou;HUANG Linsheng(National Engineering Research Center for Analysis and Application of Agro-Ecological BigData,Anhui University,Hefei 230601,China;Institute of Crops,Academy of Agricultural Sciences,Hefei 230031,China)

机构地区：[1]安徽大学农业生态大数据分析与应用技术国家地方联合工程研究中心,安徽合肥230601 [2]安徽省农业科学院作物研究所,安徽合肥230031

出　　处：《智慧农业（中英文）》2022年第1期17-28,共12页Smart Agriculture

基　　金：The Natural Science Foundation of China(31971789);The Natural Science Foundation of Anhui Province(2008085MF184)。

摘　　要：白粉病主要侵染小麦叶部,可利用卫星遥感技术进行大范围监测和评估。本研究利用多源多时相卫星遥感影像监测小麦白粉病并提升分类精度。使用四景Landat-8的热红外传感器数据(Thermal Infrared Sensor,TIRS)和20景MODIS影像的MOD11A1温度产品反演地表温度(Land Surface Temperature,LST),使用4景国产高分一号(GF-1)宽幅相机数据(Wide Field of View,WFV)提取小麦种植区和计算植被指数。首先,利用ReliefF算法优选对小麦白粉病敏感的植被指数,然后利用时空自适应反射率融合模型(Spatial and Temporal Adaptive Reflectance Fusion Model,STARFM)对Landsat-8 LST和MOD11A1数据进行时空融合。利用Z-score标准化方法对植被指数和温度数据统一量度。最后,将处理和融合后的单一时项Landsat-8LST、多时相Landsat-8 LST、累加MODIS LST和多时相Landsat-8 LST与累加MODIS LST结合的数据分别输入支持向量机(Support Vector Machine,SVM)构建了四个分类模型,即LST-SVM、SLST-SVM、MLSTSVM和SMLST-SVM,利用用户精度、生产者精度、总体精度和Kappa系数对比四个模型的分类精度。结果显示,本研究构建的SMLST-SVM取得了最高分类精度,总体精度和Kappa系数分别为81.2%和0.67,而SLST-SVM则为76.8%和0.59。表明多源多时相的LST联合SVM能够提升小麦白粉病的识别精度。Since powdery mildew(Blumeria graminis f.sp.tritici)mainly infects the foliar of wheat,satellite remote sensing technology can be used to monitor and assess it on a large scale.In this study,multisource and multitemporal satellite images were used to monitor the disease and improve the classification accuracy.Specifically,four Landsat-8 thermal infrared sensor(TIRS)and twenty MODerate-resolution imaging spectroradiometer(MODIS)temperature product(MOD11A1)were used to retrieve the land surface temperature(LST),and four Chinese Gaofen-1(GF-1)wide field of view(WFV)images was used to identify the wheat-growing areas and calculate the vegetation indices(VIs).ReliefF algorithm was first used to optimally select the vegetation index(VIs)sensitive to wheat powdery mildew,spatial-temporal fusion between Landsat-8 LST and MOD11A1 data was performed using the spatial and temporal adaptive reflectance fusion model(STARFM).The Z-score standardization method was then used to unify the VIs and LST data.Four monitoring models were then constructed through a single Landsat-8 LST,multitemporal Landsat-8 LSTs(SLST),cumulative MODIS LST(MLST)and the combination of cumulative Landsat-8and MODIS LST(SMLST)using the Support Vector Machine(SVM)classifier,that were LST-SVM,SLST-SVM,MLST-SVM and SMLST-SVM.Four assessment indicators including user accuracy,producer accuracy,overall accuracy and Kappa coefficient were used to compare the four models.The results showed that,the proposed SMLSTSVM obtained the best identification accuracies.The overall accuracy and Kappa coefficient of the SMLST-SVM model had the highest values of 81.2%and 0.67,respectively,while they were respectively 76.8%and 0.59 for the SLST-SVM model.Consequently,multisource and multitemporal LSTs can considerably improve the differentiation accuracies of wheat powdery mildew.

关 键 词：小麦白粉病 高分一号 MODIS Landsat-8 地表温度 支持向量机 

分 类 号：S435.121.4[农业科学—农业昆虫与害虫防治] TP79[农业科学—植物保护]