冬小麦长势与纹枯病遥感监测研究

发布时间：2018-03-05 07:40

本文选题：遥感技术　切入点：冬小麦　出处：《南京信息工程大学》2015年硕士论文　论文类型：学位论文

【摘要】：遥感技术因其具有可快速、无损、大面积获取地物信息的优势已被广泛应用于作物种植面积、长势、病虫害的监测中。本文利用环境卫星数据探索了利用作物光谱信息监测冬小麦长势及拔节期纹枯病病情指数,并针对研究过程中的遥感数据冬小麦种植面积提取进行了方法探索,文章主要内容总结如下：(1)基于NDVI密度分割的冬小麦种植面积提取。本文选用江苏省沭阳县冬小麦扬花期HJ-1A卫星遥感影像,基于不同地物光谱信息的差异性与可分割性,提出基于归一化植被指数·(NDVI)密度分割的冬小麦种植面积提取方法。结果表明：根据NDVI密度分割法提取冬小麦面积为8.37×104ha,面积精度为92.37%,样本精度为93.31%。基于密度分割系数(P0.5)制作沭阳县冬小麦种植分布图,获取了全县冬小麦空间分布特征信息。NDVI密度分割法能较准确地提取研究区内冬小麦种植面积,有效解决了农作物种植面积提取中混合像元问题,该方法可为南方农作物种植面积信息的快速、准确获取提供技术支持,为冬小麦长势、病害的遥感监测专题图制作提供。(2)基于光合生产模型对冬小麦长势的监测。本文以叶面积指数作为冬小麦长势的监测指标,在沐阳县荻垛镇建立小区试验,以小区试验获取的冬小麦全生育期内的生化参量及植被光谱信息,利用光合生产模型的转化模型,提出LAI的定量反演方法,模型中综合考虑了光照、温度、日长、叶面积指数等的影响,反演出全县区域的LAI,决定系数达0.8142,误差控制在了较理想范围。根据反演的LAI影像制作全县区域内冬小麦长势分级监测图,,较为直观的反映了全县范围各级冬小麦长势差异,方便农业基层技术人员的理解与应用。(3)基于遥感的纹枯病病情指数监测模型建立。基于气象资料,结合GPS实地取样调查的农学参数、光谱特征值、纹枯病病情指数,利用相关分析、多元回归的方法,构建沭阳县冬小麦纹枯病流行监测模型,模型精度达84.56%, RMSE为7.52。通过制作出的冬小麦纹枯病遥感监测信息图中,统计得到冬小麦纹枯病不同危害等级的分布与面积信息,与扬花期冬小麦长势分级图进行叠加分析,得出纹枯病对冬小麦染病后期生长发育的影响程度。该信息图信息量大、直观,方便使用者领会和应用大田冬小麦纹枯病遥感监测信息,可为基层农业植保措施的制定提供参考。
[Abstract]:Remote sensing technology because of its rapid, nondestructive, large area and obtain its information advantage has been widely used in the planting area, crop diseases and pests monitoring. This paper uses environmental satellite data to explore the blight disease index by crop spectrum information monitoring of winter wheat growth and jointing lines, and the remote sensing data of winter wheat in the process of planting area extraction method of exploration, the main contents of this paper are summarized as follows: (1) extraction of winter wheat planting area based on NDVI density segmentation. This paper chooses Shuyang County of Jiangsu province winter wheat flowering HJ-1A satellite remote sensing image, different spectral information and segmentation based on the proposed based on normalized vegetation index (NDVI), extraction of winter wheat planting area density segmentation method. The results showed that: according to the extraction of winter wheat area is 8.37 * 104ha NDVI density segmentation, area precision 92.37% samples, the accuracy of 93.31%. density coefficient based on segmentation (P0.5) production of Winter Wheat in Shuyang County planting distribution, to obtain the spatial distribution information of winter wheat.NDVI density segmentation method can accurately extract the study area of winter wheat planting area, effectively solves the problem of mixed pixel crop area extraction, the method can information for the planting area of crops in southern fast, accurate access to provide technical support for the growth of winter wheat, the production of thematic maps for monitoring the disease. (2) monitoring the photosynthetic production model of winter wheat growth. In this paper, based on the leaf area index as the monitoring index of Winter Wheat growth, the establishment of a plot experiment in Shuyang County Diduo Town, biochemical parameters and spectral information of vegetation in the whole growth period of winter wheat to obtain plots in the conversion model of photosynthetic production model, the quantitative inversion of LAI Method, were taken into account in the model of light, temperature, day length, effect of leaf area index, inverse County LAI, decision coefficient was 0.8142, the error control in the ideal range. According to the LAI image data to make county area of winter wheat growth monitoring, classification map, more intuitive to reflect the levels of winter wheat growth differences, understanding and application of agricultural technology convenient personnel. (3) the establishment of disease severity index monitoring model based on remote sensing. Based on the meteorological data, combined with the agronomic parameters GPS field sampling survey, spectral characteristic value, the disease severity index, using correlation analysis, multiple regression method, construction of sheath blight Shuyang county winter wheat epidemic monitoring model, the accuracy of the model was 84.56%, RMSE for Winter Wheat Sheath Blight monitoring information through a 7.52. produced in the statistics of Winter Wheat Sheath Blight in different hazard classes The distribution and area information, overlay analysis and flowering period of winter wheat growth classification map, we can see the degree of influence on the growth and development of Winter Wheat Sheath blight disease later. The information map of a large amount of information, convenient for users to understand the intuitive and application field of Winter Wheat Sheath Blight of remote sensing information, which can provide reference for the development of primary agricultural plant protection measures.

【学位授予单位】：南京信息工程大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S127;S512.11;S435.121.4

【引证文献】