基于Landsat8遥感影像的冬小麦种植面积提取方法研究

发布时间：2018-04-11 21:42

本文选题：遥感 + 冬小麦　；参考：《湖北大学》2016年硕士论文

【摘要】：我国是一个传统意义上的农业大国。长久以来,人口众多,耕地减少,一直是困扰我国农业发展的大问题。在这样的背景下,粮食安全便显得尤为重要。国家的长治久安,社会经济的健康稳定发展,都是建立在粮食安全这个基础之上的。小麦,是在全世界范围内大量种植的三大谷类作物之一,在我国广袤的大地上也有广泛种植,及时、准确的获得小麦作物的种植面积信息,对于维护国家粮食安全而言,其重要性是不言而喻的。在过去,农作物种植面积信息的获取通常依靠人工统计逐级上报的形式完成,这样的操作方式不仅费时费力,而且最终的统计结果往往与实际结果存在较大的偏差,因此对于当前的经济发展形式而言,该方法并不适宜。遥感技术是最近几十年发展起来的一种对地观测综合性技术,具有经济性、宏观性、时效性等很多优点,特别适合应用于农业领域。农业遥感已经成为当前精细农业发展过程中一项十分重要的技术手段。本文的研究工作主要针对冬小麦种植面积提取方法而展开,其目的就是为了更加快速、准确的提取冬小麦种植面积,并保证一定的提取精度,为大面积农作物种植面积遥感提取方法研究提供一种新的解决方案。本文选择的的研究区域是湖北省枣阳市、钟祥市,实验影像选择的是2015年3月6日的Landsat8影像,通过分析影像地物的主要特征,以辐射分辨率为依据对影像上主要地物类别的像元灰度值进行统计分析,找寻地物之间的差别,以此为条件构建决策树,提取冬小麦种植面积。通过本文的研究得到了以下结论:(1)通过分析Landsat8各个波段之间的相关性以及波段自身的像元灰度值数理统计信息,并结合野外调查及样方数据,确定了实验区最佳的波段组合方案,即将影像的第5、第4、第3波段依次赋予红色、绿色、蓝色,进行彩色合成。该彩色合成方案能够较好的突出冬小麦、油菜的特征,便于农作物识别和种植面积提取、精度验证等工作;(2)对于冬小麦种植面积较大的枣阳市,采用基于像元灰度值构建的决策树模型提取得到的冬小麦种植面积数据,其提取精度为92.00%,而对于冬小麦、油菜均有种植的钟祥市,由于冬小麦、油菜两者的光谱特征类似,因此利用基于像元灰度值构建的决策树模型提取了冬小麦、油菜总面积;(3)使用非监督分类法对经过灰度拉伸处理的钟祥市冬小麦、油菜总种植区影像进行再次分类,得到的冬小麦种植面积提取精度为92.67%,与只是用非监督分类法得到的分类结果相比,经决策树、灰度拉伸处理之后的分类精度提高了18.00%,精度提升较为明显。由此可见,根据Landsat8影像丰富的波谱信息建立的决策树模型,对于单一种植冬小麦的区域,具有较好的分类精度,且模型构建简单易操作,对于增强统计数据的时效性具有一定的积极意义。对于冬小麦、油菜容易混淆的地区,决策树模型的提取精度有限,尚无法将冬小麦、油菜精确的区分开,而通过对影像进行灰度拉伸,则可以在充分利用现有分类技术和理论的前提下,显著提高易混区冬小麦的提取精度,这是对决策树模型的很好补充。上述两种提取方法的结合,不仅提取冬小麦种植面积的速度快,而且操作简便,提取结果精度较高,这为大面积农作物种植面积提取、易混农作物区分等相关研究工作提供了一种解决问题的新思路。
[Abstract]:China is a traditional agricultural country. For a long time, a large population, the reduction of arable land, has been a big problem in China's agricultural development. In this context, food security is very important. The national long period of stability, the social economy healthy and stable development, are built on the basis of food safe. Wheat is one of the widely cultivated in the world within the scope of the three major cereal crops in China's vast land is also widely cultivated, timely, accurate information of wheat crop planting area, for the maintenance of national food security, its importance is self-evident. In the past, for planting crops area information usually rely on manual statistics reported the completion of the form, this mode of operation is not only time consuming, and the final results are often associated with actual large deviations, so For the current economic development form, this method is not suitable. The remote sensing technology is developed in recent decades an integrated earth observation technology, with the economy, macroeconomic, aging and many other advantages, particularly suitable for use in the field of agriculture. Agricultural remote sensing has become a very important technical means of the fine in the process of agricultural development. The main research work of this paper for winter wheat planting area extraction method, its purpose is to more rapid and accurate extraction of winter wheat planting area, and ensure the extraction precision, provide a new solution for the research on the remote sensing extraction method of crop planting area in large area. This paper chose to study the area is Hubei city in Jujube Province, Zhongxiang City, the experimental image is selected Landsat8 image in March 6, 2015, mainly through the analysis of image features and the characteristic of the. Based on pixel gray shot resolution image on main object category values for statistical analysis, find the difference between objects, as a condition for the construction of decision tree, the extraction of winter wheat planting area. Through the study of this paper obtained the following conclusions: (1) the value of statistical information through the gray pixel analysis between Landsat8 and the correlation between each band the band itself, and combined with field investigation and sample data, to determine the best experimentation area band combination scheme, is the image of the fifth, fourth, third bands in turn give red, green, blue, the color. The color synthesis synthesis scheme to highlight the good winter wheat, rape characteristics, identification and is convenient for crop planting area extraction and accuracy verification work; (2) for the winter wheat planting area in greater Jujube City, using the pixel gray value decision tree model is constructed based on the extracted winter Wheat planting area data, its accuracy is 92%, and for winter wheat, rape was planted in Zhongxiang City, due to winter wheat, the spectral characteristics of rape is similar to that of the pixel gray value decision tree model is constructed based on the extraction of winter wheat, rape total area; (3) the gray level of Winter Wheat in Zhongxiang City stretch processing using unsupervised classification method, the total planting area of rape image classification again, get the winter wheat planting area extraction accuracy of 92.67%, compared with the classification results is obtained by unsupervised classification method, the classification accuracy of decision tree, after gray stretch processing is improved by 18%, accuracy is improved obviously. Thus, according to the decision tree image rich spectral information of Landsat8 model for winter wheat monoculture area, has better classification accuracy, and the model is simple and easy to operate, to increase The significance of timeliness of statistical data for winter wheat, rape confused area, decision tree model extraction accuracy is limited, can not be separated from the winter wheat, rape precise area, by drawing on the gray image, you can make full use of the existing premise classification technology and theory. Easy, significantly improve the extraction accuracy of mixing region of winter wheat, which is a good supplement of decision tree model. The combination of the above two kinds of extraction methods, not only the extraction of winter wheat planting area fast and convenient operation. The extraction results with high precision, the crop planting area extraction of large area, provides a a new idea to solve the problem of mixed crop division of the relevant research work.

【学位授予单位】：湖北大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：S512.11;S127

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