發(fā)布時(shí)間：2018-04-11 21:42

  本文選題：遙感 + 冬小麥��； 參考：《湖北大學(xué)》2016年碩士論文

【摘要】：我國(guó)是一個(gè)傳統(tǒng)意義上的農(nóng)業(yè)大國(guó)。長(zhǎng)久以來(lái),人口眾多,耕地減少,一直是困擾我國(guó)農(nóng)業(yè)發(fā)展的大問(wèn)題。在這樣的背景下,糧食安全便顯得尤為重要。國(guó)家的長(zhǎng)治久安,社會(huì)經(jīng)濟(jì)的健康穩(wěn)定發(fā)展,都是建立在糧食安全這個(gè)基礎(chǔ)之上的。小麥,是在全世界范圍內(nèi)大量種植的三大谷類作物之一,在我國(guó)廣袤的大地上也有廣泛種植,及時(shí)、準(zhǔn)確的獲得小麥作物的種植面積信息,對(duì)于維護(hù)國(guó)家糧食安全而言,其重要性是不言而喻的。在過(guò)去,農(nóng)作物種植面積信息的獲取通常依靠人工統(tǒng)計(jì)逐級(jí)上報(bào)的形式完成,這樣的操作方式不僅費(fèi)時(shí)費(fèi)力,而且最終的統(tǒng)計(jì)結(jié)果往往與實(shí)際結(jié)果存在較大的偏差,因此對(duì)于當(dāng)前的經(jīng)濟(jì)發(fā)展形式而言,該方法并不適宜。遙感技術(shù)是最近幾十年發(fā)展起來(lái)的一種對(duì)地觀測(cè)綜合性技術(shù),具有經(jīng)濟(jì)性、宏觀性、時(shí)效性等很多優(yōu)點(diǎn),特別適合應(yīng)用于農(nóng)業(yè)領(lǐng)域。農(nóng)業(yè)遙感已經(jīng)成為當(dāng)前精細(xì)農(nóng)業(yè)發(fā)展過(guò)程中一項(xiàng)十分重要的技術(shù)手段。本文的研究工作主要針對(duì)冬小麥種植面積提取方法而展開(kāi),其目的就是為了更加快速、準(zhǔn)確的提取冬小麥種植面積,并保證一定的提取精度,為大面積農(nóng)作物種植面積遙感提取方法研究提供一種新的解決方案。本文選擇的的研究區(qū)域是湖北省棗陽(yáng)市、鐘祥市,實(shí)驗(yàn)影像選擇的是2015年3月6日的Landsat8影像,通過(guò)分析影像地物的主要特征,以輻射分辨率為依據(jù)對(duì)影像上主要地物類別的像元灰度值進(jìn)行統(tǒng)計(jì)分析,找尋地物之間的差別,以此為條件構(gòu)建決策樹(shù),提取冬小麥種植面積。通過(guò)本文的研究得到了以下結(jié)論:(1)通過(guò)分析Landsat8各個(gè)波段之間的相關(guān)性以及波段自身的像元灰度值數(shù)理統(tǒng)計(jì)信息,并結(jié)合野外調(diào)查及樣方數(shù)據(jù),確定了實(shí)驗(yàn)區(qū)最佳的波段組合方案,即將影像的第5、第4、第3波段依次賦予紅色、綠色、藍(lán)色,進(jìn)行彩色合成。該彩色合成方案能夠較好的突出冬小麥、油菜的特征,便于農(nóng)作物識(shí)別和種植面積提取、精度驗(yàn)證等工作;(2)對(duì)于冬小麥種植面積較大的棗陽(yáng)市,采用基于像元灰度值構(gòu)建的決策樹(shù)模型提取得到的冬小麥種植面積數(shù)據(jù),其提取精度為92.00%,而對(duì)于冬小麥、油菜均有種植的鐘祥市,由于冬小麥、油菜兩者的光譜特征類似,因此利用基于像元灰度值構(gòu)建的決策樹(shù)模型提取了冬小麥、油菜總面積;(3)使用非監(jiān)督分類法對(duì)經(jīng)過(guò)灰度拉伸處理的鐘祥市冬小麥、油菜總種植區(qū)影像進(jìn)行再次分類,得到的冬小麥種植面積提取精度為92.67%,與只是用非監(jiān)督分類法得到的分類結(jié)果相比,經(jīng)決策樹(shù)、灰度拉伸處理之后的分類精度提高了18.00%,精度提升較為明顯。由此可見(jiàn),根據(jù)Landsat8影像豐富的波譜信息建立的決策樹(shù)模型,對(duì)于單一種植冬小麥的區(qū)域,具有較好的分類精度,且模型構(gòu)建簡(jiǎn)單易操作,對(duì)于增強(qiáng)統(tǒng)計(jì)數(shù)據(jù)的時(shí)效性具有一定的積極意義。對(duì)于冬小麥、油菜容易混淆的地區(qū),決策樹(shù)模型的提取精度有限,尚無(wú)法將冬小麥、油菜精確的區(qū)分開(kāi),而通過(guò)對(duì)影像進(jìn)行灰度拉伸,則可以在充分利用現(xiàn)有分類技術(shù)和理論的前提下,顯著提高易混區(qū)冬小麥的提取精度,這是對(duì)決策樹(shù)模型的很好補(bǔ)充。上述兩種提取方法的結(jié)合,不僅提取冬小麥種植面積的速度快,而且操作簡(jiǎn)便,提取結(jié)果精度較高,這為大面積農(nóng)作物種植面積提取、易混農(nóng)作物區(qū)分等相關(guān)研究工作提供了一種解決問(wèn)題的新思路。
[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.

【學(xué)位授予單位】：湖北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：S512.11;S127

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