本文選題：甘蔗 + 植被指數(shù)　； 參考：《南京信息工程大學》2015年碩士論文

【摘要】：廣西興賓蔗區(qū)是全國最大的蔗糖生產(chǎn)基地,蔗糖業(yè)已成為當?shù)亟?jīng)濟的主導和支柱產(chǎn)業(yè)。及時準確地獲取甘蔗種植面積、監(jiān)測甘蔗長勢、進而對產(chǎn)量進行預測,對甘蔗生產(chǎn)管理、穩(wěn)定食糖市場、保障食糖安全及經(jīng)濟健康循環(huán)發(fā)展具有重要意義。目前,“3S”技術在甘蔗長勢動態(tài)監(jiān)測與產(chǎn)量預測方面的應用研究在國內(nèi)方興未艾,特別是缺乏國產(chǎn)衛(wèi)星數(shù)據(jù)的應用示例。本文選用國產(chǎn)HJ-1A/B星CCD數(shù)據(jù),以監(jiān)督分類、多時相迭代法相結合的方法提取甘蔗種植面積；通過引入隨機變量的標準差和離均差的概念并結合田間農(nóng)學觀測數(shù)據(jù)構建甘蔗長勢監(jiān)測模型；在此基礎上利用表征甘蔗長勢的植被指數(shù)和產(chǎn)量數(shù)據(jù)構建甘蔗單產(chǎn)遙感估產(chǎn)模型。主要研究成果包括以下三方面：(1)甘蔗種植面積是甘蔗遙感長勢監(jiān)測與產(chǎn)量估算的基礎內(nèi)容,基于多源數(shù)據(jù),利用多種遙感方法相結合提取甘蔗種植面積。首先,建立研究區(qū)典型地物的遙感解譯標志；其次,采用監(jiān)督分類法,結合廣西耕地圖層、坡度等本底信息數(shù)據(jù)庫獲取甘蔗可能種植區(qū)；最后,根據(jù)解譯標志逐時相設定甘蔗NDVI閡值,逐步向下通過掩膜迭代剔除其他地物信息,獲得甘蔗種植面積信息。利用野外實測樣點和農(nóng)業(yè)部門提供的部分鄉(xiāng)鎮(zhèn)甘蔗面積驗證遙感解譯結果,總體精度達91.18%,Kappa系數(shù)為0.8101,與農(nóng)業(yè)部門統(tǒng)計數(shù)據(jù)的平均相對誤差為10.17%。(2)建立了基于隨機變量理論的長勢監(jiān)測模型。針對甘蔗長勢遙感監(jiān)測缺乏統(tǒng)一分級標準的研究現(xiàn)狀,通過分析多種遙感指標與田間農(nóng)學觀測數(shù)據(jù)的相關性差異,確定增強型植被指數(shù)EVI和歸一化差值植被指數(shù)NDV1分別作為甘蔗莖伸長期和工藝成熟期的長勢監(jiān)測指標。長勢監(jiān)測模型假設甘蔗生長滿足正態(tài)分布規(guī)律,引入離均差的概念,通過離均差與標準差的差異定量研究了興賓區(qū)2009～2013年的甘蔗長勢情況。以甘蔗產(chǎn)量的整體波動情況和2013年地面觀測點數(shù)據(jù)推測的甘蔗長勢樣區(qū)以及在縣(區(qū))尺度的實際應用結果對長勢遙感監(jiān)測結果進行驗證。對比分析結果表明,基于隨機變量的標準差和離均差理論的長勢評價模型可以滿足縣(區(qū))尺度的甘蔗長勢動態(tài)監(jiān)測要求,適用于不同蔗區(qū)和多種遙感數(shù)據(jù)。(3)構建了基于植被指數(shù)的甘蔗單產(chǎn)估算模型。分析甘蔗植被指數(shù)與其單產(chǎn)的數(shù)學關系,建立了甘蔗關鍵生育期和全生育期的“遙感植被指數(shù)-甘蔗產(chǎn)量”估產(chǎn)模型。從模型本身和當年甘蔗實際單產(chǎn)的相對誤差分析模型精度,其結果表明：基于全生育期的植被指數(shù)估產(chǎn)模型效果最好,擬合系數(shù)最高,相對誤差最低；關鍵生育期以莖伸長中期的估產(chǎn)模型精度最高,其中又以線性方程模型的估產(chǎn)效果較佳；糖分積累期次之；工藝成熟期的估產(chǎn)模型擬合效果最差,相對誤差最大。
[Abstract]:Xingbin Sugarcane region in Guangxi is the largest sugarcane production base in China, which has become the leading and pillar industry of local economy. It is of great significance to obtain sugarcane planting area timely and accurately, monitor sugarcane growth and forecast the yield, which is of great significance for sugarcane production management, sugar market stability, sugar safety and economic healthy cycle development. At present, the application of "3s" technology in dynamic monitoring and yield prediction of sugarcane growth is in the ascendant in China, especially in the absence of domestic satellite data. In this paper, the CCD data of domestic HJ-1A/B star were used to extract sugarcane planting area by the method of supervised classification and multi-time iterative method. By introducing the concepts of standard deviation and deviation mean deviation of random variables and combining with the field agronomic observation data, the sugarcane growth monitoring model was constructed, and the remote sensing yield estimation model of sugarcane yield was constructed by using the vegetation index and yield data to characterize sugarcane growth. The main research results include the following three aspects: (1) Sugarcane planting area is the basic content of sugarcane remote sensing growth monitoring and yield estimation. Based on multi-source data, sugarcane planting area is extracted by using multiple remote sensing methods. First, establish the remote sensing interpretation mark of typical features in the study area; secondly, use the supervised classification method, combining with the background information database of Guangxi cultivated land layer, slope and other background information to obtain the possible sugarcane planting area; finally, According to the interpretation marker, the NDVI threshold value was set up, and the information of sugarcane planting area was obtained by removing other features information through the mask iteration step by step. The results of remote sensing interpretation were verified by using field survey samples and sugarcane area of some villages and towns provided by the agricultural sector. The Kappa coefficient is 0.8101 and the average relative error with the statistical data of the agricultural sector is 10.17. The growth monitoring model based on the theory of random variables is established. In view of the lack of unified classification standard for sugarcane growth remote sensing monitoring, the correlation difference between various remote sensing indexes and field agronomic observation data was analyzed. The enhanced vegetation index (EVI) and normalized difference vegetation index (NDV1) were determined as the growth monitoring indexes of sugarcane stem extension and process maturity respectively. The growth monitoring model assumes that sugarcane growth meets the normal distribution law and introduces the concept of deviation mean deviation. The growth of sugarcane in Xingbin district from 2009 to 2013 is quantitatively studied by the difference between the average deviation and the standard deviation. The results of remote sensing monitoring of sugarcane growth were verified by the fluctuation of sugarcane yield and the actual application results of sugarcane growth sample area and county scale based on the data of ground observation points in 2013. The results of comparative analysis show that the model based on the theory of standard deviation and mean deviation of random variables can meet the requirements of dynamic monitoring of sugarcane growth at county (district) scale. The estimation model of sugarcane yield per unit yield based on vegetation index was established for different sugarcane regions and various remote sensing data. Based on the analysis of the mathematical relationship between the vegetation index of sugarcane and its yield per unit yield, a model of "remote sensing vegetation index-sugarcane yield" for the key growth period and the whole growth period of sugarcane was established. The accuracy of the model was analyzed from the model itself and the actual yield of sugarcane. The results showed that the vegetation index based on the whole growth period had the best effect, the fitting coefficient was the highest, and the relative error was the lowest. The precision of the key growth stage was the highest in the middle stage of stem elongation, among which the linear equation model was the best; the sugar accumulation stage was the second; the fitting effect of the yield estimation model at the technological maturity stage was the worst, and the relative error was the largest.
【學位授予單位】：南京信息工程大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S566.1;S127

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