本文選題：遙感技術(shù)　切入點(diǎn)：冬小麥　出處：《南京信息工程大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：遙感技術(shù)因其具有可快速、無損、大面積獲取地物信息的優(yōu)勢(shì)已被廣泛應(yīng)用于作物種植面積、長(zhǎng)勢(shì)、病蟲害的監(jiān)測(cè)中。本文利用環(huán)境衛(wèi)星數(shù)據(jù)探索了利用作物光譜信息監(jiān)測(cè)冬小麥長(zhǎng)勢(shì)及拔節(jié)期紋枯病病情指數(shù),并針對(duì)研究過程中的遙感數(shù)據(jù)冬小麥種植面積提取進(jìn)行了方法探索,文章主要內(nèi)容總結(jié)如下：(1)基于NDVI密度分割的冬小麥種植面積提取。本文選用江蘇省沭陽縣冬小麥揚(yáng)花期HJ-1A衛(wèi)星遙感影像,基于不同地物光譜信息的差異性與可分割性,提出基于歸一化植被指數(shù)·(NDVI)密度分割的冬小麥種植面積提取方法。結(jié)果表明：根據(jù)NDVI密度分割法提取冬小麥面積為8.37×104ha,面積精度為92.37%,樣本精度為93.31%�；诿芏确指钕禂�(shù)(P0.5)制作沭陽縣冬小麥種植分布圖,獲取了全縣冬小麥空間分布特征信息。NDVI密度分割法能較準(zhǔn)確地提取研究區(qū)內(nèi)冬小麥種植面積,有效解決了農(nóng)作物種植面積提取中混合像元問題,該方法可為南方農(nóng)作物種植面積信息的快速、準(zhǔn)確獲取提供技術(shù)支持,為冬小麥長(zhǎng)勢(shì)、病害的遙感監(jiān)測(cè)專題圖制作提供。(2)基于光合生產(chǎn)模型對(duì)冬小麥長(zhǎng)勢(shì)的監(jiān)測(cè)。本文以葉面積指數(shù)作為冬小麥長(zhǎng)勢(shì)的監(jiān)測(cè)指標(biāo),在沐陽縣荻垛鎮(zhèn)建立小區(qū)試驗(yàn),以小區(qū)試驗(yàn)獲取的冬小麥全生育期內(nèi)的生化參量及植被光譜信息,利用光合生產(chǎn)模型的轉(zhuǎn)化模型,提出LAI的定量反演方法,模型中綜合考慮了光照、溫度、日長(zhǎng)、葉面積指數(shù)等的影響,反演出全縣區(qū)域的LAI,決定系數(shù)達(dá)0.8142,誤差控制在了較理想范圍。根據(jù)反演的LAI影像制作全縣區(qū)域內(nèi)冬小麥長(zhǎng)勢(shì)分級(jí)監(jiān)測(cè)圖,,較為直觀的反映了全縣范圍各級(jí)冬小麥長(zhǎng)勢(shì)差異,方便農(nóng)業(yè)基層技術(shù)人員的理解與應(yīng)用。(3)基于遙感的紋枯病病情指數(shù)監(jiān)測(cè)模型建立�；跉庀筚Y料,結(jié)合GPS實(shí)地取樣調(diào)查的農(nóng)學(xué)參數(shù)、光譜特征值、紋枯病病情指數(shù),利用相關(guān)分析、多元回歸的方法,構(gòu)建沭陽縣冬小麥紋枯病流行監(jiān)測(cè)模型,模型精度達(dá)84.56%, RMSE為7.52。通過制作出的冬小麥紋枯病遙感監(jiān)測(cè)信息圖中,統(tǒng)計(jì)得到冬小麥紋枯病不同危害等級(jí)的分布與面積信息,與揚(yáng)花期冬小麥長(zhǎng)勢(shì)分級(jí)圖進(jìn)行疊加分析,得出紋枯病對(duì)冬小麥染病后期生長(zhǎng)發(fā)育的影響程度。該信息圖信息量大、直觀,方便使用者領(lǐng)會(huì)和應(yīng)用大田冬小麥紋枯病遙感監(jiān)測(cè)信息,可為基層農(nóng)業(yè)植保措施的制定提供參考。
[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.

【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S127;S512.11;S435.121.4

【引證文獻(xiàn)】

相關(guān)會(huì)議論文 前1條

1 李衛(wèi)國;王紀(jì)華;黃文江;郭文善;;冬小麥長(zhǎng)勢(shì)TM遙感分級(jí)監(jiān)測(cè)與調(diào)優(yōu)栽培模式應(yīng)用[A];2009年中國作物學(xué)會(huì)學(xué)術(shù)年會(huì)論文摘要集[C];2009年

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本文編號(hào)：1569338