本文選題：草地類型　切入點(diǎn)：草地生物量　出處：《山東農(nóng)業(yè)大學(xué)》2015年碩士論文

【摘要】：本文針對(duì)遙感數(shù)據(jù)處理的特點(diǎn)和圖像可視化的相關(guān)特性,選取IDL作為“青海湖流域草地分類及其生物量監(jiān)測(cè)遙感系統(tǒng)”的開發(fā)語言。針對(duì)系統(tǒng)的功能需求和實(shí)際開發(fā)重點(diǎn),對(duì)如何利用IDL草地遙感數(shù)據(jù)的相關(guān)處理、數(shù)據(jù)的可視化分析和應(yīng)用等進(jìn)行了詳細(xì)的設(shè)計(jì),對(duì)系統(tǒng)的設(shè)計(jì)思路、技術(shù)路線和實(shí)現(xiàn)方法進(jìn)行了詳細(xì)的論述。結(jié)合IDL開發(fā)平臺(tái)軟件設(shè)計(jì)技術(shù),通過開發(fā)實(shí)例,在系統(tǒng)用戶交互性、可視化效果和擴(kuò)展性方面進(jìn)行嘗試。最終取得以下成果:(1)文章分析比較多種編程語言及平臺(tái),選擇經(jīng)濟(jì)、便捷的ENVI+IDL二次開發(fā)技術(shù)搭建系統(tǒng)。研究了IDL語言的特點(diǎn),重點(diǎn)深入研究各種關(guān)鍵技術(shù),分別了設(shè)計(jì)了主控模塊、草地遙感分類模塊、植被指數(shù)計(jì)算模塊、草地生物量計(jì)算模塊。系統(tǒng)能夠通過提取分析遙感數(shù)據(jù),實(shí)現(xiàn)草地快速分類,計(jì)算草地物量、覆蓋度、葉面積指數(shù)等草地參數(shù),達(dá)到對(duì)草地快速監(jiān)測(cè)的目的。(2)運(yùn)用了馬氏距離分類、歐氏距離分類、光譜角填圖法、最大似然分類、決策樹分類法對(duì)草地進(jìn)行遙感分類。其中決策樹和最大似然法的分類結(jié)果明顯優(yōu)于其它方法。其中最大似然法的分類精度最高,達(dá)到77.8%。分析了6個(gè)植被指數(shù)NDVI、RVI、DVI、EVI、MSAVI和SAVI與草地地上生物量之間均存在著不同程度的相關(guān)性;其中,RVI與生物量之間的相關(guān)性最高(相關(guān)系數(shù)0.776)。比較以各種光譜指數(shù)為自變量建立的線性、對(duì)數(shù)、二次曲線和三次曲線回歸模型。通過分析比較,最后確定以RVI為自變量的三次曲線模型y=3.9852x3-17.661x2+70.785x+65.624精度最高,R2達(dá)到0.687,是青海湖環(huán)湖地區(qū)草地生物量監(jiān)測(cè)的最佳植被指數(shù)模型。(3)系統(tǒng)搭建完成后,對(duì)青海湖流域的草地類型及生物量進(jìn)行了計(jì)算,并實(shí)現(xiàn)計(jì)算結(jié)果的快速可視化顯示。本系統(tǒng)具有友好的人機(jī)交互界面,操作簡(jiǎn)易。本系統(tǒng)集成了在草地研究中的常用的研究算法,有大量的提示信息,使研究人員不需復(fù)雜的專業(yè)軟件即可對(duì)草地遙感圖像快速處理。系統(tǒng)各功能模塊均能良好快速實(shí)現(xiàn),包括草地遙感分類模塊、植被指數(shù)計(jì)算模塊、草地生物量計(jì)算模塊等主要功能的實(shí)現(xiàn)。系統(tǒng)可視化效果較為理想。對(duì)影像做了2%線性拉伸,使得影像整體色調(diào)適中,符合人眼的視覺要求。可實(shí)現(xiàn)對(duì)圖像平移、縮放、鷹眼預(yù)覽、鼠標(biāo)取值等操作,方便圖像的瀏覽系統(tǒng)可擴(kuò)展性強(qiáng)。系統(tǒng)各模塊都獨(dú)立開發(fā),減少了相互之間的依賴,使模塊功能可依需要隨時(shí)刪減。系統(tǒng)優(yōu)化合理。編程時(shí)使用函數(shù)來代替運(yùn)行效率慢的循環(huán)語句,及時(shí)釋放內(nèi)存中的是失效的變量,優(yōu)化內(nèi)存占用量。系統(tǒng)運(yùn)行速度比較令人滿意。
[Abstract]:According to the characteristics of remote sensing data processing and the related characteristics of image visualization, this paper selects IDL as the development language of "remote sensing system for grassland classification and biomass monitoring in Qinghai Lake Basin". How to use IDL grassland remote sensing data related processing, data visual analysis and application are designed in detail. The technical route and implementation method are discussed in detail. Combined with the software design technology of IDL development platform, through the development of examples, the system user interaction, Finally, this paper analyzes and compares many programming languages and platforms, and chooses the economical and convenient secondary development technology of ENVI IDL to build a system. The characteristics of IDL language are studied. The main control module, grassland remote sensing classification module, vegetation index calculation module and grassland biomass calculation module are designed respectively. The system can extract and analyze remote sensing data to realize the rapid classification of grassland. The grassland parameters, such as grassland quantity, coverage, leaf area index and so on, were calculated to achieve the purpose of rapid monitoring of grassland. (2) Markov distance classification, Euclidean distance classification, spectral angle mapping method, maximum likelihood classification were used. Decision tree classification method is used to classify grassland by remote sensing. The result of decision tree and maximum likelihood method is obviously superior to other methods, and the maximum likelihood method has the highest classification accuracy. The correlation between the six vegetation indices (NDVI RVI VIVI and SAVI) and the aboveground biomass of grassland were analyzed. The correlation between RVI and biomass is the highest (correlation coefficient 0.776). The regression models of linear, logarithmic, conic and cubic curves, which are established by using various spectral indices as independent variables, are compared. Finally, the cubic curve model (y=3.9852x3-17.661x2 70.785x 65.624) with RVI as independent variable was determined to be the best vegetation index model for monitoring grassland biomass around Qinghai Lake, and the R2 was 0.687, which was the best vegetation index model for monitoring grassland biomass around Qinghai Lake. The grassland types and biomass in Qinghai Lake basin are calculated, and the results are visualized quickly. The system has friendly man-machine interface. Easy to operate. This system integrates the common research algorithms in grassland research, and has a lot of information. So that researchers do not need complex professional software to quickly process remote sensing images of grassland. All functional modules of the system can be realized quickly and well, including remote sensing classification module of grassland, vegetation index calculation module, The realization of the main functions such as biomass calculation module of grassland. The visualization effect of the system is relatively ideal. The image is stretched by 2% linear, which makes the overall color of the image moderate and meets the visual requirements of the human eye. The system can realize the translation and scaling of the image. Eagle eye preview, mouse values and other operations, convenient image browsing system is extensible. Each module of the system is independently developed, reducing mutual dependence, The module function can be deleted at any time according to the need. The system optimization is reasonable. In programming, the function is used to replace the slow cycle statement, and the invalid variable is released in the memory in time. Optimized memory usage. System running speed is satisfactory.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S812;S818.9

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本文編號(hào)：1653595