【摘要】：草原是構(gòu)成陸地生態(tài)系統(tǒng)的重要成分,對能量流動(dòng)和物質(zhì)循環(huán)以及人類的生存和發(fā)展起著不可估量的作用。草原植被的生長狀況能夠直接或間接地通過植被的生物、物理與化學(xué)參數(shù)(如葉面積指數(shù)和冠層水含量)反映。因此,通過研究草原植被參數(shù)動(dòng)態(tài)監(jiān)測草原生態(tài)環(huán)境狀況,及時(shí)為有關(guān)部門提供有效的科學(xué)決策,具有重要的科學(xué)意義和應(yīng)用價(jià)值。論文以青海湖流域?yàn)檠芯繀^(qū),基于多源遙感數(shù)據(jù)和地面實(shí)測數(shù)據(jù),以研究區(qū)植被冠層水含量和葉面積指數(shù)為研究對象,應(yīng)用面向?qū)ο蟮姆囱莘椒ㄟM(jìn)行定量反演。為了比較驗(yàn)證該方法的可行性和有效性,又分別從傳統(tǒng)的基于像元的物理模型和神經(jīng)網(wǎng)絡(luò)的方法進(jìn)行了反演。同時(shí),采用國產(chǎn)遙感衛(wèi)星數(shù)據(jù)(如HJ-1和GF-1)進(jìn)行研究區(qū)草原植被葉面積指數(shù)的反演,探討國產(chǎn)數(shù)據(jù)的應(yīng)用潛力和面向?qū)ο蠓椒ǖ目尚行院陀行浴Ｕ撐牡闹饕ぷ骱统晒缦?(1)應(yīng)用面向?qū)ο蟮姆椒?結(jié)合查找表算法,利用Landsat-8 OLI遙感影像數(shù)據(jù)對研究區(qū)草原植被的葉面積指數(shù)和冠層水含量進(jìn)行反演。該方法通過考慮鄰近像元的光譜信息,反演精度在一定程度上得到較好的提高。研究過程中為了解決模型反演的病態(tài)特性以及草原植被的非均勻性,對模型輸入?yún)?shù)分別從定量與定性的角度進(jìn)行了敏感性分析,將研究區(qū)內(nèi)的植被進(jìn)行分區(qū):稀疏區(qū)和密集區(qū),對其依次構(gòu)建查找表。反演結(jié)果與實(shí)測數(shù)據(jù)比較分析顯示:葉面積指數(shù)與冠層水含量的反演值與實(shí)測值的決定系數(shù)R2分別為0.88和0.81,均方根誤差RMSE分別為0.59和67.31g/m2,兩者均表現(xiàn)出較高的反演精度,從而驗(yàn)證了該方法的高度有效性。(2)利用相同的數(shù)據(jù)源(Landat-8 OLI),分別采用基于像元的物理模型方法和神經(jīng)網(wǎng)絡(luò)方法對研究區(qū)的草原植被葉面積指數(shù)與冠層水含量進(jìn)行定量反演。研究中通過將該兩種方法與面向?qū)ο蟮姆椒ㄟM(jìn)行對比驗(yàn)證,說明后者的優(yōu)越性。通過將該兩種方法的反演結(jié)果與地面實(shí)測值進(jìn)行對比驗(yàn)證,結(jié)果顯示:基于像元的物理模型方法反演的葉面積指數(shù)和冠層水含量與實(shí)測值的決定系數(shù)R2分別為0.87和0.78,均方根誤差分別為0.62和80.11 g/m2;而神經(jīng)網(wǎng)絡(luò)方法的反演值與實(shí)測值的決定系數(shù)R2分別為0.84和0.72,均方根誤差分別為0.65和99.95 g/m2。對比本文提出方法的反演結(jié)果說明其具有較好的反演精度。(3)以前面應(yīng)用的三種方法為基礎(chǔ),采用國產(chǎn)遙感衛(wèi)星數(shù)據(jù)(如HJ-1和GF-1)進(jìn)行研究區(qū)草原植被葉面積指數(shù)的反演。一方面為了探討國產(chǎn)數(shù)據(jù)與國外遙感數(shù)據(jù)的數(shù)據(jù)質(zhì)量與應(yīng)用潛力,另一方面也是為了進(jìn)一步驗(yàn)證本文提出的方法即面向?qū)ο蠓椒ǖ目尚行院陀行�。結(jié)果表明:采用相同的數(shù)據(jù)源,面向?qū)ο蠓椒ǖ姆囱菥容^高,基于像元的物理模型方法次之;而相同的方法,Landsat-8影像數(shù)據(jù)應(yīng)用的性能較高,其次是高分一號影像數(shù)據(jù)。
[Abstract]:Grassland is an important component of terrestrial ecosystem, which plays an inestimable role in energy flow, material circulation and human survival and development. The growth of grassland vegetation can be directly or indirectly reflected by the biological, physical and chemical parameters (such as leaf area index and canopy water content). Therefore, it is of great scientific significance and application value to study the dynamic monitoring of grassland ecological environment by studying the parameters of grassland vegetation, and to provide effective scientific decision for relevant departments in time. Taking Qinghai Lake Basin as the study area, based on multi-source remote sensing data and ground measured data, and taking the water content and leaf area index of vegetation canopy in the study area as the research object, the paper uses the object-oriented inversion method to carry out quantitative inversion. In order to compare the feasibility and effectiveness of the proposed method, the traditional methods based on pixel physical model and neural network are inversed respectively. At the same time, using domestic remote sensing satellite data (such as HJ-1 and GF-1) to invert the leaf area index of grassland vegetation in the study area, the application potential of domestic data and the feasibility and effectiveness of object-oriented method are discussed. The main work and achievements are as follows: (1) the leaf area index and canopy water content of steppe vegetation in the study area are inversed by using Landsat-8 OLI remote sensing image data by using object-oriented method and lookup table algorithm. The inversion accuracy is improved to a certain extent by taking into account the spectral information of adjacent pixels. In order to solve the pathological characteristics of model inversion and the nonuniformity of grassland vegetation, the sensitivity analysis of model input parameters from quantitative and qualitative perspectives was carried out. The vegetation in the study area is divided into sparse area and dense area, and the lookup table is constructed in turn. The comparison and analysis of the inversion results and the measured data show that the coefficient R2 of the inversion value and the measured value of the leaf area index and the canopy water content are 0.88 and 0.81, respectively, and the root mean square error (RMSE) is 0.59 and 67.31 g / m2, respectively. Both of them have high inversion accuracy. This method is highly effective. (2) the same data source (Landat-8 OLI),) is used to quantitatively invert the leaf area index of grassland vegetation and the water content of canopy in the study area by using the method of physical model based on pixel and the method of neural network respectively. In the study, the superiority of the two methods is proved by comparing them with the object-oriented method. The inversion results of the two methods are compared with the measured data on the ground. The results show that the coefficient R2 of leaf area index and canopy water content and measured values are 0.87 and 0.78, respectively, and the root mean square error are 0.62 and 80.11 g / m2, respectively. The determined coefficient R2 of the measured values is 0.84 and 0.72, and the root mean square error is 0.65 and 99.95 g / m2, respectively. The inversion results show that this method has good inversion accuracy. (3) based on the three methods used in this paper, we use domestic remote sensing satellite data (such as HJ-1 and GF-1) to invert the grassland vegetation leaf area index in the study area. On the one hand, in order to discuss the data quality and application potential of domestic and foreign remote sensing data, on the other hand, it is necessary to further verify the feasibility and effectiveness of the proposed method, namely, the object-oriented method. The results show that with the same data source, the retrieval accuracy of object-oriented method is higher, the physical model method based on pixel is the second, and the same method Landsat-8 image data has higher performance, followed by high-score No. 1 image data.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S812

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