發(fā)布時間：2018-03-05 15:54

  本文選題：毛竹　切入點(diǎn)：生物量　出處：《安徽農(nóng)業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：毛竹在我國是分布最廣、面積最大的竹種,具有速生豐產(chǎn)、用途廣泛、再生能力強(qiáng)、經(jīng)濟(jì)價值高和可持續(xù)更新等特點(diǎn)。據(jù)不完全統(tǒng)計,我國毛竹林面積386.83萬ha,約占竹林面積的70%,占全世界竹林面積20%,在維護(hù)生態(tài)平衡方面發(fā)揮明顯的作用。借助遙感技術(shù)對毛竹生物量的研究將為毛竹固碳能力的研究提供基礎(chǔ)數(shù)據(jù)。LiDAR遙感技術(shù)可以獲得植被高精度、高密度的三維坐標(biāo)數(shù)據(jù),并可構(gòu)建植被的三維立體模型,進(jìn)而反演植被生物量。將LiDAR技術(shù)應(yīng)用在毛竹生物量遙感估測上將為今后毛竹林生物量估測提供更多手段。本文立足于研究機(jī)載LiDAR數(shù)據(jù)與機(jī)載高光譜數(shù)據(jù)分別反演毛竹林地上部分生物量的可行性,并且比較反演精度。以安徽省黃山市作為飛行區(qū)域,獲取機(jī)載LiDAR數(shù)據(jù)與機(jī)載高光譜數(shù)據(jù);在飛行航跡內(nèi)調(diào)查50塊(有效44塊)毛竹林樣地并計算生物量。分別提取樣地范圍內(nèi)不同遙感數(shù)據(jù)特征變量作為自變量,樣地生物量作為因變量,建立基于不同遙感數(shù)據(jù)源的反演模型。比較分析兩種反演模型精度的原因。主要研究結(jié)論如下:(1)機(jī)載LiDAR數(shù)據(jù)經(jīng)過歸一化處理消除了地形因子的影響;點(diǎn)云分類使用軟件分類與手工編輯的方法,區(qū)分開了地面點(diǎn),植被點(diǎn)以及噪聲點(diǎn);點(diǎn)云統(tǒng)計定義高于地面2m的點(diǎn)為毛竹林反射點(diǎn),避免了雜灌等植被的影響。因此用于提取變量的點(diǎn)全部是毛竹林的反射點(diǎn)。(2)機(jī)載LiDAR數(shù)據(jù)經(jīng)過預(yù)處理在ENVI IDL模塊下編程統(tǒng)計點(diǎn)云信息作為自變量,地面調(diào)查獲取的毛竹林生物量作為因變量,使用SPSS 22軟件進(jìn)行多元線性回歸分析可以建立反演模型f1:InW=5.024+0.101×Inh50+0.226×Inhmax-0.318×Ind15+0.582×Inc,該模型可解釋生物量64.13%的變動。這表明借助機(jī)載LiDAR技術(shù)反演毛竹林地上部分生物量可行。(3)機(jī)載高光譜數(shù)據(jù)經(jīng)過處理,使用ENVI軟件提取位置變量、面積變量、植被指數(shù)、原始波段以及地面調(diào)查樣地平均高作為自變量,地面調(diào)查獲取的生物量作為因變量,使用SPSS 22軟件多元線性回歸分析的方法建立反演模型f2:W2=1.514+0.765×Dr+4.324×SDr-1.602×VI2+0.937 hmean,,該模型可解釋毛竹林生物量58.3%的變動。表明借助機(jī)載高光譜技術(shù)反演毛竹林地上部分生物量是可行的。(4)機(jī)載LiDAR數(shù)據(jù)與機(jī)載高光譜數(shù)據(jù)提取特征變量配合地面調(diào)查數(shù)據(jù)建立毛竹林地上部分生物量反演模型是可行的,通過比較各自模型的決定系數(shù)(R2)、復(fù)決定系數(shù)(Ra2)、絕對均方根誤差(RMSE)以及自相關(guān)性檢驗(yàn)(DW)發(fā)現(xiàn)機(jī)載LiDAR數(shù)據(jù)建立的反演模型精度優(yōu)于機(jī)載高光譜數(shù)據(jù)建立的反演。
[Abstract]:Phyllostachys pubescens is the most widely distributed and the largest bamboo species in China. It has the characteristics of fast growing and high yield, wide use, strong regeneration ability, high economic value and sustainable renewal, etc. According to incomplete statistics, The area of Phyllostachys pubescens forest in China is three million eight hundred and sixty-eight thousand and three hundred ha. about 70% of the total area of bamboo forest, accounting for 20% of the world's bamboo forest area, which plays an obvious role in maintaining ecological balance. Provide basic data. LiDAR remote sensing technology to obtain high accuracy vegetation, High-density three-dimensional coordinate data, and can build three-dimensional vegetation model, The application of LiDAR technology to the remote sensing estimation of bamboo biomass will provide more means for estimating the biomass of Phyllostachys pubescens in the future. This paper is based on the study of airborne LiDAR data and airborne hyperspectral data respectively. Feasibility of aboveground biomass of Phyllostachys pubescens forest, Taking Huangshan City, Anhui Province as flight area, the airborne LiDAR data and airborne hyperspectral data are obtained. In flight track, 50 plots (44 effective plots) were investigated and biomass was calculated. The characteristic variables of different remote sensing data were extracted as independent variables and biomass of sample plots as dependent variables. The inversion models based on different remote sensing data sources are established, and the reasons for the accuracy of the two inversion models are compared and analyzed. The main conclusions are as follows: 1) the airborne LiDAR data are normalized to eliminate the influence of terrain factors; Point cloud classification uses the method of software classification and manual editing to distinguish ground points, vegetation points and noise points, and point cloud statistical definition above 2 m above the ground is the reflection point of Phyllostachys pubescens forest. Therefore, all the points used to extract the variables are the reflection points of bamboo forest. The airborne LiDAR data are pre-processed and programmed under the ENVI IDL module to calculate the point cloud information as independent variables. The biomass of Phyllostachys pubescens forest obtained by ground survey was taken as dependent variable. The inversion model f1: Inw5.024 0.101 脳 Inh50 0.226 脳 Inhmax-0.318 脳 Ind15 0.582 脳 Inc can be established by using SPSS 22 software for multivariate linear regression analysis. This model can explain the variation of biomass 64.13%. This indicates that it is feasible to retrieve aboveground biomass of Phyllostachys pubescens forest by using airborne LiDAR technique. The spectral data are processed, Using ENVI software to extract location variables, area variables, vegetation index, original wave band and average height of ground survey samples as independent variables, and biomass obtained from ground survey as dependent variables. The inversion model f2: W2t1. 514 1. 765 脳 Dr 4. 324 脳 SDr-1.602 脳 VI2 0. 937 hMeV was established by using SPSS 22 software multivariate linear regression analysis. The model can explain the variation of biomass 58.3% of Phyllostachys pubescens forest. It shows that the airborne hyperspectral technique can be used to estimate the aboveground biomass of Phyllostachys pubescens forest. Row. 4) it is feasible to establish a model of aboveground biomass inversion of Phyllostachys pubescens forest by extracting characteristic variables from airborne LiDAR data and airborne hyperspectral data combined with ground survey data. By comparing the determination coefficients of their respective models, the complex determination coefficients, the absolute root mean square error (RMSE) and the autocorrelation test (DW), it is found that the inversion model built by airborne LiDAR data is more accurate than that established by airborne hyperspectral data.
【學(xué)位授予單位】：安徽農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：S795.7

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