發(fā)布時間：2018-03-06 14:37

  本文選題：人口空間化　切入點：光學(xué)影像　出處：《中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：人口密度是GDP、城市發(fā)展、生態(tài)環(huán)境等多方面的重要指標(biāo)。目前最常規(guī)的人口數(shù)據(jù)獲取方法是人口統(tǒng)計數(shù)據(jù),具有權(quán)威、系統(tǒng)、規(guī)范的特點,能夠用于反映具體行政單元的人口情況,但其內(nèi)部差異體現(xiàn)受到限制。同時,重大自然災(zāi)害的損失評估、人口分布的變化研究、GDP發(fā)展規(guī)劃中需要以自然單元的人口開展評價研究,以行政單元為單位的統(tǒng)計數(shù)據(jù)難以滿足其要求。與之相比,人口數(shù)據(jù)空間化可以彌補(bǔ)統(tǒng)計數(shù)據(jù)的制約,滿足研究的需要,提供空間化的人口數(shù)據(jù)。本研究的目的在于進(jìn)行區(qū)域人口空間化方法的研究,通過總結(jié)與分析現(xiàn)有的基于遙感的人口空間化方法,將其分為兩類:利用遙感數(shù)據(jù)提取的信息層與利用遙感數(shù)據(jù)的圖像特征。然而,現(xiàn)存的方法難以滿足區(qū)域人口空間化的尺度或者難以與人口密度建立直接聯(lián)系。因此,本研究提出一種基于建筑密度的人口空間化方法,既能滿足區(qū)域人口空間化的尺度要求,又能與人口密度建立直接關(guān)系。本研究選擇了京津冀為研究區(qū)域,結(jié)合了光學(xué)與SAR影像,提出了從提取建筑區(qū)到估算建筑密度再到人口空間化的一系列方法。研究首先提出考慮空間信息的改進(jìn)變差函數(shù)方法,實現(xiàn)了整個京津冀地區(qū)建筑區(qū)的提取;然后結(jié)合光學(xué)與SAR影像,利用分類回歸樹算法,估算了京津冀地區(qū)的建筑密度;最后結(jié)合建筑密度與GDP數(shù)據(jù)實現(xiàn)了京津冀地區(qū)的人口空間化。首先通過研究中高分辨率SAR影像中農(nóng)村建筑區(qū)與城市建筑區(qū)的紋理特征,總結(jié)分析了傳統(tǒng)變差函數(shù)方法在農(nóng)村建筑區(qū)造成錯分的原因。在此基礎(chǔ)上,提出了一種考慮空間信息的改進(jìn)變差函數(shù)方法,用于突出農(nóng)村建筑區(qū),抑制周邊非建筑區(qū),降低錯分誤差,并將此方法應(yīng)用于整個京津冀地區(qū)。通過選取8個樣本區(qū)進(jìn)行精度驗證,結(jié)果表明,改進(jìn)方法的平均檢測率為86.81%,錯分率為15.62%,漏分率為13.19%。隨后分析了使用單一數(shù)據(jù)源進(jìn)行建筑密度估算的弊端,并使用了光學(xué)與SAR影像的特征組合,即光譜反射率,歸一化指數(shù)及后向散射強(qiáng)度,利用分類回歸樹算法,使用不同的特征組合構(gòu)建了回歸模型,最后估算了整個京津冀地區(qū)的建筑密度。結(jié)果表明,建筑密度的估算結(jié)果與實際結(jié)果R2達(dá)到0.7831。最后,根據(jù)建筑密度的估算結(jié)果,結(jié)合3種不同的人口分配單元進(jìn)行了京津冀地區(qū)的人口空間化并以176個縣區(qū)的人口統(tǒng)計數(shù)據(jù)進(jìn)行精度驗證。結(jié)果表明,在單獨基于建筑密度進(jìn)行人口空間化的模型中,以京津冀各市為人口分配單元的模型具備最高的精度,R2為0.6001。然而,考慮到建筑密度只是表達(dá)二維平面信息,而人口往往分布三維空間,因此研究中加入了GDP數(shù)據(jù)為各縣市賦以權(quán)值,以經(jīng)濟(jì)發(fā)達(dá)程度來表征建筑物的三維高度信息,結(jié)果表明,加入GDP數(shù)據(jù)后,模型的R2達(dá)到0.7515。
[Abstract]:Population density is an important indicator of GDP, urban development, ecological environment, etc. At present, the most conventional method of obtaining population data is demographic data, with authoritative, systematic and normative characteristics. Can be used to reflect the demographic situation of a specific administrative unit, but its internal differences are limited... at the same time, damage assessment of major natural disasters, Research on the change of population Distribution; in the development planning of GDP, it is necessary to carry out evaluation research with the population of natural units, and the statistical data based on administrative units cannot meet its requirements. In contrast, the spatialization of population data can make up for the constraints of statistical data. To meet the needs of research and to provide spatialized population data. The purpose of this study is to carry out a study on the spatial methods of regional population, by summarizing and analysing existing methods of population spatialization based on remote sensing, It is divided into two categories: the information layer extracted from remote sensing data and the image features using remote sensing data. However, the existing methods are difficult to satisfy the spatial scale of regional population or to establish a direct connection with population density. In this study, a method of population spatialization based on building density is proposed, which can not only meet the scale requirements of regional population spatialization, but also establish a direct relationship with population density. In this study, Beijing-Tianjin-Hebei is chosen as the study area. Combining the optical and SAR images, a series of methods from extracting the building area to estimating the building density to the spatialization of the population are proposed. Firstly, an improved variation function method considering spatial information is proposed. The whole building area of Beijing-Tianjin-Hebei region is extracted, and then the building density of Beijing-Tianjin-Hebei region is estimated by using the classification regression tree algorithm combined with optical and SAR images. Finally, the spatialization of population in Beijing-Tianjin-Hebei region is realized by combining building density and GDP data. Firstly, the texture features of rural and urban building areas in the middle and high resolution SAR images are studied. This paper summarizes and analyzes the causes of the misdivision caused by the traditional variation function method in the rural building area. On this basis, an improved variation function method considering spatial information is proposed, which can be used to outshine the rural building area and restrain the surrounding non-building area. This method is applied to the whole Beijing-Tianjin-Hebei region. The accuracy of this method is verified by selecting 8 sample areas, and the results show that, The average detection rate of the improved method is 86.81, the error rate is 15.622,the leakage rate is 13.199.The disadvantages of using a single data source to estimate the building density are analyzed, and the characteristic combination of optical and SAR images is used, that is, spectral reflectivity. The normalized index and backscatter intensity are used to construct the regression model by using the classification regression tree algorithm and different feature combinations. Finally, the building density of the whole Beijing-Tianjin-Hebei region is estimated. The results show that, The result of building density estimation and the actual result R2 are 0.7831. Finally, according to the estimate result of building density, Combined with three different population distribution units, the population of Beijing-Tianjin-Hebei region is spatialized, and the precision of population statistics of 176 counties and districts is verified. The results show that, in the model of population spatialization based on building density alone, The model of population distribution unit in Beijing-Tianjin-Hebei city has the highest precision (R ~ 2 = 0.661). However, considering that building density only expresses two-dimensional plane information, population is often distributed in three dimensional space. Therefore, GDP data are added to the study to assign weights to each county and city, and the three-dimensional height information of buildings is represented by the degree of economic development. The results show that the R2 of the model reaches 0.7515 after the addition of GDP data.
【學(xué)位授予單位】：中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：C924.2;P237

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