本文選題：固定資產(chǎn)投資　切入點(diǎn)：遙感監(jiān)測(cè)　出處：《中國(guó)地質(zhì)大學(xué)(北京)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：固定資產(chǎn)投資對(duì)城市景觀格局變化、城市經(jīng)濟(jì)發(fā)展有舉足輕重的作用,其動(dòng)態(tài)監(jiān)測(cè)技術(shù)研究也是近年來(lái)的研究熱點(diǎn)。本文選取了河南省中牟縣和重慶市北碚區(qū)兩個(gè)研究區(qū),以固定資產(chǎn)投資與城市擴(kuò)張、投資項(xiàng)目特征提取方法、城市景觀格局的驅(qū)動(dòng)機(jī)制作為切入點(diǎn)監(jiān)測(cè)不同尺度的投資態(tài)勢(shì)。主要研究?jī)?nèi)容與結(jié)論如下:(1)通過(guò)構(gòu)建1990年至2010年的全國(guó)各區(qū)域全社會(huì)固定資產(chǎn)投資面板數(shù)據(jù)并進(jìn)行系數(shù)修正,結(jié)合遙感人工解譯的1990年至2010年的632個(gè)城市的主城區(qū)擴(kuò)張面積空間分布,建立二者的回歸模型,揭示了宏觀尺度固定資產(chǎn)投資規(guī)模的趨勢(shì)及分布規(guī)律,提出利用遙感監(jiān)測(cè)建成區(qū)擴(kuò)張監(jiān)測(cè)投資規(guī)模的方法。揭示了固定資產(chǎn)投資存在的“虹吸”效應(yīng)。數(shù)據(jù)說(shuō)明,隨著中國(guó)改革的不斷推進(jìn),投資規(guī)模與擴(kuò)張面積的相關(guān)性持續(xù)減弱,投資對(duì)經(jīng)濟(jì)的驅(qū)動(dòng)力權(quán)重有所下降。單位擴(kuò)張面積的固定資產(chǎn)投資額這一土地投入指標(biāo)顯示,中國(guó)城市的擴(kuò)張集約節(jié)約效果顯著。(2)提出了針對(duì)高分辨率、超高分辨率遙感數(shù)據(jù)的投資特征提取方法。實(shí)驗(yàn)證明,HSD特征訓(xùn)練得到的隨機(jī)森林機(jī)器學(xué)習(xí)以提取目標(biāo)建筑物的方法為從超高分辨率遙感影像中剔除土壤這一方向提供了一種魯棒性較強(qiáng)的方法。而且,隨機(jī)森林分類器移植性遠(yuǎn)遠(yuǎn)高于傳統(tǒng)方法,可用于提取投資監(jiān)測(cè)領(lǐng)域的多種監(jiān)測(cè)目標(biāo)。此外,針對(duì)塔吊這一特殊的投資項(xiàng)目在建配套設(shè)備,使用數(shù)學(xué)形態(tài)學(xué)和幾何特征相結(jié)合的算法進(jìn)行精確的定位和數(shù)量提取,并進(jìn)行了實(shí)驗(yàn)驗(yàn)證。(3)建立了適合遙感投資監(jiān)測(cè)的包括15個(gè)類別的投資項(xiàng)目監(jiān)測(cè)體系。針對(duì)可監(jiān)測(cè)項(xiàng)目構(gòu)建了特征指數(shù)BBI及IPBI,應(yīng)用面向?qū)ο蟮姆诸愃惴?結(jié)合光學(xué)紋理等多種特征值,對(duì)彩板房等臨時(shí)建筑物等對(duì)象進(jìn)行解譯,有效獲取了微觀尺度的投資熱點(diǎn)分布。(4)通過(guò)對(duì)平原研究區(qū)中牟縣和山地研究區(qū)北碚區(qū)的近15年四個(gè)時(shí)相的土地利用景觀類別動(dòng)態(tài)的監(jiān)測(cè),構(gòu)建基于平原與山區(qū)的景觀格局指數(shù)框架,對(duì)不同固定資產(chǎn)投資結(jié)構(gòu)對(duì)景觀格局變化的驅(qū)動(dòng)差異性進(jìn)行探索性分析。
[Abstract]:Fixed asset investment plays an important role in the change of urban landscape pattern and the development of urban economy. The research on dynamic monitoring technology is also a hot topic in recent years. This paper selects Zhongmou County in Henan Province and Beibei District in Chongqing as two research areas. The method of extracting the characteristics of fixed assets investment and urban expansion, The driving mechanism of urban landscape pattern is used as the starting point to monitor the investment situation of different scales. The main research contents and conclusions are as follows: (1) by constructing the data of fixed assets investment panel of the whole society from 1990 to 2010 in the whole country and modifying the coefficient, Combined with the spatial distribution of the expansion area in 632 cities from 1990 to 2010, the regression model of them is established, and the trend and distribution law of the scale of fixed asset investment in macro scale are revealed. This paper puts forward a method of using remote sensing to monitor the investment scale of the established area, and reveals the siphon effect of the fixed asset investment. The data show that the correlation between the investment scale and the expansion area continues to weaken with the development of China's reform. Investment has a lower driving force on the economy. Investment in fixed assets per unit expansion area, a land input index, shows that the intensive expansion of Chinese cities has significant savings. An investment feature extraction method for ultra-high resolution remote sensing data. It is proved by experiments that the method of random forest machine learning based on HSD feature training to extract target buildings is to remove soil from ultra-high resolution remote sensing images. Provides a robust approach. And, The transplantability of stochastic forest classifier is much higher than that of traditional methods, and it can be used to extract various monitoring targets in the field of investment monitoring. Using mathematical morphology and geometric features of the algorithm for accurate location and quantity extraction, The monitoring system of 15 kinds of investment items suitable for remote sensing investment monitoring is established. The feature index BBI and IPBI are constructed for the monitored projects, and the object-oriented classification algorithm is applied. Combining with the optical texture and other characteristic values, the objects such as temporary buildings, such as color plate houses, are interpreted. The distribution of investment hot spots on the micro scale is obtained effectively. The dynamic monitoring of land use landscape types in the past 15 years in Zhongmou County, plain research area, and Beibei, mountainous research area, is carried out. The landscape pattern index framework based on plain and mountain area is constructed to analyze the driving difference of different fixed asset investment structure on landscape pattern change.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：F283

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