喀斯特城市水体、不透水面、植被与地表温度关系研究
发布时间：2018-11-08 10:30
【摘要】：城市化的快速发展,导致城市人口和建筑面积大量增加,自然地表逐渐被大量人工不透水地表例如沥青路面、混凝土等代替。由于城市地表覆盖类型的改变,造成地表与空气中的能量、水分交换发生了改变,形成了城区温度高于郊区温度的局地小气候效应,即城市热岛效应。近些年来,城市热岛效应对人类生活环境造成的影响也是日渐明显。遥感数据能够获得大面积城市地面温度,是一种快捷、有效的技术手段,本研究中以遥感数据来研究喀斯特城市桂林热环境变化情况及其影响因子,其目的是为改善桂林人居环境、科学的方法进行环境管理提供理论和技术上的支撑。选取覆盖桂林市主城区的美国Landsat 5 TM卫星2006年、2009年、2010年三幅图像,反演地表温度和描述不透水面、水体、植被的遥感影响参数。对桂林市地表温度进行正规化处理,分析地表热状况变化情况。对GVI、NDVI、PV、RVI、MSAVI、SAVI、DVI七种植被指数进行剖面分析,通过对其均值和标准差的分析得出植被覆盖度比较适合作为植被参数用来与地表温度进行分析,对地域差异并没有其他植被参数敏感。定量分析植被覆盖度与地表温度的关系,分别统计不同等级植被覆盖度区域的平均温度,发现植被覆盖率比较高的地方,气温均值相对较低。对植被变化进行时空分析并与地表温度进行回归分析,发现植被与地表温度成负相关关系。定量分析不透水面,发现不透水面与地表温度呈明显的正相关关系,利用NDBBI模型提取建筑用地,回归分析发现NDBBI与植被水体成负相关关系。定量分析桂林市水体与地表温度的关系,发现水体与地表温度成明显的负相关关系。缨帽变换以及主成分分析进一步分析与地表温度相关的因素,结果表明绿度分量与地表温度紧密相关,不透水面对地表温度的升温效果超过植被和水体对地表温度的降温效果时,城市的热岛效应将会更加明显。鉴于Landsat 5卫星热红外波段地面分辨率为120m×120m,反演只能获取该分辨率的地表温度。为了获取30m×30m地面分辨率的地表温度,构建120m×120m地表温度与相关遥感参数的神经网络模型,并将学习训练获得的模型应用于输入30m×30m的遥感参数。根据各种地表遥感参数与地表温度的相关系数,以及与地表温度进行回归拟合的判定系数,选取绿度植被指数、归一化植被指数、修改型调整植被指数、比值植被指数、植被覆盖度、修改型归一化差值水体指数、归一化差值裸地与建筑用地指数、不透水面率作为遗传神经网络模型进行训练和测试的输入。论文中对选取输入数据的方法进行了验证,并且证明以相关系数以及回归分析系数为判断原则的方法是可行的。
[Abstract]:With the rapid development of urbanization, the urban population and the building area increase greatly, and the natural surface is gradually replaced by a large number of artificial impervious surfaces such as asphalt pavement, concrete and so on. Because of the change of urban surface cover type, the energy and water exchange between the surface and the air have changed, and the local microclimate effect, which is the urban heat island effect, which is higher than the suburban temperature in urban area, has been formed. In recent years, the urban heat island effect on human living environment is increasingly obvious. Remote sensing data can obtain large area of urban surface temperature, which is a fast and effective technical means. In this study, remote sensing data is used to study the changes of thermal environment and its influencing factors in Guilin, a karst city. The purpose is to provide theoretical and technical support for improving the living environment of Guilin and carrying out scientific environmental management. Three images of Landsat 5 TM satellite covering the main urban area of Guilin in 2006, 2009 and 2010 were selected to retrieve the surface temperature and describe the remote sensing parameters of impermeable surface, water body and vegetation. The surface temperature of Guilin city was regularized and the change of surface heat condition was analyzed. Through the analysis of the mean value and standard deviation of GVI,NDVI,PV,RVI,MSAVI,SAVI,DVI seven vegetation indices, it is concluded that vegetation coverage is more suitable to be used as a vegetation parameter to analyze the surface temperature. No other vegetation parameters are sensitive to regional differences. Quantitative analysis of the relationship between vegetation coverage and surface temperature, statistics of the average temperature of different grades of vegetation coverage areas, it is found that where the vegetation coverage is relatively high, the mean temperature is relatively low. The spatial and temporal analysis of vegetation change and the regression analysis between vegetation and surface temperature showed that vegetation had a negative correlation with surface temperature. Quantitative analysis of impermeable surface showed that impermeable surface was positively correlated with surface temperature. NDBBI model was used to extract construction land. Regression analysis showed that NDBBI had a negative correlation with vegetation water body. Quantitative analysis of the relationship between water body and surface temperature in Guilin City shows that there is an obvious negative correlation between water body and surface temperature. Tasseled hat transformation and principal component analysis (PCA) further analyzed the factors related to the surface temperature. The results showed that the green component was closely related to the surface temperature. The urban heat island effect will be more obvious when the surface temperature warming effect of impermeable water is higher than that of vegetation and water body. Since the ground resolution of the thermal infrared band of Landsat 5 satellite is 120m 脳 120m, the surface temperature can only be obtained by inversion. In order to obtain the surface temperature of 30m 脳 30m ground resolution, the neural network model of 120m 脳 120m surface temperature and related remote sensing parameters is constructed, and the model obtained by learning and training is applied to input 30m 脳 30m remote sensing parameters. According to the correlation coefficient between surface remote sensing parameters and surface temperature, and the decision coefficient of regression fitting with surface temperature, the green vegetation index, normalized vegetation index, modified vegetation index and ratio vegetation index are selected. Vegetation coverage, modified normalized difference water index, normalized difference index of bare land and building land, impermeable surface rate are used as inputs for training and testing of genetic neural network model. In this paper, the method of selecting input data is verified, and it is proved that the method based on correlation coefficient and regression analysis coefficient is feasible.
【学位授予单位】：广西师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P423.7;TP183;P407
本文编号：2318196
[Abstract]:With the rapid development of urbanization, the urban population and the building area increase greatly, and the natural surface is gradually replaced by a large number of artificial impervious surfaces such as asphalt pavement, concrete and so on. Because of the change of urban surface cover type, the energy and water exchange between the surface and the air have changed, and the local microclimate effect, which is the urban heat island effect, which is higher than the suburban temperature in urban area, has been formed. In recent years, the urban heat island effect on human living environment is increasingly obvious. Remote sensing data can obtain large area of urban surface temperature, which is a fast and effective technical means. In this study, remote sensing data is used to study the changes of thermal environment and its influencing factors in Guilin, a karst city. The purpose is to provide theoretical and technical support for improving the living environment of Guilin and carrying out scientific environmental management. Three images of Landsat 5 TM satellite covering the main urban area of Guilin in 2006, 2009 and 2010 were selected to retrieve the surface temperature and describe the remote sensing parameters of impermeable surface, water body and vegetation. The surface temperature of Guilin city was regularized and the change of surface heat condition was analyzed. Through the analysis of the mean value and standard deviation of GVI,NDVI,PV,RVI,MSAVI,SAVI,DVI seven vegetation indices, it is concluded that vegetation coverage is more suitable to be used as a vegetation parameter to analyze the surface temperature. No other vegetation parameters are sensitive to regional differences. Quantitative analysis of the relationship between vegetation coverage and surface temperature, statistics of the average temperature of different grades of vegetation coverage areas, it is found that where the vegetation coverage is relatively high, the mean temperature is relatively low. The spatial and temporal analysis of vegetation change and the regression analysis between vegetation and surface temperature showed that vegetation had a negative correlation with surface temperature. Quantitative analysis of impermeable surface showed that impermeable surface was positively correlated with surface temperature. NDBBI model was used to extract construction land. Regression analysis showed that NDBBI had a negative correlation with vegetation water body. Quantitative analysis of the relationship between water body and surface temperature in Guilin City shows that there is an obvious negative correlation between water body and surface temperature. Tasseled hat transformation and principal component analysis (PCA) further analyzed the factors related to the surface temperature. The results showed that the green component was closely related to the surface temperature. The urban heat island effect will be more obvious when the surface temperature warming effect of impermeable water is higher than that of vegetation and water body. Since the ground resolution of the thermal infrared band of Landsat 5 satellite is 120m 脳 120m, the surface temperature can only be obtained by inversion. In order to obtain the surface temperature of 30m 脳 30m ground resolution, the neural network model of 120m 脳 120m surface temperature and related remote sensing parameters is constructed, and the model obtained by learning and training is applied to input 30m 脳 30m remote sensing parameters. According to the correlation coefficient between surface remote sensing parameters and surface temperature, and the decision coefficient of regression fitting with surface temperature, the green vegetation index, normalized vegetation index, modified vegetation index and ratio vegetation index are selected. Vegetation coverage, modified normalized difference water index, normalized difference index of bare land and building land, impermeable surface rate are used as inputs for training and testing of genetic neural network model. In this paper, the method of selecting input data is verified, and it is proved that the method based on correlation coefficient and regression analysis coefficient is feasible.
【学位授予单位】：广西师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P423.7;TP183;P407
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