基于厘米级高分辨率无人机影像,应用面向对象方法(Object-Based Image Analysis,OBIA)对吉林省伊通县椽子沟流域的横坡改垄、地埂植物带、生态恢复乔木林、生态恢复草地等水土保持措施进行自动精准识别。应用超绿指数(Excess Green Inde...基于厘米级高分辨率无人机影像,应用面向对象方法(Object-Based Image Analysis,OBIA)对吉林省伊通县椽子沟流域的横坡改垄、地埂植物带、生态恢复乔木林、生态恢复草地等水土保持措施进行自动精准识别。应用超绿指数(Excess Green Index,ExG)、超红指数(Excess Red Index,ExR)、归一化差异指数(Normalized Difference Index,NDI)等光谱指数,形状的主方向、形状指数等形状特征,均值(Mean)、方差(Variance)、对比度(Contrast)等纹理特征进行措施的特征提取。结果表明:研究区水土保持措施识别的总体精度可达91.24%,Kappa系数为0.87;对垄台、垄沟等线性水土保持措施总体精度可达72.33%,Kappa系数为0.63。基于厘米级无人机影像,应用面向对象方法基本可实现对黑土区水土保持措施的精准识别,也可对垄台垄沟等线性措施进行自动识别,研究结果可为水土保持措施实施范围及完好程度的动态监测提供参考依据。展开更多
Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure t...Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.展开更多
文摘基于厘米级高分辨率无人机影像,应用面向对象方法(Object-Based Image Analysis,OBIA)对吉林省伊通县椽子沟流域的横坡改垄、地埂植物带、生态恢复乔木林、生态恢复草地等水土保持措施进行自动精准识别。应用超绿指数(Excess Green Index,ExG)、超红指数(Excess Red Index,ExR)、归一化差异指数(Normalized Difference Index,NDI)等光谱指数,形状的主方向、形状指数等形状特征,均值(Mean)、方差(Variance)、对比度(Contrast)等纹理特征进行措施的特征提取。结果表明:研究区水土保持措施识别的总体精度可达91.24%,Kappa系数为0.87;对垄台、垄沟等线性水土保持措施总体精度可达72.33%,Kappa系数为0.63。基于厘米级无人机影像,应用面向对象方法基本可实现对黑土区水土保持措施的精准识别,也可对垄台垄沟等线性措施进行自动识别,研究结果可为水土保持措施实施范围及完好程度的动态监测提供参考依据。
基金supported by the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals(CBAS2022ORP06)the Strategic Priority Research Program of Chinese Academy of Sciences(XDA19040500)+4 种基金the National Natural Science Foundation of China(42171372 and 42171379)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2021227)the Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University(19I02)the National Earth System Science Data Center(www.geodata.cn)Xiangming Xiao was supported by the U.S.National Science Foundation(1911955)。
基金supported by the National Basic Research Program (973) of China (No. 2008CB418104)the Major Programs of the Chinese Academy of Sciences (No. KZCX1-YW-14-4-1)the National Natural Science Foundation of China (No. 40901265)
文摘Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.
