利用光学遥感技术可以快速高效地获取大范围的地表水体信息,但是在洪灾监测等应用中,常常伴随多云、降雨等恶劣天气,难以及时获得高质量的光学遥感图像。合成孔径雷达(Synthetic Aperture Radar,SAR)技术具有全天时、全天候的特点,不受...利用光学遥感技术可以快速高效地获取大范围的地表水体信息,但是在洪灾监测等应用中,常常伴随多云、降雨等恶劣天气,难以及时获得高质量的光学遥感图像。合成孔径雷达(Synthetic Aperture Radar,SAR)技术具有全天时、全天候的特点,不受阴雨天气的影响,相比光学遥感具有明显的优势。本文首先总结和分析了目前基于星载SAR数据提取水体的方法和存在的问题,以Sentinel-1数据为例,为了弥补单极化阈值法和哨兵一号双极化水体指数(Sentinel-1 Dual-Polarized Water Index,SDWI)方法的不足,提出了双极化第一主成分水体指数(Dual-Polarized First-Principal-Component Water Index,DFWI)。其次,为了解决雷达阴影容易与水体相互混淆的问题,提出了3种升降轨极化SAR水体提取方法:升降轨VH极化方法(AscendingDescending VH-Polarization Water Index,AD-VH)、升降轨VV极化方法(Ascending-Descending VV-Polarization,AD-VV)和升降轨双极化第一主成分水体指数(Ascending-Descending Dual-Polarized First-Principal-Component Water Index,AD-DFWI)。最后,选择云南省洱海和土耳其Hatay 2个研究区进行水体提取实验,分别对应水体的常态化监测和应急监测等不同的应用场景。根据用户精度、生产者精度、虚警率和F1-score共4种指标对上述不同方法得到的水体提取结果进行精度评价和分析。实验结果表明:(1)本文所提方法相比单极化阈值法和SDWI法的分类精度明显提高,在2个研究区中,精度相对最高的方法均为AD-DFWI法,F1-score指标分别达到了97.83%和88.33%;(2)升降轨极化SAR水体提取方法不仅较好地解决了雷达阴影和水体相互混淆的问题,而且图像直方图中双峰分布特点更加显著,水体和非水体的可分离性更高,综合性能更好。本文提出的方法未来可以为水体提取和洪灾监测等应用提供参考。展开更多
This study uses green patent data from 264 cities in China between 2006 and 2020 to examine the evolution of spatial patterns in urban green technology innovation(GTI)across the country and identify the underlying dri...This study uses green patent data from 264 cities in China between 2006 and 2020 to examine the evolution of spatial patterns in urban green technology innovation(GTI)across the country and identify the underlying driving factors.Moran’s I index,Getis-Ord Gi*index,standard deviation ellipse,and geographical detector were used for the analysis.The findings indicate an increase in the overall level of GTI within Chinese cities.Provincial capitals,cities along the eastern coast,and planned cities emerge as the prominent“highlands”of GTI,whereas the“lowlands”of GTI predominantly lie in the western and northeastern regions,forming the spatial pattern of“hot in the east and center of the country,cold in the northwest and the northeast.”The distribution center of gravity of GTI is toward the southwest of China.The distribution pattern is in the“northeast–southwest”direction,which is characterized by“diffusion,”followed by“agglomeration.”Differences in economic development have the highest determining power on the spatial differentiation of GTI in Chinese cities,whereas differences in environmental regulation and industrial structure have the lowest degree of relative influence.The interaction between any two factors contributes to an amplified explanatory power in understanding the differences in GTI.展开更多
文摘利用光学遥感技术可以快速高效地获取大范围的地表水体信息,但是在洪灾监测等应用中,常常伴随多云、降雨等恶劣天气,难以及时获得高质量的光学遥感图像。合成孔径雷达(Synthetic Aperture Radar,SAR)技术具有全天时、全天候的特点,不受阴雨天气的影响,相比光学遥感具有明显的优势。本文首先总结和分析了目前基于星载SAR数据提取水体的方法和存在的问题,以Sentinel-1数据为例,为了弥补单极化阈值法和哨兵一号双极化水体指数(Sentinel-1 Dual-Polarized Water Index,SDWI)方法的不足,提出了双极化第一主成分水体指数(Dual-Polarized First-Principal-Component Water Index,DFWI)。其次,为了解决雷达阴影容易与水体相互混淆的问题,提出了3种升降轨极化SAR水体提取方法:升降轨VH极化方法(AscendingDescending VH-Polarization Water Index,AD-VH)、升降轨VV极化方法(Ascending-Descending VV-Polarization,AD-VV)和升降轨双极化第一主成分水体指数(Ascending-Descending Dual-Polarized First-Principal-Component Water Index,AD-DFWI)。最后,选择云南省洱海和土耳其Hatay 2个研究区进行水体提取实验,分别对应水体的常态化监测和应急监测等不同的应用场景。根据用户精度、生产者精度、虚警率和F1-score共4种指标对上述不同方法得到的水体提取结果进行精度评价和分析。实验结果表明:(1)本文所提方法相比单极化阈值法和SDWI法的分类精度明显提高,在2个研究区中,精度相对最高的方法均为AD-DFWI法,F1-score指标分别达到了97.83%和88.33%;(2)升降轨极化SAR水体提取方法不仅较好地解决了雷达阴影和水体相互混淆的问题,而且图像直方图中双峰分布特点更加显著,水体和非水体的可分离性更高,综合性能更好。本文提出的方法未来可以为水体提取和洪灾监测等应用提供参考。
基金National Natural Science Foundation of China,No.42171172Natural Science Foundation of Guangdong Province,No.2021A1515012248Major Program of the National Social Science Fund of China,No.21ZDA011。
文摘This study uses green patent data from 264 cities in China between 2006 and 2020 to examine the evolution of spatial patterns in urban green technology innovation(GTI)across the country and identify the underlying driving factors.Moran’s I index,Getis-Ord Gi*index,standard deviation ellipse,and geographical detector were used for the analysis.The findings indicate an increase in the overall level of GTI within Chinese cities.Provincial capitals,cities along the eastern coast,and planned cities emerge as the prominent“highlands”of GTI,whereas the“lowlands”of GTI predominantly lie in the western and northeastern regions,forming the spatial pattern of“hot in the east and center of the country,cold in the northwest and the northeast.”The distribution center of gravity of GTI is toward the southwest of China.The distribution pattern is in the“northeast–southwest”direction,which is characterized by“diffusion,”followed by“agglomeration.”Differences in economic development have the highest determining power on the spatial differentiation of GTI in Chinese cities,whereas differences in environmental regulation and industrial structure have the lowest degree of relative influence.The interaction between any two factors contributes to an amplified explanatory power in understanding the differences in GTI.
