Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment a...Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment around the stations have influenced observa- tions of air temperature. When the observational data from urban stations are applied in the interpolation of national or regional scale air temperature dataset, they could lead to overes- timation of regional air temperature and inaccurate assessment of warming. In this study, the underlying surface surrounding 756 meteorological stations across China was identified based on remote sensing images over a number of time intervals to distinguish the rural sta- tions that ＇entered＇ into cities. Then, after removing the observational data from these stations which have been influenced by urban expansion, a dataset of background air temperatures was generated by interpolating the observational data from the remaining rural stations. The mean urban heat island effect intensity since 1970 was estimated by comparing the original observational records from urban stations with the background air temperature interpolated. The result shows that urban heat island effect does occur due to urban expansion, with a higher intensity in winter than in other seasons. Then the overestimation of regional air tem- perature is evaluated by comparing the two kinds of grid datasets of air temperature which are respectively interpolated by all stations＇ and rural stations＇ observational data. Spatially, the overestimation is relatively higher in eastern China than in the central part of China; however, both areas exhibit a much higher effect than is observed in western China. We concluded that in the last 40 years the mean temperature in China increased by about 1.58℃, of which about 0.01℃ was attributed to urban expansion, with a contribution of up to 0.09℃ in the core areas from the overestimation of air temperature.展开更多
With the ongoing development of economy and urbanization in China, the change of land use types has attracted more and more attention. In this study we focused on the urban development of Shenzhen City, Guangdong Prov...With the ongoing development of economy and urbanization in China, the change of land use types has attracted more and more attention. In this study we focused on the urban development of Shenzhen City, Guangdong Province, analyzing Landsat 5 TM and Landsat 8 OLI data. We used an SVM based classification, a land transfer matrix approach, a directional growth analysis method and we calculated the inversion of land surface temperature to derive information of land cover changes that occurred in the time period between 1987 and 2017. The results are combined with Shenzhen’s economy, transportation policy and other aspects to find the driving forces of the urban development. The results show that during the observed 30 years, the area of construction land has increased significantly. Most of it is converted from other lands, and some of them are reclaimed. Most rapidly developing are areas west and northwest of the Bao’an, Nanshan and Longhua. The vegetated areas decreased slightly. Caused by the continuous increase of the construction land, the so-called heat island effect emerges slightly but continuously.展开更多
DATA AND COMPUTILITY ISLANDS IN REMOTE SENSING FOR EO The rapid advancement of Earth observation(EO)capabilities is driving an explosive increase in remote sensing data.There is an urgent need for advanced processing ...DATA AND COMPUTILITY ISLANDS IN REMOTE SENSING FOR EO The rapid advancement of Earth observation(EO)capabilities is driving an explosive increase in remote sensing data.There is an urgent need for advanced processing techniques to unleash their application value.1 Generalist EO intelligence refers to the ability to provide unified support for qualitative interpretation,quantitative inversion,and interactive dialogue across diverse EO data and tasks.It has attracted significant attention recently,prompting academia,industry,and government to invest substantial resources.2 Through developing remote sensing foundation models(RSFMs),generalist EO intelligence can ultimately offer humanity a shared spatial-temporal intelligence service in various fields(e.g.,agriculture,forestry,and oceanography).3 However,a critical question remains:have we truly unleashed the potential of RSFMs for generalist EO intelligence?Despite the vast volume of remote sensing data,their distribution is often fragmented and decentralized due to privacy concerns,storage bottlenecks,industrial competition,and geo-information security.This fragmentation leads to data islands,which limit the full utilization of multi-source remote sensing data.Moreover,computility(i.e.,computational resources)typically develops in isolation,inadequately supporting the large-scale training and application of RSFMs.展开更多
基金National 973 Program of China, No.2010CB950900Swedish Research Links, No.2006-24724-44416-13
文摘Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment around the stations have influenced observa- tions of air temperature. When the observational data from urban stations are applied in the interpolation of national or regional scale air temperature dataset, they could lead to overes- timation of regional air temperature and inaccurate assessment of warming. In this study, the underlying surface surrounding 756 meteorological stations across China was identified based on remote sensing images over a number of time intervals to distinguish the rural sta- tions that ＇entered＇ into cities. Then, after removing the observational data from these stations which have been influenced by urban expansion, a dataset of background air temperatures was generated by interpolating the observational data from the remaining rural stations. The mean urban heat island effect intensity since 1970 was estimated by comparing the original observational records from urban stations with the background air temperature interpolated. The result shows that urban heat island effect does occur due to urban expansion, with a higher intensity in winter than in other seasons. Then the overestimation of regional air tem- perature is evaluated by comparing the two kinds of grid datasets of air temperature which are respectively interpolated by all stations＇ and rural stations＇ observational data. Spatially, the overestimation is relatively higher in eastern China than in the central part of China; however, both areas exhibit a much higher effect than is observed in western China. We concluded that in the last 40 years the mean temperature in China increased by about 1.58℃, of which about 0.01℃ was attributed to urban expansion, with a contribution of up to 0.09℃ in the core areas from the overestimation of air temperature.
文摘With the ongoing development of economy and urbanization in China, the change of land use types has attracted more and more attention. In this study we focused on the urban development of Shenzhen City, Guangdong Province, analyzing Landsat 5 TM and Landsat 8 OLI data. We used an SVM based classification, a land transfer matrix approach, a directional growth analysis method and we calculated the inversion of land surface temperature to derive information of land cover changes that occurred in the time period between 1987 and 2017. The results are combined with Shenzhen’s economy, transportation policy and other aspects to find the driving forces of the urban development. The results show that during the observed 30 years, the area of construction land has increased significantly. Most of it is converted from other lands, and some of them are reclaimed. Most rapidly developing are areas west and northwest of the Bao’an, Nanshan and Longhua. The vegetated areas decreased slightly. Caused by the continuous increase of the construction land, the so-called heat island effect emerges slightly but continuously.
基金supported by the National Natural Science Foundation of China under grants 42030102 and 42371321 and by the Ant Group。
文摘DATA AND COMPUTILITY ISLANDS IN REMOTE SENSING FOR EO The rapid advancement of Earth observation(EO)capabilities is driving an explosive increase in remote sensing data.There is an urgent need for advanced processing techniques to unleash their application value.1 Generalist EO intelligence refers to the ability to provide unified support for qualitative interpretation,quantitative inversion,and interactive dialogue across diverse EO data and tasks.It has attracted significant attention recently,prompting academia,industry,and government to invest substantial resources.2 Through developing remote sensing foundation models(RSFMs),generalist EO intelligence can ultimately offer humanity a shared spatial-temporal intelligence service in various fields(e.g.,agriculture,forestry,and oceanography).3 However,a critical question remains:have we truly unleashed the potential of RSFMs for generalist EO intelligence?Despite the vast volume of remote sensing data,their distribution is often fragmented and decentralized due to privacy concerns,storage bottlenecks,industrial competition,and geo-information security.This fragmentation leads to data islands,which limit the full utilization of multi-source remote sensing data.Moreover,computility(i.e.,computational resources)typically develops in isolation,inadequately supporting the large-scale training and application of RSFMs.
