Mobile SVG可以作为LBS地理信息存储和交换的一个开放式的标准,本文提出基于Mobile SVG的服务器端和移动端负载平衡的LBS架构,对SVG空间数据的存储、SVG空间信息表达、服务器端数据分块、数据分级传输、移动端数据表现,SVG数据存储等关...Mobile SVG可以作为LBS地理信息存储和交换的一个开放式的标准,本文提出基于Mobile SVG的服务器端和移动端负载平衡的LBS架构,对SVG空间数据的存储、SVG空间信息表达、服务器端数据分块、数据分级传输、移动端数据表现,SVG数据存储等关键技术进行了研究,结合实验提出了可行的技术方案。展开更多
Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of ...Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening(Ts HARP) method and a binary linear model are compared to downscale the original daytime FengY un 2 F(FY-2 F) land surface temperature(LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land(SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error(RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data.展开更多
Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geogr...Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geographic Information System） software in GIS sector. However, those table data contain much unnecessary information that do not need for a certain project. Using the raw data can increase processing times and reduce performance of geoprocessing tools. This study shows steps of how the raw data are being processed using ArcGIS ModelBuilder and Python script.展开更多
Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method(HASM) is proposed, and HASM_Big is developed to handle very...Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method(HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem(LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations.A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model.Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion,HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.展开更多
Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehe...Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.展开更多
基金Under the auspices of the Natural Science Foundation of China(No.41571418,41401471)Qing Lan Projectthe Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening(Ts HARP) method and a binary linear model are compared to downscale the original daytime FengY un 2 F(FY-2 F) land surface temperature(LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land(SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error(RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data.
文摘Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geographic Information System） software in GIS sector. However, those table data contain much unnecessary information that do not need for a certain project. Using the raw data can increase processing times and reduce performance of geoprocessing tools. This study shows steps of how the raw data are being processed using ArcGIS ModelBuilder and Python script.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41541010, 41701456, 41421001, 41590840 & 91425304)the Key Programs of the Chinese Academy of Sciences (Grant No. QYZDY-SSW-DQC007)the Cultivate Project of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (Grant No. TSYJS03)
文摘Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method(HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem(LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations.A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model.Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion,HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.
基金Supported by the National Key Technologies Research and Development Program of the Ministry of Science and Technology of China during the 12th Five-Year Plan Period(Nos.2011BAD32B01 and 2012BAH29B02)
文摘Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.
