Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the ro...Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.展开更多
近些年城市大气污染问题尤为突出,其中PM2.5、PM10等污染物是引起雾霾天气的重要因素.本文基于2007—2016年10年中全国主要城市SO2、NO2、PM10等污染物因子的年平均浓度变化,利用Ocean Data View软件分析主要城市大气污染主控因子(二氧...近些年城市大气污染问题尤为突出,其中PM2.5、PM10等污染物是引起雾霾天气的重要因素.本文基于2007—2016年10年中全国主要城市SO2、NO2、PM10等污染物因子的年平均浓度变化,利用Ocean Data View软件分析主要城市大气污染主控因子(二氧化硫、氮氧化物以及颗粒物)的排放特征及其成因.结果表明:各污染物的区域性分布明显,污染物浓度变化的总体趋势北方高于南方,SO2、NO2、PM10年平均浓度北方分别高于南方108.15%、7.60%、48.36%;从大气污染组分来看,颗粒物的增长速度最快,石家庄2007—2016年PM10增速为28.10%;而SO2的污染物浓度在下降,乌鲁木齐的降速为84.10%.展开更多
The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns.However,cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lea...The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns.However,cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss,which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes.If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency,highest accuracy,best applicability,and greatest robustness at different scales remains debatable to date.Therefore,in the present study,four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales.The experimental results revealed that in terms of spatial distribution,the chlorophyll-a concentration products generated by averaging method(Chl1-AV/AVW)and GSM model(Chl1-GSM)presented a relatively high data coverage frequency in Case Ⅰ water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions,the chlorophyll-a concentration products generated by the Neural Network algorithm(Chl2)presented high data coverage frequency in the estuarine coastal zone Case 2 water regions.The chlorophyll-a concentration products generated by the OC5 algorithm(ChlOC5)presented high data coverage frequency in Case I water regions and the turbid Case Ⅱ water regions.In terms of absolute precision,the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions,and the Chl2 product performed well only in Case Ⅱ estuarine coastal zones,while presenting large errors in absolute precision in the Case Ⅰ water regions.The ChlOC5 product presented a higher precision in Case Ⅰ and Case Ⅱ water regions,with a better and more stable performance in both regions compared to the other products.展开更多
High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estima...High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.展开更多
Consistent bio-optical properties across multiple ocean color satellites are the key prerequisite to merging products from these satellites,thereby enhancing spatial coverage and extending temporal spans.However,due t...Consistent bio-optical properties across multiple ocean color satellites are the key prerequisite to merging products from these satellites,thereby enhancing spatial coverage and extending temporal spans.However,due to factors such as sensor specifics and separate data processing algorithms,bio-optical properties(e.g.,remote sensing reflectance,R_(rs))from different ocean color missions exhibit varying discrepancies in oceanic,coastal,and inland waters.Here,we introduce a cross-satellite atmospheric correction(CSAC)scheme,which could greatly improve the consistency of R_(rs)products between MODIS-Aqua and other satellite ocean color missions.Specifically,using an inclusive high-quality R_(rs)dataset of oceanic waters obtained from MODIS-Aqua as the reference,and as an example,top-of-atmosphere reflectance from SeaWiFS(Sea-Viewing Wide Field-of-View Sensor)is directly processed to MODIS-Aqua-equivalent R_(rs)(R^(MA−eqv)_(rs))via CSAC.As a demonstration,for independent space-time matched measurements between MODIS-Aqua and SeaWiFS,the mean absolute percent difference(MAPD)between RMA−eqv rs and MODIS-Aqua R_(rs)ranges from 5.9%to 22.2%across wavelengths from 412 to 667 nm.In contrast,the MAPD values between the NASA standard SeaWiFS and MODIS-Aqua R_(rs)products range from 10.1%to 55.1%for the same spectral bands.These results highlight the potential of CSAC in obtaining consistent R_(rs)products and,subsequently,R_(rs)-derived bio-optical properties,from various ocean color satellites,facilitating extensive and long-term ocean color observations of the global ocean.展开更多
作为能源技术与互联网信息技术相融合的产物,能源互联网的构建离不开大型数据服务中心互联的支撑。然而,当前互联网数据中心(Internet Data Center,IDC)巨大能耗所带来的成本和环境压力,突显出对IDC能耗管理的重要性。文中基于实时电价...作为能源技术与互联网信息技术相融合的产物,能源互联网的构建离不开大型数据服务中心互联的支撑。然而,当前互联网数据中心(Internet Data Center,IDC)巨大能耗所带来的成本和环境压力,突显出对IDC能耗管理的重要性。文中基于实时电价和多电力市场构成的能源互联网市场环境,在考虑IDC散热成本、碳排放成本以及服务延迟约束的基础上,以IDC负荷周期内总的能耗成本最小化为目标,建立了IDC数据负荷在多时空尺度下的优化调度模型,并采用反馈分支定界算法对模型求解。最后,通过算例仿真验证所提方案的正确性,仿真结果表明该技术可以显著降低IDC的能耗成本。展开更多
基金funded by National High Technology Research and Development Program of China (863 Program,2012AA092303)Project of Shanghai Science and Technology Innovation (12231203900)+2 种基金Industrialization Program of National Development and Reform Commission (2159999)National Science and Technology Support Program (2013BAD13B01)Shanghai Leading Academic Discipline Project
文摘Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.
文摘近些年城市大气污染问题尤为突出,其中PM2.5、PM10等污染物是引起雾霾天气的重要因素.本文基于2007—2016年10年中全国主要城市SO2、NO2、PM10等污染物因子的年平均浓度变化,利用Ocean Data View软件分析主要城市大气污染主控因子(二氧化硫、氮氧化物以及颗粒物)的排放特征及其成因.结果表明:各污染物的区域性分布明显,污染物浓度变化的总体趋势北方高于南方,SO2、NO2、PM10年平均浓度北方分别高于南方108.15%、7.60%、48.36%;从大气污染组分来看,颗粒物的增长速度最快,石家庄2007—2016年PM10增速为28.10%;而SO2的污染物浓度在下降,乌鲁木齐的降速为84.10%.
基金funded by the Project for Fostering Outstanding Young talents of Henan Academy of Sciences(No.210401001)Special Project for Team Building of Henan Academy of Sciences(No.200501007)+1 种基金Science and Technology Research Project of Henan Province(Nos.212102310424,222102320467,and 212102310024)Major Scientific Research Focus Project of Henan Academy of Sciences(No.210101007).
文摘The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns.However,cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss,which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes.If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency,highest accuracy,best applicability,and greatest robustness at different scales remains debatable to date.Therefore,in the present study,four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales.The experimental results revealed that in terms of spatial distribution,the chlorophyll-a concentration products generated by averaging method(Chl1-AV/AVW)and GSM model(Chl1-GSM)presented a relatively high data coverage frequency in Case Ⅰ water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions,the chlorophyll-a concentration products generated by the Neural Network algorithm(Chl2)presented high data coverage frequency in the estuarine coastal zone Case 2 water regions.The chlorophyll-a concentration products generated by the OC5 algorithm(ChlOC5)presented high data coverage frequency in Case I water regions and the turbid Case Ⅱ water regions.In terms of absolute precision,the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions,and the Chl2 product performed well only in Case Ⅱ estuarine coastal zones,while presenting large errors in absolute precision in the Case Ⅰ water regions.The ChlOC5 product presented a higher precision in Case Ⅰ and Case Ⅱ water regions,with a better and more stable performance in both regions compared to the other products.
基金Supported by the National Key Research and Development Program of China (2018YFC1506501, 2018YFA0605503, and2016YFB0501502)Special Program of Gaofen Satellites (04-Y30B01-9001-18/20-3-1)National Natural Science Foundation of China (41871230 and 41871231)。
文摘High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.
基金support from the National Natural Science Foundation of China(#42430107 and#42250710150)the National Key Research and Development Program of China(2022YFC3104903)Fujian Satellite Data Development,Co.,Ltd.,and Fujian Haisi Digital Technology Co.,Ltd.
文摘Consistent bio-optical properties across multiple ocean color satellites are the key prerequisite to merging products from these satellites,thereby enhancing spatial coverage and extending temporal spans.However,due to factors such as sensor specifics and separate data processing algorithms,bio-optical properties(e.g.,remote sensing reflectance,R_(rs))from different ocean color missions exhibit varying discrepancies in oceanic,coastal,and inland waters.Here,we introduce a cross-satellite atmospheric correction(CSAC)scheme,which could greatly improve the consistency of R_(rs)products between MODIS-Aqua and other satellite ocean color missions.Specifically,using an inclusive high-quality R_(rs)dataset of oceanic waters obtained from MODIS-Aqua as the reference,and as an example,top-of-atmosphere reflectance from SeaWiFS(Sea-Viewing Wide Field-of-View Sensor)is directly processed to MODIS-Aqua-equivalent R_(rs)(R^(MA−eqv)_(rs))via CSAC.As a demonstration,for independent space-time matched measurements between MODIS-Aqua and SeaWiFS,the mean absolute percent difference(MAPD)between RMA−eqv rs and MODIS-Aqua R_(rs)ranges from 5.9%to 22.2%across wavelengths from 412 to 667 nm.In contrast,the MAPD values between the NASA standard SeaWiFS and MODIS-Aqua R_(rs)products range from 10.1%to 55.1%for the same spectral bands.These results highlight the potential of CSAC in obtaining consistent R_(rs)products and,subsequently,R_(rs)-derived bio-optical properties,from various ocean color satellites,facilitating extensive and long-term ocean color observations of the global ocean.
文摘作为能源技术与互联网信息技术相融合的产物,能源互联网的构建离不开大型数据服务中心互联的支撑。然而,当前互联网数据中心(Internet Data Center,IDC)巨大能耗所带来的成本和环境压力,突显出对IDC能耗管理的重要性。文中基于实时电价和多电力市场构成的能源互联网市场环境,在考虑IDC散热成本、碳排放成本以及服务延迟约束的基础上,以IDC负荷周期内总的能耗成本最小化为目标,建立了IDC数据负荷在多时空尺度下的优化调度模型,并采用反馈分支定界算法对模型求解。最后,通过算例仿真验证所提方案的正确性,仿真结果表明该技术可以显著降低IDC的能耗成本。
