Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has...Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.展开更多
基于碳卫星的遥感是一种正在发展的大范围高精度CO_(2)监测方法,但当监测对象为我国长三角区域这种大空间尺度时,碳卫星数据会存在时空稀疏性的问题。本文提出了一种新的模型ST-SAN(space time soft attention network),旨在提高碳卫星...基于碳卫星的遥感是一种正在发展的大范围高精度CO_(2)监测方法,但当监测对象为我国长三角区域这种大空间尺度时,碳卫星数据会存在时空稀疏性的问题。本文提出了一种新的模型ST-SAN(space time soft attention network),旨在提高碳卫星数据的高时空分辨率XCO_(2)(大气CO_(2))浓度估算精度。本文将2016—2020年的多源数据(包括人类活动数据、气象数据和植被数据)与碳卫星数据结合,生成空间分辨率为0.05°的无间隙XCO_(2)日浓度数据集。通过ST-SAN模型对这些数据进行训练和预测。实验结果表明,重建后的XCO_(2)数据集与OCO-2卫星数据和地面站点数据具有高度一致性,验证了本方法在高时空分辨率XCO_(2)浓度估算中的有效性。展开更多
A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single po...A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.展开更多
Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(...Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(XCO_(2))since 2014.In this study,the OCO-2 XCO_(2)products were compared between in-situ data from the Total Carbon Column Network(TCCON)and Global Monitoring Division(GMD),and modeling data from CarbonTracker2019 over global ocean and land.Results showed that the OCO-2 XCO_(2)data are consistent with the TCCON and GMD in situ XCO_(2)data,with mean absolute biases of 0.25×10^(-6)and 0.67×10^(-6),respectively.Moreover,the OCO-2 XCO_(2)data are also consistent with the CarbonTracker2019 modeling XCO_(2)data,with mean absolute biases of 0.78×10^(-6)over ocean and 1.02×10^(-6)over land.The results indicated the high accuracy of the OCO-2 XCO_(2)product over global ocean which could be applied to estimate the air-sea CO_(2)flux.展开更多
Bone morphogenetic proteins are osteoinductive factors which have gained popularity in orthopaedicsurgery and especially in spine surgery. The use of recombinant human bone morphogenetic protein-2 has been officially ...Bone morphogenetic proteins are osteoinductive factors which have gained popularity in orthopaedicsurgery and especially in spine surgery. The use of recombinant human bone morphogenetic protein-2 has been officially approved by the United States Food and Drug Administration only for single level anterior lumbar interbody fusion, nevertheless it is widely used by many surgeons with off-label indications. Despite advantages in bone formation, its use still remains a controversial issue and several complications have been described by authors who oppose their wide use.展开更多
Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,f...Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.展开更多
Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to ...Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.展开更多
文摘Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.
文摘基于碳卫星的遥感是一种正在发展的大范围高精度CO_(2)监测方法,但当监测对象为我国长三角区域这种大空间尺度时,碳卫星数据会存在时空稀疏性的问题。本文提出了一种新的模型ST-SAN(space time soft attention network),旨在提高碳卫星数据的高时空分辨率XCO_(2)(大气CO_(2))浓度估算精度。本文将2016—2020年的多源数据(包括人类活动数据、气象数据和植被数据)与碳卫星数据结合,生成空间分辨率为0.05°的无间隙XCO_(2)日浓度数据集。通过ST-SAN模型对这些数据进行训练和预测。实验结果表明,重建后的XCO_(2)数据集与OCO-2卫星数据和地面站点数据具有高度一致性,验证了本方法在高时空分辨率XCO_(2)浓度估算中的有效性。
基金Supported by the National Natural Science Foundation of China(61370212)the Research Fund for the Doctoral Program of Higher Education of China(20122304130002)+1 种基金the Natural Science Foundation of Heilongjiang Province(ZD 201102)the Fundamental Research Fund for the Central Universities(HEUCFZ1213,HEUCF100601)
文摘A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.
基金The National Key Research and Development Programme of China under contract No.2017YFA0603004the Fund of Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)(Zhanjiang Bay Laboratory)under contract No.ZJW-2019-08+1 种基金the National Natural Science Foundation of China under contract Nos 41825014,41676172 and 41676170the Global Change and Air-Sea Interaction Project of China under contract Nos GASI-02-SCS-YGST2-01,GASI-02-PACYGST2-01 and GASI-02-IND-YGST2-01。
文摘Atmospheric CO_(2)is one of key parameters to estimate air-sea CO_(2)flux.The Orbiting Carbon Observatory-2(OCO-2)satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide(XCO_(2))since 2014.In this study,the OCO-2 XCO_(2)products were compared between in-situ data from the Total Carbon Column Network(TCCON)and Global Monitoring Division(GMD),and modeling data from CarbonTracker2019 over global ocean and land.Results showed that the OCO-2 XCO_(2)data are consistent with the TCCON and GMD in situ XCO_(2)data,with mean absolute biases of 0.25×10^(-6)and 0.67×10^(-6),respectively.Moreover,the OCO-2 XCO_(2)data are also consistent with the CarbonTracker2019 modeling XCO_(2)data,with mean absolute biases of 0.78×10^(-6)over ocean and 1.02×10^(-6)over land.The results indicated the high accuracy of the OCO-2 XCO_(2)product over global ocean which could be applied to estimate the air-sea CO_(2)flux.
文摘Bone morphogenetic proteins are osteoinductive factors which have gained popularity in orthopaedicsurgery and especially in spine surgery. The use of recombinant human bone morphogenetic protein-2 has been officially approved by the United States Food and Drug Administration only for single level anterior lumbar interbody fusion, nevertheless it is widely used by many surgeons with off-label indications. Despite advantages in bone formation, its use still remains a controversial issue and several complications have been described by authors who oppose their wide use.
基金supported by National Natural Science Foundation of China(Grant No.41901382)Open Fund of State Key Laboratory of Remote Sensing Science(Grant No.OFSLRSS201917)the HZAU research startup fund(No.11041810340,No.11041810341).
文摘Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.
基金Supported by the National Key Research and Development Program of China(2018YFC1506601)National Natural Science Foundation of China(91437220)+1 种基金China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002 and GYHY201206008)China Meteorological Administration“Meteorological Data Quality Control and Multi-source Data Fusion and Reanalysis”project。
文摘Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.
