无人船环境感知是无人船智能航行的关键技术之一,目前主要依赖于可获取目标空间位置的激光雷达和提供目标类别信息的光学设备。为获得复杂海上环境下目标多维感知信息,提出一种无人船载激光雷达-相机的融合感知方法,融合PR-YOLOv8视觉...无人船环境感知是无人船智能航行的关键技术之一,目前主要依赖于可获取目标空间位置的激光雷达和提供目标类别信息的光学设备。为获得复杂海上环境下目标多维感知信息,提出一种无人船载激光雷达-相机的融合感知方法,融合PR-YOLOv8视觉检测结果和激光雷达三维点云,实现了海上目标高精度识别和空间定位。首先,利用标定板进行激光雷达和相机联合标定,构建了两传感器间的投影关系。随后,对于雷达分支,对目标点云聚类拟合,提取目标的特征信息并投影至图像;对于相机分支,基于YOLOv8提出PR-YOLOv8目标检测模型,获得高识别精度的目标检测边界框。最后,结合两分支检测结果,提出一种新的代价构建因子DSIoU(Distance-Scale Intersection over Union)关联目标,并结合贝叶斯理论,实现了多源感知信息的融合。采用青岛近海和内湖船只感知实验,验证了所提出方法的可行性和有效性。展开更多
Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and ...Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.展开更多
Endowed with opportunities from both land and ocean,coastal areas attract expanding human populations and economic activities.At the same time,they face growing societal and environmental pressures from both the above...Endowed with opportunities from both land and ocean,coastal areas attract expanding human populations and economic activities.At the same time,they face growing societal and environmental pressures from both the above river catchments and the bordering sea due to climate change,ecosystem degradation,and expansion of built-up areas.Despite the accumulation of human population,economic activities,and environmental impacts,we lack social-ecological systems analysis on water-related risks to world’s coastal human population.To address this research gap,we analyze the spatial extent of six globally important water stressors to people within the world’s coastal zone(100 km from the coastal line)and classify this zone globally into 12 groups by distance from the coastline and elevation from the mean sea level.Adopting the approaches of the UN Sendai Framework and IPCC,we produce risk maps from the stressor maps by multiplying them with population exposure and vulnerability.For most risks,geographical hotspots are the Chinese coast,Bay of Bengal,Gujarat,and the Island of Java.The analysis reveals fundamental differences between water stressors and related risks,often mixed in scholarly literature.Both manifest specific geographic patterns and latitudinal profiles.Our study highlights the importance of high-resolution spatial analysis of vulnerability,exposure,and risks posed by water related stressors in the world’s coastal zone,in a manner prompted by key policy bodies to promote policy design and shared responsibility for managing stress-prone areas.展开更多
Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observat...Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.展开更多
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.展开更多
海冰密集度是描述海冰特征的重要参数,准确获取海冰密集度对研究全球气候变化具有重要意义。针对北极夏季海冰密集度反演精度较低的问题,本文通过对微波辐射传输模型中的海冰发射率和初始海冰密集度进行优化估算,改善了微波辐射传输模...海冰密集度是描述海冰特征的重要参数,准确获取海冰密集度对研究全球气候变化具有重要意义。针对北极夏季海冰密集度反演精度较低的问题,本文通过对微波辐射传输模型中的海冰发射率和初始海冰密集度进行优化估算,改善了微波辐射传输模型对夏季观测亮温的大气校正效果,从而优化被动微波海冰密集度的反演结果,本研究采用2019年6—9月的FY-3D/MWRI亮温数据,分别利用优化前和优化后的ASI2算法(ASI2和ASI2E),结合固定系点(FTP)与动态系点(DTP),分别获得了4套夏季北极海冰密集度数据(ASI2-FTP、ASI2-DTP、ASI2E-FTP、ASI2E-DTP),并利用14景MODIS影像对结果进行了精度验证。研究结果表明,本研究提出的优化方法可有效提高北极夏季海冰密集度的反演精度,其中该优化方法对基于固定系点的反演改进尤为明显,其优化后的均方根误差(root mean square error,ERMSE)由21.9%减小到15.43%,偏差(bias,|B_(bias)|)由-12.40%下降到-6.01%。4种反演方法中,基于动态系点的算法优化后(ASI2E-DTP)表现尤为明显,其E_(RMSE)和B_(bias)分别为14.33%和-4.53%。展开更多
文摘无人船环境感知是无人船智能航行的关键技术之一,目前主要依赖于可获取目标空间位置的激光雷达和提供目标类别信息的光学设备。为获得复杂海上环境下目标多维感知信息,提出一种无人船载激光雷达-相机的融合感知方法,融合PR-YOLOv8视觉检测结果和激光雷达三维点云,实现了海上目标高精度识别和空间定位。首先,利用标定板进行激光雷达和相机联合标定,构建了两传感器间的投影关系。随后,对于雷达分支,对目标点云聚类拟合,提取目标的特征信息并投影至图像;对于相机分支,基于YOLOv8提出PR-YOLOv8目标检测模型,获得高识别精度的目标检测边界框。最后,结合两分支检测结果,提出一种新的代价构建因子DSIoU(Distance-Scale Intersection over Union)关联目标,并结合贝叶斯理论,实现了多源感知信息的融合。采用青岛近海和内湖船只感知实验,验证了所提出方法的可行性和有效性。
基金supported by the National Natural Science Foundation of China(Grant Nos.42027804,41775026,and 41075012)。
文摘Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.
基金support from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(Grant agreement No.819202)the Research Council of Finland’s Flagship Programme and Doctoral Education Pilot under project Digital Waters(Grant No.359248)funded by the Research Council of Finland's Flagship ProgrammeStrategic Research Council(SRC)through project‘Water&Food’(Grant No.365512).
文摘Endowed with opportunities from both land and ocean,coastal areas attract expanding human populations and economic activities.At the same time,they face growing societal and environmental pressures from both the above river catchments and the bordering sea due to climate change,ecosystem degradation,and expansion of built-up areas.Despite the accumulation of human population,economic activities,and environmental impacts,we lack social-ecological systems analysis on water-related risks to world’s coastal human population.To address this research gap,we analyze the spatial extent of six globally important water stressors to people within the world’s coastal zone(100 km from the coastal line)and classify this zone globally into 12 groups by distance from the coastline and elevation from the mean sea level.Adopting the approaches of the UN Sendai Framework and IPCC,we produce risk maps from the stressor maps by multiplying them with population exposure and vulnerability.For most risks,geographical hotspots are the Chinese coast,Bay of Bengal,Gujarat,and the Island of Java.The analysis reveals fundamental differences between water stressors and related risks,often mixed in scholarly literature.Both manifest specific geographic patterns and latitudinal profiles.Our study highlights the importance of high-resolution spatial analysis of vulnerability,exposure,and risks posed by water related stressors in the world’s coastal zone,in a manner prompted by key policy bodies to promote policy design and shared responsibility for managing stress-prone areas.
基金Supported by the National Natural Science Foundation of China(Nos.T2261149752,41976163,42476172)。
文摘Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.
基金supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
文摘海冰密集度是描述海冰特征的重要参数,准确获取海冰密集度对研究全球气候变化具有重要意义。针对北极夏季海冰密集度反演精度较低的问题,本文通过对微波辐射传输模型中的海冰发射率和初始海冰密集度进行优化估算,改善了微波辐射传输模型对夏季观测亮温的大气校正效果,从而优化被动微波海冰密集度的反演结果,本研究采用2019年6—9月的FY-3D/MWRI亮温数据,分别利用优化前和优化后的ASI2算法(ASI2和ASI2E),结合固定系点(FTP)与动态系点(DTP),分别获得了4套夏季北极海冰密集度数据(ASI2-FTP、ASI2-DTP、ASI2E-FTP、ASI2E-DTP),并利用14景MODIS影像对结果进行了精度验证。研究结果表明,本研究提出的优化方法可有效提高北极夏季海冰密集度的反演精度,其中该优化方法对基于固定系点的反演改进尤为明显,其优化后的均方根误差(root mean square error,ERMSE)由21.9%减小到15.43%,偏差(bias,|B_(bias)|)由-12.40%下降到-6.01%。4种反演方法中,基于动态系点的算法优化后(ASI2E-DTP)表现尤为明显,其E_(RMSE)和B_(bias)分别为14.33%和-4.53%。
