Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city planners.Coastal regions,geohazard-prone areas,and highly populated cities repres...Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city planners.Coastal regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new methods.Over the last decades,many efforts have been spent on improving Earth’s surface monitoring,including intensifying Earth Observation(EO)operations by the major national space agencies.They oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development opportunities in the geoscience field.For instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific communities.This manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further developments.The main findings are related to i)the analysis of flood risk in China,ii)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,iii)the analysis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;and iv)the use of SAR-based technologies for marine applications.展开更多
The upper mixed layer depth(h)has a significant seasonal variation in the real ocean and the low-order statistics of Langmuir turbulence are dramatically influenced by the upper mixed layer depth.To explore the influe...The upper mixed layer depth(h)has a significant seasonal variation in the real ocean and the low-order statistics of Langmuir turbulence are dramatically influenced by the upper mixed layer depth.To explore the influence of the upper mixed layer depth on Langmuir turbulence under the condition of the wind and wave equilibrium,the changes of Langmuir turbulence characteristics with the idealized variation of the upper mixed layer depth from very shallow(h=5 m)to deep enough(h=40 m)are studied using a non-hydrostatic large eddy simulation model.The simulation results show that there is a direct entrainment depth induced by Langmuir turbulence(h_(LT))within the thermocline.The normalized depthaveraged vertical velocity variance is smaller and larger than the downwind velocity variance for the ratio of the upper mixed layer to a direct entrainment depth induced by Langmuir turbulence h/h_(LT)<1 and h/h_(LT)>1,respectively,indicating that turbulence characteristics have the essential change(i.e.,depth-averaged vertical velocity variance(DAVV)DADV for Langmuir turbulence)between h/h_(LT)<1 and h/h_(LT)>1.The rate of change of the normalized depth-averaged low-order statistics for h/h_(LT)<1 is much larger than that for h/h_(LT)>1.The reason is that the downward pressure perturbation induced by Langmuir cells is strongly inhibited by the upward reactive force of the strong stratified thermocline for h/h_(LT)<1 and the eff ect of upward reactive force on the downward pressure perturbation becomes weak for h/h_(LT)>1.Hence,the upper mixed layer depth has significant influences on Langmuir turbulence characteristics.展开更多
Poyang Lake is a freshwater lake in China which is a vital winter habitat for many kinds of wildlife and a critical component of the regional ecology.Here,we use Landsat satellite imagery to systematically assess habi...Poyang Lake is a freshwater lake in China which is a vital winter habitat for many kinds of wildlife and a critical component of the regional ecology.Here,we use Landsat satellite imagery to systematically assess habitat characteristic changes from 1990 to 2021.Four machine learning methods including random forest(RF),gradient boosting tree(GBT),support vector machine(SVM)and classification and regression trees(CART)are analyzed by comparing the overall accuracy and Kappa coefficients.The results show that the accuracy of random forest is higher than that of the other three machine learning methods.The long-term characteristics of Poyang Lake winter habitat types are extracted from Landsat satellite images using the RF method.These results show that the mudflat area was larger than water surface and sand.After 2012,more mudflat area had been converted into grassland,which is related to the early onset of the winter dry season in the Poyang Lake area.The habitats were scattered and fragmented from 1990 to 1998;after 1997−1998,however,the degree of landscape patch density and interference decreased,indicating a decreased impact of human-related interference and natural factors on the evolution of habitats in and around Poyang Lake.展开更多
基金supported by the DRAGON 5 ESA-MOST GREENISH project[grant number 58351].
文摘Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city planners.Coastal regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new methods.Over the last decades,many efforts have been spent on improving Earth’s surface monitoring,including intensifying Earth Observation(EO)operations by the major national space agencies.They oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development opportunities in the geoscience field.For instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific communities.This manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further developments.The main findings are related to i)the analysis of flood risk in China,ii)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,iii)the analysis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;and iv)the use of SAR-based technologies for marine applications.
基金Supported by the National Key Research and Development Program of China(No.2018YFC1405701)the National Natural Science Foundation of China(Nos.92158204,41506001,42076026,41876017,42176027)+2 种基金the Project supported by Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(No.GML2019ZD0304)the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City(University of Macao)(No.SKL-IoTSC(UM)-2021-2023/ORPF/A20/2022)the State Key Laboratory of Tropical Oceanography(No.LTOZZ2101)。
文摘The upper mixed layer depth(h)has a significant seasonal variation in the real ocean and the low-order statistics of Langmuir turbulence are dramatically influenced by the upper mixed layer depth.To explore the influence of the upper mixed layer depth on Langmuir turbulence under the condition of the wind and wave equilibrium,the changes of Langmuir turbulence characteristics with the idealized variation of the upper mixed layer depth from very shallow(h=5 m)to deep enough(h=40 m)are studied using a non-hydrostatic large eddy simulation model.The simulation results show that there is a direct entrainment depth induced by Langmuir turbulence(h_(LT))within the thermocline.The normalized depthaveraged vertical velocity variance is smaller and larger than the downwind velocity variance for the ratio of the upper mixed layer to a direct entrainment depth induced by Langmuir turbulence h/h_(LT)<1 and h/h_(LT)>1,respectively,indicating that turbulence characteristics have the essential change(i.e.,depth-averaged vertical velocity variance(DAVV)DADV for Langmuir turbulence)between h/h_(LT)<1 and h/h_(LT)>1.The rate of change of the normalized depth-averaged low-order statistics for h/h_(LT)<1 is much larger than that for h/h_(LT)>1.The reason is that the downward pressure perturbation induced by Langmuir cells is strongly inhibited by the upward reactive force of the strong stratified thermocline for h/h_(LT)<1 and the eff ect of upward reactive force on the downward pressure perturbation becomes weak for h/h_(LT)>1.Hence,the upper mixed layer depth has significant influences on Langmuir turbulence characteristics.
基金supported by the National Basic Research Program of China(No.2021YFB3900400).
文摘Poyang Lake is a freshwater lake in China which is a vital winter habitat for many kinds of wildlife and a critical component of the regional ecology.Here,we use Landsat satellite imagery to systematically assess habitat characteristic changes from 1990 to 2021.Four machine learning methods including random forest(RF),gradient boosting tree(GBT),support vector machine(SVM)and classification and regression trees(CART)are analyzed by comparing the overall accuracy and Kappa coefficients.The results show that the accuracy of random forest is higher than that of the other three machine learning methods.The long-term characteristics of Poyang Lake winter habitat types are extracted from Landsat satellite images using the RF method.These results show that the mudflat area was larger than water surface and sand.After 2012,more mudflat area had been converted into grassland,which is related to the early onset of the winter dry season in the Poyang Lake area.The habitats were scattered and fragmented from 1990 to 1998;after 1997−1998,however,the degree of landscape patch density and interference decreased,indicating a decreased impact of human-related interference and natural factors on the evolution of habitats in and around Poyang Lake.
