The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has b...The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.展开更多
In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial ...In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial vehicle based on scene matching(GTLUAVSM)is proposed.The sugges-ted approach entails completing scene matching through a feature matching algorithm.Then,multi-sensor registration is optimized by robust estimation based on homologous registration.Finally,basemap generation and model solution are utilized to improve basemap correspondence and accom-plish aerial image positioning.Theoretical evidence and experimental verification demonstrate that GTLUAVSM can improve localization accuracy,speed,and precision while minimizing reliance on task equipment.展开更多
The important role of greenway non-mortorized systems in urban sustainable development was summarized,pointing out their potential value in improving the ecological environment,promoting healthy living,and enhancing c...The important role of greenway non-mortorized systems in urban sustainable development was summarized,pointing out their potential value in improving the ecological environment,promoting healthy living,and enhancing community connections.Based on the analysis on some cases of urban greenway construction in China,specific transformation models and strategies were proposed for greenway construction,which could integrate green spaces with non-mortorized system,so as to enhance the comprehensive efficiency of urban linear spaces.展开更多
Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in th...Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index（NDVI） and an improved Carnegie-Ames-Stanford（CASA） model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination（R2） of 0.65 and root mean square error（RMSE） of 20.86 g C/（m^2·a）. Temporally, annual NPP of the Ili River Valley was 599.19 g C/（m^2·a） and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of –3.51 g C/（m^2·a）. However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.展开更多
Low-carbon economic development is a strategy that is emerging in response to global climate change. Being the third-largest energy base in the world, Central Asia should adopt rational and efficient energy utilizatio...Low-carbon economic development is a strategy that is emerging in response to global climate change. Being the third-largest energy base in the world, Central Asia should adopt rational and efficient energy utilization to achieve the sustainable economic development. In this study, the logarithmic mean Divisia index(LMDI) decomposition method was used to explore the influence factors of CO2 emissions in Central Asia(including Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan and Turkmenistan) during the period 1992–2014. Moreover, decoupling elasticity and decoupling index based on the LMDI decomposition results were employed to explore the relationship between economic growth and CO2 emissions during the study period. Our results show that the total CO2 emissions decreased during the period 1992–1998, influenced by the collapse of the Soviet Union in 1991 and the subsequent financial crisis. After 1998, the total CO2 emissions started to increase slowly along with the economic growth after the market economic reform. Energy-related CO2 emissions increased in Central Asia, mainly driven by economic activity effect and population effect, while energy intensity effect and energy carbon structure effect were the primary factors inhibiting CO2 emissions. The contribution percentages of these four factors(economic activity effect, population effect, energy intensity effect and energy carbon structure effect) to the total CO2 emissions were 11.80%, 39.08%, –44.82% and –4.32%, respectively, during the study period. Kazakhstan, Uzbekistan and Turkmenistan released great quantities of CO2 with the annual average emissions of 189.69×106, 45.55×106 and 115.38×106 t, respectively. In fact, their economic developments depended on high-carbon energies. The decoupling indices clarified the relationship between CO2 emissions and economic growth, highlighting the occurrence of a ’’weak decoupling’’ between these two variables in Central Asia. In conclusion, our results indicate that CO2 emissions are still not completely decoupled from economic growth in Central Asia. Based on these results, we suggest four key policy suggestions in this paper to help Central Asia to reduce CO2 emissions and build a resource-conserving and environment-friendly society.展开更多
Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area...Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area is important for breeding area planning,production value estimation,ecological survey,and storm surge prevention.However,as the aquaculture area expands,the seawater background becomes increasingly complex and spectral characteristics differ dramatically,making it difficult to determine the aquaculture area.In this study,we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features(RCF)network model to extract the aquaculture area.Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao.The results demonstrate that this method does not require land and water separation of the area in advance,and good extraction can be achieved in the areas with more sediment and waves,with an extraction accuracy>93%,which is suitable for large-scale aquaculture area extraction.Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high,reaching a higher vulnerability level than other parts.展开更多
基金financially supported by the National Key R&D Program of China(Grant No.2022YFB4200705)the National Natural Science Foundation of China(Grant No.52109146)。
文摘The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.
基金the National Key R&D Program of China(2022YFF0604502).
文摘In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial vehicle based on scene matching(GTLUAVSM)is proposed.The sugges-ted approach entails completing scene matching through a feature matching algorithm.Then,multi-sensor registration is optimized by robust estimation based on homologous registration.Finally,basemap generation and model solution are utilized to improve basemap correspondence and accom-plish aerial image positioning.Theoretical evidence and experimental verification demonstrate that GTLUAVSM can improve localization accuracy,speed,and precision while minimizing reliance on task equipment.
文摘The important role of greenway non-mortorized systems in urban sustainable development was summarized,pointing out their potential value in improving the ecological environment,promoting healthy living,and enhancing community connections.Based on the analysis on some cases of urban greenway construction in China,specific transformation models and strategies were proposed for greenway construction,which could integrate green spaces with non-mortorized system,so as to enhance the comprehensive efficiency of urban linear spaces.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19030204)the West Light Program of Chinese Academy of Sciences(2015-XBQN-B-17)
文摘Net primary productivity（NPP）, as an important variable and ecological indicator in grassland ecosystems, can reflect environmental change and the carbon budget level. The Ili River Valley is a wetland nestled in the hinterland of the Eurasian continent, which responds sensitively to the global climate change. Understanding carbon budget and their responses to climate change in the ecosystem of Ili River Valley has a significant effect on the adaptability of future climate change and sustainable development. In this study, we calculated the NPP and analyzed its spatio-temporal pattern of the Ili River Valley during the period 2000–2014 using the normalized difference vegetation index（NDVI） and an improved Carnegie-Ames-Stanford（CASA） model. Results indicate that validation showed a good performance of CASA over the study region, with an overall coefficient of determination（R2） of 0.65 and root mean square error（RMSE） of 20.86 g C/（m^2·a）. Temporally, annual NPP of the Ili River Valley was 599.19 g C/（m^2·a） and showed a decreasing trend from 2000 to 2014, with an annual decrease rate of –3.51 g C/（m^2·a）. However, the spatial variation was not consistent, in which 55.69% of the areas showed a decreasing tendency, 12.60% of the areas remained relatively stable and 31.71% appeared an increasing tendency. In addition, the decreasing trends in NPP were not continuous throughout the 15-year period, which was likely being caused by a shift in climate conditions. Precipitation was found to be the dominant climatic factor that controlled the inter-annual variability in NPP. Furthermore, the correlations between NPP and climate factors differed along the vertical zonal. In the medium-high altitudes of the Ili River Valley, the NPP was positively correlated to precipitation and negatively correlated to temperature and net radiation. In the low-altitude valley and high-altitude mountain areas, the NPP showed a negative correlation with precipitation and a weakly positive correlation with temperature and net radiation. The results suggested that the vegetation of the Ili River Valley degraded in recent years, and there was a more complex mechanism of local hydrothermal redistribution that controlled the growth of vegetation in this valley ecosystem.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19030204)the West Light Foundation of the Chinese Academy of Sciences (2015-XBQN-17)
文摘Low-carbon economic development is a strategy that is emerging in response to global climate change. Being the third-largest energy base in the world, Central Asia should adopt rational and efficient energy utilization to achieve the sustainable economic development. In this study, the logarithmic mean Divisia index(LMDI) decomposition method was used to explore the influence factors of CO2 emissions in Central Asia(including Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan and Turkmenistan) during the period 1992–2014. Moreover, decoupling elasticity and decoupling index based on the LMDI decomposition results were employed to explore the relationship between economic growth and CO2 emissions during the study period. Our results show that the total CO2 emissions decreased during the period 1992–1998, influenced by the collapse of the Soviet Union in 1991 and the subsequent financial crisis. After 1998, the total CO2 emissions started to increase slowly along with the economic growth after the market economic reform. Energy-related CO2 emissions increased in Central Asia, mainly driven by economic activity effect and population effect, while energy intensity effect and energy carbon structure effect were the primary factors inhibiting CO2 emissions. The contribution percentages of these four factors(economic activity effect, population effect, energy intensity effect and energy carbon structure effect) to the total CO2 emissions were 11.80%, 39.08%, –44.82% and –4.32%, respectively, during the study period. Kazakhstan, Uzbekistan and Turkmenistan released great quantities of CO2 with the annual average emissions of 189.69×106, 45.55×106 and 115.38×106 t, respectively. In fact, their economic developments depended on high-carbon energies. The decoupling indices clarified the relationship between CO2 emissions and economic growth, highlighting the occurrence of a ’’weak decoupling’’ between these two variables in Central Asia. In conclusion, our results indicate that CO2 emissions are still not completely decoupled from economic growth in Central Asia. Based on these results, we suggest four key policy suggestions in this paper to help Central Asia to reduce CO2 emissions and build a resource-conserving and environment-friendly society.
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402003)the National Natural Science Foundation of China(No.41671436)the Innovation Project of LREIS(No.O88RAA01YA)
文摘Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area is important for breeding area planning,production value estimation,ecological survey,and storm surge prevention.However,as the aquaculture area expands,the seawater background becomes increasingly complex and spectral characteristics differ dramatically,making it difficult to determine the aquaculture area.In this study,we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features(RCF)network model to extract the aquaculture area.Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao.The results demonstrate that this method does not require land and water separation of the area in advance,and good extraction can be achieved in the areas with more sediment and waves,with an extraction accuracy>93%,which is suitable for large-scale aquaculture area extraction.Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high,reaching a higher vulnerability level than other parts.
