Rapid and timely testing is essential to minimize the COVID-19 spread. Decision makers and policy planners need to determine the equal distribution and accessibility of testing sites. This study mainly examines the sp...Rapid and timely testing is essential to minimize the COVID-19 spread. Decision makers and policy planners need to determine the equal distribution and accessibility of testing sites. This study mainly examines the spatial equality of COVID-19 testing sites that maintain a zero COVID policy in Guangzhou City. The study has identified the spatial disparities of COVID testing sites, characteristics of testing locations, and accessibility. The study has obtained information on COVID testing sites in Guangzhou City and population data. Point pattern analyses, Euclidian distance and allocation, and network analyses are the main methods used to achieve the research objectives, and 1183 total COVID testing sites can be recognized in Guangzhou City. Results revealed that spatial disparities could be noticed over the study area. Testing locations of Guangzhou City are highly clustered. The most significant testing sites are located in Haizhu District, which has the third largest population. The highest population density can be identified in Yuexiu District. However, only 94 testing sites are located there. According to all the results, higher disparities can be identified, and a lack of testing sites is located in the north part of the study area. Some people in the northern part have to travel more than 10 km to reach a testing site. Finally, this paper suggests increasing the number of testing sites in the north and south parts of the study area and keeping the same distribution, considering the area, total population, and population density. This kind of research will be helpful to decision-makers in making proper decisions to maintain a zero COVID policy.展开更多
The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorolog...The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.展开更多
Landsat-9 is the latest satellite of the Landsat program and provides useful agricultural remote sensing data.The earlier the crop distribution data is obtained,the greater the value of the data.However,whether Landsa...Landsat-9 is the latest satellite of the Landsat program and provides useful agricultural remote sensing data.The earlier the crop distribution data is obtained,the greater the value of the data.However,whether Landsat-9 image is suitable for the early-season mapping of winter garlic and winter wheat is a problem worthy of attention.Therefore,this study evaluates the potential to use two Landsat-9 images,acquired on 13 December 2021 and 30 January 2022,for the early-season identification of winter garlic and winter wheat in China.According to J-M(Jeffries-Matusita)distances,we evaluated the separability of winter garlic,winter wheat,and other ground objects based on the two Landsat-9 images.Then,winter garlic and winter wheat were extracted by using unsupervised classification method,i.e.the IsoData and K-means clustering algorithms,and supervised classification method,i.e.the Random Forest(RF),and Support Vector Machine(SVM)algorithms.The separability between garlic and wheat in January is stronger than that in December.The classification overall accuracy based on the Landsat-9 image on 30 January 2022 is 92.03% with a kappa coefficient of 0.87 using the SVM algorithm.This period is about 4 months earlier than the crop harvest period.Landsat-9 images have good potentiality for early-season mapping of winter garlic and winter wheat.展开更多
Poyang Lake experiences cyclical height changes annually,serving as a prime focus for highly dynamic water resource research.However,its complex environment and cloudy climate make timely and accurate mapping of water...Poyang Lake experiences cyclical height changes annually,serving as a prime focus for highly dynamic water resource research.However,its complex environment and cloudy climate make timely and accurate mapping of water resources diffi cult,as well as more accurate ecological and water resource studies.We introduced the Tanh activation function and constructed a new optical water index,the Combined Water Index(CWl),to improve the accuracy and automation level of water identifi cation.Meanwhile,in view of the cloudy weather conditions in Poyang Lake,we integrated the microwave images and proposed the New SAR Water Index(NSWI),which effectively supplemented the observation data.We established a 37-year-long observation experience of the water of Poyang Lake from 1986 to 2023.Third,we introduced water level,cumulative outflow from the Three Gorges Dam,and precipitation data to build a predictive model for random forests with reliable accuracy.Finally,we realized the spatial inversion of the predicted area and constructed a daily-scale water dataset of Poyang Lake(2016-2021).This study offers critical data support for advancing Poyang Lake ecological research and water resource management.展开更多
Interpreting the relationship between urban heat island (UHI) and urban vegetation is a basis for understanding the impacts of underlying surfaces on UHL The calculation of UHI intensity (UHII) requires observations f...Interpreting the relationship between urban heat island (UHI) and urban vegetation is a basis for understanding the impacts of underlying surfaces on UHL The calculation of UHI intensity (UHII) requires observations from paired stations in both urban and rural areas.Due to the limited number of paired meteorological stations,many studies have used remotely sensed land surface temperature,but these time-series land surface temperature data are often heavily affected by cloud cover and other factors.These factors,together with the algorithm for inversion of land surface temperature,lead to accuracy problems in detecting the UHII,especially in cities with weak UHII.Based on meteorological observations from the Oklahoma Mesonet,a world-class network,we quantified the UHII and trends in eight cities of the Great Plains,USA,where data from at least one pair of urban and rural meteorological stations were available.We examined the changes and variability in urban temperature,UHII,vegetation condition (as measured by enhanced vegetation index,EVI),and evapotranspiration (ET).We found that both UHI and urban cold islands (UCI) occurred among the eight cities during 2000-2014 (as measured by impervious surface area).Unlike what is generally considered,UHII in only three cities significantly decreased as EVI and ET increased (p < 0.1),indicating that the UHI or UCI cannot be completely explained simply from the perspective of the underlying surface.Increased vegetative cover (signaled by EVI) can increase ET,and thereby effectively mitigate the UHI.Each study station clearly showed that the underlying surface or vegetation affects urban-rural temperature,and that these factors should be considered during analysis of the UHI effect over time.展开更多
文摘Rapid and timely testing is essential to minimize the COVID-19 spread. Decision makers and policy planners need to determine the equal distribution and accessibility of testing sites. This study mainly examines the spatial equality of COVID-19 testing sites that maintain a zero COVID policy in Guangzhou City. The study has identified the spatial disparities of COVID testing sites, characteristics of testing locations, and accessibility. The study has obtained information on COVID testing sites in Guangzhou City and population data. Point pattern analyses, Euclidian distance and allocation, and network analyses are the main methods used to achieve the research objectives, and 1183 total COVID testing sites can be recognized in Guangzhou City. Results revealed that spatial disparities could be noticed over the study area. Testing locations of Guangzhou City are highly clustered. The most significant testing sites are located in Haizhu District, which has the third largest population. The highest population density can be identified in Yuexiu District. However, only 94 testing sites are located there. According to all the results, higher disparities can be identified, and a lack of testing sites is located in the north part of the study area. Some people in the northern part have to travel more than 10 km to reach a testing site. Finally, this paper suggests increasing the number of testing sites in the north and south parts of the study area and keeping the same distribution, considering the area, total population, and population density. This kind of research will be helpful to decision-makers in making proper decisions to maintain a zero COVID policy.
文摘The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.
基金supported by the Henan Provincial Science and Technology Research and Development Plan Joint Fund,China[grant number 222103810029]Open Fund of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions(Henan University),Ministry of Education[grant number GTYR202204]+4 种基金the National Natural Science Foundation of China[grant number 32130066]Open Fund of Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem[grant number 2023XYQN01]Natural Science Foundation of China[grant number 42301104]Postdoctoral Fellowship Program of CPSF[grant number GZC20230677]the major project of the Collaborative Innovation Center on Yellow River Civilization,jointly built by the Henan province and the Ministry of Education[grant number 2020M19].
文摘Landsat-9 is the latest satellite of the Landsat program and provides useful agricultural remote sensing data.The earlier the crop distribution data is obtained,the greater the value of the data.However,whether Landsat-9 image is suitable for the early-season mapping of winter garlic and winter wheat is a problem worthy of attention.Therefore,this study evaluates the potential to use two Landsat-9 images,acquired on 13 December 2021 and 30 January 2022,for the early-season identification of winter garlic and winter wheat in China.According to J-M(Jeffries-Matusita)distances,we evaluated the separability of winter garlic,winter wheat,and other ground objects based on the two Landsat-9 images.Then,winter garlic and winter wheat were extracted by using unsupervised classification method,i.e.the IsoData and K-means clustering algorithms,and supervised classification method,i.e.the Random Forest(RF),and Support Vector Machine(SVM)algorithms.The separability between garlic and wheat in January is stronger than that in December.The classification overall accuracy based on the Landsat-9 image on 30 January 2022 is 92.03% with a kappa coefficient of 0.87 using the SVM algorithm.This period is about 4 months earlier than the crop harvest period.Landsat-9 images have good potentiality for early-season mapping of winter garlic and winter wheat.
基金supported by the National Natural Science Foundation of China[grant number 42471333]Natural Science Foundation of Henan[grant number 252300421198]+2 种基金Open Fund of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions(Henan University),Ministry of Education[grant number GTYR202204]the National Natural Science Foundation of China[grant number 32130066]Xinyang Academy of Ecological Research Open Foundation[grant number 2023XYQN01].
文摘Poyang Lake experiences cyclical height changes annually,serving as a prime focus for highly dynamic water resource research.However,its complex environment and cloudy climate make timely and accurate mapping of water resources diffi cult,as well as more accurate ecological and water resource studies.We introduced the Tanh activation function and constructed a new optical water index,the Combined Water Index(CWl),to improve the accuracy and automation level of water identifi cation.Meanwhile,in view of the cloudy weather conditions in Poyang Lake,we integrated the microwave images and proposed the New SAR Water Index(NSWI),which effectively supplemented the observation data.We established a 37-year-long observation experience of the water of Poyang Lake from 1986 to 2023.Third,we introduced water level,cumulative outflow from the Three Gorges Dam,and precipitation data to build a predictive model for random forests with reliable accuracy.Finally,we realized the spatial inversion of the predicted area and constructed a daily-scale water dataset of Poyang Lake(2016-2021).This study offers critical data support for advancing Poyang Lake ecological research and water resource management.
基金research grants from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040301)the National Science Foundation EPSCoR program of American (ILA-1301789)+3 种基金the National Natural Science Foundation of China (Grant Nos.41671425 and 41401504)HENU-CPGIS Collaborative Fund (JOF201701)the Key Research Program of Frontier Sciences by the Chinese Academy of Sciences (QYZDB-SSW-DQC005)the “Thousand Youth Talents Plan.”.
文摘Interpreting the relationship between urban heat island (UHI) and urban vegetation is a basis for understanding the impacts of underlying surfaces on UHL The calculation of UHI intensity (UHII) requires observations from paired stations in both urban and rural areas.Due to the limited number of paired meteorological stations,many studies have used remotely sensed land surface temperature,but these time-series land surface temperature data are often heavily affected by cloud cover and other factors.These factors,together with the algorithm for inversion of land surface temperature,lead to accuracy problems in detecting the UHII,especially in cities with weak UHII.Based on meteorological observations from the Oklahoma Mesonet,a world-class network,we quantified the UHII and trends in eight cities of the Great Plains,USA,where data from at least one pair of urban and rural meteorological stations were available.We examined the changes and variability in urban temperature,UHII,vegetation condition (as measured by enhanced vegetation index,EVI),and evapotranspiration (ET).We found that both UHI and urban cold islands (UCI) occurred among the eight cities during 2000-2014 (as measured by impervious surface area).Unlike what is generally considered,UHII in only three cities significantly decreased as EVI and ET increased (p < 0.1),indicating that the UHI or UCI cannot be completely explained simply from the perspective of the underlying surface.Increased vegetative cover (signaled by EVI) can increase ET,and thereby effectively mitigate the UHI.Each study station clearly showed that the underlying surface or vegetation affects urban-rural temperature,and that these factors should be considered during analysis of the UHI effect over time.
