Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geos...Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.展开更多
This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spat...This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.展开更多
Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal...Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.展开更多
Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precip...Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.展开更多
In general, geospatial data can be divided into two formats, raster and vector formats. A raster consists of a matrix of cells where each cell contains a value representing quantitative information, such as temperatur...In general, geospatial data can be divided into two formats, raster and vector formats. A raster consists of a matrix of cells where each cell contains a value representing quantitative information, such as temperature, vegetation intensity, land use/cover, elevation, etc. A vector data consists of points, lines and polygons representing location or distance or area of landscape features in graphical forms. Many raster data are derived from remote sensing techniques using sophisticated sensors by quantitative approach and many vector data are generated from GIS processes by qualitative approach. Among them, land use/cover data is frequently used in many GIS analyses and spatial modeling processes. However, proper use of quantitative and qualitative geospatial data is important in spatial modeling and decision making. In this article, we discuss common geospatial data formats, their origins and proper use in spatial modelling and decision making processes.展开更多
The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wir...The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wireless networking technologies, the improvement in computational power of handheld devices such as smartphones, tablet PCs, ultra-mobile personal computers (UMPCs) and netbook computers allow field users to connect, store and stream large amounts of geospatial data from the web-server. Nowadays, geospatial data collection is more flexible and timely manner. In this paper we discuss field data collection using a smartphone and web-based GIS system, which collects, integrates, visualizes and analyzes the collected data in real-time. We built a web-GIS system for creating a user account, acquiring coordinates from GPS embedded devices or wireless access points, and providing a user-friendly survey form. The collected data can be visualized and analyzed by performing thematic mapping, labeling, symbolizing, querying and generating a summary report. We tested this system on a university campus management system, in which we collected information on illegal disposal sites and parking events within the university campus.展开更多
In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management to...In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.展开更多
Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity...Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.展开更多
Poverty threatens human development especially for developing countries,so ending poverty has become one of the most important United Nations Sustainable Development Goals(SDGs).This study aims to explore China’s pro...Poverty threatens human development especially for developing countries,so ending poverty has become one of the most important United Nations Sustainable Development Goals(SDGs).This study aims to explore China’s progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model.The poverty reduction efficiency(PRE)is measured by the difference in the out-of-poverty rates(which measures the probability of being not poor)of 2016 and 2019.The study shows that the probability of poverty in all regions of China has shown an overall decreasing trend(PRE=0.264),which indicates that the progress in poverty reduction during this period is significant.The Hu Huanyong Line(Hu Line)shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest China.From 2016 to 2019,the centroid of China’s out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line,indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to 2019.The results imply that the government’s future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor areas.This study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government’s poverty reduction policy.展开更多
This study investigates trends in extreme precipitation events(EPEs)across Antarctica from 1979 to 2023,analyzing changes in EPE frequency,intensity,and the proportion of extreme to total precipitation.Using Self-Orga...This study investigates trends in extreme precipitation events(EPEs)across Antarctica from 1979 to 2023,analyzing changes in EPE frequency,intensity,and the proportion of extreme to total precipitation.Using Self-Organizing Map(SOM)techniques,the study distinguishes the contributions from thermodynamic,dynamic,and interaction components in explaining these trends.Positive EPE occurrence trends are observed across the Bellingshausen and Weddell Seas,Dronning Maud Land,and parts of the Southern Ocean,with declines limited to Queen Mary Land.Thermodynamic factors,responsible for 96.0%of the overall trend,are driven by increased water vapor content in polar air masses.Dynamic contributions,representing 10.8%,are linked to a strengthened Amundsen Sea Low(ASL)associated with the Southern Annular Mode(SAM)and Pacific South American(PSA)trends.Interaction effects make a slightly negative contribution(-6.8%)to the overall trend.Variations in water vapor transport and vertical velocity tied to annual 500-hPa geopotential height anomalies further explain EPE trends.These findings provide insight into the atmospheric processes that influence Antarctic EPEs,with implications for understanding the climatic impact on the polar environment.展开更多
The March 11,2011,MW9.0 Tohoku-Oki earthquake,in Japan,caused rapid strain release near the epicenter,while the Boso Peninsula,located farther away,experienced stress redistribution,leading to changes in the recurrenc...The March 11,2011,MW9.0 Tohoku-Oki earthquake,in Japan,caused rapid strain release near the epicenter,while the Boso Peninsula,located farther away,experienced stress redistribution,leading to changes in the recurrence interval of slow slip events(SSEs)and regional strain.This study focuses on three detected post-2011 Boso SSEs,utilizing a segmented model displacement time series measured by Global Navigation Satellite System(GNSS)to calculate velocity and strain rate fields for eight periods before,during,and after the SSEs.Results show that the 2011 earthquake and the three SSEs significantly alter the velocity field in the Boso region,with SSE velocities predominantly oriented southeast,reaching maximum values of 26.9 cm/a,10.6 cm/a,and 38.5 cm/adnearly opposite to non-SSE periods.After the third SSE,the velocity field nearly returns to its pre-earthquake state,with a maximum of 1.8 cm/a.The maximum shear strain rates during the three SSEs are 25.88×10^(-7) a^(-1),11.38×10^(-7) a^(-1),and 29.02×10^(-7) a^(-1)(i.e.,per annum),significantly higher than those during non-slow slip periods,with principal strain rates following a similar pattern.The spatial distribution of strain rates during the SSEs indicates greater deformation compared to the non-slip periods,dominated by northwest-southeast extension and southwest-northeast compression.Spatiotemporal analysis reveals a strong correlation between seismic frequency and strain rate during the SSEs,with time correlation coefficients of 0.85,0.88,and 0.9.Although larger accumulated strain results in stronger strain release during the latter two SSEs,not all strain is fully released,suggesting that earthquake swarms accompanying the SSEs may contribute to the partial release of unreleased strain.This study,through the analysis of GNSS data,evaluates the spatiotemporal distribution of strain fields during periodic SSEs,contributing to further research on strain accumulation and release,and aiding in the analysis of this regional seismic activity.展开更多
Various slow slip events(SSEs)with distinct characteristics have been detected globally,particularly in regions with dense Global Navigation Satellite Systems(GNSS)networks.In the Hikurangi subduction zone of New Zeal...Various slow slip events(SSEs)with distinct characteristics have been detected globally,particularly in regions with dense Global Navigation Satellite Systems(GNSS)networks.In the Hikurangi subduction zone of New Zealand,SSEs frequently occur alongside seismic activity,especially in the Manawatu and Kapiti regions.This study analyzes the 2021-2023 Kapiti-Manawatu long-term SSE using daily displacement data(2019-2023)from 53 GPS stations.The network inversion filter(NIF)method is applied to extract slow slip signals,revealing spatial migration with alternating slip between Kapiti and Manawatu,characterized by distinct phases of acceleration and deceleration.Manawatu exhibits higher slip rates,exceeding 4 cm/month,with greater cumulative slip and surface displacement than Kapiti.A moderate temporal correlation(coefficient 0.59)between seismic activity in the region and slip acceleration in Manawatu suggests that seismic events may contribute to the slip,while no significant correlation is observed in Kapiti.展开更多
Widespread soil acidification driven by nitrogen(N)fertilization and precipitation challenges the conventional notion of the long-term stability of soil inorganic carbon(SIC)in agroecosystems.However,the changes in SI...Widespread soil acidification driven by nitrogen(N)fertilization and precipitation challenges the conventional notion of the long-term stability of soil inorganic carbon(SIC)in agroecosystems.However,the changes in SIC with precipitation and N fertilization remain ambiguous.Based on 4,000+soil samples collected in the 1980s and 2010s and by developing machine learning models to fill the missing SIC of soil samples,this study generated 3,697 paired soil samples between the two periods and then investigated the cropland SIC change and explored its relationship with precipitation and N fertilization across the Sichuan Basin,China.The results showed an overall SIC loss,with a decline of the mean SIC by 15.73%.SIC change varied with initial soil pH and initial SIC and exhibited an exponential relationship with soil pH change,indicating the changing role of carbonates in providing acid-buffering capacity.There was a parabolical relationship between the magnitude of SIC decline and N fertilizer rates,and low N fertilizer rates contributed to a reduction in SIC loss,while SIC loss was promoted by N fertilization occurred when N fertilizing rates exceeded 250 kg ha^(-1) yr^(-1).The change in SIC showed a sinusoidal variation with precipitation,with 950 mm being the threshold controlling whether SIC increased or decreased.Meanwhile,N fertilization did not alter the sinusoidal relationship between SIC change and precipitation.In areas with rainfall<950 mm,the high N fertilizer rate did not cause SIC loss,while higher precipitation could also cause larger SIC loss in areas with lower N fertilizer rates.These results suggest that SIC dynamics are jointly driven by precipitation and N fertilization and are controlled by acid-buffering mechanisms associated with initial pH and SIC,with precipitation being the predominant driver.These findings emphasize the need for more regional soil observations and in-depth studies of SIC change and its mechanisms for accurately estimating SIC change.展开更多
提出利用GPS参考网估计电离层延迟、卫星相位偏差的算法,用于实现区域内精密单点定位(Precise Point Positioning,PPP)的整周模糊度快速固定.利用站间距约为100～200km的参考网进行实验,结果表明:电离层延迟的内插和外推精度均优于1dm,...提出利用GPS参考网估计电离层延迟、卫星相位偏差的算法,用于实现区域内精密单点定位(Precise Point Positioning,PPP)的整周模糊度快速固定.利用站间距约为100～200km的参考网进行实验,结果表明:电离层延迟的内插和外推精度均优于1dm,卫星相位偏差估值的日内变化不超过0.2周;此外,单天内不同时刻始,固定PPP整周模糊度所需时长最多不超过10min,且当模糊度成功固定后,三维位置解较之相应浮点解的精度改善优于80%.新算法可望解决PPP普遍存在的收敛时间过长问题,增强了PPP技术的实用性.展开更多
全球范围内大量布设的GNSS（Global Navigation Satellite System）参考网为精密定位、导航和授时等应用提供了丰富的数据资源.基于局域参考网,先后发展了若干侧重实现双频精密定位的技术,如NRTK（Network Real Time Kinematic）,PPP（P...全球范围内大量布设的GNSS（Global Navigation Satellite System）参考网为精密定位、导航和授时等应用提供了丰富的数据资源.基于局域参考网,先后发展了若干侧重实现双频精密定位的技术,如NRTK（Network Real Time Kinematic）,PPP（Precise Point Positioning）和PPP-RTK等.其中,PPP-RTK融合了NRTK和PPP的技术优势,是目前相关研究的热点.本文改进了利用局域参考网提取各类改正信息的算法,以便于实现单频PPP-RTK,具体步骤包括：1）逐参考站实施非组合PPP,并固定已知站星距和卫星钟差,预估电离层延迟、浮点模糊度等参数;2）联合所有参考站的PPP模糊度预估值,通过重新参数化,形成一组双差整周模糊度和接收机、卫星相位偏差;3）固定双差整周模糊度,精化求解卫星相位偏差和各参考站PPP电离层延迟.基于网解中用到的卫星轨道和钟差,以及网解所提供的卫星相位偏差和（内插的）电离层延迟,参考网内的单频流动站即可实施PPP-RTK.基于澳大利亚某连续运行参考站网和流动站的实测数据,考察了：1）参考网数据处理中,双差模糊度的固定成功率（98.89%）和卫星相位偏差估值的时间稳定性（各连续弧段优于0.2周）;2）流动站处电离层延迟的内插精度（优于10cm）;3）单天内任一历元起算,固定静态（动态）单频PPP整周模糊度所需时长（均不超过10min）;4）模糊度固定前后,单频动态PPP的定位精度（模糊度固定后,平面和天顶RMS分别优于5cm和10cm;模糊度固定前,相应RMS仅为28～53cm）.展开更多
文摘Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.
基金supported by the National Natural Science Foundation of China (40930101,40971218)the 948 Program,Ministry of Agriculture of China (2009-Z31)the Foundation for National Non-Profit Scientific Institution,Ministry of Finance of China (IARRP-2010-2)
文摘This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.
文摘Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.
基金funded by the Center for Spatial Information Science and Systems at George Mason University, USABayes Ahmed is a Commonwealth Scholar funded by the UK govt
文摘Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.
文摘In general, geospatial data can be divided into two formats, raster and vector formats. A raster consists of a matrix of cells where each cell contains a value representing quantitative information, such as temperature, vegetation intensity, land use/cover, elevation, etc. A vector data consists of points, lines and polygons representing location or distance or area of landscape features in graphical forms. Many raster data are derived from remote sensing techniques using sophisticated sensors by quantitative approach and many vector data are generated from GIS processes by qualitative approach. Among them, land use/cover data is frequently used in many GIS analyses and spatial modeling processes. However, proper use of quantitative and qualitative geospatial data is important in spatial modeling and decision making. In this article, we discuss common geospatial data formats, their origins and proper use in spatial modelling and decision making processes.
文摘The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wireless networking technologies, the improvement in computational power of handheld devices such as smartphones, tablet PCs, ultra-mobile personal computers (UMPCs) and netbook computers allow field users to connect, store and stream large amounts of geospatial data from the web-server. Nowadays, geospatial data collection is more flexible and timely manner. In this paper we discuss field data collection using a smartphone and web-based GIS system, which collects, integrates, visualizes and analyzes the collected data in real-time. We built a web-GIS system for creating a user account, acquiring coordinates from GPS embedded devices or wireless access points, and providing a user-friendly survey form. The collected data can be visualized and analyzed by performing thematic mapping, labeling, symbolizing, querying and generating a summary report. We tested this system on a university campus management system, in which we collected information on illegal disposal sites and parking events within the university campus.
文摘In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.
文摘Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.
基金supported by the National Key Research and Development Program of China[grant number 2019YFB2102903]the National Natural Science Foundation of China[grant number 41801306]+1 种基金the“CUG Scholar”Scientific Research Funds at China University of Geosciences(Wuhan)[grant number 2022034]a grant from State Key Laboratory of Resources and Environmental Information System.
文摘Poverty threatens human development especially for developing countries,so ending poverty has become one of the most important United Nations Sustainable Development Goals(SDGs).This study aims to explore China’s progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model.The poverty reduction efficiency(PRE)is measured by the difference in the out-of-poverty rates(which measures the probability of being not poor)of 2016 and 2019.The study shows that the probability of poverty in all regions of China has shown an overall decreasing trend(PRE=0.264),which indicates that the progress in poverty reduction during this period is significant.The Hu Huanyong Line(Hu Line)shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest China.From 2016 to 2019,the centroid of China’s out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line,indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to 2019.The results imply that the government’s future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor areas.This study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government’s poverty reduction policy.
基金supported by the National Key R&D Program of China(2022YFE0106300)Norges Forskningsråd(328886).
文摘This study investigates trends in extreme precipitation events(EPEs)across Antarctica from 1979 to 2023,analyzing changes in EPE frequency,intensity,and the proportion of extreme to total precipitation.Using Self-Organizing Map(SOM)techniques,the study distinguishes the contributions from thermodynamic,dynamic,and interaction components in explaining these trends.Positive EPE occurrence trends are observed across the Bellingshausen and Weddell Seas,Dronning Maud Land,and parts of the Southern Ocean,with declines limited to Queen Mary Land.Thermodynamic factors,responsible for 96.0%of the overall trend,are driven by increased water vapor content in polar air masses.Dynamic contributions,representing 10.8%,are linked to a strengthened Amundsen Sea Low(ASL)associated with the Southern Annular Mode(SAM)and Pacific South American(PSA)trends.Interaction effects make a slightly negative contribution(-6.8%)to the overall trend.Variations in water vapor transport and vertical velocity tied to annual 500-hPa geopotential height anomalies further explain EPE trends.These findings provide insight into the atmospheric processes that influence Antarctic EPEs,with implications for understanding the climatic impact on the polar environment.
基金funded by the National Natural Science Foundation of China(41704031,42374040)the Natural Science Foundation of Jiangxi Science and Technology Department(20232BAB203073)the Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake,Ministry of Natural Resources(MEMI-2021-2022-29).
文摘The March 11,2011,MW9.0 Tohoku-Oki earthquake,in Japan,caused rapid strain release near the epicenter,while the Boso Peninsula,located farther away,experienced stress redistribution,leading to changes in the recurrence interval of slow slip events(SSEs)and regional strain.This study focuses on three detected post-2011 Boso SSEs,utilizing a segmented model displacement time series measured by Global Navigation Satellite System(GNSS)to calculate velocity and strain rate fields for eight periods before,during,and after the SSEs.Results show that the 2011 earthquake and the three SSEs significantly alter the velocity field in the Boso region,with SSE velocities predominantly oriented southeast,reaching maximum values of 26.9 cm/a,10.6 cm/a,and 38.5 cm/adnearly opposite to non-SSE periods.After the third SSE,the velocity field nearly returns to its pre-earthquake state,with a maximum of 1.8 cm/a.The maximum shear strain rates during the three SSEs are 25.88×10^(-7) a^(-1),11.38×10^(-7) a^(-1),and 29.02×10^(-7) a^(-1)(i.e.,per annum),significantly higher than those during non-slow slip periods,with principal strain rates following a similar pattern.The spatial distribution of strain rates during the SSEs indicates greater deformation compared to the non-slip periods,dominated by northwest-southeast extension and southwest-northeast compression.Spatiotemporal analysis reveals a strong correlation between seismic frequency and strain rate during the SSEs,with time correlation coefficients of 0.85,0.88,and 0.9.Although larger accumulated strain results in stronger strain release during the latter two SSEs,not all strain is fully released,suggesting that earthquake swarms accompanying the SSEs may contribute to the partial release of unreleased strain.This study,through the analysis of GNSS data,evaluates the spatiotemporal distribution of strain fields during periodic SSEs,contributing to further research on strain accumulation and release,and aiding in the analysis of this regional seismic activity.
基金funded by the National Natural Science Foundation of China(41704031,42374040)the Natural Science Foundation of Jiangxi Science and Technology Department(20232BAB203073)the Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake,Ministry of Natural Resources(MEMI-2021-2022-29).
文摘Various slow slip events(SSEs)with distinct characteristics have been detected globally,particularly in regions with dense Global Navigation Satellite Systems(GNSS)networks.In the Hikurangi subduction zone of New Zealand,SSEs frequently occur alongside seismic activity,especially in the Manawatu and Kapiti regions.This study analyzes the 2021-2023 Kapiti-Manawatu long-term SSE using daily displacement data(2019-2023)from 53 GPS stations.The network inversion filter(NIF)method is applied to extract slow slip signals,revealing spatial migration with alternating slip between Kapiti and Manawatu,characterized by distinct phases of acceleration and deceleration.Manawatu exhibits higher slip rates,exceeding 4 cm/month,with greater cumulative slip and surface displacement than Kapiti.A moderate temporal correlation(coefficient 0.59)between seismic activity in the region and slip acceleration in Manawatu suggests that seismic events may contribute to the slip,while no significant correlation is observed in Kapiti.
基金supported by the National Natural Science Foundation of China(42330707 and 41930647)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China(72221002)the Science and Technology Plan of Sichuan Province,China(2022NSFSC0104).
文摘Widespread soil acidification driven by nitrogen(N)fertilization and precipitation challenges the conventional notion of the long-term stability of soil inorganic carbon(SIC)in agroecosystems.However,the changes in SIC with precipitation and N fertilization remain ambiguous.Based on 4,000+soil samples collected in the 1980s and 2010s and by developing machine learning models to fill the missing SIC of soil samples,this study generated 3,697 paired soil samples between the two periods and then investigated the cropland SIC change and explored its relationship with precipitation and N fertilization across the Sichuan Basin,China.The results showed an overall SIC loss,with a decline of the mean SIC by 15.73%.SIC change varied with initial soil pH and initial SIC and exhibited an exponential relationship with soil pH change,indicating the changing role of carbonates in providing acid-buffering capacity.There was a parabolical relationship between the magnitude of SIC decline and N fertilizer rates,and low N fertilizer rates contributed to a reduction in SIC loss,while SIC loss was promoted by N fertilization occurred when N fertilizing rates exceeded 250 kg ha^(-1) yr^(-1).The change in SIC showed a sinusoidal variation with precipitation,with 950 mm being the threshold controlling whether SIC increased or decreased.Meanwhile,N fertilization did not alter the sinusoidal relationship between SIC change and precipitation.In areas with rainfall<950 mm,the high N fertilizer rate did not cause SIC loss,while higher precipitation could also cause larger SIC loss in areas with lower N fertilizer rates.These results suggest that SIC dynamics are jointly driven by precipitation and N fertilization and are controlled by acid-buffering mechanisms associated with initial pH and SIC,with precipitation being the predominant driver.These findings emphasize the need for more regional soil observations and in-depth studies of SIC change and its mechanisms for accurately estimating SIC change.
文摘提出利用GPS参考网估计电离层延迟、卫星相位偏差的算法,用于实现区域内精密单点定位(Precise Point Positioning,PPP)的整周模糊度快速固定.利用站间距约为100～200km的参考网进行实验,结果表明:电离层延迟的内插和外推精度均优于1dm,卫星相位偏差估值的日内变化不超过0.2周;此外,单天内不同时刻始,固定PPP整周模糊度所需时长最多不超过10min,且当模糊度成功固定后,三维位置解较之相应浮点解的精度改善优于80%.新算法可望解决PPP普遍存在的收敛时间过长问题,增强了PPP技术的实用性.
基金国家自然科学重点基金(41231064)国家重点基础研究发展计划项目(2012CB825604)+3 种基金国家高技术研究发展计划(2012AA121803)国家自然科学基金(41374043)大地测量与地球动力学国家重点实验室开放基金(SKLGED2013-1-6-E)the Positioning Program Project 1.19 "Multi-GNSS PPP-RTK Network Processing" of the Cooperative Research Centre for Spatial Information(CRC-SI)联合资助
文摘全球范围内大量布设的GNSS（Global Navigation Satellite System）参考网为精密定位、导航和授时等应用提供了丰富的数据资源.基于局域参考网,先后发展了若干侧重实现双频精密定位的技术,如NRTK（Network Real Time Kinematic）,PPP（Precise Point Positioning）和PPP-RTK等.其中,PPP-RTK融合了NRTK和PPP的技术优势,是目前相关研究的热点.本文改进了利用局域参考网提取各类改正信息的算法,以便于实现单频PPP-RTK,具体步骤包括：1）逐参考站实施非组合PPP,并固定已知站星距和卫星钟差,预估电离层延迟、浮点模糊度等参数;2）联合所有参考站的PPP模糊度预估值,通过重新参数化,形成一组双差整周模糊度和接收机、卫星相位偏差;3）固定双差整周模糊度,精化求解卫星相位偏差和各参考站PPP电离层延迟.基于网解中用到的卫星轨道和钟差,以及网解所提供的卫星相位偏差和（内插的）电离层延迟,参考网内的单频流动站即可实施PPP-RTK.基于澳大利亚某连续运行参考站网和流动站的实测数据,考察了：1）参考网数据处理中,双差模糊度的固定成功率（98.89%）和卫星相位偏差估值的时间稳定性（各连续弧段优于0.2周）;2）流动站处电离层延迟的内插精度（优于10cm）;3）单天内任一历元起算,固定静态（动态）单频PPP整周模糊度所需时长（均不超过10min）;4）模糊度固定前后,单频动态PPP的定位精度（模糊度固定后,平面和天顶RMS分别优于5cm和10cm;模糊度固定前,相应RMS仅为28～53cm）.
