Remote sensing inversion and prediction of land use land cover in the middle reaches of the Yangtze River basin, China.

Land use and land cover (LULC) changes are dynamic and have been extensively studied; the change in LULC has become a crucial factor in decision making for planners and conservationists owing to its impact on natural ecosystems. Deriving accurate LULC data and analyzing their changes are important for assessing the energy balance, carbon balance, and hydrological cycle in a region. Therefore, we investigated the best classification method from the four methods and analyzed the change in LULC in the middle Yangtze River basin (MYRB) from 2001 to 2020 using the Google Earth Engine (GEE). The results suggest that (1) GEE platform enables to rapidly acquire and process remote sensing images for deriving LULC, and the random forest (RF) algorithm was able to calculate the highest overall accuracy and kappa coefficient (KC) of 87.7% and 0.84, respectively; (2) forestland occupied the largest area from 2001 to 2020, followed by water bodies and buildings. During the study period, there was a significant change in area occupied by both water bodies (overall increase of 46.2%) and buildings (decrease of 14.3% from 2001 to 2005); and (3) the simulation of LULC in the MYRB area was based on the primary drivers in the area, of which elevation changes had the largest effect on LULC changes. The patch generated land use simulation model (PLUS) was used to produce the simulation, with an overall accuracy and KC of 89.6% and 0.82, respectively. This study not only was useful for understanding the spatial and temporal characteristics of LULC in the MYRB, but also offered the basis for the simulation of ecological quality in this region.

Multivariable integrated risk assessment for cyanobacterial blooms in eutrophic lakes and its spatiotemporal characteristics.

Climate change has catalyzed the global expansion of cyanobacterial blooms in eutrophic , lakes and threatens water security. In most studies, the cyanobacterial bloom risk levels in lakes were evaluated using field-collected data from multiple indicators or spatially continuous data from one cyanobacteria-related indicator. Nevertheless, the occurrence of cyanobacterial blooms in lakes has clear spatial heterogeneity and is affected by numerous factors. Therefore, we developed a multivariable integrated risk assessment framework for cyanobacterial blooms in lakes using five spatially continuous datasets to estimate the risk level of cyanobacterial blooms at the pixel scale (250 m). The spatial and temporal variations in cyanobacterial bloom risk levels from May 1, 2002, to October 31, 2020, were investigated for three typical eutrophic lakes in China: Lakes Taihu, Chaohu, and Dianchi. Seasons and regions of high cyanobacterial bloom risk were identified for each lake. Environmental characteristics were discussed. A long-term investigation revealed that owing to its warm climate, the cyanobacterial risk levels in summer and autumn were much higher than those in the other two seasons. At the synoptic scale, Lake Taihu had a lower cyanobacterial bloom risk than Lakes Chaohu and Dianchi. A further comparison found that precipitation, wind speed, and temperature were responsible for the differences in cyanobacterial bloom risk levels among the three lakes. At the pixel scale, the risk map indicated that the cyanobacterial bloom risk levels of Lake Taihu were unevenly distributed, and the cyanobacterial bloom risk of the lakeshore was higher than that of the other subregions. Nutrient levels played the most critical role in the regional differences in cyanobacterial bloom risk levels in a lake. While the differences of cyanobacterial bloom risk levels in three lakes were resulted by the climates. Bloom events were defined and classified as "long-term bloom" or "flash bloom" according to their duration (over or below a year). Overall, this study can assist in advanced water management with a pixel-scale evaluation of cyanobacterial bloom risk levels.

Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning.

Cyanobacterial blooms in most lakes exhibit extraordinary changes in time and space. Herein, a cyanobacterial prediction model was designed for Lake Taihu based on a machine learning method. This method can generate temporally continuous (24 moments throughout the day) cyanobacterial data at a fine spatial scale of 9 km. The hourly meteorological data for 24 moments of the day were obtained from ERA5-Land data. Areal coverage of cyanobacterial blooms was derived from the hourly Geostationary Ocean Color Imager reflectance data observed only eight times a day (from ~8:00 to ~15:00, UTC+8). The cyanobacterial and meteorological data of eight moments in Lake Taihu from 2011 to 2020 were used to design the prediction model. The results were compared and validated employing nine training strategies to determine the best cyanobacterial prediction model for Lake Taihu (R = 0.42; root mean square error = 0.10). With the best-fitted model utilizing meteorological data (2011-2020), the area coverage of cyanobacterial blooms at the other 16 moments during a day were estimated. Based on this, the regional and temporal characteristics of diurnal bloom variation were evaluated at an hourly scale. The results indicated that the hourly variations in the areal coverage of cyanobacterial blooms at 24 moments of the day had similar patterns in each subregion of Lake Taihu with minor seasonal variations. The six meteorological variables adopted to construct the model had similar diurnal changes but with diverse value ranges among the seasons. Further analysis revealed that three meteorological variables (temperature, surface pressure, and evaporation) were positively related to diurnal bloom variations at an hourly scale. Overall, these results illustrate that meteorological conditions can affect the occurrence of cyanobacterial blooms at multiple time scales (e.g., hourly, daily, or monthly). The developed cyanobacterial prediction model can provide cyanobacterial data when cyanobacterial data is unavailable for the target waterbody.

Will carbon trading reduce spatial inequality? A spatial analysis of 200 cities in China.

The rising concentration of carbon in the atmosphere leads to increasing climate change, and it has become a worldwide consensus to reduce emissions. Considering the degree of economic development and industrial structure of different regions and the vast differences in the spatial distribution of clean energy reserves, it is essential to develop localized emission reduction programs. This study investigates the changes in city GDP after implementing carbon pricing policies. The results show that the carbon pricing policy could effectively reduce inequalities between "rich" and "poor" regions. The Moran index before and after the implementation of the policy decreases from 0.416 to 0.401. We also found spatial clustering patterns of carbon emissions, with the main drivers of carbon emissions differing significantly between developed and developing cities, resource-based and industrial cities, and southern and northern cities in China. The most crucial driver of carbon emissions is still the demand for economic development, which can explain more than 30% of carbon emissions. This study focuses on the impact of carbon market & carbon pricing on poverty alleviation and carbon reduction, makes up for the lack of "spatial justice" in the existing studies and provides a feasible carbon reduction plan for different cities.

A systematic review and quantitative meta-analysis of the relationships between driving forces and cyanobacterial blooms at global scale.

The global expansion of cyanobacterial blooms poses a major risk to the safety of freshwater resources. As a result, many explorations have been performed at a regional scale to determine the underlying impact mechanism of cyanobacterial blooms for one or several waterbodies. However, two questions still need to be answered quantitatively at a global scale to assist the water management. One is to specify which factors were often selected as the driving forces of cyanobacterial blooms, and the other is to estimate their quantitative relationships. For that, this paper applied a systematic literature review for 41 peer-reviewed studies published before May 2021 and a statistical meta-analysis based on the Pearson's or Spearman's correlation coefficients from 27 studies. These results showed that the water quality, hydraulic conditions, meteorological conditions and nutrient levels were often considered the driving forces of cyanobacterial blooms in global freshwater systems. Among these, meteorological conditions and nutrient level had the highest probability of being chosen as the driving force. In addition, knowledge of the quantitative relationships between these driving forces and cyanobacterial blooms was newly synthesized based on the correlation coefficients. The results indicated that, at a global scale, meteorological conditions were negatively related to cyanobacterial blooms, and other driving forces, such as water quality, hydraulic conditions and nutrient levels, were positively related to cyanobacterial blooms. In addition, the measurement indicators of these driving forces had diverse forms. For example, the nutrient level can be measured by the concentration of different forms of nitrogen or phosphorus, which may lead to different results in correlation analysis. Thus, a subgroup meta-analysis was necessary for the subdivided driving forces and cyanobacterial blooms, which had a better accuracy. Overall, the synthesized knowledge can help guide advanced cyanobacteria-centered water management, especially when the necessary cyanobacterial data of targeting waterbodies are inaccessible.

Land use/land cover prediction and analysis of the middle reaches of the Yangtze River under different scenarios.

Land use and land cover (LULC) projections are critical for climate models to predict the impacts of LULC change on the Earth system. Different assumptions and policies influence LULC changes, which are a key factor in the decisions of planners and conservationists. Therefore, we predicted and analyzed LULC changes in future scenarios (SSP1-26, SSP2-45, SSP5-85) in the middle reaches of the Yangtze River basin (MYRB). We obtain historical (i.e., 2005-2020) LULC data from the Google Earth Engine (GEE) platform using the random forest (RF) classification method. LULC data for different future scenarios are also obtained by the driving factors of LULC changes in future shared socioeconomic pathways (SSPs), representative concentration pathways (RCPs) (SSP-RCP) scenarios (i.e., 2035-2095) and the patch-generated land use simulation (PLUS) model. The major findings are as follows: (1) simulation using the PLUS model based on the acquired classification data and the selected drivers can obtain accurate land use data in MYRB and a Kappa coefficient of 89.6% and 0.82, respectively; (2) as for the LULC changes in the MYRB, forests increased by 3.9% and decreased by 1.2% in the SSP1-26 and SSP5-85 scenarios, respectively, while farmland decreased by 9.2% and increased by 13.4% in SSP 1-26 and SSP 2-45, respectively, during 2080-2095; and (3) the main conversions in LULC in the MYRB were farmland to forest, forests/water bodies to farmland, and forests/grasslands to farmland/buildings in SSP1-2.6, SSP2-4.5, and SSP 5-8.5, respectively. This can be mainly attributed to gross domestic product (GDP), population (POP), temperature, and precipitation. Overall, this study not only contributes to the understanding of the mechanisms of LULC changes in the MYRB but also provides a basis for ecological and climatic studies.

Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations.

Occurrence of cyanobacterial blooms in most lakes has dramatic changes in time and space. However, most current studies only focused on daily or seasonal scales to obtain a relatively coarse resolution result. To explore the possibility of fine changes occurring within a day in Lake Taihu (China), the area coverage of surface cyanobacterial blooms was quantified from the hourly Geostationary Ocean Color Imager (GOCI) data using a GOCI-derived cyanobacterial index. Based on that, diurnal change characteristics were explored at two scales, and the environmental impacts were investigated. For that, an classification method was first designed to identify the types of diurnal change patterns of cyanobacterial blooms automatically. This method classified the patterns into four types, including the decreasing (Type1), decreasing first and then increasing (Type2), increasing (Type3), increasing first and then decreasing (Type4). Based on that, the types of diurnal change patterns of blooms in Lake Taihu (from April 1, 2011 to October 31, 2020) were identified at pixel (500 m) and synoptic scales. Results indicated that Type1 and Type3 were two hot diurnal change patterns of blooms, and lakeshore was the hotspot occurring severe diurnal changes, and autumn was the hot season occurring frequent diurnal changes. Specifically, hotspot of Type1 was lakeshore, while hotspot of Type3 was Central Regions. Environmental impacts were analyzed at two scales. At pixel scale (500 m), diurnal variation of temperature affected the regional occurence of each type ofdiurnal changes patterns of blooms, and the afternoon temperature played the most critical role (p < 0.001, N = 8316). The occurrence frequency of Type1 was positively (R = 0.41) related with the afternoon temperature, and the occurrence frequency of Type3 was negatively (R = -0.37) related with it. Diurnal variation of wind speed was another key factor impacting the occurrence of obvious diurnal blooms changes, and the wind impacts should be distinguished when the wind speed was over or below 3.5 m/s. At synoptic scale, the interaction of multi environmental factors influenced the diurnal change degree of blooms area, and the environmental contributions were 71%.Comparing with the existing manual classifying workat synoptic scale, the designed classification method can identify the types of diurnal change patterns of blooms at a higher spatial resolution (500 m). These explorations on diurnal dynamics of cyanobacterial blooms in Lake Taihu provide a new insight for advanced cyanobacteria dynamics studies and regional water management.

Spatiotemporal dynamics of wetlands and their driving factors based on PLS-SEM: A case study in Wuhan.

Globally, wetlands have been severely damaged due to natural environment and human activities. Understanding the spatiotemporal dynamics of wetlands and their driving forces is essential for their effective protection. This study proposes a research framework to explore the interaction between the natural environment and human activities and its impact on wetland changes, by introducing Partial Least Squares Structural Equation Modeling (PLS-SEM) and Geographically Weighted Regression (GWR) model, then applying the methodology in Wuhan, a typical wetland city in China. The validity and reliability evaluation indicated that the PLS-SEM model is reasonable. The results showed that the area of wetlands in Wuhan decreased by 10.98% in 1990-2018 and four obvious direct pathways of influence were found. Positive soil and terrain conditions are conducive to maintaining wetlands, while rapid urbanization drastically reduce the distribution of wetlands. It is remarkable that the impact of climate on wetlands is gradually shifting from positive to negative. Furthermore, four potential indirect impact pathways affecting wetland distribution shown that urbanization and climate enhance the negative impact of terrain on wetland distribution, while their impacts on soil weaken soil's direct positive impact. This study provides a quantitative methodology for determining the causes of wetland loss; it can also be applied to other cities or regions, which is essential for applying more effective measures to protect wetlands.

Eco-environmental impacts of dams in the Yangtze River Basin, China.

Nearly half large dams of China have been built in the Yangtze River Basin (YRB) and the eco-environmental impacts of existing dams remain elusive. Here we present a spatio-temporal approach to measuring the eco-environmental impacts of dams and its long-term changes. We also develop a new metric, the dam eco-environmental effect index (DEEI), that quickly identifies the eco-environmental impacts on dams over 36 years. Underlying the analysis are the revised universal soil loss equation (RUSLE), the generalized boosted regression modeling (GBM), the generalized linear model (GLM), stepwise multiple regression, trend analysis, soil erosion and sediment yield balance equation, and sample entropy used to identify the eco-environmental impacts of dams on yearly timescales. We find that the accumulated negative environmental effects of constructed dams have increased significantly and has led to large-scale hydrophysical and human health risk affecting the Yangtze River Basins downstream (i.e. Jianghan-Lushui-Northeastern Hubei, Dongting Lake District, Yichang-Jianli, and Qingjiang) and reservoir areas (i.e. Wanxian-Miaohe, Miaohe-Huanglingmiao, and Huanglingmiao-Yichang). We also provide observational evidence that dam construction has reduced the complexity of short-term (1-12 months) in runoff and sediment loads. This spatial pattern seems to reflect a filtering effect of the dams on the temporal and spatial patterns of runoff and sediment. Three Gorges Dam (TGD) has a significant impact on the complexity of the runoff and sediment loads in the mainstream of the Yangtze River. This enhanced impact is attributed to the high trapping efficiency of the dam and its associated large reservoir. This assessment may underestimate the cumulative effect of the dam because it does not consider the future effects of the planned dam. Our study provides a quantitative methodology for finding the relative change rate of eco-environmental impact on dams, which is the first step towards addressing the extent, process, and magnitude of the dam-induced effects.

Multi-source sensor based urban habitat and resident health sensing: A case study of Wuhan, China.

The COVID-19 pandemic undoubtedly has a great impact on the world economy, especially the urban economy. It is urgent to study the environmental pathogenic factors and transmission route of it. We want to discuss the relationship between the urban living environment and the number of confirmed cases at the community scale, and examine the driving forces of community infection (e.g., environment, ecology, convenience, livability, and population density). Besides, we hope that our research will help make our cities more inclusive, safe, resilient, and sustainable. 650 communities with confirmed COVID-19 cases in Wuhan were selected as the research objects. We utilize deep learning semantic segmentation technology to calculate the Visible Green Index (VGI) and Sky View Factor (SVF) of street view and use Partial Least Squares Structural Equation Modeling (PLS-SEM) to study the driving forces of pandemic situation. Temperature and humidity information recorded by sensors was also used for urban sensing. We find that the more SVF has a certain inhibitory effect on the virus transmission, but contrary to our intuitive perception, higher VGI has a certain promotion effect. Also, the structural equation model constructed in this paper can explain the variance of 28.9% of the number of confirmed cases, and results (path coef.) demonstrate that residential density of community (0.517) is a major influencing factor for pandemic cases, whereas convenience of community living (0.234) strongly influence it. Communities with good suitability of community human settlement (e.g., construction time, price) are safer in the face of pandemic events. Does the influence of SVF and VGI on the results of the pandemic situation mean that sunlight can effectively block the spread of the virus? This spatial heterogeneity in different communities is helpful for us to explore the environmental transmission route of COVID-19.

A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling.

The online public opinion is the sum of public views, attitudes and emotions spread on major public health emergencies through the Internet, which maps out the scope of influence and the disaster situation of public health events in real space. Based on the multi-source data of COVID-19 in the context of a global pandemic, this paper analyzes the propagation rules of disasters in the coupling of the spatial dimension of geographic reality and the dimension of network public opinion, and constructs a new gravity model-complex network-based geographic propagation model of the evolution chain of typical public health events. The strength of the model is that it quantifies the extent of the impact of the epidemic area on the surrounding area and the spread of the epidemic, constructing an interaction between the geographical reality dimension and online public opinion dimension. The results show that: The heterogeneity in the direction of social media discussions before and after the "closure" of Wuhan is evident, with the center of gravity clearly shifting across the Yangtze River and the cyclical changing in public sentiment; the network model based on the evolutionary chain has a significant community structure in geographic space, divided into seven regions with a modularity of 0.793; there are multiple key infection trigger nodes in the network, with a spatially polycentric infection distribution.

A risk assessment method for remote sensing of cyanobacterial blooms in inland waters.

The widespread occurrence of Cyanobacterial blooms (CABs) in inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatially continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, the dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial, and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA was executed and validated in three different lakes of Wuhan urban agglomeration (WUA) with different trophic states. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in the east part of Lake LongGan was higher than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification data and results of the existing study, including trophic level, ecology risk, and algal extent, the MIRA method was valuable for accurate and spatially continuous identifying the risk of CABs in inland waters with potential eutrophication trends.

Urban drought challenge to 2030 sustainable development goals.

In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs). Yet, the current literatures on drought have not placed sufficient emphasis on urban drought challenge in achieving the United Nations' 2030 Agenda for Sustainable Development. This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts. Then, four sub-categories of urban drought are proposed, including precipitation-induced, runoff-induced, pollution-induced, and demand-induced urban droughts. These sub-categories can support city stakeholders in taking drought mitigation actions and advancing the following SDGs: SDG 6 "Clean water and sanitation", SDG 11 "Sustainable cities and communities", SDG 12 "Responsible production and consumption", SDG 13 "Climate actions", and SDG 15 "Life on land". To further support cities in taking concrete actions in reaching the listed SDGs, this perspective proposes five actions that city stakeholders can undertake in enhancing drought resilience and preparedness:1) Raising public awareness on water right and water saving; 2) Fostering flexible reliable, and integrated urban water supply; 3) Improving efficiency of urban water management; 4) Investing in sustainability science research for urban drought; and 5) Strengthening resilience efforts via international cooperation. In short, this review contains a wealth of insights on urban drought and highlights the intrinsic connections between drought resilience and the 2030 SDGs. It also proposes five action steps for policymakers and city stakeholders that would support them in taking the first step to combat and mitigate the impacts of urban droughts.

Effects of spatio-temporal landscape patterns on land surface temperature: a case study of Xi'an city, China.

A city is a mixed ecosystem of nature, economy, and society and is simultaneously transforming natural areas and adapting to nature. Urbanization causes the population to expand rapidly, leading to rapid expansions of scale. Consequently, the proportions of impermeable surfaces (ISs) and greenspaces (GSs) change drastically, which has a considerable influence on the urban thermal environment. The aim of this study was to research the effects of spatio-temporal landscape patterns on land surface temperature (LST) and between GS and IS in the city of Xi'an using the urban-rural gradient, the moving split-window algorithm (MSA), multiple grid resolutions, and landscape metrics based on three-phase Landsat data. The results showed that there was a significantly positively correlated with IS density and significantly negatively correlated with the GS density from the urban center to rural areas. Over the past 25 years, the main urban area of Xi'an has expanded by nearly 6.2 times its initial size. The correlation between IS density and LST increased with increasing grid size, and the correlation between GS density and LST increased with decreasing grid size. Thus, LST is highly sensitive to the ISs and GSs at particular grid sizes. The correlation coefficients of the ISs and GSs with LST increased with decreasing grid size during 1992-2016. Hence, the LST was less sensitive to IS and the GS densities in conjunction with larger grid sizes. The class area (CA) and the landscape shape index (LSI) of the ISs were significantly positively correlated with the LST, whereas the CA and largest patch index (LPI) of the GSs were negatively correlated with the LST. The LST of the ISs in 1992, 2006, and 2016 were 1.6, 1.8, and 3.9 °C higher, respectively, than those of the GSs, indicating that GSs are important to lowering urban LSTs. Therefore, the government and urban planning departments should consider optimizing the spatial patterns of ISs and GSs to fully exploit the cooling effect of optimally configured GSs, which would be conducive to the sustainable development of the urban ecological environment.

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin.

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm.

The efficient data access of streaming vehicle data is the foundation of analyzing, using and mining vehicle data in smart cities, which is an approach to understand traffic environments. However, the number of vehicles in urban cities has grown rapidly, reaching hundreds of thousands in number. Accessing the mass streaming data of vehicles is hard and takes a long time due to limited computation capability and backward modes. We propose an efficient streaming spatio-temporal data access based on Apache Storm (ESDAS) to achieve real-time streaming data access and data cleaning. As a popular streaming data processing tool, Apache Storm can be applied to streaming mass data access and real time data cleaning. By designing the Spout/bolt workflow of topology in ESDAS and by developing the speeding bolt and other bolts, Apache Storm can achieve the prospective aim. In our experiments, Taiyuan BeiDou bus location data is selected as the mass spatio-temporal data source. In the experiments, the data access results with different bolts are shown in map form, and the filtered buses' aggregation forms are different. In terms of performance evaluation, the consumption time in ESDAS for ten thousand records per second for a speeding bolt is approximately 300 milliseconds, and that for MongoDB is approximately 1300 milliseconds. The efficiency of ESDAS is approximately three times higher than that of MongoDB.

Droughts in India from 1981 to 2013 and Implications to Wheat Production.

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation.

Relationship between air quality and economic development in the provincial capital cities of China.

Air pollution in China has become increasingly severe with rapid economic growth in recent years. We analyzed the relationship between the gross regional product (GRP) per capita and the Integrated Air Pollution Index (IAPI) in all the provincial capital cities in China from 2003 to 2014 and clustered them into six urban development patterns. These patterns are as follows: inverse U-shaped, N-1-shaped, N-2-shaped, U-shaped, linear decline, and stable. The majority of the provincial capitals are N-1, N-2, and U types, suggesting that the air quality is deteriorating currently or will deteriorate in the future. Meteorological conditions and industrial structure are taken into consideration when testing the environmental Kuznets curve (EKC) hypothesis between the economy and air pollutant concentration. Results show that there exists no direct relationship between three main pollutants and GRP per capita, while an inverse U-shaped relationship with the secondary industry and a U-shaped relationship with the tertiary industry. These results will be a meaningful reference for policy makers to develop policies that coordinate the environmental protection and economic development.

Representing Geospatial Environment Observation Capability Information: A Case Study of Managing Flood Monitoring Sensors in the Jinsha River Basin.

Sensor inquirers cannot understand comprehensive or accurate observation capability information because current observation capability modeling does not consider the union of multiple sensors nor the effect of geospatial environmental features on the observation capability of sensors. These limitations result in a failure to discover credible sensors or plan for their collaboration for environmental monitoring. The Geospatial Environmental Observation Capability (GEOC) is proposed in this study and can be used as an information basis for the reliable discovery and collaborative planning of multiple environmental sensors. A field-based GEOC (GEOCF) information representation model is built. Quintuple GEOCF feature components and two GEOCF operations are formulated based on the geospatial field conceptual framework. The proposed GEOCF markup language is used to formalize the proposed GEOCF. A prototype system called GEOCapabilityManager is developed, and a case study is conducted for flood observation in the lower reaches of the Jinsha River Basin. The applicability of the GEOCF is verified through the reliable discovery of flood monitoring sensors and planning for the collaboration of these sensors.

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City.

The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems.