Most air pollution research has focused on assessing the urban landscape effects of pollutants in megacities, little is known about their associations in small- to mid-sized cities. Considering that ...the biggest urban growth is projected to occur in these smaller-scale cities, this empirical study identifies the key urban form determinants of decadal-long fine particulate matter (PM
) trends in all 626 Chinese cities at the county level and above. As the first study of its kind, this study comprehensively examines the urban form effects on air quality in cities of different population sizes, at different development levels, and in different spatial-autocorrelation positions. Results demonstrate that the urban form evolution has long-term effects on PM
level, but the dominant factors shift over the urbanization stages: area metrics play a role in PM
trends of small-sized cities at the early urban development stage, whereas aggregation metrics determine such trends mostly in mid-sized cities. For large cities exhibiting a higher degree of urbanization, the spatial connectedness of urban patches is positively associated with long-term PM
level increases. We suggest that, depending on the city's developmental stage, different aspects of the urban form should be emphasized to achieve long-term clean air goals.
Urbanization is a global problem with emergent properties. The difference in temperature between urban and rural surfaces is one such property that affects health, energy consumption budgets, ...regional planning and climate. We used remotely sensed datasets and gridded population to estimate the magnitude of thermal differentials (urban heat islands and/or sinks), the timing of heat differential events, and the controlling variables. The global scope of the study provides a consistent analytical environment that enables identification of the key factors that contribute to deleterious heat differentials. We propose new indices of thermal differential and use them to show particular prevalence of heat islands and sinks in arid regions. A variable ranking analysis indicates that development intensity, vegetation amount and the size of the urban metropolis are the most important urban variables to predict heat differentials. Population was of lesser importance in this study. Urban structure indices were also ranked lower, though a different measurement scale qualifies this conclusion. The results support the paradigm of compact development and incorporation of vegetation to the urban infrastructure.
•We characterize and map surface urban heat islands and sinks globally for 2010.•Alteration of thermal inertia patterns is implicated in the creation of heat islands and sinks.•Urban area, vegetation and development are within-city controls of urban heat islands and sinks.
Research on global environmental change requires new data processing and analysis tools that can integrate heterogeneous geospatial data from real-time in situ measurement, remote sensing (RS) and ...geographic information systems (GISs) at the global scale. The rapid growth of virtual globes for global geospatial information management and display holds promise to meet such a requirement. Virtual globes, Google Earth in particular, enable scientists around the world to communicate their data and research findings in an intuitive three-dimensional (3D) global perspective. Different from traditional GIS, virtual globes are low cost and easy to use in data collection, exploration and visualization. Since 2005, a considerable number of papers have been published in peer-reviewed journals and proceedings from a variety of disciplines. In this review, we examine the development and applications of Google Earth and highlight its merits and limitations for Earth science studies at the global scale. Most limitations are not unique to Google Earth, but to all virtual globe products. Several recent efforts to increase the functionalities in virtual globes for studies at the global scale are introduced. The power of virtual globes in their current generations is mostly restricted to functions as a ‘geobrowser’; a better virtual globe tool for Earth science and global environmental change studies is described.
Although mapping activities of urban land change have been widely carried out, detailed information on urban development in time over rapid urbanization areas would have been lost in most studies ...with multi-year intervals. Here we provide a two-stage framework of long-term mapping of urban areas at an annual frequency in Beijing, China, over the period from 1984 to 2013. Classification for each year was carried out initially based on a number of Landsat scenes within that year using spectral information from a base image plus NDVI time series derived from all scenes. A temporal consistency check involving both temporal filtering and heuristic reasoning was then applied to the sequence of classified urban maps for further improvement. We assessed this time-series of urban maps based on two schemes. One is change detection in rapidly developing areas over the past three decades, and the other is accuracy assessment over the whole region in four selected years (i.e., 1984, 1990, 2000 and 2013). Based on validation using independent samples, the OAs (overall accuracies) of these four years are 96%, 93%, 92% and 95%, respectively. Meanwhile, the average accuracy of change detection for all years is 83%. In addition, the proposed temporal consistency check was found to be able to make considerable improvements (about 6%) to the overall accuracies and results of change detection. The resultant urban land sequence revealed that the average growth rates were 47.51±4.17km2/year, 34.65±2.90km2/year and 99.48±1.3km2/year for 1984–1990, 1990–2000 and 2000–2013, respectively.
•An annual sequence of urban land has been produced in Beijing over a 30-year period.•Many Landsat images have been employed to make full use of their temporal contexts.•A temporal consistency check was conducted to make the sequence more reasonable.•The growth rates are different in Beijing during the past three decades.
Productive wetland systems at land–water interfaces that provide unique ecosystem services are challenging to study because of water dynamics, complex surface cover and constrained field access. We ...applied object-based image analysis and supervised classification to four 32-m Beijing-1 microsatellite images to examine broad-scale surface cover composition and its change during November 2007–March 2008 low water season at Poyang Lake, the largest freshwater lake-wetland system in China (>
4000
km
2). We proposed a novel method for semi-automated selection of training objects in this heterogeneous landscape using extreme values of spectral indices (SIs) estimated from satellite data. Dynamics of the major wetland cover types (Water, Mudflat, Vegetation and Sand) were investigated both as transitions among primary classes based on maximum membership value, and as changes in memberships to all classes even under no change in a primary class. Fuzzy classification accuracy was evaluated as match frequencies between classification outcome and a) the best reference candidate class (MAX function) and b) any acceptable reference class (RIGHT function). MAX-based accuracy was relatively high for Vegetation (≥
90%), Water (≥
82%), Mudflat (≥
76%) and the smallest-area Sand (≥
75%) in all scenes; these scores improved with the RIGHT function to 87–100%. Classification uncertainty assessed as the proportion of fuzzy object area within a class at a given fuzzy threshold value was the highest for all classes in November 2007, and consistently higher for Mudflat than for other classes in all scenes. Vegetation was the dominant class in all scenes, occupying 41.2–49.3% of the study area. Object memberships to Vegetation mostly declined from November 2007 to February 2008 and increased substantially only in February–March 2008, possibly reflecting growing season conditions and grazing. Spatial extent of Water both declined and increased during the study period, reflecting precipitation and hydrological events. The “fuzziest” Mudflat class was involved in major detected transitions among classes and declined in classification accuracy by March 2008, representing a key target for finer-scale research. Future work should introduce Vegetation sub-classes reflecting differences in phenology and alternative methods to discriminate Mudflat from other classes. Results can be used to guide field sampling and top-down landscape analyses in this wetland.
► We present wetland object-based analysis and change detection at Poyang Lake, China. ► We propose novel methods to select training objects and analyze class uncertainty. ► Important fuzzy membership changes were detected even with no primary class change. ► Changes in Vegetation class membership reflected grazing and temperature variation. ► The fuzziest Mudflat class was sensitive to flooding and needs finer-scale research.
The flavivirus NS5 harbors a methyltransferase (MTase) in its N-terminal ≈ 265 residues and an RNA-dependent RNA polymerase (RdRP) within the C-terminal part. One of the major interests and ...challenges in NS5 is to understand the interplay between RdRP and MTase as a unique natural fusion protein in viral genome replication and cap formation. Here, we report the first crystal structure of the full-length flavivirus NS5 from Japanese encephalitis virus. The structure completes the vision for polymerase motifs F and G, and depicts defined intra-molecular interactions between RdRP and MTase. Key hydrophobic residues in the RdRP-MTase interface are highly conserved in flaviviruses, indicating the biological relevance of the observed conformation. Our work paves the way for further dissection of the inter-regulations of the essential enzymatic activities of NS5 and exploration of possible other conformations of NS5 under different circumstances.
The life cycles and transmission of most infectious agents are inextricably linked with climate. In spite of a growing level of interest and progress in determining climate change effects on ...infectious disease, the debate on the potential health outcomes remains polarizing, which is partly attributable to the varying effects of climate change, different types of pathogen-host systems, and spatio-temporal scales. We summarize the published evidence and show that over the past few decades, the reported negative or uncertain responses of infectious diseases to climate change has been growing. A feature of the research tendency is the focus on temperature and insect-borne diseases at the local and decadal scale. Geographically, regions experiencing higher temperature anomalies have been given more research attention; unfortunately, the Earth's most vulnerable regions to climate variability and extreme events have been less studied. From local to global scales, agreements on the response of infectious diseases to climate change tend to converge. So far, an abundance of findings have been based on statistical methods, with the number of mechanistic studies slowly growing. Research gaps and trends identified in this study should be addressed in the future.
•The reported negative or uncertain CC-ID relations consistently grow.•Temperature and insect-borne diseases is a long-lasting heat topic.•Most vulnerable regions to climate variability have not gain enough attention.•CC-ID relations tend to be less controversial at larger spatial scale.
The RNA-dependent RNA polymerases (RdRPs) encoded by the RNA viruses are a unique class of nucleic acid polymerases. Each viral RdRP contains a 500-600 residue catalytic module with palm, fingers, ...and thumb domains forming an encircled human right hand architecture. Seven polymerase catalytic motifs are located in the RdRP palm and fingers domains, comprising the most conserved parts of the RdRP and are responsible for the RNA-only specificity in catalysis. Functional regions are often found fused to the RdRP catalytic module, resulting in a high level of diversity in RdRP global structure and regulatory mechanism. In this review, we surveyed all 46 RdRP-sequence available virus families of the positive-strand RNA viruses listed in the 2018b collection of the International Committee on Virus Taxonomy (ICTV) and chose a total of 49 RdRPs as representatives. By locating hallmark residues in RdRP catalytic motifs and by referencing structural and functional information in the literature, we were able to estimate the N- and C-terminal boundaries of the catalytic module in these RdRPs, which in turn serve as reference points to predict additional functional regions beyond the catalytic module. Interestingly, a large number of virus families may have additional regions fused to the RdRP N-terminus, while only a few of them have such regions on the C-terminal side of the RdRP. The current knowledge on these additional regions, either in three-dimensional (3D) structure or in function, is quite limited. In the five RdRP-structure available virus families in the positive-strand RNA viruses, only the
family has the 3D structural information resolved for such regions. Hence, future efforts to solve full-length RdRP structures containing these regions and to dissect the functional contribution of them are necessary to improve the overall understanding of the RdRP proteins as an evolutionarily integrated group, and our analyses here may serve as a guideline for selecting representative RdRP systems in these studies.
Surface water is the most dynamic land‐cover type. Transitions between water and nonwater types (such as vegetation and ice) can happen momentarily. More frequent mapping is necessary to study the ...changing patterns of water. However, monitoring of long‐term global water changes at high spatial resolution and in high temporal frequency is challenging. Here we report the generation of a daily global water map data set at 500‐m resolution from 2001 to 2016 based on the daily reflectance time series from Moderate Resolution Imaging Spectroradiometer. Each single‐date image is classified into three types: water, ice/snow, and land. Following temporal consistency check and spatial‐temporal interpolation for missing data, we conducted a series of validation of the water data set. The producer's accuracy and user's accuracy are 94.61% and 93.57%, respectively, when checked with classification results derived from 30‐m resolution Landsat images. Both the producer's accuracy and user's accuracy reached better than 90% when compared with manually interpreted large‐sized sample units (≥1,000 m × 1,000 m) collected in a previous global land cover mapping project. Generally, the global inland water area reaches its maximum (~3.80 × 106 km2) in September and minimum (~1.50 × 106 km2) in February during an annual cycle. Short‐duration water bodies, sea level rise effects, different types of rice field use can be detected from the daily water maps. The size distribution of global water bodies is also discussed from the perspective of the number of water bodies and the corresponding water area. In addition, the daily water maps can precisely reflect water freezing and help correct water areas with inconsistent cloud flags in the MOD09GA quality assessment layer.
Plain Language Summary
Daily global inland surface water maps are produced from more than 1.9 million frames of satellite images for the period of 2001–2016 with a spatial resolution of 500 m. From this time series of maps, we found that the inland surface water on Earth varies greatly in area within an annual cycle. It can reach more than 3.8 million square kilometers in September and reduce to approximately 1.5 million square kilometers in February. We demonstrate that (1) short‐duration water bodies in arid areas, which are particularly important to life, can be detected from these daily water maps; (2) sea level rise effects on land submersion can be detected over some gentle‐slope coasts like west Florida; and (3) different types of rice field use exist in the world. For example, in California, United States, rice fields are filled with water after harvest to help create a wetland environment for wild birds in the winter.
Key Points
Global inland water has a dramatic seasonal variation
Short duration water bodies, sea level rise effects, and various types of rice field use can be detected
An innovative phenology-based classification method was developed to map corn and soybean in multiple years using training data limited to a single year. Unlike traditional mapping efforts mainly ...based on multi-spectral image data, the classifier employed by this method takes phenological metrics as the major input. Phenological metrics represent crop characteristics related to crop calendar and progress such as the timing of emergence, maturity, harvest, etc. While considerable inter-annual variability exists among remotely sensed images from different years, phenological characteristics of each crop type are relatively consistent for a long period of time. Therefore, it is assumed that phenological metrics can be used to classify crop types in multiple years with the same rules, which is a valuable feature not possessed by traditional classification inputs.
The classification method was tested in mapping corn and soybean, which are two dominant summer crop types in the central United States, and the experiment was carried out for Doniphan County, Kansas during years 2006–2010. Over 100 Landsat TM and ETM+images in this period were utilized and phenological metrics were calculated from Enhanced Vegetation Index time series using techniques including image segmentation and curve-fitting. Several sets of input variables, ranging from multi-spectral features of selected dates, which are widely used in traditional mapping efforts, to phenological metrics and derived measurements such as accumulated temperature, were tested using a random forest classifier. When the classifier was trained by reference data collected in the same year as that of remotely sensed data, most sets of input variables yielded accuracies higher than 88%. However, when the training data used by the classifier were obtained in a year different from the mapping years, only input sets containing phenological metrics were able to achieve acceptable accuracies greater than 80%. The use of phenological metrics as classification inputs avoids the restrictive requirements of a large ground reference dataset, enabling frequent and routine crop mapping without repeated collection of reference data.
•We mapped cropland in multiple years without recursive training data collection.•Using phenology we applied trained classifier to other years without reformulation.•Phenological metrics improve the extendability of the random forest classifier.
PDF
