The effects of sensor spatial resolution and changing grain size on fragmentation indices in urban landscape

Satellite images are now used as the primary source of spatial information for landscape pattern analysis. Choosing data of proper resolution has always been a big problem for landscape pattern analysis using satellite images. However, the effects of remote sensor spatial resolution on urban landscape fragmentation have never been reported. In this study, landscape fragmentation of Shanghai was analysed using land use maps produced from TM and IRS‐PAN images, attempting to discover the effects of remote sensor spatial resolution on urban landscape fragmentation. Four landscape fragmentation indices were computed along a 51 km long and 9 km wide transect cutting across Shanghai with a moving window to detect the behaviour of landscape indices along the gradient of land use zones. The results showed that fragmentation indices were sensitive to varied spatial resolution and grain size, indicating that fine resolution data and small grain size of about 7.5 m might be optimal for urban fragmentation analysis. The behaviour of landscape indices along the gradient of land use zones suggests that urban fragmentation can be distinguished as occurring in two phases: dissection introduced by roads and shrinkage caused primarily by urban sprawl.     

Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China

Urbanization is taking place at an unprecedented rate around the world, particularly in China in the past few decades. One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). Understanding the effects of landscape pattern on UHI is crucial for improving the ecology and sustainability of cities. This study investigated how landscape composition and configuration would affect UHI in the Shanghai metropolitan region of China, based on the analysis of land surface temperature (LST) in relation to normalized difference vegetation index (NDVI), vegetation fraction (Fv), and percent impervious surface area (ISA). Two Landsat ETM+ images acquired on March 13 and July 2, 2001 were used to estimate LST, Fv, and percent ISA. Landscape metrics were calculated from a high spatial resolution (2.5 × 2.5 m) land-cover/land-use map. Our results have showed that, although there are significant variations in LST at a given fraction of vegetation or impervious surface on a per-pixel basis, NDVI, Fv, and percent ISA are all good predictors of LST on the regional scale. There is a strong negative linear relationship between LST and positive NDVI over the region. Similar but stronger negative linear relationship exists between LST and Fv. Urban vegetation could mitigate the surface UHI better in summer than in early spring. A strong positive relationship exists between mean LST and percent ISA. The residential land is the biggest contributor to UHI, followed by industrial land. Although industrial land has the highest LST, it has limited contribution to the overall surface UHI due to its small spatial extend in Shanghai. Among the residential land-uses, areas with low- to-middle-rise buildings and low vegetation cover have much high temperatures than areas with high-rise buildings or areas with high vegetation cover. A strong correlation between the mean LST and landscape metrics indicates that urban landscape configuration also influences the surface UHI. These findings are helpful for understanding urban ecology as well as land use planning to minimize the potential environmental impacts of urbanization.     

Knowledge patterns and spatial dynamics of industrial districts in knowledge cities: Hsinchu,Taiwan

In the era of a global knowledge economy, urban regions that seek to increase their competitive edge and become destinations for talent and investment have little chance of achieving these goals without forming effective knowledge-based urban development strategies. Hence, the development of clusters of knowledge-based corporations has become an important strategic factor in increasing the competitiveness of knowledge cities. Whereas previous studies have tended to focus on the characteristics of local clusters and the causes of their success, empirical studies of the long-term development of local knowledge-based industries are few. Accordingly, this investigation takes the knowledge city region-Hsinchu as its subject, and quantitatively analyzes the correlation between the spatial dynamics of knowledge in major industries and innovation based on empirical data. This finding shows that steadily developing industries in the Hsinchu region have continued to strengthen their new knowledge of product development and innovation. An overview of innovative activities of firms also revealed that their knowledge patterns have been changing from patterns of internal dependency to a locality-based, broader networking and agglomeration pattern.     

An analysis of deforestation patterns in the extractive reserves of Acre,Amazonia from satellite imagery: A landscape ecological approach

Classified multi-temporal Landsat satellite imagery for one of the extractive reserves in Acre, Amazonia was classified into forest and cleared forest patches. Three indices of landscape structure were applied to the classified satellite imagery to characterize the impact of social and economic processes on the development of the forest landscape. The Lacunarity Index was used to measure landscape homogeneity, the Korcak Patchiness Index to measure the distribution of patches according to their size and the Area-Perimeter Fractal Exponent to measure change in the shape of cleared forest patches. The changing shape, size and spatial frequency of patches in the forest landscape showed that major changes in the forest landscape occurred between 1975-1989. Pre-1975 the region was mainly exploited by family groups who formed an extractive economy based on natural resources. By 1985 the economy had changed, with larger more regular patches indicating a transformed economy based on a settled and well developed farming economy. The techniques are important for measuring the human impact on fragile tropical ecosystems.     

Regression analysis between environmental factors and earthquake-damaged trace: a case study in Wenchuan County,China

This article discusses the integration of remote sensing (RS), geographic information system (GIS), GPS and ground survey technologies to establish the accuracy of supervised classification. N _e, the proportion of earthquake-damaged trace in a specific region, as a relative value expresses the influence of the characteristics of an area on the distribution of earthquake-damaged trace; it reflects the earthquake-damaged trace's distribution characteristics better. Four factors – distance to central fault, slope, elevation and distance to river – were selected to quantify the association between environmental factors and N _e by regression analysis. The results show the following. (1) The association between N _e and distance to river (R ²?=?0.99) and between N _e and distance to central fault (R ²?=?1.00) is better modelled by an inverse second-order line. N _e is largest within 0.5 km of the river and within 5 km of the central fault. When the distance is more than 2.0 km from the river and more than 30 km from the central fault, N _e dramatically decreases. (2) The association between N _e and elevation (R ²?=?0.94) is better modelled by the exponential decay line. With an increasing elevation, N _e decreases with the weakening of human activities. (3) The association between N _e and slope (R ²?=?0.93) is better modelled by exponential growth. With an increasing slope, N _e first decreases and then increases. (4) A possible mechanism is that the earthquake energy first acts on the area near the central fault. The erosion of the river leads to a steep slope; the unstable geologic structure is easily destroyed by the effect of seismic waves. Consequently, earthquake-damaged trace and N _e are greater in regions near the central fault and river bank than in other regions.     

Spectroscopy of sediments in the Ganges-Brahmaputra delta: Spectral effects of moisture, grain size and lithology

The lithologic composition and grain size distribution of sediments are primary determinants of their inherent reflectance properties. However, moisture content is also known to have a strong influence on reflectances of soils and sediments. If the effects of sediment composition, grain size and moisture content could be distinguished spectrally, it might be possible to map these properties at synoptic scales using hyperspectral, or perhaps even broadband, remote sensing. Mapping the spatiotemporal distribution of sediment composition and moisture content could provide unique constraints on both the processes by which the sediments are deposited as well as the constraints they may impose on subsequent water flow and sediment transport. The Ganges-Brahmaputra delta (GBD) is formed by the convergence of these two great rivers and is superlative in both size and geologic activity. Sediment redistribution and channel migration associated with the annual floods disrupt the lives of hundreds of thousands of people living on the GBD but is also critical for maintaining the delta area fertile and above sea level. The 30+ year archive of Landsat imagery could provide a basis for spatiotemporal analysis of these fluvial dynamics if sediment properties could be inferred or measured from reflectance spectra. However, before confronting the challenge of broadband detection we must understand the spectral properties of the sediments under more controlled laboratory conditions. Bidirectional reflectance spectroscopy of 109 sediment samples from the GBD yields a spectral mixing space that appears to be structured by variations in moisture content, grain size and possibly lithology. Although the individual Empirical Orthogonal Functions of the Principal Components do not correspond to unique absorption features, clustering within the mixing space is clearly influenced by moisture content and grain size. Laboratory spectra of sediment reflectance measured under varying moisture content yield distinct trajectories through the spectral mixing space for different grain size distributions of sieved sediments. These variations in moisture content account for > 98% of spectral variance observed in these samples. Drying trajectories of coarse, fine and mixed sediments are distinct and suggest that moisture and grain size might be spectrally distinguishable. These results are consistent with Angstrom's hypothesis of moisture-driven spectral absorption but more controlled experiments are necessary to test the hypothesis rigorously.     

Comparison between on- and off-campus behaviour and adaptability in online learning: a case from China

More and more universities and colleges are providing online courses not only for on-campus students but also for off-campus students. Tutors have to consider the differences between on- and off-campus students in order to improve effective instruction. Comparisons are made in this paper between on- and off-campus performances in online learning from four areas: learning time, path of browsing courseware, intercommunication and adaptability towards online learning. The last two areas are emphasized. Multiple approaches were adopted to collect data, which include questionnaires, posted documents, online logs, interviews and observations. This study shows that the rush time of online learning, paths of browsing courseware and favourite intercommunication means of on- and off-campus students are similar. But there are also some differences between these two groups such as competence of self-learning, enthusiasm of interpersonal exchange, dependence on tutors, feeling of learning stress, etc.     

Towards a multidimensional view of tourist mobility patterns in cities: A mobile phone data perspective

The last decade has witnessed a wealth of studies on characterizing human mobility patterns using movement datasets. Such efforts have highlighted a few salient dimensions of individual travel behavior relevant to urban planning and policy analysis. Despite the fruitful research outcomes, most of the findings are drawn upon urban residents. The behavioral characteristics of other population groups, such as tourists, remain underexplored. In this study, we introduce an analytical framework to gain insights into tourist mobility patterns. By analyzing mobile phone trajectories of international travelers to three different cities in South Korea, we introduce nine mobility indicators to capture different facets of tourist travel behavior (e.g., duration of stay in a city, spatial extent of activities, location visited and trips conducted, and mobility diversity), and examine their statistical properties across cities. An eigendecomposition approach is then introduced to better understand the interdependency of these mobility indicators and inherent variations among individual travelers. Based on the eigendecomposition results, we further employ a dimension reduction technique to describe the key characteristics of each traveler. Since the mobile phone dataset captures the nationality of tourists, we use such information to quantify the behavioral heterogeneity of travelers across countries and regions. Finally, we select a few traveler groups with distinctive mobility patterns in each city and examine the spatial patterns of their activities. Substantial differences are observed among traveler groups in their spatial preferences. The implications for location recommendation and deployment of tourism services (e.g., transportation) are discussed. We hope the study brings a synergy between classic human mobility analysis and the emerging field of tourism big data. The framework can be applied or extended to compatible datasets to understand travel behavior of tourists, residents, and special population groups in cities.     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

Spatial relationships between snow contaminant content, grain size, and surface temperature from multispectral images of Mt. Rainier, Washington (USA)

We use multispectral MODIS/ASTER Airborne Simulator (MASTER) data collected at Mt. Rainier, Washington (USA) to map spatial covariance between snowpack properties and to evaluate techniques for quantitative estimation of reflectance, grain size, and temperature. The late-August MASTER images reveal a distinct pattern of snow contaminant content, grain size, and temperature related to a recent snowfall and late-summer melting. Spatial correlation between grain size and temperature patterns suggests that rapid destructive metamorphism of the fresh snow occurred when temperatures were near 0 °C. We use 10 specific locations to evaluate hemispherical-directional reflectance factor (HDRF), grain size, and temperature retrievals. We map relative snow contaminant content using visible (0.4-0.8 μm) HDRF spectra. Atmospheric correction and topographic modeling limit the accuracy of HDRF estimates. We use MASTER-derived spectra near 1.8 and 2.2 μm to estimate optical grain size (by comparison to modeled layers of ice spheres) and physical grain size (by comparison to measured spectra with known physical grain size and by correlation to ground measurements). Estimated physical grain sizes were less than estimated optical grain sizes. Differing definitions of optical and physical grain sizes could contribute to this discrepancy. Limitations at 1.8 and 2.2 μm, including reduced discrimination between larger grain radii (>∼500 μm physical, >∼200 μm optical) and low signal-to-noise ration with atmospheric effects and decreasing solar irradiance, suggest that grain size retrieval may be improved at other wavelengths (e.g., 1.1 μm). Accounting for uncertainty in emissivity, atmospheric correction, and detector noise, we estimate systematic errors in our radiant temperatures at <1.8 °C. This study shows both strengths and limitations for coregistered visible, short-wave infrared, and thermal infrared images to estimate snowpack properties and reveal their spatial coherence.     

Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area: a case study in Shanghai,China

Many application fields need land surface temperature (LST) with simultaneous high spatial and temporal resolution, which can be achieved through the disaggregation technique. Most published methods built an assumed scale-independent relationship between LST and predictor variables derived from coarse spatial resolution data. However, LST disaggregation in the heterogeneous areas, especially urban areas, is very difficult to achieve and there are few studies on it. In this article, we propose an adjusted stratified stepwise regression method for temperature disaggregation in urban areas. Landsat Enhanced Thematic Plus (ETM+) data from Shanghai, China, were used to construct remote-sensing indices that are related to LST variance and retrieve LST at 60 and 480 m spatial resolution, respectively. Different stepwise regression models at 480 m resolution were built for two stratified regions according to normalized difference vegetation index (NDVI) distribution, and then each independent variable at 60 m resolution was adjusted to calculate disaggregated LST by considering its relationship with the 480 m resolution image. By using LST retrieved directly from ETM+ band 6 at 60 m spatial resolution as the reference, the proposed method comprising resampling disaggregation, the thermal data sharpening model (TsHARP)/disaggregation procedure for radiometric surface temperature (DisTrad) technique, and the LST-principal component analysis (PCA) regression algorithm were verified and compared. The results show that the temperature distribution estimated using the proposed method is most consistent with that of the reference LST in this heterogeneous study area, and that the precision improves significantly, especially for the low vegetation fraction region.     

Land deformation monitoring using coherent target-neighbourhood networking method combined with polarimetric information: a case study of Shanghai,China

In this article, an advanced approach for land deformation monitoring using synthetic aperture radar (SAR) interferometry combined with polarimetric information is presented. The linear and nonlinear components of the deformation, the error of the digital elevation model (DEM) and the atmospheric artefacts can be achieved by a coherent target (CT)-neighbourhood networking approach. In order to detect recent land deformation in Shanghai, China, 12 ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization images acquired from January 2006 to August 2008 are employed for deformation analysis. Over a 2.5-year period, two deformation velocity fields from HH and VV modes over Shanghai are derived using the CT-neighbourhood networking SAR interferometry (InSAR), then integrated into a final deformation map by a fusion scheme. It is found that the annual subsidence rates in the study area range from??20 to 10 mm year^?1 and the average subsidence rate in the downtown area reaches??7.5 mm year^?1, which is consistent with the local government statistics published in 2007.     

Comparison of FFT,direct inversion,and conjugate gradient methods for use in pseudo-spectral methods

Direct matrix inversion methods and the conjugate gradient method are compared with the FFT for speed when used in pseudo-spectral calculations. It is demonstrated that in some cases either of these methods is competitive with an FFT. This is especially true when using direct methods in two dimensions.     

Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan,China

According to the UN Population Reference Bureau, 1.4 billion more people will have settled in urban areas by 2030. One of the key environmental effects of rapid urbanization is the urban heat island (UHI) effect. Understanding the mechanism of surface UHIs associated with land-use/land-cover (LULC) change patterns is important for improving the ecology and sustainability of cities. In this article, time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data were used to extract LULC data and land surface temperature (LST) data for the city of Jinan, China, from 1987 to 2011, a period during which the city experienced rapid urbanization. With the aid of a geographical information system (GIS) and remote sensing (RS) approach, the changes in this urban area’s LULC were explored, and the impact of these changes on the spatiotemporal patterns and underlying driving forces of the surface UHI effect were further quantitatively characterized. The results show that significant changes in land use and land cover occurred over the study period, with loss of farmland, forest, and shrub vegetation to urban use, leading to spatial growth of impervious surfaces. Consequently, the land surface characteristics and spatiotemporal patterns of the UHI have changed drastically. According to the seasonal and inter-annual variations in intensity of UHIs, mean differences in UHI intensity between city centre, peri-urban, and nearby rural areas were stronger during summer and spring and weaker during winter and autumn. Spatially, there were significant LST gradients from the city centre to surrounding rural areas. The city centre exhibited higher LSTs and remarkable variation in LSTs, while the surrounding rural areas exhibited lower LSTs and lower variation in LSTs. Moreover, the analysis of LSTs and indices showed that great differences of temperature even existed in a LULC type except for variations between different LULC types. In addition, a local-level analysis revealed that the intensity of the UHI effect is proportional to the size of the urban area, the population density, and the frequent occurrence of certain activities.     

Comparison between on- and off-campus behaviour and adaptability in online learning: A case from China

More and more universities and colleges are providing online courses not only for on-campus students but also for off-campus students. Tutors have to consider the differences between on- and off-campus students in order to improve effective instruction. Comparisons are made in this paper between on- and off-campus performances in online learning from four areas: learning time, path of browsing courseware, intercommunication and adaptability towards online learning. The last two areas are emphasized. Multiple approaches were adopted to collect data, which include questionnaires, posted documents, online logs, interviews and observations. This study shows that the rush time of online learning, paths of browsing courseware and favourite intercommunication means of on- and off-campus students are similar. But there are also some differences between these two groups such as competence of self-learning, enthusiasm of interpersonal exchange, dependence on tutors, feeling of learning stress, etc.     

Characterisation of trunk muscle activation amplitude patterns during a simulated checkstand operation with continuously changing flexor and lateral moment demands

While the typical physical exposure to modern-day workers has changed from heavy to low level repetitive demands, there is limited research that examines light occupations. This study examined trunk muscle recruitment strategies in response to a simulated checkout operation. Surface electromyography and kinematic variables were recorded from 29 healthy subjects. Four principal patterns accounted for 95.3% of the variation. Significant differences in scores captured different strategies in response to reach conditions and external moment directions. Synergistic co-activation of ipsilateral back sites and differential activation among external oblique and erector spinae sites suggests that the central nervous system may control different regions of the trunk musculature to optimally account for asymmetrical demands. The strategy between the internal oblique and back extensor sites suggests that a specific co-activation strategy may be needed during lighter work. During low-load occupational tasks, several recruitment strategies were required to maintain spinal stability and account for changing external moments.

Statement of Relevance: Different recruitment strategies found in response to changing external moments offer new insights into neuromuscular control for lighter work. Specifically, multiple trunk muscle sites interact in a complex manner, taking into account task specificity and individual variation that are valuable in workstation design, evaluating injury risk and estimating spinal loads.     

Developing future human-centered smart cities: Critical analysis of smart city security,Data management,and Ethical challenges

As the globally increasing population drives rapid urbanization in various parts of the world, there is a great need to deliberate on the future of the cities worth living. In particular, as modern smart cities embrace more and more data-driven artificial intelligence services, it is worth remembering that (1) technology can facilitate prosperity, wellbeing, urban livability, or social justice, but only when it has the right analog complements (such as well-thought out policies, mature institutions, responsible governance); and (2) the ultimate objective of these smart cities is to facilitate and enhance human welfare and social flourishing. Researchers have shown that various technological business models and features can in fact contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In the light of these observations, addressing the philosophical and ethical questions involved in ensuring the security, safety, and interpretability of such AI algorithms that will form the technological bedrock of future cities assumes paramount importance. Globally there are calls for technology to be made more humane and human-centered. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain.     

APACS: a system for the automatic analysis and classification of conceptual patterns

Many existing inductive learning systems have been developed under the assumption that the learning tasks are performed in a noise-free environment. To cope with most real-world problems, it is important that a learning system be equipped with the capability to handle uncertainty. In this paper, we first identify the various sources of uncertainty that may be encountered in a noisy problem domain. Next, we present a method for the efficient acquisition of classification rules from training instances which may contain inconsistent, incorrect, or missing information. This algorithm consists of three phases: ( i ) the detection of inherent patterns in a set of noisy training data; ( ii ) the construction of classification rules based on these patterns; and ( iii ) the use of these rules to predict the class membership of an object. The method has been implemented in a system known as APACS (automatic pattern analysis and classification system). This system has been tested using both real-life and simulated data, and its performance is found to be superior to many existing systems in terms of efficiency and classification accuracy. Being able to handle uncertainty in the learning process, the proposed algorithm can be employed for applications in real-world problem domains involving noisy data.     

On the analysis of exponential queuing systems with randomly changing arrival rates: Stability conditions and finite buffer scheme with a resume level

This paper considers a single server exponential queue with random fluctuations in the intensity of the arrival process. The motivation being the modelling of random changes in traffic patterns. This random intensity model does not obey the independence assumption made in queuing theory. Necessary and sufficient conditions for the stability or ergodicity of the queuing process are obtained via analytic techniques using Jury's stability criteria, often used to study discrete time control systems. The effect of such fluctuations is then studied for a finite resume level queue which is often used in flow control. Exact performance measures are computed and are compared with existing results.