Abstract:In this paper, we present surface urban heat island (SUHI) analysis of Shanghai (China) based on the change in land use and land cover using satellite Landsat images from 2002 to 2013. With the rapid development of urbanization, urban ecological and environmental issues have aroused widespread concern. The urban heat island (UHI) effect is a crucial problem, as its generation and evolution are closely related to social and economic activities. Land-use and land-cover change (LUCC) is the key in analyzing the UHI effect. Shanghai, one of China's major economic, financial and commercial centers, has experienced high development density for several decades. A tremendous amount of farmland and vegetation coverage has been replaced by an urban impervious surface, leading to an intensive SUHI effect, especially in the city's center. Luckily, the SUHI trend has slowed due to reasonable urban planning and relevant green policies since the 2010 Expo. Data analyses demonstrate that an impervious surface (IS) has a positive correlation with land surface temperature (LST) but a negative correlation with vegetation and water. Among the three factors, impervious surface is the most relevant. Therefore, the policy implications of land use and control of impervious surfaces should pay attention to the relief of the current SUHI effect in Shanghai.Keywords: surface urban heat island (SUHI); land-use and land-cover change (LUCC); land surface temperature (LST); Shanghai; NDVI; MNDWI

Substantial exposure to exhaled air occurs within a 0.5-m radius of patients receiving NPPV. Medical wards should be designed with an architectural aerodynamics approach and knowledge of air/particle dispersion from common mechanical ventilatory techniques.

Abstract:In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and ecological environmental sensitivity to create reasonable evaluation indicators to analyze urban land carrying capacity based on ecological sensitivity in the rapidly developing megacity of Hangzhou, China. In the study, ecological sensitivity is grouped into four levels: non-sensitive, lightly sensitive, moderately sensitive, and highly sensitive. The results show that the ecological sensitivity increases progressively from the center to the periphery. The results also show that ULCC is determined by ecologically sensitive levels and that the ULCC is categorized into four levels. Even though it is limited by the four levels, the ULCC still has a large margin if compared with the current population numbers. The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future. However, it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China.

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Abstract:Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. Based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pléiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images.

Abstract:In this study, we assess the urban heat island (UHI) effect using remote sensing data, a phenomenon emerging under the background of global warming and urbanization. With the rapid development of satellite technology, remote sensing images are widely applied to evaluate the UHI effect on rapidly-urbanized areas in recent years. In the study, we applied Landsat 8 data to estimate the land surface temperature (LST) in the case study of Shenzhen and Hong Kong. The methods of the mono-window algorithm (MWA), single-channel method (SCM), Qin's split-window algorithm (SWA-Q) and Sobrino's split-window algorithm (SWA-S) are used to calculate the LST from Landsat

Abstract:In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual-and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual-and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.

Canadian emergency management planners have historically ignored the self-motivated evacuation procedures of people who cannot initially choose the safest evacuation areas. In densely developed urban areas, open spaces can be seen as ideal evacuation areas and should thus be included in shelter planning. In this study, the public open spaces in Great Victoria were selected as the study area and evaluated using GIS technologies. A multi-criteria TOPSIS evaluation model was used to conduct comprehensive quantitative evaluations of the open spaces’ safety, accessibility, and availability. Through hybrid process, service area, and POI aggregation coupling analyses, a model is created that provides an overall evaluation at the district level. In addition to providing a model for evaluating open spaces as emergency shelters, applicable to most Canadian cities, this study emphasizes the importance and disadvantages of open space emergency shelters in Canada, which have heretofore been ignored by decision makers. In Great Victoria, we found that the distribution of open spaces does not match the dynamics of the population distribution, meaning that through inadequate preparation some districts lack a safe evacuation place—this in an area where people are at high risk of earthquake disasters and their subsequent effects.