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 A tremendous amount of farmland and vegetation coverage has been replaced by an urban impervious surface, leading to an intensive SUHI effect, especially in the city's center.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.