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The increase in the density of high-rise buildings is making urban air flow increasingly complex, and the wind environment conditions in urban public spaces are considerably changing. At present, few studies focus on the relationship between wind environment and the master plan of urban public spaces or attempt to improve the wind environment of public spaces from a landscape design perspective. To resolve this gap in information, this study explored the relationship among wind speed, seasonal comfort level in Nanjing, and different types of planting patterns. Computational fluid dynamics (CFD) software was used to quantify the seasonal wind environment of Xuanwumen Square in Nanjing. Three planting designs were created on the basis of the results, and their CFD results were compared among one another. Comparison results show that the second planting design provides the best wind environment, and its comfort levels are 2.26, 1.92, and 2.09 out of 5 in winter, summer, and total. Understanding of the relationship among different landscape designs and their impacts on wind environment supports designers in improving the wind environment of Xuanwumen Square in Nanjing. This study provides new insights into relating urban public space design with the quantitative research of wind environment in the future. [ABSTRACT FROM AUTHOR]

Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas. [ABSTRACT FROM AUTHOR]