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Research in urban ecology has recently focused on the field of ecological security. Ecological security should be assessed at different scales, from global (to understand some processes of global change) to regional (to understand processes in specific areas) to urban (to consider the processes of urban development). Remote sensing (RS) and geographical information systems (GISs) are widely utilized in ecological security evaluation, with remotely sensed images applied to determine the structure and quantitative indicators of urban ecological systems and GIS to manipulate, analyse, and visualize multisource information in urban ecology. This article focuses on urban ecological security assessment based on multitemporal RS information and GIS. As a case study, the changes in spatial patterns of urban ecological security in Xuzhou City are evaluated during its transformation from a coal mining industrial city to a modern one with decreasing mining production. The popular ‘pressure–state–response’ conceptual model is used to construct an evaluation framework according to geographical properties and data availability. A hierarchical weighted model is adopted to take different factors into account and determine the state of ecological security in the study area. Using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as the main RS data source, the derived parameters, such as vegetation index, urban thermal information, and landscape pattern, are integrated to depict the structure and properties of the urban ecological system. Other ancillary information, including mining pressure, water pollution pressure, population density, and ecological flexibility are collected and managed by ArcGIS. The GIS is then used to manage ecological factors derived from remotely sensed data, statistical reports, and geographical data, to evaluate and analyse urban ecological security, and to generate quantitative evaluation and spatial patterns of ecological security. The resulting maps of urban ecological security situations in 1987, 1994, 2000, 2005, and 2007 are analysed in both temporal and spatial dimensions. By analysing urban ecological security in both temporal and spatial dimensions, it is concluded that the ecological security of Xuzhou City is under increasing pressure. Influenced by the pressure of urbanization and mining industry development, the unsafe areas of Xuzhou City have greatly expanded, and with accelerating speed. Finally, suggestions for improving urban ecological security are provided. [ABSTRACT FROM PUBLISHER]

In order to promote the application of Beijing-1 small satellite (BJ-1) remote sensing data, the multispectral and panchromatic images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining. An improved Intensity-Hue-Saturation (IHS) fusion algorithm is proposed to fuse panchromatic and multispectral images, in which intensity component and panchromatic image are combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree. This improved IHS fusion algorithm outperforms traditional IHS fusion method to a certain extent, evidenced by its ability in preserving spectral information and enhancing spatial details. Dempster-Shafer (D-S) evidence theory was adopted to combine the outputs of three member classifiers to generate the final classification map with higher accuracy than that by any individual classifier. Based on this study, we conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological environment degradation in mining areas, and the proposed fusion algorithms at data and decision levels can integrate the advantages of multi-resolution images and multiple classifiers, improve the overall accuracy and produce a more reliable land cover map. [ABSTRACT FROM AUTHOR]

Multiple source satellite datasets, including the Gaofen (GF) series and Zhuhai-1 hyperspectral, are provided to detect and monitor the floods. Considering the complexity of land cover changes within the flooded areas and the different characteristics of the multi-source remote sensing dataset, we proposed a new coarse-to-fine framework for detecting floods at a large scale. Firstly, the coarse results of the water body were generated by the binary segmentation of GF-3 SAR, the water indexes of GF-1/6 multispectral, and Zhuhai-1 hyperspectral images. Secondly, the fine results were achieved by the deep neural networks with noisy-label learning. More specifically, the Unet with the T-revision is adopted as the noisy label learning method. The results demonstrated the reliability and accuracy of water mapping retrieved by the noisy learning method. Finally, the differences in flooding patterns in different regions were also revealed. We presented examples of Poyang Lake to show the results of our framework. The rapid and robust flood monitoring method proposed is of great practical significance to the dynamic monitoring of flood situations and the quantitative assessment of flood disasters based on multiple Chinese satellite datasets. [ABSTRACT FROM AUTHOR]

Separate impacts of building and tree on the urban thermal environment have been studied extensively, but their combined impacts, especially from both the horizontal (i.e., two-dimensional (2D)) and vertical (i.e., three-dimensional (3D)) perspectives remain largely unclear. Based on satellite thermal data and elaborate 2D and 3D urban morphology, herein we simultaneously investigate the separate and combined impacts of building and tree over Nanjing in China from both the 2D and 3D perspectives. We further examine the day–night contrast together with the sensitivity of such impacts to scale. Our results show that, when compared with urban structures from a single dimension, the combination of 2D and 3D structures is more capable of predicting urban land surface temperatures (LSTs) for both day and night. The assessments further illustrate that the separate and combined impacts of building and tree on LSTs are usually more significant when the spatial scale increases. As for the separate impacts of building and tree, 2D structure affects more urban thermal environment than 3D structure at all spatial scales during the day, but an opposite trend occurs at night. Moreover, for the combined impact of building and tree on LST across different scales, daytime and nighttime LSTs are respectively dominated by 2D and 3D building structures. Combining 2D and 3D structures improves the explained LST variation by 7.3%–11.1% and 25.3%–37.7% for day and night, respectively when compared to using 2D structures only. These findings emphasize the need to incorporate both 2D and 3D urban morphology to improve the urban thermal environment. [Display omitted] • Separate and combined impacts of building and tree on LST are analyzed from both 2D and 3D perspectives. • Sensitivity of the relative importance of 2D and 3D structures on LST to scale is examined. • Combining 2D and 3D structures better predicts LST than features of a single dimension. • 2D building structure affects more LST than 3D building structure during the day, but opposite at night. [ABSTRACT FROM AUTHOR]