The spatio-temporal patterns of macrofaunal fouling assemblages were quantitatively investigated in the nearshore waters of the South China Sea. The work was undertaken by deploying seasonal panels at two sites (H-site, L-site) for one year, and the fouling communities on the panels were examined and analyzed. The results indicated that species composition of assemblages was obviously different between the two sites. At both sites the assemblages were characteristic with solitary dominant species throughout the year, with Amphibalanus reticulates dominating at H-site and Hydroides elegans at L-site. Shannon index and biomass of the assemblages varied with depth and season at both sites. At H-site the total biomass in summer and autumn were significantly higher than those in spring and winter, while at L-site the assemblage biomass also differed significantly among the four seasons, and the greatest biomass occurred at the depth of 2.0 m in winter. The abundance of all seasonal samples in non-metric multidimensional scaling was clustered as one group at L-site and three groups at H-site. The environmental factors were more likely to be related to the variation of fouling assemblages. Furthermore, it also suggests that in tropical seas the integrated adaptability would qualify a species for dominating a fouling assemblage despite its short life cycle, rather than the usually assumed only species with long life span. This study reveals the complexity and characteristic dynamics of macrofaunal fouling assemblages in the tropical habitats, and the results would provide valuable knowledge for biodiversity and antifouling research. 相似文献
The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution(HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping. 相似文献
Using more than three million Landsat satellite images, this research developed the first global impervious surface area (GISA) dataset from 1972 to 2019. Based on 120,777 independent and random reference sites from 270 cities all over the world, the omission error, commission error, and F-score of GISA are 5.16%, 0.82%, and 0.954, respectively. Compared to the existing global datasets, the merits of GISA include: (1) It provided the global ISA maps before the year of 1985, and showed the longest time span (1972–2019) and the highest accuracy (in terms of a large number of randomly selected and third-party validation sample sets); (2) it presented a new global ISA mapping method including a semi-automatic global sample collection, a locally adaptive classification strategy, and a spatio-temporal post-processing procedure; and (3) it extracted ISA from the whole global land area (not from an urban mask) and hence reduced the underestimation. Moreover, on the basis of GISA, the long time series global urban expansion pattern (GUEP) has been calculated for the first time, and the pattern of continents and representative countries were analyzed. The two new datasets (GISA and GUEP) produced in this study can contribute to further understanding on the human’s utilization and reformation to nature during the past half century, and can be freely download from http://irsip.whu.edu.cn/resources/dataweb.php.
