Advances and Practices on the Research, Prevention and Control of Land Subsidence in Coastal Cities

Land subsidence severely threatens most of the coastal plains around the world where high productive industrial and agricultural activities and urban centers are concentrated. Coastal subsidence damages infrastructures and exacerbates the effect of the sea-level rise at regional scale. Although it is a well-known process, there is still much more to be improved on the monitoring, mapping and modeling of ground movements, as well as the understanding of controlling mechanisms. The International Geoscience Programme recently approved an international project(IGCP 663) aiming to bring together worldwide researchers to share expertise on subsidence processes typically occurring in coastal areas and cities, including basic research, monitoring and observation, modelling and management. In this paper, we provide the research communities and potential stakeholders with the basic information to join the participating teams in developing this project. Specifically, major advances on coastal subsidence studies and information on well-known and new case studies of land subsidence in China, Italy, The Netherlands, Indonesia, Vietnam and Thailand are highlighted and summarized. Meanwhile, the networking, dissemination, annual meeting and field trip are briefly introduced.

Impact,Mechanism,Monitoring of Land Subsidence in Coastal Cities(Annual Work of IGCP 663)

Land subsidence is a worldwide geohazard consisting in the lowering of the ground surface due to natural and human-induced processes occurring in the shallow and deep subsoil.Over the last two decades,land subsidence has caused damages and widespread impacts to a variety of infrastructures in coastal cities(Ma et al.,2011;Liu et al.,2016;Minderhoud et al.,2018).

Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical applications.First,change indicators constructed from incoherent feature only cannot characterize the change objects accurately.Second,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel meaningless.In this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images.The coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change pixels.Afterwards,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each patch.An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.