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With the rapid growth of data volume and the development of artificial intelligence technology, deep-learning methods are a new way to model land subsidence. We utilized a long short-term memory (LSTM) model, a deep-learning-based time-series processing method to model the land subsidence under multiple influencing factors. Land subsidence has non-linear and time dependency characteristics, which the LSTM model takes into account. This paper modelled the time variation in land subsidence for 38 months from 2011 to 2015. The input variables included the change in land subsidence detected by InSAR technology, the change in confined groundwater level, the thickness of the compressible layer and the permeability coefficient. The results show that the LSTM model performed well in areas where the subsidence is slight but poorly in places with severe subsidence. [ABSTRACT FROM AUTHOR]

Subsidence can now be routinely mapped on a national scale thanks to ESA's Sentinel-1 sensors and advanced scalable SqueeSAR® processing. In order to be integrated into existing monitoring programmes, the SqueeSAR® datasets can be calibrated with GNSS measurements. The dense spatial coverage of SqueeSAR® deformation maps captures local deformation phenomena, and with appropriate calibration, can advance the understanding of regional deformation trends. The regular and reliable SAR image acquisitions by Sentinel-1, as well as significant improvements in the scalability of SqueeSAR® processing allow regular updates of deformation maps on a national scale. Filtering the large amount of data for relevant information is achieved by using an algorithm to detect changes in displacement trends. [ABSTRACT FROM AUTHOR]

The proposal in this paper is to make accessible the hydrology analysis tools that were developed by our research team in the past years through an interoperable Spatial Data Infrastructure. To this aim we chose to develop add-ons for the geOrchestra OGC-compliant platform. Such add-ons trigger algorithms and retrieve their output in real time through OGC standard WPS. We then introduce a watershed WPS add-on and its functioning modes. In so doing we exemplify the fact that the use of OGC standards make it straightforward (and transparent to the user operating a common web browser) to remotely trigger a process on a distant server, then apply it to distant data present on a remote cartographic server, and drop the outcome onto a third-party cartographic server while visualizing it all on a browser. [ABSTRACT FROM AUTHOR]

The analysis of the vertical movements of the soil in the Po River plane of the Emilia-Romagna Region (Italy) was updated through an interferometric analysis referred to the 2011–2016 time-span. This activity is a continuation of previous studies on the state of knowledge of vertical soil movements in the same area, analyzed firstly by levelling and GNSS and more recently by SAR interferometry for the periods 1992–2000, 2002–2006, 2006–2011, on behalf of the Emilia-Romagna Region. The survey area analysed was approximately 13 300 km 2 , which corresponds to the territory of the regional plain. The interferometric dataset was calibrated through the use of velocity time series of several permanent GNSS stations. Among the 36 stations analysed, 22 were included in the study area: 16 were used for the calibration and 6 as check points). The velocities required for the calibration of the SAR analysis were calculated in the period following the important seismic events that struck the territory of the Emilia Romagna Region in May 2012. The interferometric analysis was carried out by TRE ALTAMIRA using the SqueeSAR™ technology. In particular, in order to update the interferometric dataset to 2016, it was necessary to perform a joint processing of the available RADARSAT-1 data and of the data acquired by the RADARSAT-2 satellite using a specific operating mode of the SqueeSAR™ algorithm known as stitching; this approach allowed the joint processing of images acquired in the same geometry by these two satellites. The study of the time series of the GNSS permanent stations used to provide the velocity datum to the interferometric analysis, is described, and the results of the SqueeSAR™ interferometric processing are reported. Statistical analyses on the spatial distribution and the type of scatterers have been performed during the screening and validation procedures of the dataset, and for the identification and removal of the outliers. Finally, the resulting map is described in order to analyse the measured soil movements with respect to the results obtained in past analyses, and the possible geological and human-induced causes, which could have produced them. [ABSTRACT FROM AUTHOR]

Published suspended sediment data for Arctic rivers is scarce. Suspended sediment rating curves for three medium to large rivers of the Russian Arctic were obtained using various curve-fitting techniques. Due to the biased sampling strategy, the raw datasets do not exhibit log-normal distribution, which restricts the applicability of a log-transformed linear fit. Non-linear (power) model coefficients were estimated using the Levenberg-Marquardt, Nelder-Mead and Hooke-Jeeves algorithms, all of which generally showed close agreement. A non-linear power model employing the Levenberg-Marquardt parameter evaluation algorithm was identified as an optimal statistical solution of the problem. Long-term annual suspended sediment loads estimated using the non-linear power model are, in general, consistent with previously published results. [ABSTRACT FROM AUTHOR]