Over the past few decades, the global population and the built environment’s vulnerability to natural hazards have risen dramatically. As a result, decisive actions, such as the SENDAI framework, have emerged to foster a global culture of successful disaster risk reduction policies, including actions to mitigate the social and economic impact of geohazards. The effective study of natural disasters requires meticulous and precise monitoring of their triggering factors, with ground- and space-based techniques. The integration of GNSS and SAR observations through the establishment of permanent infrastructures, i.e., Continuously Operating Reference Stations (CORS) networks and arrays of Corner Reflectors (CRs), may form a seamless ground displacement monitoring system. The current research literature provides fragmented guidelines, regarding the co-location of SAR and GNSS permanent infrastructures. Furthermore, there exist no guidelines for the determination of the most suitable locations using a holistic approach, in terms of criteria and required data. The purpose of this paper is to present a semi-automatic multicriteria site suitability analysis and evaluation of candidate sites for the installation of a permanent CORS and two CRs; one for each pass, taking into account various parameters and criteria. The first results demonstrate that the collocation of SAR and GNSS permanent infrastructures, utilizing a holistic criteria-based approach, is successful and complies with all the literature’s requirements.

The objective of this paper is to introduce CyCLOPS, a novel strategic research infrastructure unit, and present its current progress of implementation, and integration in the National geodetic, geophysical and geotechnical infrastructure of the government-controlled areas of the Republic of Cyprus. CyCLOPS is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation under the grant agreement RIF/INFRASTRUCTURES/1216/0050. CyCLOPS is developed via the collaboration of the Cyprus University of Technology (CUT) and the German Aerospace Center (DLR), and supported by the Cyprus Geological Survey Department and the Department of Lands and Surveys. The main objective of CyCLOPS is to establish an integrated infrastructure for space-based monitoring of geohazards using the most prominent earth observation technologies (EO), such as GNSS and InSAR. Furthermore, the infrastructure will densify and form the backbone for the definition of the next generation national datum of the Republic of Cyprus. Eleven Tier-1/2 state-of-the-art GNSS CORS, precise weather stations, tiltmeters and specifically designed InSAR triangular trihedral corner reflectors will be deployed, in a collocated fashion, at selected locations throughout the government-controlled areas of Cyprus. The collocated configuration will be established and installed to be compliant with the most stringent CORS monumentation specifications, support all current GNSS constellations and SAR missions. Finally, one of CyCLOPS’ fundamental aims is to actively contribute to the on-going efforts and growing demand for more precise positioning services and high-quality modern reference frames, in conformity with the recommendations of the UN-GGIM (and its Subcommittee of Geodesy) to establish and enhance national geodetic infrastructures to support the sustainable management of geospatial information on the changing Earth.

In the last five years, the urban development of the city of Limassol has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development arises several concerns about the aggravation of potential land subsidence in the Limassol coastal front. Forty six Copernicus Sentinel-1 acquisitions from 2017 to 2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). A case study for the identification and analysis of the persistent scatterers (PS) in pixels in a series of interferograms and the quantity of the land displacements in the line of sight of the Limassol coastal front is presented in this research, with subsidence rates up to about (−5 to 4 mm/year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, considering that there is a significant number of skyscrapers planned or currently under construction, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.

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<div> <p><span data-contrast="auto">Landslides constitute a significant geohazard causing human losses and significantly affecting the economy worldwide. Earth Observation and the exploitation of the freely available Copernicus datasets, such as the Sentinel-1 and Sentinel-2 satellite images, can assist in the systematic monitoring of landslides overcoming the restrictions arising from in situ measurements. This study shows how the Google Earth Engine (GEE) platform can be utilised for the rapid mapping of landslides and effectively integrate both passive and active satellite data to enhance the results’ reliability. The GEE is a cloud computing platform designed to store and process huge datasets for scientific analysis and visualization of geospatial datasets where open-source images are acquired by several satellites.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":0,"335559740":240}"> </span></p> </div> <div> <p><span data-contrast="auto">For this study, Ground Range Detection (GRD) Sentinel-1 and multispectral Sentinel-2 satellite data were utilised for a time period between 2016 and 2021. Multitemporal SAR change detection was conducted to identify potential landslides using GRD Sentinel-1 satellite images. Moreover, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Moisture Index (SMI) and Bare Soil Index (BSI) indices were used for the multispectral data. Multi-temporal image composites were created for the two periods. Furthermore, for all image collections, the calculated spectral indices were added as new bands to all images, and the maximum value for each pixel of the vegetation indices was taken. Following, the difference image for each spectral index was created based on two methods, i.e., the first method was based on subtracting the two time periods, and the second one on subtracting each year from the total average for the time period from 2016 until 2021. The possible events were then masked using the thresholding technique based on the trial-and-error procedure where the analyst adjusts manually the thresholds and evaluates the resulting image until satisfied. Based on the results derived from the abovementioned processing, the use of the second method, i.e., subtracting each year from the average, based on the NDVI spectral index provides better results. The proposed methodology was tested in Paphos city in Cyprus because of the occurrence of numerous landslide events in this area, based on the landslide inventory provided by the Geological Survey Department of the Ministry of Agriculture, Rural Development and Environment. The results of this study were validated using high-resolution images from Google Earth in combination with the data from the Geological Survey department.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":0,"335559740":240}"> </span></p> <div> <p><strong><span data-contrast="none">Acknowledgements</span></strong><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":240}"> </span></p> </div> <div> <p><span data-contrast="none">The authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology. The authors would also like to thank the Geological Survey Department of the Ministry of Agriculture, Rural Development and Environment for the provision of the landslide inventory.</span></p> </div> </div>