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Article

Integration of Multi-Source Datasets for Assessing Ground Swelling/Shrinking Risk in Cyprus: The Case Studies of Pyrgos–Parekklisia and Moni

by
Athanasios V. Argyriou
1,2,
Maria Prodromou
1,3,
Christos Theocharidis
1,3,
Kyriaki Fotiou
1,3,
Stavroula Alatza
4,
Constantinos Loupasakis
5,
Zampela Pittaki-Chrysodonta
1,
Charalampos Kontoes
4,
Diofantos G. Hadjimitsis
1,3 and
Marios Tzouvaras
1,3,*
1
ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus
2
Laboratory of Geophysical-Satellite Remote Sensing & Archaeoenvironment (GeoSat ReSeArch Lab), Institute for Mediterranean Studies, Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Greece
3
Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus
4
National Observatory of Athens, Operational Unit BEYOND Centre for Earth Observation Research and Satellite Remote Sensing IAASARS/NOA, 15236 Athens, Greece
5
Laboratory of Engineering Geology and Hydrogeology, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(17), 3185; https://doi.org/10.3390/rs16173185 (registering DOI)
Submission received: 18 July 2024 / Revised: 18 August 2024 / Accepted: 21 August 2024 / Published: 28 August 2024

Abstract

The determination of swelling/shrinking phenomena, from natural and anthropogenic activity, is examined in this study through the synergy of various remote sensing methodologies. For the period of 2016–2022, a time-series InSAR analysis of Sentinel-1 satellite images, with a Coherent Change Detection procedure, was conducted to calculate the Normalized Coherence Difference. These were combined with Sentinel-2 multispectral data by exploiting the Normalized Difference Vegetation Index to create multi-temporal image composites. In addition, ALOS-Palsar DEM derivatives highlighted the geomorphological characteristics, which, in conjunction with the satellite imagery outcomes and other auxiliary spatial datasets, were embedded within a Multi-Criteria Decision Analysis (MCDA) model. The synergy of the remote sensing and GIS techniques’ applicability within the MCDA model highlighted the zones undergoing seasonal swelling/shrinking processes in Pyrgos–Parekklisia and Moni regions in Cyprus. The accuracy assessment of the produced final MCDA outcome provided an overall accuracy of 72.4%, with the Kappa statistic being 0.66, indicating substantial agreement of the MCDA outcome with the results from a Persistent Scatterer Interferometry analysis and ground-truth observations. Thus, this study offers decision-makers a powerful procedure to monitor longer- and shorter-term swelling/shrinking phenomena.
Keywords: data fusion; optical; SAR; geohazards observatory; multi-criteria decision analysis data fusion; optical; SAR; geohazards observatory; multi-criteria decision analysis

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MDPI and ACS Style

Argyriou, A.V.; Prodromou, M.; Theocharidis, C.; Fotiou, K.; Alatza, S.; Loupasakis, C.; Pittaki-Chrysodonta, Z.; Kontoes, C.; Hadjimitsis, D.G.; Tzouvaras, M. Integration of Multi-Source Datasets for Assessing Ground Swelling/Shrinking Risk in Cyprus: The Case Studies of Pyrgos–Parekklisia and Moni. Remote Sens. 2024, 16, 3185. https://doi.org/10.3390/rs16173185

AMA Style

Argyriou AV, Prodromou M, Theocharidis C, Fotiou K, Alatza S, Loupasakis C, Pittaki-Chrysodonta Z, Kontoes C, Hadjimitsis DG, Tzouvaras M. Integration of Multi-Source Datasets for Assessing Ground Swelling/Shrinking Risk in Cyprus: The Case Studies of Pyrgos–Parekklisia and Moni. Remote Sensing. 2024; 16(17):3185. https://doi.org/10.3390/rs16173185

Chicago/Turabian Style

Argyriou, Athanasios V., Maria Prodromou, Christos Theocharidis, Kyriaki Fotiou, Stavroula Alatza, Constantinos Loupasakis, Zampela Pittaki-Chrysodonta, Charalampos Kontoes, Diofantos G. Hadjimitsis, and Marios Tzouvaras. 2024. "Integration of Multi-Source Datasets for Assessing Ground Swelling/Shrinking Risk in Cyprus: The Case Studies of Pyrgos–Parekklisia and Moni" Remote Sensing 16, no. 17: 3185. https://doi.org/10.3390/rs16173185

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