A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders.
Over the preceding decades, climate change has affected precipitation, the most common factor triggering landslides. The aim of this study is to highlight this impact by examining the precipitation trends in the Chania regional unit, Greece, with the help of the precipitation time series provided by 21 local meteorological stations covering a period from 1955 to 2020. The analysis also focuses on the extreme precipitation events of February 2019, where the monthly cumulated precipitation amount reached 1225 mm, one of the highest ever recorded in Greece. Moreover, an inventory of past and recent landslides was created and the intensity–duration landslide precipitation thresholds were evaluated. Daily simulations of precipitation from three state-of-the-art regional climate models were used to analyze precipitation patterns under two representative concentration pathways (RCPs), 4.5 and 8.5, for the period 2030–2060. The application of the estimated precipitation thresholds on the daily future precipitation projections revealed an increase in the following decades of the precipitation events that can activate a landslide and, therefore, highlighted the climate change impact. Moreover, the mean annual precipitation of the preceding 10 years was evaluated and used along with local hydro-geological data and the recent landslide inventory, providing approximately a 5% more effective landslide susceptibility map compared with the relative maps produced by using the mean annual precipitation evaluated for the control period (1976–2005) and for the preceding 30 years. Thus, landslide susceptibility emerges as a dynamic process and the landslide susceptibility map needs to be regularly updated due to the significant and ongoing changes in precipitation because of climate change.
Every year landslides cause many fatalities and destroy numerous infrastructures around the world. Due to their catastrophic results, scientific research studies are conducted, on a continuous basis, trying to determine the controlling and triggering factors, and to evaluate their contribution-weight to that phenomenon. In this direction, many of these studies use multicriteria decision analysis methods as they are quite effective and can be applied rather quickly. However, a large percentage of the new studies that use these methods, is usually devoted to the analysis of many previous research studies and the validation of their results, which usually leads to serious delays and requires significant resources. In this research, 82 relevant past studies are evaluated, and their results are integrated into a worldwide geospatial database, to present its potential as a decision-making tool, during the landslide susceptibility assessment. As it is revealed the results of its statistical and spatial correlation with the examined region’s prevailing parameters in a geographical information system environment, can provide critical indications- suggestions to a researcher and along with the applicability of the multicriteria decision analysis methods, that contain the use of other experts’ knowledge and experience, to lead to the rapid identification of the most critical landslide causal factors and the initial evaluation of their contribution-weight. These indications accelerate significant the whole process and reduce the risk for possible biased conclusions, which can render the whole method ineffective. Moreover, this study highlights the geodatabase’s potential to incorporate open-access data, from external spatial databases and to use them, during the process of the landslide susceptibility assessment.
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