Integrating expert knowledge with statistical analysis for landslide susceptibility assessment at regional scale

Abstract: In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale

Underwater geophysical prospection in ancient Olous, Crete

We employ electrical resistivity tomography and magnetic gradiometry methods to the ultra-shallow submerged and littoral archaeological site of Olous. This allows reconstruction of the built environment that nowadays lie below the sea bottom, thus completing the respective archaeological evidence

GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method

The main aim of this paper is landslide susceptibility assessment using fuzzy expert-based modeling. Factors that influence landslide occurrence, such as elevation, slope, aspect, lithology, land cover, precipitation and seismicity were considered. Expert-based fuzzy weighting (EFW) approach was used to combine these factors for landslide susceptibility mapping (Peloponnese, Greece). This method produced a landslide susceptibility map of the investigated area. The landslides under investigation have more or less same characteristics: lateral based and downslope shallow movement of soils or rocks. The validation of the model reveals, that predicted susceptibility levels are found to be in good agreement with the past landslide occurrences. Hence, the obtained landslide susceptibility map could be acceptable, for landslide hazard prevention and mitigation at regional scale

GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method

The main aim of this paper is landslide susceptibility assessment using fuzzy expert-based modeling. Factors that influence landslide occurrence, such as elevation, slope, aspect, lithology, land cover, precipitation and seismicity were considered. Expert-based fuzzy weighting (EFW) approach was used to combine these factors for landslide susceptibility mapping (Peloponnese, Greece). This method produced a landslide susceptibility map of the investigated area. The landslides under investigation have more or less same characteristics: lateral based and downslope shallow movement of soils or rocks. The validation of the model reveals, that predicted susceptibility levels are found to be in good agreement with the past landslide occurrences. Hence, the obtained landslide susceptibility map could be acceptable, for landslide hazard prevention and mitigation at regional scale

Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

The main purpose of this study is to explore the impact of analysis scale on the performance of a quantitative model for landslide susceptibility assessment through empirical analyses in the northern Peloponnese, Greece. A multivariate statistical model like logistic regression (LR) was applied at two different scales (a regional and a more detailed scale). Due to this scale difference, the implementation of the model was based on two landslide inventories representing in a different way the landslide occurrence (as point and polygon features), and two datasets of similar geo-environmental factors characterized by a different size of grid cells (90 m and 20 m). Model performance was tested by a standard validation method like receiver operating characteristics (ROC) analysis. The validation results in terms of accuracy (about 76%) and prediction ability (Area under the Curve (AUC) = 0.84) of the model revealed that the more detailed scale analysis is more appropriate for landslide susceptibility assessment and mapping in the catchment under investigation than the regional scale analysis

Evaluation of landslide susceptibility assessment methods by using geoinformatics

The knowledge about the spatial occurrence probability of a natural hazard is particularly useful for its mitigation, and estimation of the potential amount of damages and losses that it may cause. However, the acquisition of this knowledge requires dealing with important issues such as the existence of complex relationships among factors related to the occurrence of such a hazard, the lack of relevant data, and the integration of the dynamic changes taking place in the environment. The modern technological advances in the field of Geosciences allow the simulation of natural hazards. For this reason, by using the capabilities of Geoinformatics-based technologies (such as Geographical Information Systems, Remote Sensing, etc.), the present PhD thesis aims to the figuration of spatial predictions of landslide occurrence. The target predictions were derived from the landslide susceptibility assessment through empirical analyses in Greece. In terms of this assessment, the impact of the change of analysis scale on the performance of selected models was examined. A set of qualitative, quantitative and integrated models was applied in two different analysis scales (regional and more detailed), and by extension, in two areas with different size in Greece (system of catchments in northern Peloponnese, and Selinous river catchment, respectively). Due to the difference in the scale analysis, similar datasets of landslides and causal factors were collected which were characterized by different spatial resolution (with cell size 90 and 20 meters, respectively). The performance of the models was evaluated comparatively through specific validation methods. Furthermore, for each of the two analysis scales, the existence of spatial non-stationarity in the relationships between the landslide occurrence and the analyzed factors was investigated. Finally, the impact expected to have the “optimal” susceptibility result on the socio-economic elements of the study area in the more detailed analysis scale was examined through the landslide vulnerability assessment.Η γνώση σχετικά με τη χωρική πιθανότητα εκδήλωσης ενός φυσικού κινδύνου είναι ιδιαιτέρως χρήσιμη για τον μετριασμό του, και την εκτίμηση του πλήθους των ενδεχόμενων ζημιών και απωλειών που μπορεί να προκαλέσει. Ωστόσο, η απόκτηση αυτής της γνώσης απαιτεί την αντιμετώπιση σημαντικών ζητημάτων, όπως είναι η ύπαρξη των πολύπλοκων σχέσεων μεταξύ των παραγόντων που σχετίζονται με την εκδήλωση ενός τέτοιου κινδύνου, η έλλειψη σχετικών δεδομένων, και η ενσωμάτωση των δυναμικών αλλαγών που λαμβάνουν χώρα στο περιβάλλον. Οι σύγχρονες τεχνολογικές εξελίξεις στον επιστημονικό κλάδο των Γεωεπιστημών επιτρέπουν την προσομοίωση των φυσικών κινδύνων. Γι’ αυτό το λόγο, αξιοποιώντας τις δυνατότητες των βασισμένων στη Γεωπληροφορική τεχνολογιών (όπως Συστήματα Γεωγραφικών Πληροφοριών, Τηλεπισκόπηση, κ.ά.), η παρούσα διδακτορική διατριβή στοχεύει στη διαμόρφωση χωρικών προβλέψεων εκδήλωσης κατολισθήσεων. Οι επιδιωκόμενες προβλέψεις προέκυψαν από την εκτίμηση της επιδεκτικότητας σε εκδήλωση κατολίσθησης μέσω εμπειρικών αναλύσεων στον Ελληνικό χώρο. Στα πλαίσια αυτής της εκτίμησης, εξετάστηκε η επίδραση της μεταβολής της κλίμακας ανάλυσης στην απόδοση επιλεγμένων μοντέλων. Ένα σύνολο ποιοτικών, ποσοτικών και ενοποιημένων μοντέλων εφαρμόστηκε σε δύο διαφορετικές κλίμακες ανάλυσης (περιφερειακή και λεπτομερέστερη), και κατ’ επέκταση, σε δύο διαφορετικού μεγέθους περιοχές του Ελληνικού χώρου (σύστημα λεκανών απορροής της βόρειας Πελοποννήσου, και λεκάνη απορροής του ποταμού Σελινούντα, αντιστοίχως). Λόγω της διαφοράς της κλίμακας ανάλυσης, παρόμοια σύνολα δεδομένων κατολισθήσεων και αιτιολογικών παραγόντων συλλέχθηκαν τα οποία χαρακτηρίζονταν από διαφορετική χωρική ανάλυση (με μέγεθος ψηφίδας 90 και 20 μέτρα, αντιστοίχως). Η απόδοση των μοντέλων αξιολογήθηκε συγκριτικώς μέσω εξειδικευμένων μεθόδων επικύρωσης. Επιπλέον, για κάθε μια από τις δύο κλίμακες ανάλυσης, διερευνήθηκε η ύπαρξη χωρικής μη-στασιμότητας στις σχέσεις μεταξύ της εκδήλωσης κατολισθήσεων και των αναλυθέντων παραγόντων. Τέλος, η επίδραση που αναμένεται να έχει η επαλήθευση του «βέλτιστου» αποτελέσματος επιδεκτικότητας στα κοινωνικο-οικονομικά στοιχεία της περιοχής μελέτης στη λεπτομερέστερη κλίμακα ανάλυσης, εξετάστηκε μέσω της εκτίμησης της τρωτότητας σε εκδήλωση κατολίσθησης

GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

: In this paper, bivariate statistical analysis modeling was applied and validated to derive a landslide susceptibility map of Peloponnese (Greece) at a regional scale. For this purpose, landslide-conditioning factors such as elevation, slope, aspect, lithology, land cover, mean annual precipitation (MAP) and peak ground acceleration (PGA), and a landslide inventory were analyzed within a GIS environment. A landslide dataset was realized using two main landslide inventories. The landslide statistical index method (LSI) produced a susceptibility map of the study area and the probability level of landslide occurrence was classified in five categories according to the best classification method from three different methods tested. Model performance was checked by an independent validation set of landslide events. The accuracy of the final result was evaluated by receiver operating characteristics (ROC) analysis. The prediction ability was found to be 75.2% indicating an acceptable susceptibility map obtained from the GIS-based bivariate statistical model

Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale

In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale

Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping

Among several natural and anthropogenic conditioning factors that control slope instability, heavy rainfall is a key factor in terms of triggering landslide events. In the Mediterranean region, Crete suffers the frequent occurrence of heavy rainstorms that act as a triggering mechanism for landslides. The Mediterranean island of Crete suffers from frequent occurrences of heavy rainstorms, which often trigger landslides. Therefore, the spatial and temporal study of recent storm/landslide events and the projection of potential future events is crucial for long-term sustainable land use in Crete and Mediterranean landscapes with similar geomorphological settings, especially with climate change likely to produce bigger and more frequent storms in this region. Geoinformatic technologies, mainly represented by remote sensing (RS) and Geographic Information Systems (GIS), can be valuable tools towards the analysis of such events. Considering an administrative unit of Crete (municipality of Rethymnon) for investigation, the present study focused on using RS and GIS-based approaches to: (i) detect landslides triggered by heavy rainstorms during February 2019; (ii) determine the interaction between the triggering factor of rainfall and other conditioning factors; and (iii) estimate the spatial component of a hazard map by spatially indicating the possibility for rainfall-triggered landslides when similar rainstorms take place in the future. Both landslide detection and hazard mapping outputs were validated by field surveys and empirical analysis, respectively. Based on the validation results, geoinformatic technologies can provide an ideal methodological framework for the acquisition of landslide-related knowledge, being particularly beneficial to land-use planning and decision making, as well as the organization of emergency actions by local authorities

Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha−1) with October (about 61 t ha−1) in 2016, and winter (about 96 t ha−1) with February (146 t ha−1) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha−1), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion