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Dr. Christos Polykretis
Lab of Geophysical - Satellite Remote Sensing and Archaeo-environment (GeoSat ReSeArch), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology - Hellas (FORTH)

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0 Cartography
0 Environmental Analysis
0 Geography
0 Landslide
0 Remote Sensing

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Journal article
Published: 27 July 2021 in Big Earth Data
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Among the environmental threats, the intensification of natural hazards, such as soil erosion may threaten the integrity and value of cultural heritage sites. In this framework, the present study’s main objective was to identify archaeological sites susceptible by soil erosion, taking the case study of Chania prefecture in Crete Island. Remotely sensed and other available geospatial datasets were analyzed in a GIS-based empirical model, namely Unit Stream Power Erosion and Deposition (USPED), to estimate the average annual soil loss and deposition rates due to water-induced erosion in the study area. The resultant erosion map was then intersected with the locations and surrounding zones of the known archaeological sites for identifying the sites and the portions of their vicinity being at risk. The results revealed that Chania prefecture and its cultural heritage are significantly affected by both soil loss and deposition processes. Between the two processes, soil loss was found to be more intensive, influencing a larger part of the prefecture (especially to the west) as well as a higher amount of archaeological sites. The extreme and high soil loss classes were also detected to cover the most considerable portion of the sites’ surrounding area. The identification of the archaeological sites being most exposed to soil erosion hazard can constitute a basis for cultural heritage managers in order to take preventive preservation measures and develop specific risk mitigation strategies.

ACS Style

Christos Polykretis; Dimitrios D. Alexakis; Manolis G. Grillakis; Athos Agapiou; Branka Cuca; Nikos Papadopoulos; Apostolos Sarris. Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece. Big Earth Data 2021, 1 -19.

AMA Style

Christos Polykretis, Dimitrios D. Alexakis, Manolis G. Grillakis, Athos Agapiou, Branka Cuca, Nikos Papadopoulos, Apostolos Sarris. Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece. Big Earth Data. 2021; ():1-19.

Chicago/Turabian Style

Christos Polykretis; Dimitrios D. Alexakis; Manolis G. Grillakis; Athos Agapiou; Branka Cuca; Nikos Papadopoulos; Apostolos Sarris. 2021. "Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece." Big Earth Data , no. : 1-19.

Journal article
Published: 13 May 2021 in Water Resources Research
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The European Space Agency (ESA), through the Climate Change Initiative (CCI), is currently providing nearly 4 decades of global satellite‐observed, fully homogenized soil moisture data for the uppermost 2–5 cm of the soil layer. These data are valuable as they comprise one of the most complete remotely sensed soil moisture data sets available in time and space. One main limitation of the ESA CCI soil moisture data set is the limited soil depth at which the moisture content is represented. In order to address this critical gap, we (a) estimate and calibrate the Soil Water Index using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then (b) leverage machine learning techniques and physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration. We use this calibration to assess the root‐zone soil moisture for the period 2001–2018. The results are compared against the European Centre for Medium‐Range Weather Forecasts, ERA5 Land, and the Famine Early Warning Systems Network Land Data Assimilation System reanalyses soil moisture data sets, showing a good agreement, mainly over mid latitudes. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large‐scale soil moisture‐related studies.

ACS Style

Manolis G. Grillakis; Aristeidis G. Koutroulis; Dimitrios D. Alexakis; Christos Polykretis; Ioannis N. Daliakopoulos. Regionalizing Root‐Zone Soil Moisture Estimates From ESA CCI Soil Water Index Using Machine Learning and Information on Soil, Vegetation, and Climate. Water Resources Research 2021, 57, 1 .

AMA Style

Manolis G. Grillakis, Aristeidis G. Koutroulis, Dimitrios D. Alexakis, Christos Polykretis, Ioannis N. Daliakopoulos. Regionalizing Root‐Zone Soil Moisture Estimates From ESA CCI Soil Water Index Using Machine Learning and Information on Soil, Vegetation, and Climate. Water Resources Research. 2021; 57 (5):1.

Chicago/Turabian Style

Manolis G. Grillakis; Aristeidis G. Koutroulis; Dimitrios D. Alexakis; Christos Polykretis; Ioannis N. Daliakopoulos. 2021. "Regionalizing Root‐Zone Soil Moisture Estimates From ESA CCI Soil Water Index Using Machine Learning and Information on Soil, Vegetation, and Climate." Water Resources Research 57, no. 5: 1.

Journal article
Published: 01 February 2021 in Applied Geography
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Research efforts related to better understanding and capture of the fertility trends over the time are considered to be of significant interest in the fields of demography and social sciences. In Greece, the overall relationships between fertility and numerous determinants that influence it have been explored at national level. However, the possible spatial heterogeneity in these relationships has not been investigated at any spatial level. The main objective of this study was to explore the spatial stratified heterogeneity in the relationships between fertility and seven socio-economic determinants at the municipality level of Crete Island, Greece. By using demographic data from two censuses in 2001 and 2011, the fertility was measured as crude birth rate (CBR) and the determinants were created as attributes of urbanization, population density, immigration, marriage, female educational level, female unemployment and male employment. The spatial distributions of fertility and determinants for each year as well as their temporal changes were firstly identified. The majority of municipalities of Crete presented high fertility levels in 2001. Between 2001 and 2011, they showed a decline trend, with some exceptions from municipalities located in the north and south of its four prefectures. Furthermore, the Geographical Detector (GeoDetector) technique was applied to reveal the impact of each determinant and their interactions on fertility. The output results revealed that there was obvious spatial stratified heterogeneity of fertility in the island, which could mostly be explained by the immigration in 2001, and the urbanization and population density in 2011. The impact of marriage and female educational level was lower in both years and different (either positive or negative) among the years. The single effects of the prominent determinants were found to be significantly improved from their interactions.

ACS Style

Christos Polykretis; Dimitrios D. Alexakis. Spatial stratified heterogeneity of fertility and its association with socio-economic determinants using Geographical Detector: The case study of Crete Island, Greece. Applied Geography 2021, 127, 102384 .

AMA Style

Christos Polykretis, Dimitrios D. Alexakis. Spatial stratified heterogeneity of fertility and its association with socio-economic determinants using Geographical Detector: The case study of Crete Island, Greece. Applied Geography. 2021; 127 ():102384.

Chicago/Turabian Style

Christos Polykretis; Dimitrios D. Alexakis. 2021. "Spatial stratified heterogeneity of fertility and its association with socio-economic determinants using Geographical Detector: The case study of Crete Island, Greece." Applied Geography 127, no. : 102384.

Journal article
Published: 15 September 2020 in ISPRS International Journal of Geo-Information
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Research efforts focusing on better understanding and capture of mortality progression over the time are considered to be of significant interest in the field of demography. On a demographic basis, mortality can be expressed by different physical parameters. The main objective of this study is the assessment and mapping of four such parameters at the European scale, during the time period 1993–2013. Infant mortality (parameter θ), population aging (parameter ξ), and individual and population mortality due to unexpected exogenous factors/events (parameter κ and λ, respectively) are represented from these parameters. Given that their estimation is based on demographics by age and cause of death, and in order to be examined and visualized by gender, time-specific mortality and population demographic data with respect to gender, age, and cause of death was used. The resulting maps present the spatial patterns of the estimated parameters as well as their variations over the examined period for both male and female populations of 22 European countries in all.

ACS Style

Panagiotis Andreopoulos; Christos Polykretis; Alexandra Tragaki. Assessment and Mapping of Spatio-Temporal Variations in Human Mortality-Related Parameters at European Scale. ISPRS International Journal of Geo-Information 2020, 9, 547 .

AMA Style

Panagiotis Andreopoulos, Christos Polykretis, Alexandra Tragaki. Assessment and Mapping of Spatio-Temporal Variations in Human Mortality-Related Parameters at European Scale. ISPRS International Journal of Geo-Information. 2020; 9 (9):547.

Chicago/Turabian Style

Panagiotis Andreopoulos; Christos Polykretis; Alexandra Tragaki. 2020. "Assessment and Mapping of Spatio-Temporal Variations in Human Mortality-Related Parameters at European Scale." ISPRS International Journal of Geo-Information 9, no. 9: 547.

Journal article
Published: 17 August 2020 in Water
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We present and assess a method to estimate missing values in daily precipitation time series for the Mediterranean island of Crete. The method involves a quantile mapping methodology originally developed for the bias correction of climate models’ output. The overall methodology is based on a two-step procedure: (a) assessment of missing values from nearby stations and (b) adjustment of the biases in the probability density function of the filled values towards the existing data of the target. The methodology is assessed for its performance in filling-in the time series of a dense precipitation station network with large gaps on the island of Crete, Greece. The results indicate that quantile mapping can benefit the filled-in missing data statistics, as well as the wet day fraction. Conceptual limitations of the method are discussed, and correct methodology application guidance is provided.

ACS Style

Manolis G. Grillakis; Christos Polykretis; Stelios Manoudakis; Konstantinos D. Seiradakis; Dimitrios D. Alexakis. A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series. Water 2020, 12, 2304 .

AMA Style

Manolis G. Grillakis, Christos Polykretis, Stelios Manoudakis, Konstantinos D. Seiradakis, Dimitrios D. Alexakis. A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series. Water. 2020; 12 (8):2304.

Chicago/Turabian Style

Manolis G. Grillakis; Christos Polykretis; Stelios Manoudakis; Konstantinos D. Seiradakis; Dimitrios D. Alexakis. 2020. "A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series." Water 12, no. 8: 2304.

Journal article
Published: 29 July 2020 in Remote Sensing
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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.

ACS Style

Christos Polykretis; Dimitrios Alexakis; Manolis Grillakis; Stelios Manoudakis. 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. Remote Sensing 2020, 12, 2439 .

AMA Style

Christos Polykretis, Dimitrios Alexakis, Manolis Grillakis, Stelios Manoudakis. 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. Remote Sensing. 2020; 12 (15):2439.

Chicago/Turabian Style

Christos Polykretis; Dimitrios Alexakis; Manolis Grillakis; Stelios Manoudakis. 2020. "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." Remote Sensing 12, no. 15: 2439.

Journal article
Published: 29 April 2020 in CATENA
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Soil erosion constitutes an increasing thread for soil productivity and food security. Projected changes in Eastern Mediterranean rainfall regime show less but more intense rainfall events that are expected to affect the soil erosion processes. Research on the field has shown that the accurate estimation of rainfall erosivity dependents on the temporal resolution of rainfall data. Nonetheless, the scarcity of high frequency rainfall recordings, leads to the development of methodologies exploiting lower frequency data. This work aims to fill the gap of a rainfall erosivity formula that exploits the daily timescale rainfall data for the island of Crete at Eastern Mediterranean. An unprecedented daily and a sub-hourly rainfall station networks were used to calibrate and validate the formula. The calibrated formula is used along with the future climate projections, to estimate the potential future changes rainfall erosivity under three climate scenarios. Results for the future projections show moderate changes in the rainfall erosivity in the near future for all the assessed scenarios, with the Representative Concentration Pathway (RCP) 2.6 to project the largest change with increased erosivity. Even larger and of different direction are the changes projected for the far future and especially for the RCP2.6 and RCP8.5, with the former to show further increase while the latter a significant decrease in the rainfall erosivity. Attribution analysis shows that those changes are driven by the combination of the reduction in the number of the erosive events with a simultaneous increase in the events’ intensity in all cases, but of different magnitudes which define the final sign of change in the rainfall erosivity. Finally, this study provides evidence that rainfall erosivity in Crete is higher comparing to previous studies’ findings, yet, close to literature values that focus on the central and Eastern Mediterranean region.

ACS Style

Manolis G. Grillakis; Christos Polykretis; Dimitrios D. Alexakis. Past and projected climate change impacts on rainfall erosivity: Advancing our knowledge for the eastern Mediterranean island of Crete. CATENA 2020, 193, 104625 .

AMA Style

Manolis G. Grillakis, Christos Polykretis, Dimitrios D. Alexakis. Past and projected climate change impacts on rainfall erosivity: Advancing our knowledge for the eastern Mediterranean island of Crete. CATENA. 2020; 193 ():104625.

Chicago/Turabian Style

Manolis G. Grillakis; Christos Polykretis; Dimitrios D. Alexakis. 2020. "Past and projected climate change impacts on rainfall erosivity: Advancing our knowledge for the eastern Mediterranean island of Crete." CATENA 193, no. : 104625.

Original paper
Published: 06 February 2020 in Bulletin of Engineering Geology and the Environment
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Landslide susceptibility is the likelihood of landslide occurrence, in a specific place and time. The identification of the potential relationships between landslide susceptibility and conditioning factors is very important towards landslide hazard mitigation. In this paper, we implement a local statistical analysis model geographically weighted regression, in two catchment areas located in northern Peloponnese, Greece. For this purpose, we examined the following eight conditioning factors: elevation, slope, aspect, lithology, land cover, proximity to the drainage network, proximity to the road network, and proximity to faults. Moreover, the relationship between these factors and landsliding in the study area is examined. The local statistical analysis model was also evaluated by finding its differences with the performance of a standard global statistical model logistic regression. The results indicated that the global statistical model can be enhanced by the application of a local model. The outputs of the proposed approach favored a better understanding of the factors influencing landslide occurrence and may be beneficial to local authorities and decision-makers dealing with the mitigation of landslide hazard.

ACS Style

Christos Chalkias; Christos Polykretis; Efthimios Karymbalis; Mauro Soldati; Alessandro Ghinoi; Maria Ferentinou. Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression. Bulletin of Engineering Geology and the Environment 2020, 79, 2799 -2814.

AMA Style

Christos Chalkias, Christos Polykretis, Efthimios Karymbalis, Mauro Soldati, Alessandro Ghinoi, Maria Ferentinou. Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression. Bulletin of Engineering Geology and the Environment. 2020; 79 (6):2799-2814.

Chicago/Turabian Style

Christos Chalkias; Christos Polykretis; Efthimios Karymbalis; Mauro Soldati; Alessandro Ghinoi; Maria Ferentinou. 2020. "Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression." Bulletin of Engineering Geology and the Environment 79, no. 6: 2799-2814.

Journal article
Published: 18 January 2020 in Remote Sensing
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The main objective of this study was to explore the impact of various spectral indices on the performance of change vector analysis (CVA) for detecting the land cover changes on the island of Crete, Greece, between the last two decades (1999–2009 and 2009–2019). A set of such indices, namely, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), albedo, bare soil index (BSI), tasseled cap greenness (TCG), and tasseled cap brightness (TCB), representing both the vegetation and soil conditions of the study area, were estimated on Landsat satellite images captured in 1999, 2009, and 2019. Change vector analysis was then applied for five different index combinations resulting to the relative change outputs. The evaluation of these outputs was performed towards detailed land cover maps produced by supervised classification of the aforementioned images. The results from the two examined periods revealed that the five index combinations provided promising performance results in terms of kappa index (with a range of 0.60–0.69) and overall accuracy (with a range of 0.86–0.96). Moreover, among the different combinations, the use of NDVI and albedo were found to provide superior results against the other combinations.

ACS Style

Christos Polykretis; Manolis Grillakis; Dimitrios Alexakis. Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece. Remote Sensing 2020, 12, 319 .

AMA Style

Christos Polykretis, Manolis Grillakis, Dimitrios Alexakis. Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece. Remote Sensing. 2020; 12 (2):319.

Chicago/Turabian Style

Christos Polykretis; Manolis Grillakis; Dimitrios Alexakis. 2020. "Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece." Remote Sensing 12, no. 2: 319.

Journal article
Published: 09 August 2019 in Geosciences
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The main purpose of this study is to comparatively assess the susceptibility of earthquake-triggered landslides in the island of Lefkada (Ionian Islands, Greece) using two different statistical analysis models, a bivariate model represented by frequency ratio (FR), and a multivariate model represented by logistic regression (LR). For the implementation of the models, the relationship between geo-environmental factors contributing to landslides and documented events related to the 17th November 2015 earthquake was investigated by geographic information systems (GIS)-based analysis. A landslide inventory with events attributed to the specific earthquake was prepared using satellite imagery interpretation and field surveys. Eight factors: Elevation, slope angle, slope aspect, distance to main road network, distance to faults, land cover, geology, and peak ground acceleration (PGA), were considered and used as thematic data layers. The prediction capability of the models and the accuracy of the resulting susceptibility maps were tested by a standard validation method, the receiver operator characteristic (ROC) analysis. Based on the validation results, the output map with the highest reliability could potentially constitute an ideal basis for use within regional spatial planning as well as for the organization of emergency actions by local authorities.

ACS Style

Christos Polykretis; Kleomenis Kalogeropoulos; Panagiotis Andreopoulos; Antigoni Faka; Andreas Tsatsaris; Christos Chalkias. Comparison of Statistical Analysis Models for Susceptibility Assessment of Earthquake-Triggered Landslides: A Case Study from 2015 Earthquake in Lefkada Island. Geosciences 2019, 9, 350 .

AMA Style

Christos Polykretis, Kleomenis Kalogeropoulos, Panagiotis Andreopoulos, Antigoni Faka, Andreas Tsatsaris, Christos Chalkias. Comparison of Statistical Analysis Models for Susceptibility Assessment of Earthquake-Triggered Landslides: A Case Study from 2015 Earthquake in Lefkada Island. Geosciences. 2019; 9 (8):350.

Chicago/Turabian Style

Christos Polykretis; Kleomenis Kalogeropoulos; Panagiotis Andreopoulos; Antigoni Faka; Andreas Tsatsaris; Christos Chalkias. 2019. "Comparison of Statistical Analysis Models for Susceptibility Assessment of Earthquake-Triggered Landslides: A Case Study from 2015 Earthquake in Lefkada Island." Geosciences 9, no. 8: 350.

Abstract
Published: 01 January 2019 in Proceedings
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Multi-temporal Land use and Land cover (LULC) monitoring is a crucial parameter for assessing an area’s landscape ecology regime. LULC changes can be effectively used to describe dynamics of both urban or rural environments and vegetation patterns as an important indicator of ecological environments. In this context, spatial land use properties can be quantified by using a set of landscape metrics. Landscape metrics capture inherent spatial structure of the environment and are used to enhance interpretation of spatial pattern of the landscape. This study aims to monitor diachronically the LULC regime of the island of Crete, Greece with the use of Landsat satellite imageries (Landsat 5, Landsat-7 and Landsat-8) in terms of soil erosion. For this reason, radiometric and atmospheric corrections are applied to all satellite products and unsupervised classification algorithms are used to develop detail LULC maps of the island. The LULC classes are developed by generalizing basic CORINE classes. Following, various landscape metrics are applied to estimate the temporal changes in LULC patterns of the island. The results denote that the diachronic research of spatial patterns evolution can effectively assist to the investigation of the structure, function and landscape pattern changes.

ACS Style

Dimitrios D. Alexakis; Christos Polykretis. Studying Land Use and Land Cover Spatial Patterns Distribution in Crete, Greece with Means of Satellite Remote Sensing. Proceedings 2019, 30, 66 .

AMA Style

Dimitrios D. Alexakis, Christos Polykretis. Studying Land Use and Land Cover Spatial Patterns Distribution in Crete, Greece with Means of Satellite Remote Sensing. Proceedings. 2019; 30 (1):66.

Chicago/Turabian Style

Dimitrios D. Alexakis; Christos Polykretis. 2019. "Studying Land Use and Land Cover Spatial Patterns Distribution in Crete, Greece with Means of Satellite Remote Sensing." Proceedings 30, no. 1: 66.

Journal article
Published: 12 July 2018 in Geosciences
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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.

ACS Style

Christos Polykretis; Antigoni Faka; Christos Chalkias. Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece. Geosciences 2018, 8, 261 .

AMA Style

Christos Polykretis, Antigoni Faka, Christos Chalkias. Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece. Geosciences. 2018; 8 (7):261.

Chicago/Turabian Style

Christos Polykretis; Antigoni Faka; Christos Chalkias. 2018. "Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece." Geosciences 8, no. 7: 261.

Original paper
Published: 09 April 2018 in Natural Hazards
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The main purpose of this study is to compare the performance of two statistical analysis models like weight of evidence and logistic regression (LR) with a soft computing model like artificial neural networks for landslide susceptibility assessment. These models were applied for the Selinous River drainage basin (northern Peloponnese, Greece) in order to map landslide susceptibility and rate the importance of landslide causal factors. A landslide inventory was prepared using satellite imagery interpretation and field surveys. Eight causal factors including altitude, slope angle, slope aspect, distance to road network, distance to drainage network, distance to tectonic elements, land cover, and lithology were considered. Model performance was tested with receiver operator characteristic analysis. The validation findings revealed that the three models show promising results since they give good accuracy values. However, the LR model proved to be relatively superior in estimating landslide susceptibility throughout the study area.

ACS Style

Christos Polykretis; Christos Chalkias. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Natural Hazards 2018, 93, 249 -274.

AMA Style

Christos Polykretis, Christos Chalkias. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Natural Hazards. 2018; 93 (1):249-274.

Chicago/Turabian Style

Christos Polykretis; Christos Chalkias. 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models." Natural Hazards 93, no. 1: 249-274.

Original paper
Published: 17 July 2017 in Bulletin of Engineering Geology and the Environment
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In this paper, an adaptive neuro-fuzzy modeling (ANFIS) is applied in order to map landslide susceptibility for a Mediterranean catchment (Peloponnese, Greece). The relationship between landslides and factors influencing their occurrence is investigated in GIS environment. Seven conditioning factors, including elevation, slope angle, profile curvature, stream density, distance to main roads, geology, and vegetation were considered in the analysis. Six ANFIS models with different membership functions were developed to generate the corresponding landslide susceptibility maps. The outputs, representing the probability level of landslide occurrence, were grouped into five classes. They were then evaluated using an independent dataset of landslide events in two different validation methods: receiver operating characteristics (ROC) analysis and success and prediction rates. The majority of the calculated area under the curve values for the two validation methods was in the range 0.70–0.90 indicating between fair and very good prediction accuracy for the six models. These values also showed that the prediction accuracy depends on the membership functions examined in the ANFIS modeling. Among these functions, the difference of two sigmoidally shaped (Dsigmf) and product of two sigmoidally shaped (Psigmf) presented the highest prediction accuracy.

ACS Style

Christos Polykretis; Christos Chalkias; Maria Ferentinou. Adaptive neuro-fuzzy inference system (ANFIS) modelingfor landslide susceptibility assessment in a Mediterranean hillyarea. Bulletin of Engineering Geology and the Environment 2017, 78, 1173 -1187.

AMA Style

Christos Polykretis, Christos Chalkias, Maria Ferentinou. Adaptive neuro-fuzzy inference system (ANFIS) modelingfor landslide susceptibility assessment in a Mediterranean hillyarea. Bulletin of Engineering Geology and the Environment. 2017; 78 ():1173-1187.

Chicago/Turabian Style

Christos Polykretis; Christos Chalkias; Maria Ferentinou. 2017. "Adaptive neuro-fuzzy inference system (ANFIS) modelingfor landslide susceptibility assessment in a Mediterranean hillyarea." Bulletin of Engineering Geology and the Environment 78, no. : 1173-1187.

Journal article
Published: 01 March 2016 in Geosciences
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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.

ACS Style

Christos Chalkias; Christos Polykretis; Maria Ferentinou; Efthimios Karymbalis. Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale. Geosciences 2016, 6, 14 .

AMA Style

Christos Chalkias, Christos Polykretis, Maria Ferentinou, Efthimios Karymbalis. Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale. Geosciences. 2016; 6 (1):14.

Chicago/Turabian Style

Christos Chalkias; Christos Polykretis; Maria Ferentinou; Efthimios Karymbalis. 2016. "Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale." Geosciences 6, no. 1: 14.

Journal article
Published: 20 August 2014 in Geosciences
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: 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.

ACS Style

Christos Chalkias; Maria Ferentinou; Christos Polykretis. GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece. Geosciences 2014, 4, 176 -190.

AMA Style

Christos Chalkias, Maria Ferentinou, Christos Polykretis. GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece. Geosciences. 2014; 4 (3):176-190.

Chicago/Turabian Style

Christos Chalkias; Maria Ferentinou; Christos Polykretis. 2014. "GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece." Geosciences 4, no. 3: 176-190.

Journal article
Published: 27 April 2014 in Bulletin of Engineering Geology and the Environment
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The main scope of this study is to compare the performance of a conventional statistical method like the landslide susceptibility index (LSI) and a soft computing method like artificial neural networks (ANNs). These models were applied in order to realistically map landslide susceptibility (LS) in the Krathis and Krios drainage basins in northern Peloponnesus. The relationship between landslides and various conditioning factors contributing to their occurrence was investigated through geographic information system-based analysis. A landslide inventory was realised using aerial-photos, satellite images and field surveys. Eight conditioning factors, including land cover, geology, elevation, slope, aspect, distance to road network, distance to drainage network, distance to structural elements, were considered. Subsequently, LS maps were produced using LSI and ANNs, and they were then compared and validated accordingly. Model performance was checked by an independent validation set of landslide events. For the validation process, the receiver operating curve was drawn and the area-under-the-curve (AUC) values were calculated. The calculated AUC values were 0.852 for the LSI model, and 0.842 for the ANNs; thus, both methods seem to lead to quite similar results. Based on these results, with an average percentage of correctly predicting landslides of about 84 %, model validation confirms that extrapolation results are very good, and that both models can be used to mitigate hazards related to landslides, and to aid in generalised land-use planning assessment purposes.

ACS Style

Christos Polykretis; Maria Ferentinou; Christos Chalkias. A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bulletin of Engineering Geology and the Environment 2014, 74, 27 -45.

AMA Style

Christos Polykretis, Maria Ferentinou, Christos Chalkias. A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bulletin of Engineering Geology and the Environment. 2014; 74 (1):27-45.

Chicago/Turabian Style

Christos Polykretis; Maria Ferentinou; Christos Chalkias. 2014. "A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece)." Bulletin of Engineering Geology and the Environment 74, no. 1: 27-45.

Journal article
Published: 02 April 2014 in ISPRS International Journal of Geo-Information
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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.

ACS Style

Christos Chalkias; Maria Ferentinou; Christos Polykretis. GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method. ISPRS International Journal of Geo-Information 2014, 3, 523 -539.

AMA Style

Christos Chalkias, Maria Ferentinou, Christos Polykretis. GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method. ISPRS International Journal of Geo-Information. 2014; 3 (2):523-539.

Chicago/Turabian Style

Christos Chalkias; Maria Ferentinou; Christos Polykretis. 2014. "GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method." ISPRS International Journal of Geo-Information 3, no. 2: 523-539.