Landslide Inventory Database Research Articles

Landslide Susceptibility and Risk Mapping in the Tectonic Ensemble Comprising of Eastern Himalayan Zone, Northeast India and Bhutan using Logistic Regression and Random Forest Techniques

Abstract The mountainous terrain of the East-Northeast tectonic ensemble covers an approximate area of 3,11,420 km2 comprising the Eastern Himalayan zone, Northeast India and Bhutan is prone to mass movements. The increasing trend of landslide occurrences in the last few decades sets loud alarm bells for mapping the landslide hotspot zones in terms of Landslide Susceptibility and Risk. Initially, a landslide inventory of around 9751 landslides has been prepared, of which 6826 (70%) has been randomly picked as training dataset and the remaining 2925 (30%) as the testing dataset. Thereafter, Random Forest (RF) and binary Logistic Regression (LR) based Landslide Susceptibility Zonation (LSZ) have been prepared through twelve predictor layers viz. Slope Angle, Slope Aspect, Slope Curvature, Distance to Drainage, Distance to Lineament, Landform, Surface Geology, Distance to Road, Normalized Difference Vegetation Index (NDVI), Landuse/ Landcover (LULC), Rainfall and Epicentre Proximity. Both the Landslide Susceptibility Index (LSI) maps are divided into five classes, viz. ‘low’, ‘moderate’, ‘high’, ‘very high’ and ’severe’, which are then validated statistically by drawing a comparison with the 30% testing chronological landslide inventory database. The statistical index based accuracy assessment in terms of Area Under the Curve (AUC) exhibits an LR model AUC of 0.776 and RF model AUC of 0.820. However, it has been observed that the RF model strongly correlates with the testing inventory dataset exhibiting that around 46% of the landslide-prone terrain is classified in the ‘high to severe’ zones with 41% inventory landslides occurring in these zones. Integrating the Random Forest (RF) based LSI thematic layer with the socio-economic vulnerability layers like the Number of Households and Population Density have demarcated around 20.78% of the region under ‘very high to severe’ socio-economic risk as convoluted by landslide susceptibility in the terrain. The present findings are expected to be useful in urban development and town planning with appropriate slope management and land-use planning.

GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh

The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9–12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management.

Environmental challenge of landslide in geothermal areas: an introductory study

Landslides in geothermal areas are one of the environmental challenges that are attributed to their volcanic and hydrothermal systems. However, the inventory of landslides in geothermal areas remains considerably limited. This may implicate pitfalls, where characterisation of historical events is an essential practice in disaster risk management. Accordingly, this exploratory study aims to characterise landslide events in 19 geothermal areas with historical landslides. This introductory study was sourced from electronic news, scientific publications, and authoritative institutions. The collected records were cross-examined and integrated into comparable attributes. The resulting characteristics display the physical features, environmental factors, and mitigation measures as follows: (1) landslides commonly occur in small to extremely large volumes. However, serious tolls may be caused even by small-to modest-sized landslides. (2) Landslides may not be preceded by triggering factors but by their gradually weakened environment. (3) Although there are scattered sources and unclear information related to underreported landslides and understudied mitigation, respective authorities conduct rapid measures following the landslide event. Correspondingly, it is highly recommended to further improve and establish a landslide inventory database in geothermal areas that constitutes a more comprehensive overview for better risk management.

SAR data and field surveys combination to update rainfall-induced shallow landslide inventory

The Campania region has been recurrently hit by severe landslides in volcanoclastic deposits. The city of Naples, and in particular the Camaldoli and Agnano hills (Phlegraean Fields), also suffered several landslide crises in weathered volcanoclastic rocks as a consequence of intense rainfalls or wildfires. To identify slope failures phenomena occurred in the winter season 2019–2020 an innovative procedure has been proposed. The purpose of this procedure is to highlight areas where major land cover changes occurred within our area of study, which can be potentially related to mass movements. The amplitude of spaceborne SAR images has been exploited for the change detection analysis and the output derived from the segmentation procedure has been compared with field observations. The amplitude-based method has been already applied in the detection of landslides, but never on the event with limited extensions, such as for this application. The achieved outcomes allowed the mapping of 62 new landslides that have been used to update the current landslide inventory database. This type of information is expected to help decision-makers with land planning and risk assessment.

Landslide susceptibility assessment in expansion areas of the rapidly growing city of Cuenca (Ecuador)

Landslides can have significant effects on settlements and human life, especially when mobilized volumes are large and runout velocity is high. In case of high- intensity events, landslide mitigation is not always possible so that the best way to prevent disasters is to reduce the exposed value in susceptible areas. Landslide risk assessment is a fundamental tool for reducing loss induced by landslides, especially considering the impact of high-intensity landslides on the socio- economic development of developing countries. Risk mitigation requires, at least, to be based on a susceptibility scenario and on inventory of existing settlements. On this basis, a study concerning landslide impacting settlements in the city of Cuenca (Ecuador) has been carried out using a landslide inventory database (LID) and supply energy contract database representing a proxy of buildings presence. The susceptibility analysis indicates that most of the slope surrounding the city of Cuenca, where the greatest urban expansion has taken place, is highly prone to instability. In addition, multi-temporal supply energy contract analysis highlights that expansion of urban areas is occurring at a steady rate and such extension is corresponding to an increase in areas susceptible to landslide.

Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China

This study aims to investigate the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation. Wanzhou section of the Three Gorges Reservoir area, China, frequently suffering numerous landslides, is chosen as an example. The class-weighted algorithm focuses on the class-imbalanced issue of landslide and non-landslide samples, and it can turn the class-imbalanced issue into a cost-sensitive machine learning by setting unequal weights for different classes, which contribute to improving the accuracy of landslide susceptibility evaluation. The landslide inventory database was produced by field investigation and remote sensing images derived from Google Earth. Of the 233 landslides in the inventory, 40% were used for validation, and the remaining 60% were used for training purposes. Twelve environmental parameters (elevation, slope, aspect, curvature, distance to river, NDVI, NDWI, rainfall, seismic intensity, land use, TRI, lithology) were treated as inputs of the models to produce a landslide susceptibility map (LSM). The AUC value, Balanced accuracy, and Geometric mean score were utilized to estimate the quality of models. The result shows that the weighted models (weighted logistic regression (WLR), weighted LightGBM (WLightGBM), weighted random forest (WRF) have higher AUC values, Balanced accuracy, and Geometric mean scores than those of unweighted methods, which demonstrates that the weighted models exhibit better than unweighted models, with the WRF model having the best performance. The landslide susceptibility map of the Wanzhou section displays that the high and very high landslide susceptibility zones are mainly distributed on both sides of the river. The insights from this research will be useful for ameliorating the landslide susceptibility mapping and the prevention and mitigation for the Wanzhou section.

Landslide inventory validation and susceptibility mapping in the Gerecse Hills, Hungary

ABSTRACT Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but the database is out-of-date. Here we address the problem of revising the National Landslides Cadastre landslide inventory database by creating a landslide susceptibility map with a multivariate model based on likelihood ratio functions. The model is applied to the TanDEM-X DEM (0.4″ res.), the current landslide inventory of the area, and data acquired from geological maps. By comparing the distributions of four variables in the landslide and non-landslide area with grid computation methods, the model yields landslide susceptibility estimates for the study area. The estimations show to what extent a certain area is similar to the sample areas, therefore, its likelihood to be affected by landslides in the future. The accuracy of the model predictions was checked in the field and compared to the results of our previous study using the SRTM-1 DEM for a similar analysis. The model gave accurate estimates when certain correction measures were applied to the input datasets. The limitations of the model, the input datasets, and the suggested correction measures are also discussed.

Spatial modelling of shallow landslide susceptibility: a study from the southern Western Ghats region of Kerala, India.

ABSTRACT The study was undertaken to produce the landslide susceptibility maps by using Dempster–Shafer, Bayesian probability and logistic regression methods for the southern Western Ghats, Kerala, India. A landslide inventory database of 82 landslides is prepared and used for landslide susceptibility modelling. Twelve landslide conditioning factors including lithology, geomorphological features, slope angle, soil texture, distance from stream, distance from road, distance from lineaments, land use/land cover, slope curvature, rainfall, topographic wetness index and relative relief are extracted from the spatial database and used for modelling. Multi-collinearity among the independent variables were tested and landslide susceptibility maps are constructed. The constructed models were validated with sensitivity, specificity, classification accuracy, ROC-AUC, root mean square error (RMSE) and kappa index. The Bayesian probability model obtained highest ROC-AUC (0.833), sensitivity (0.870), specificity (0.800) and kappa index (0.667) with least RMSE (0.4550) in validation phase. In addition, the study reveals that the agricultural areas have 10°–40° slopes falling on the denudational structural hills are extremely susceptible to landslide occurrence with extended influence from distance from roads, distance from streams and soil texture. The predicted model is trustworthy for future land use planning in the southern Western Ghats to mitigate the risk from landslide hazard.

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km2 (WofE& RBF-SVM), 474 km2 (WofE& Linear-SVM), 501km2 (WofE& Polynomial-SVM), and 498 km2 (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km2. The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts.

Preliminary landslide mapping in Greenland

The landslide of 17 June 2017 in Karrat Fjord, central West Greenland, highlighted the need for a better understanding of landslides and landslide-generated tsunamis in Greenland and motivated a landslide screening project in 2018, led by the Geological Survey of Denmark and Greenland (GEUS; see also Svennevig et al. this volume). A central part of this project was to conduct a preliminary mapping of Quaternary and historical landslides in Greenland – the first effort of its kind. The main objective was to establish a landslide inventory database that can be used to identify areas prone to landslides and serve as a tool for gaining a better understanding of where, when and why catastrophic landslides take place in Greenland.
 This paper describes the workflow used to produce the preliminary landslide inventory of Greenland and discusses some of the initial results. To date (June 2019), I have mapped 564 landslides with the vast majority situated in the Nuussuaq Basin between Sigguup Nunaa (Svartenhuk Halvø), and Qeqertarsuaq (Disko) in West Greenland (Fig. 1). The inventory mapping is mainly based on observations and analyses of remotely sensed imagery and pre-existing geological maps. The mapping coverage was not systematic for all of Greenland, but focused on postglacial, potentially tsunamigenic landslides in inhabited coastal regions, i.e. on relatively large landslides on coastal slopes, mainly in West Greenland and small areas of East Greenland. However, smaller and inland landslides were included when they were encountered. Similarly, the less inhabited parts of Greenland were provisionally screened, but call for more thorough, systematic mapping in the future.

A CitSci app for landslide data collection

Depending on the increase in the world population and climate changes, the number of disasters have increased gradually. To cope with natural hazards, comprehensive disaster management strategies must be developed and implemented. Among the natural hazards, landslides are one of the most harmful and they cause serious economic losses and human deaths throughout the world. To reduce these losses, comprehensive regional landslide susceptibility and hazard assessments must be performed and the mechanism of landslides must be understood clearly. If a landslide inventory database is inaccurate and incomplete both spatially and temporally, assessment of regional landslide susceptibility and hazard includes more or less uncertainties. Consequently, new approaches are needed to reduce or even to eliminate the uncertainties. For this reason, the purposes of the present study are to describe the potential role of Citizen Science (CitSci) in landslide researches and to present a simple and user-friendly mobile app for the collection of the essential data from landslides. It is expected that the use of CitSci in landslide researches would increase and help greatly for the provision of comprehensive data. In addition, the spatial distribution of the data to be collected may be correlated with the human population and the settlement density.

Analysis of Landslide Vulnerabilities in Small and Medium-Sized Cities of ColombiaⅠ: With Focus on Physical Vulnerability

Colombia has experienced many problems in its urban areas due to increase in population in these areas. In particular, because of recurring slope disasters in urban areas, it has become necessary for the Colombian government to analyze these hazards. In this study, we selected a test bed with a high-potential source area in the small and medium city and conducted the physical vulnerability assessment of the test bed. First, we collected and analyzed the causal factors and landslide inventory. Using the landslide inventory and spatial information database, we performed a susceptibility assessment to identify high-potential landslide source areas. Next, we defined the parameters (travel angle, maximum velocity, etc.) for the Flow-R model analysis, which we used to calculate the propagation zone of debris flow. For the vulnerability assessment, we used road sections to define the boundary zones. Finally, we developed and evaluated the physical vulnerability map of Soacha using the propagation zones of debris flow and the boundary zones. The physical vulnerability assessment technique developed through this study will provide useful information for future urban planning and disaster prevention in Colombia. Keywords: Landslide, Physical Vulnerability, Spatial Information, Flow Analysis

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

LANDSLIDE SUSCEPTIBILITY ASSESSMENT USING GEOGRAPHICAL INFORMATION SYSTEM BASED LOGISTIC REGRESSION SYSTEM BETWEEN ÇUBUK-KALECİK (ANKARA) AND ŞABANÖZÜ (ÇANKIRI) REGIONS

Landslides occur frequently in particular is in certain parts in our country as well as in the world, is caused from time to time to return to natural disasters and signifi cant social and economic losses. In this study, landslide susceptibility analysis Çubuk-Kalecik (Ankara) between Şabanözü (Çankırı) were performed totally 2360 km2 area. According to the inventory map that represents about 5,16 % of the spatial distribution of landslide at study area, landslide movement is generally seen as the type of the existing rotational slip. According to the Turkish Landslide Inventory database prepared by MTA, 876 landslides covering 122 km2 were identifi ed in the study area. A total of twelve independent variables for the landslide conditioning factors were used in the susceptibility assessments, being as lithological maps, landform classifi cation, digital elevation model, slope, profi le, plan, and tangent curvatures, roughness index, slope / aspect ratio, stream power index, topographic position index. Landslide susceptibility assessment was carried out using multivariate logistic regression method, one of statistical methods. The mapping unit 25 *25 m pixels are used for statistical assessment. The obtained probability values of landslide susceptibility maps are very low and very high was evaluated in fi ve grades in the range. Performance evaluation of susceptibility maps were performed with using receiver operating characteristic (ROC) and prediction success rate curves. The area under the ROC curves were found to be in 0,794. Susceptibility map, high and very susceptible regions correspond to 27 % of the study area. 78 % of the landslides are in medium, high and very susceptible regions.The accomplished landslide susceptibility map with relatively high performance could be used during the medium scale planning strategies.

A public Cloud-based China’s Landslide Inventory Database (CsLID): development, zone, and spatiotemporal analysis for significant historical events, 1949-2011

Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China.

Updated landslide inventory of the area between the Furiano and Rosmarino creeks (Sicily, Italy)

ABSTRACTA 1:10,000 scale landslide inventory map has been prepared for the area between the Furiano and Rosmarino creeks, in the Nebrodi Mountains (north-eastern Sicily, Italy), a territory highly prone to slope failures, due to the local geological and geomorphological settings and intense rainfall. The landslide inventory database included within the Hydrogeological Setting Plan of the Sicily Region has been used as a starting point for this work. The updated inventory map has been compiled through a combination of conventional approaches (i.e. aerial photo-interpretation and field surveys) and new remote sensing techniques (ground deformation measurements obtained by interferometric analysis of satellite Synthetic Aperture Radar images). The new landslide inventory consists of 566 events, classified according to their typology and state of activity.

Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, Iran

The main objective of this research was to establish a semiautomated object-based image analysis (OBIA) methodology for locating landslides. We have detected and delineated landslides within a study area in north-western Iran using normalized difference vegetation index (NDVI), brightness, and textural features derived from satellite imagery (IRS-ID and SPOT-5) in combination with slope and flow direction derivatives from a digital elevation model (DEM) and topographically oriented gray-level cooccurrence matrices (GLCMs). We utilized particular combinations of these information layers to generate objects by applying multiresolution segmentation in a sequence of feature selection and object classification steps. The results were validated by using a landslide inventory database including 109 landslide events. In this study, a combination of these parameters led to a high accuracy of landslide delineation yielding an overall accuracy of 93.07%. Our results confirm the potential of OBIA for accurate delineation of landslides from satellite imagery and, in particular, the ability of OBIA to incorporate heterogeneous parameters such as DEM derivatives and surface texture measures directly in a classification process. The study contributes to the establishment of geographic object-based image analysis (GEOBIA) as a paradigm in remote sensing and geographic information science.

A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping

Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine causal landslide factors identified by local landslide experts. Fuzzy set theory was first integrated with an analytical hierarchy process (AHP) in order to use pairwise comparisons to compare LSM criteria for ranking purposes. FMFs were then applied in order to determine the criteria weights to be used in the development of a landslide susceptibility map. Finally, a landslide inventory database was used to validate the LSM map by comparing it with known landslides within the study area. Results indicated that the integration of fuzzy set theory with AHP produced significantly improved accuracies and a high level of reliability in the resulting landslide susceptibility map. Approximately 53% of known landslides within our study area fell within zones classified as having “very high susceptibility”, with the further 31% falling into zones classified as having “high susceptibility”.

Estimating landslide susceptibility through a artificial neural network classifier

A landslide susceptibility analysis is performed through an artificial neural network (ANN) algorithm, in order to model the nonlinear relationship between landslide manifestation and geological and geomorphological parameters. The proposed methodology can be divided into two distinctive phases. In the first phase, the methodology introduces a specific distance metric, the Mahalanobis distance metric, to improve the selection of non-landslide records that “enriches” the training database and provides the model with the necessary data during the training phase. In the second phase, the methodology develops a ANN model that was capable of minimizing the effect of over-fitting by monitoring in parallel the testing data during the training phase and terminating the process of learning when a certain acceptable criteria are achieved. The model was capable in identifying unstable areas, expressed by a landslide susceptibility index. The proposed methodology has been applied in the County of Xanthi, in the northern part of Greece, an area where a well-established landslide database existed. The landslide-related parameters that had been taken in account in the analysis were the following: lithology, distance from geological boundaries, distance from tectonic features, elevation, slope inclination, slope orientation, distance from hydrographic network and distance from road network. These parameters have been normalized and reclassified and used as input variables, while the description of a given area as landslide/non-landslide was assumed to be the output variable. The final outcome of the model was a geospatial product, which expressed the landslide susceptibility index and when compared with an up-to-date landslide inventory database showed satisfactory results.

Multitemporal landslides inventory map updating using spaceborne SAR analysis

Deep seated gravitational slope deformation and slow moving landslides on large areas were analyzed by spaceborne SAR interferometry: a test site in the Italian Alps of about 300km2 was selected for updating pre-existing landslide inventory maps based on the advanced interferometric processing technique (A-DInSAR).SAR images from ERS-1/2 satellites (1995–2000) and from Envisat satellite (2002–2009) have been used, allowing the deferred-time analysis of past movements and the record of recent slope movements. In the multi-temporal updated landslide inventory database, the characteristics of the landslides were highlighted: geometry, state of activity, typology, monitoring systems, interventions, source of information and the updating time and actions. Furthermore, for each landslide area, the occurrence of persistent scatterers points and the statistical description of their velocities were reported. This methodology may allow the systematic updating of landslides inventory maps keeping all information on each landslide, becoming the basic tool for the realization and updating of thematic maps such as the landslide susceptibility map.