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A quintessential component of any type of landslide studies, like susceptibility mapping, risk assessment and identifying the role of influencing parameters, is a landslide inventory map (LIM). LIM helps to analyse the spatial and temporal characteristics of landslides, and is also vital for constructing a landslide early warning system. Thus, LIM plays a vital role in landslide disaster risk reduction processes. As a paradigm work, this study aims at creating a relatively complete landslide inventory dataset for the 2018 rainfall-triggered landslide in a small sector of the south of the Western Ghats, called Kodagu in Karnataka, India. Integration of field investigation, and visual interpretation of pre- and post-landslide images of the Google Earth and Sentinel-2A satellite data were used to construct this LIM. Field investigation was aimed at two components: (i) to verify the created inventory from satellite imageries and (ii) to map those landslides that could not be identified in the images due to non-availability of images or cloud covered images or for any other reasons. The final, newly created LIM comprised 267 landslides: 89 through field investigation, and 178 by image interpretation. Of these, 153 are shallow slides and 114 are debris flow, with major damages attributed to debris flow. The created LIM is uploaded in GitHub and can be freely downloaded by researchers and students for further studies. This LIM was further used to generate a landslide susceptibility map (LSM) using machine learning techniques. This empirical method of LSM was done in Google Colab, and the results show that Random Forest as the best model for the study area. Majority of the landslides are confined within the slope range of 14°-29°, elevation between 970 and 1100 m as well as 1200 and 1700 m, slope aspect corresponding to southwest and west direction, and convex surfaces, especially near roads within 750 m. [ABSTRACT FROM AUTHOR]

Lunching satellites equipped with the Synthetic Aperture Radar (SAR) sensor for interferometry applications has created a great chance to detect and monitor extremely slow, slow and even quick ground deformations. Accordingly, the objective of this research is to compare and assess capabilities of the ASAR and PALSAR sensors to detect, monitor and map landslides in part of the Zagros mountain range occurring in the south of Isfahan, Iran. In addition, generating an accurate landslide distribution map of the study area by combined usage of the ASAR and PALSAR images in two different passes was evaluated. Two image datasets including 17 PALSAR and 21 ASAR images acquired by the ALOS and ENVISAT satellites, respectively were processed by the Differential Synthetic Aperture Radar Interferometry (DInSAR) method. 16 interferogram pair images of the sensors were found to be suitable for the analysis based on an initial processing. 914 landslides were identified covering an area of 13,578 ha by processing the radar images and field studies as well. The maximum vertical displacement was estimated to be 1.58 m during 6 May 2010 to 2 September 2011 by analysis of both the PALSAR and ASAR pair images. The PALSAR images performed more efficient than ASAR images to map the landslides distribution due to the higher wavelength and subsequently more wave penetration and also lower incoherency. However, the ASAR can distinguish the landslides in more details because of its lower wavelength. It was concluded that the DInSAR method is more favorable for identification of the landslides with smaller area and higher frequency. [ABSTRACT FROM AUTHOR]

Detailed and accurate geomorphological historical landslide inventory maps are an invaluable source of information for many research topics and applications. Their systematic preparation worldwide has been advised by many researchers as it may foster our knowledge on landslides, their spatial and temporal distribution, their potential interaction with the built environment, their contribution to landscape dynamics, their response to climate change in the past. Due to the extreme variability of the morphological and radiometric elements that can reveal historical landslides, geomorphological historical landslide inventory maps are produced by expert interpretation, which makes it a time consuming and expensive process, which often discourages wide area mapping activities. In this paper we present a new geomorphological historical landslide inventory map for a 1,460 km² area in the Daunia Apennines, the northern-western sector of the Apulia (Puglia) region, in Southern Italy. The inventory contains 17,437 landslides classified according to relative age, type of movement and estimated depth. Landslides were mapped according to rigorous and reproducible criteria applied by two teams of expert photo interpreters to two sets of stereoscopic aerial photographs taken in 1954/55 and 2003. The dataset consists of a digital archive publicly available at https://doi.pangaea.de/10.1594/PANGAEA.942427 (Cardinali et al., 2022). [ABSTRACT FROM AUTHOR]

The Cinque Terre National Park (38 km2) has recently recorded a remarkable increase in tourists involved in outdoor activities such as hiking. However, due to the actual slope degradation issues that affect this outstanding terraced landscape, hiking trails are increasingly threatened by the impact of landslides. This produces severe landslide-related risk scenarios for hikers. In this study, a landslide inventory map (at 1:45000 scale) has been prepared through the revision/update of previous database and the analysis of air-photos and satellite images coupled with field surveys. Furthermore, a quantitative evaluation of the interaction between landslides and hiking trails has been performed by the definition of a Footpath Landslide Index. More than 400 landslides were identified highlighting that these phenomena represent the main source of hazard. The findings of this study reveal that side-hill trails are the most hazardous since hikers can be potentially affected by flow-like or fast-moving landslides.

This paper reports a feasible alternative to compile a landslide inventory map (LIM) from remote sensing datasets using the application of an artificial intelligence–driven methodology. A deep convolutional neural network model, called LanDCNN, was developed to generate segmentation maps of landslides, and its performance was compared with the benchmark model, named U-Net, and other conventional object-based methods. The landslides that occurred in Lantau Island, Hong Kong, were taken as the case study, in which the pre- and post-landslide aerial images, and a rasterized digital terrain model (DTM) were used. The assessment reveals that LanDCNN trained with bitemporal images and DTM yields the smoothest and most semantically meaningfully LIM, compared to other methods. This LIM is the most balanced segmentation results, represented by the highest F1 measure among all analyzed cases. With the encoding capability of LanDCNN, the application of DTM as the input renders better LIM production, especially when the landslide signatures are relatively subtle. With the computational setup used in this study, LanDCNN requires ~ 3 min to map landslides from the datasets of approximately 25 km2 in area and with a resolution of 0.5 m. In short, the proposed landslide mapping framework, featured LanDCNN, is scalable to handle the vast amount of remote sensing data from different types of measurements within a short processing period. [ABSTRACT FROM AUTHOR]

Landslide detection and mapping are essential issues for reducing impact of such natural disasters, and for improving the future built-up expansion and planning strategies, especially in developing countries where a reasonable land-use design is an important concern for sustainable growth and environmental management. Armenia is a landlocked country and its urban development is strongly tied to the improvement of infrastructures, which must takes into account the environmental setting and the slope instability of the area, in order to identify risks and possible damages to settlements and economic activities. The use of satellite-based Earth Observation data has advanced significantly in the last decade and has turned out to be very useful for measuring and monitoring slow-moving surface deformation phenomena with millimetric precision. In this framework, this study aims at providing a remote sensing-based Landslide Inventory Map (LIM) and a Landslide Susceptibility Map (LSM) over Dilijan (Armenia) area, performed within the Secondary Cities Urban Development in Armenia project. In particular, LIM and LSM in the study area were produced by using ground deformation measurements derived from satellite Synthetic Aperture Radar (SAR) data, acquired by ALOS and ENVISAT sensors from 2003 up to 2010, and integrated with photo-interpretation of recent optical images and morphological analysis of Digital Elevation Model (DEM). Given the extensive presence of vegetation in the area of interest, satellite SAR images were processed to produce both SqueeSAR™ and Temporary Coherent Scatterers data, which are PSI (Persistent Scatterer Interferometry) data conceived as evolution of PSInSAR™ approach and particularly suited for non-urban and rural areas characterized by low density of coherent terrain benchmarks over time. Landslide mapping produced through this work identifies the most hazardous landslide-affected and landslide-prone areas around Dilijan city, and can be used for further estimating environmental risks for urban infrastructure development in the area.

The Cinque Terre National Park (38 km2) has recently recorded a remarkable increase in tourists involved in outdoor activities such as hiking. However, due to the actual slope degradation issues that affect this outstanding terraced landscape, hiking trails are increasingly threatened by the impact of landslides. This produces severe landslide-related risk scenarios for hikers. In this study, a landslide inventory map (at 1:45000 scale) has been prepared through the revision/update of previous database and the analysis of air-photos and satellite images coupled with field surveys. Furthermore, a quantitative evaluation of the interaction between landslides and hiking trails has been performed by the definition of a Footpath Landslide Index. More than 400 landslides were identified highlighting that these phenomena represent the main source of hazard. The findings of this study reveal that side-hill trails are the most hazardous since hikers can be potentially affected by flow-like or fast-moving landslides. [ABSTRACT FROM AUTHOR]

The Earth's climate system is clearly warming. Unquestionably, climate change has an impact on landslides by affecting the stability of both naturally occurring and artificially constructed slopes. Less certain are the type, extent, degree, and direction of the changes in stability conditions as well as the location, abundance, activity, and frequency of landslides in response to the expected climate changes. Climate change threatens global ecosystems, causing severe human and economic losses through sea level rise, hurricanes, flooding, landslides, and heat waves. Landslides in high mountains could become more common as a result of these changes. The high-speed electric train project in Egypt seeks to provide sustainable transportation, lessen climate change, and enhance public health. The study area was conducted along a 70-km track in the Eastern Desert, Egypt, across steep and rugged basement rocks of the Red Sea mountain chain (RSM). The geographic pinpointing of landslide susceptibility zones plays a significant role in promoting hazards mitigation in mountainous areas, such as RSM. The aim of this research project is to create a landslide susceptibility map (LSM) utilizing multi-criteria decision analysis (MCDA) in the region using information value (IV) and analytical hierarchy process (AHP) approaches that encompass the third proposed track of the high-speed electric railroad Qena-Hurghada between the RSM. The landslide inventory map, which has a total of 12 single landslide locations, was created based on fieldwork using hand-held GPS and Landsat 8 images. The two decision-making techniques were used to contrast the LSM factors based on the knowledge of experts using the qualitative method of AHP with the statistical approach that depends on the field data IV. Ten landslide-influencing parameters taken into consideration are slope, lithology, rainfall distribution, distance to fault, distance to track, seismicity of area, distance to epicenter, land cover, distance to stream, and aspect. The parameters were obtained using LANDSAT8 and SRTM DEM of a 30 m resolution, vectorized lithological structure units from a 1:2000, 000 map, and data between (1913–2000) for yearly precipitation and (1997–2018) for seismic activity of the research area. A GIS-based method produces a themed layer map for each geoenvironmental element, weighing each parameter after examination. A LSM was produced using a combination of thematic layers that were then separated into four grades: low, moderate, high, and very high, based on the intensity of the landslide vulnerability. According to AUC data, the IV model has a better success rate 0.85 than the AHP model 0.82. The study's models, especially when applied to the IV method, showed good accuracy in forecasting landslide vulnerability. The method shows rising landslide percentages with susceptibility and delivers accurate results that are compatible with the landslide inventory map. Environmental decisions often lead to unintended consequences due to lost information, incorrect factor selection, limited expert expertise, and ignoring uncertainty. This investigation emphasizes the importance of LSM as a tool for decision-makers to minimize landslide losses and damage. • Climate change is causing landslides, affecting both natural and artificial slopes. A research project in Egypt created a landslide susceptibility map (LSM) using MCDA and AHP approaches to address high mountain landslide threats. The study, conducted in Egypt, used GPS and Landsat 8 images to create a landslide inventory map of 12 single landslide locations. The LSM was divided into four grades based on landslide vulnerability intensity. The models demonstrated good accuracy in forecasting landslide vulnerability, emphasizing its importance for decision-makers. [ABSTRACT FROM AUTHOR]

This paper illustrates the potential of Sentinel-1 for landslide detection, mapping and characterization with the aim of updating inventory maps and monitoring landslide activity. The study area is located in Molise, one of the smallest regions of Italy, where landslide processes are frequent. The results achieved by integrating Differential Synthetic Aperture Radar Interferometry (DInSAR) deformation maps and time series, and Geographical Information System (GIS) multilayer analysis (optical, geological, geomorphological, etc.) are shown. The adopted methodology is described followed by an analysis of future perspectives. Sixty-two landslides have been detected, thus allowing the updating of pre-existing landslide inventory maps. The results of our ongoing research show that Sentinel-1 might represent a significant improvement in terms of exploitation of SAR data for landslide mapping and monitoring due to both the shorter revisit time (up to 6 days in the close future) and the wavelength used, which determine an higher coherence compared to other SAR sensors.

In the last decade, development in remote sensing techniques has opened new avenues for studying the evolution of landscapes dominated by mass wasting processes. Conventional methods including field reconnaissance are time-consuming and resource-intensive. Thus, it is worth taking advantage of the high-resolution digital elevation model (HRDEM) to identify landslide features remotely and investigate landslide morphology. This research proposes a new technique of landslide feature identification and morphology mapping using computer-aided methods to enhance the visual interpretation of HRDEM. These computer-aided methods involve deep exploration of topographic information provided by HRDEM. In addition to the HRDEM, nine diverse HRDEM derivatives were used to maximise the morphological information captured by HRDEM. To compact and to better understand the morphological information, original HRDEM derivatives were transformed into the principal component (PC) domain. Based on PC composition provided by three initial PCs, it was possible to identify morphological signatures of landslides and represent them as the detailed landslide surface morphology maps. The presented methodology serves as an alternative means of landslide characterisation. It permitted the evaluation of slope morphology and the ability to reassess recent and future landslide activity on a comparative basis. [ABSTRACT FROM AUTHOR]

Landslide inventory maps are commonly prepared through the visual interpretation of stereoscopic aerial photographs and field checks. Stereoscopic satellite images can also be interpreted visually to recognize and map landslides. When interpreting stereoscopic imagery, shadows can conceal the photographic elements typical of landslides, hampering the recognition and mapping of the landslides. To mitigate the problem, we propose a method that exploits normalized difference vegetation index (NDVI) images and digital stereoscopy for the 3D visual recognition and mapping of landslides in shadowed areas. We tested the method in the 25 km2 Pogliaschina catchment, northern Italy, where intense rainfall caused abundant landslides on 25 October 2011. Using a PLANAR® StereoMirror™ digital stereoscope, we prepared an event landslide inventory map (E-LIM) through the visual interpretation of a pair of NDVI images obtained from a WorldView-2 stereoscopic multispectral bundle. We compared the event inventory with two independent E-LIMs for the same area and landslide event. The 3D vision of the NDVI stereoscopic image pair maximized the use of the radiometric (color and tone) and the terrain (elevation, slope, relief, and convexity) information captured by the stereoscopic multispectral images, allowing for the recognition of more landslides and more landslide areas than the other E-LIMs in the shadowed areas. Our results confirm that use of NDVI images facilitates the visual recognition and mapping of landslides in terrain affected by shadows. We expect that the proposed method can help trained interpreters to map landslides more accurately in areas affected by shadows. [ABSTRACT FROM AUTHOR]

Landslide inventory mapping (LIM) plays an important role in hazard assessment and hazard relief. Even though much research has taken place in past decades, there is space for improvements in accuracy and the usability of mapping systems. In this paper, a new landslide inventory mapping framework is proposed based on the integration of the majority voting method and the multiscale segmentation of a postevent images, making use of spatial feature of landslide. Compared with some similar state-of-the-art methods, the proposed framework has three advantages: 1) the generation of LIM is almost automatic; 2) the framework can achieve more accurate results because it takes into account the landslide spatial information in an irregular manner through multisegmentation of the postevent image and object-based majority voting (MV); and 3) it needs less parameter tuning. The proposed framework was applied to four landslide sites on Lantau Island, Hong Kong. Compared with existing methods, including region level set evolution (RLSE), edge level set evolution (ELSE) and change detection Markov random field (CDMRF) methods, quantitative evaluation shows the proposed framework is competitive in terms of Completeness. The framework outperformed RLSE, ELSE, and CDMRF for the four experiments by more than 9% in Correctness and by 8% in Quality. To the authors’ knowledge, this is the first-time that landslide spatial information has been utilized through the integration of multiscale segmentation of postevent image with the MV approach to obtain LIM using high spatial resolution remote sensing images. The approach is also of wide generality and applicable to other kinds of land cover change detection using remote sensing images. [ABSTRACT FROM PUBLISHER]

Landslide detection and mapping are essential issues for reducing impact of such natural disasters, and for improving the future built-up expansion and planning strategies, especially in developing countries where a reasonable land-use design is an important concern for sustainable growth and environmental management. Armenia is a landlocked country and its urban development is strongly tied to the improvement of infrastructures, which must takes into account the environmental setting and the slope instability of the area, in order to identify risks and possible damages to settlements and economic activities. The use of satellite-based Earth Observation data has advanced significantly in the last decade and has turned out to be very useful for measuring and monitoring slow-moving surface deformation phenomena with millimetric precision. In this framework, this study aims at providing a remote sensing-based Landslide Inventory Map (LIM) and a Landslide Susceptibility Map (LSM) over Dilijan (Armenia) area, performed within the Secondary Cities Urban Development in Armenia project. In particular, LIM and LSM in the study area were produced by using ground deformation measurements derived from satellite Synthetic Aperture Radar (SAR) data, acquired by ALOS and ENVISAT sensors from 2003 up to 2010, and integrated with photo-interpretation of recent optical images and morphological analysis of Digital Elevation Model (DEM). Given the extensive presence of vegetation in the area of interest, satellite SAR images were processed to produce both SqueeSAR™ and Temporary Coherent Scatterers data, which are PSI (Persistent Scatterer Interferometry) data conceived as evolution of PSInSAR™ approach and particularly suited for non-urban and rural areas characterized by low density of coherent terrain benchmarks over time. Landslide mapping produced through this work identifies the most hazardous landslide-affected and landslide-prone areas around Dilijan city, and can be used for further estimating environmental risks for urban infrastructure development in the area. [ABSTRACT FROM AUTHOR]

A 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. [ABSTRACT FROM PUBLISHER]

This paper illustrates the potential of Sentinel-1 for landslide detection, mapping and characterization with the aim of updating inventory maps and monitoring landslide activity. The study area is located in Molise, one of the smallest regions of Italy, where landslide processes are frequent. The results achieved by integrating Differential Synthetic Aperture Radar Interferometry (DInSAR) deformation maps and time series, and Geographical Information System (GIS) multilayer analysis (optical, geological, geomorphological, etc.) are shown. The adopted methodology is described followed by an analysis of future perspectives. Sixty-two landslides have been detected, thus allowing the updating of pre-existing landslide inventory maps. The results of our ongoing research show that Sentinel-1 might represent a significant improvement in terms of exploitation of SAR data for landslide mapping and monitoring due to both the shorter revisit time (up to 6 days in the close future) and the wavelength used, which determine an higher coherence compared to other SAR sensors. [ABSTRACT FROM AUTHOR]

Slope deformation is one of the typical geohazards that causes an extensive economic damage in mountainous regions. As such, they are usually intensively monitored by means of modern expertise commonly by national geological or emergency services. Resulting landslide susceptibility maps, or landslide inventories, offer an overview of areas affected by previously activated landslides as well as slopes known to be unstable currently. Current slope instabilities easily transform into a landslide after various triggering factors, such as an intensive rainfall or a melting snow cover. In these inventories, the majority of the existing landslide-affected slopes are marked as either stable or active, after a continuous investigative work of the experts in geology. In this paper we demonstrate the applicability of Sentinel-1A satellite SAR interferometry (InSAR) to assist by identifying slope movement activity and use the information to update national landslide inventories. This can be done reliably in cases of semi-arid regions or low vegetated slopes. We perform several analyses based on multitemporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides. [ABSTRACT FROM AUTHOR]

A significant number of landslides have occurred along the upper Indus River which resulted in temporary landslide dams. All of these debris dams eventually breach, often causing catastrophic flooding downstream. Remote sensing data can aid in the recognition of many of these features in otherwise inaccessible areas. Regional level landslide inventory maps have recently been prepared, which also show the locations of historic landslide dams. The research reported in this paper was a reconnaissance-level study, aimed at identifying those slides and rock avalanches that are or were more than 500-m long, crossing at least five adjacent contours. Representative examples of the various types of landslides were evaluated using topographic expression, aided by field reconnaissance and literature review. Many of the landslide features described in the study area exceed an entrained debris volume of 10 million m³, which are often described as mega landslides like the Gol-Gone, Satpara, and Katzarah rockslide dams. Cross canyon profiles were also prepared of selected landslides to estimate the likely block kinematics controlling the slopes failure, using the three-dimensional analyst extension of common computer software. The majority of the historic mega landslide events appear to be seismically triggered. The research reported in this paper suggests that the highest concentration of landslide dams were observed in the tectonically active Nanga Perbat Haramosh Massif region, where the river flows through narrow gorges, and/or where active thrust faults cross the river. Most of these features appear to be structurally controlled, translating along suites of planar preexisting discontinuities, such as bedding, jointing, foliation, lithologic contacts, shear zones, or faults. These features often form landslide dams if they empty into perennial river channels and often leave telltale signs of their short-lived existence. These signs include elevated stream terraces, lacustrine deposits (within and upstream of the temporary blockages), local sediment fans which form about the margins of short-lived reservoirs, deeply incised gorges, and scattered outbreak flood debris along the flanks of the river channel, often blocking side canyon tributaries. If infrastructure, such as highways, pipelines, or transmission lines were to be constructed into the study area, the landslide features should merit additional study and field verification to ascertain the validity of the interpreted features. [ABSTRACT FROM AUTHOR]

The combination of high-quality landslide inventories and geomorphic attributes that can be derived and analyzed by geographic information systems can provide powerful and straightforward input into mapping landscape-wide landslide susceptibility. A methodology for generating landslide susceptibility maps can assist land managers in reducing risk to populations and economic land use from landslide hazards, particularly in areas prone to landslide disasters, such as tropical mountain ranges. Pico de Orizaba is the highest stratovolcano in Mexico, and throughout its geologic history, it has had large landslides triggered by flank collapse as well as small landslides triggered by high seasonal rainfall on terrains covered by poorly consolidated materials. The present work analyzes the distribution of small landslides embedded in volcanic landforms to characterize slope instability. The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano has been selected as a study area. In the area, landforms are ascertained through aerial photographs, field investigations, and an adaptation of the Landslide Hazard Zonation Protocol of the Washington State Department of Natural Resources, Forest Practices Division in a GIS-based technology. For each landform, a semi-quantitative overall susceptibility rating is derived by using the landslide area rate and the landslide frequency rate. This analysis divides the watershed into seven mass-wasting landforms that are assigned slope stability susceptibility ratings from low to very high. The overall susceptibility rating for this watershed is very high. The technique and its implementation are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present a geomorphological landslide inventory map for an intermountain catchment in the southern Italian Apennines. The study area is seismically active, and it is characterized by high uplift rates produced by Quaternary tectonics. A total of 531 landslides of different types, relative age, and sizes, including some kilometer-scale relict landslides were mapped through the visual interpretation of 1:33,000-scale stereoscopic aerial photographs, and dedicated field surveys. Analysis of the inventory map revealed that recent landslides consist chiefly of reactivations of older landslides, and of the new landslides formed in pre-existing landslide deposits, triggered primarily by intense rainfall events, or prolonged rainfall periods. We expect that the inventory will be used for the evaluation of landslide susceptibility and hazard in the area, and to investigate the long-term geomorphological evolution of a portion of the southern Apennines. [ABSTRACT FROM AUTHOR]

In volcanic terrains, dormant stratovolcanoes are very common and can trigger landslides and debris flows continually along stream systems, thereby affecting human settlements and economic activities. It is important to assess their potential impact and damage through the use of landslide inventory maps and landslide models. In Mexico, numerous geographic information systems (GIS)-based applications have been used to represent and assess slope stability. However, there is no practical and standardized landslide mapping methodology under a GIS. This work provides an overview of the ongoing research project from the Institute of Geography at the National Autonomous University of Mexico that seeks to conduct a multi-temporal landslide inventory and produce a landslide susceptibility map by using GIS. The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The method encompasses two main levels of analysis to assess landslide susceptibility. First, the project aims to derive a landslide inventory map from a representative sample of landslides using aerial orthophotographs and field work. Next, the landslide susceptibility is modelled by using multiple logistic regression implemented in a GIS platform. The technique and its implementation of each level in a GISs-based technology is presented and discussed. [ABSTRACT FROM AUTHOR]

On 1 October 2009, a high intensity storm hit the Ionian coast of Sicily, SWof Messina, Italy. The Santo Stefano di Briga rain gauge, located 2 km W of the Ionian coast, recorded 225 mm of rain in seven hours. The intense rainfall event triggered abundant slope failures, and resulted in widespread erosion and deposition of debris along ephemeral drainage channels, extensive inundation, and local modifications of the coastline. Landslides occurred in a territory prone to slope failures, due to the local geological and geomorphological settings. Many landslides were related to the presence of roads lacking adequate drainage. Abandoned terraced slopes lacking proper drainage, and unmaintained dry walls were also related to slope failures. Damage was particularly severe in small villages and at several sites along the transportation network. The shallow landslides and the inundation resulted in 37 fatalities, including 31 deaths and six missing persons, and innumerable injured people. After the event, an accurate landslide inventory map was prepared for the Briga and the Giampilieri catchments. The map shows: (i) the distribution of the event landslides triggered by the 1 October 2009 rainfall event; (ii) the distribution of the pre-existing slope failures; and (iii) other geomorphological features related to fluvial processes and slope movements. The landslide inventory map was prepared at 1:10,000 scale through a combination of field surveys and photo-interpretation of pre-event and post-event, stereoscopic and pseudo-stereoscopic, aerial photography. Different types of aerial photographs were analysed visually to prepare the landslide inventory map. The event landslides were mapped through the interpretation of pseudostereoscopic colour photographs taken shortly after the event at 1:3500 scale, combined with digital stereoscopic photographs at approximately 1:4500 scale, taken in November 2009. The pre-event landslides and the associated geomorphological features were mapped using 1:33,000 scale aerial photographs flown in 1954, 1955, and 2005. The event and pre-existing landslides were checked in the field in the period October- November 2009. [ABSTRACT FROM AUTHOR]

Large, deep-seated landslides are common features in the Flemish Ardennes (Belgium). As most of these old (>100 years) landslides are located under forest in this hilly region, aerial photograph interpretation is not an appropriate landslide mapping method. This study tested the potential of LIDAR (Light Detection and Ranging) images for mapping old landslides under forest. Landslide inventory maps were created for a 125 km² area by applying the expert knowledge of seven geomorphologists to LIDAR-derived hillshade, slope and contour line maps in a GIS environment. Each of the seven LIDAR-based landslide inventories was compared (i) with the other six, (ii) with a detailed field survey-based inventory, and (iii) with a comparable study in which topographic data were extracted from a topographical map. The combination of the percentage of field landslides indicated by an expert and the percentage of positional discrepancies (expressed in terms of positional mismatch) were used to evaluate the quality of the LIDAR-based inventory maps. High-quality LIDAR-derived landslide inventory maps contain more than 70 per cent of the landslides mapped during the field survey, and have positional discrepancies smaller than 70 per cent when compared with the field survey-based inventory map. Four experts and the combination map of all experts satisfied these criteria. Together the seven experts indicated all landslides mapped in the field. Importantly, LIDAR enabled the experts to find ten new landslides and to correct the boundaries of eleven (of the 77) landslides mapped during the field survey. Hence, this study showed that large-scale LIDAR-derived maps analysed by experienced geomorphologists can significantly improve field survey-based inventories of landslides with a subdued morphology in hilly regions. [ABSTRACT FROM AUTHOR]

Abstract: Large deep-seated landslides with a shear surface deeper than 3 m and a mean affected area of 4.2 ha are common features in the Flemish Ardennes. None of these deep-seated landslides are dated, but they are assumed to be rather old (>100 years). Because most of these landslides are located under forest, aerial photo interpretation commonly used for the creation of landslide inventories is not a suitable tool to map the landslides in the Flemish Ardennes. Therefore, an intensive 100-day field survey was carried out by two geomorphologists in a 430-km2 study area. This resulted in a landslide inventory map, indicating the location of 135 large deep-seated landslides. But field surveys are time consuming and, thus, very expensive. Therefore, a cheaper and faster mapping technique was tested. A hillshade map was constructed for the study area in a GIS (IDRISI32) from a 5-m resolution digital elevation model (DEM). Seven experts were given 1 h to indicate all the hillslope sections, which they suspected to be possible landslides on a copy of the aforementioned map (scale 1:100,000). In total, this exercise took only 1 day (i.e., 7 person hours). Large differences in the number of presumed landslides and the extent of the hillslopes thought to be affected by landslides were reported among the seven experts. The polygon and pixel efficiency were introduced to estimate the quality of the landslide maps based on hillshade maps and expert knowledge. Compared to the field survey-based landslide inventory, the quality of the landslide inventories based on the hillshade maps and expert knowledge was relatively low. Experts familiar with the study area obtained somewhat better results than experts who visited the study area only once. A combination of the seven expert maps did not result in a good inventory map because too many unaffected hillslopes were incorrectly indicated as affected by landslides. The results obtained in this study are comparable to an investigation carried out by (Wills, C.J., McCrink, T.P., 2002. Comparing landslide inventories, the map depends on the method. Environmental and Engineering Geoscience 8, 279–293). Although the tested method can never replace a detailed field survey, taking into account the proposed improvements, it can be used for the creation of a regional inventory map of old landslides in a densely forested area where light detection and ranging (LIDAR) data are unavailable. [Copyright &y& Elsevier]