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Litoseliti, Aspasia, Ioannis K. Koukouvelas, Konstantinos G. Nikolakopoulos, and Vasiliki Zygouri. "An Event-Based Inventory Approach in Landslide Hazard Assessment: The Case of the Skolis Mountain, Northwest Peloponnese, Greece." ISPRS International Journal of Geo-Information 9, no. 7 (July 20, 2020): 457. http://dx.doi.org/10.3390/ijgi9070457.
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Assessment of landslide hazard across mountains is imperative for public safety. Pre- and post-earthquake landslide mapping envisage that landslides show significant size changes during earthquake activity. One of the purposes of earthquake-induced landslide investigation is to determine the landslide state and geometry and draw conclusions on their mobility. This study was based on remote sensing data that covered 72 years, and focused on the west slopes of the Skolis Mountains, in the northwest Peloponnese. On 8 June 2008, during the strong Movri Mountain earthquake (Mw = 6.4), we mapped the extremely abundant landslide occurrence. Historical seismicity and remote sensing data indicate that the Skolis Mountain west slope is repeatedly affected by landslides. The impact of the earthquakes was based on the estimation of Arias intensity in the study area. We recognized that 89 landslides developed over the last 72 years. These landslides increased their width (W), called herein as inflation or their length (L), termed as enlargement. Length and width changes were used to describe their aspect ratio (L/W). Based on the aspect ratio, the 89 landslides were classified into three types: I, J, and Δ. Taluses, developed at the base of the slope and belonging to the J- and Δ-landslide types, are supplied by narrow or irregular channels. During the earthquakes, the landslide channels migrated upward and downward, outlining the mobility of the earthquake-induced landslides. Landslide mobility was defined by the reach angle. The reach angle is the arctangent of the landslide’s height to length ratio. Furthermore, we analyzed the present slope stability across the Skolis Mountain by using the landslide density (LD), landslide area percentage (LAP), and landslide frequency (LF). All these parameters were used to evaluate the spatial and temporal landslide distribution and evolution with the earthquake activity. These results can be considered as a powerful tool for earthquake-induced landslide disaster mitigation
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Su, Xiaojun, Yi Zhang, Xingmin Meng, Mohib Ur Rehman, Zainab Khalid, and Dongxia Yue. "Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR." Remote Sensing 14, no. 19 (September 30, 2022): 4907. http://dx.doi.org/10.3390/rs14194907.
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The Hunza Valley, in the northwestern Karakoram Mountains, North Pakistan, is a typical region with many towns and villages, and a dense population and is prone to landslides. The present study completed landslide identification, updating a comprehensive landslide inventory and analysis. First, the ground surface deformation was detected in the Hunza Valley by SBAS-InSAR from ascending and descending datasets, respectively. Then, the locations and boundaries were interpreted and delineated, and a comprehensive inventory of 118 landslides, including the 53 most recent InSAR identified active landslides and 65 landslides cited from the literature, was completed. This study firstly named all 118 landslides, considering the demand for globally intensive research and hazard mitigation. Finally, the deformation, spatial–topographic development, and distribution characteristics in the Hunza Valley scale and three large significant landslides were analyzed. Information on 72 reported landslides was used to construct an empirical power law relationship linking landslide area (AL) to volume (VL) (VL = 0.067 × AL1.351), and this formula predicted the volume of 118 landslides in this study. We discovered that the landslides from the literature, which were interpreted from optical images, had lower levels of velocity, area, elevation, and height. The SBAS-InSAR-detected active landslide was characterized by higher velocity, larger area, higher elevation, larger slope gradient, larger NDVI (normalized difference vegetation index), and greater height. The melting glacier water and rainfall infiltration from cracks on the landslide’s upper part may promote the action of a push from gravity on the upper part. Simultaneously, the coupling of actions from river erosion and active tectonics could have an impact on the stability of the slope toe. The up-to-date comprehensive identification and understanding of the characteristics and mechanism of landslide development in this study provide a reference for the next step in landslide disaster prevention and risk assessment.
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Luetzenburg, Gregor, Kristian Svennevig, Anders A. Bjørk, Marie Keiding, and Aart Kroon. "A national landslide inventory for Denmark." Earth System Science Data 14, no. 7 (July 11, 2022): 3157–65. http://dx.doi.org/10.5194/essd-14-3157-2022.
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Abstract. Landslides are a frequent natural hazard occurring globally in regions with steep topography. Additionally, landslides play an important role in landscape evolution by transporting sediment downslope. Landslide inventory mapping is a common technique to assess the spatial distribution and extent of landslides in an area of interest. High-resolution digital elevation models (DEMs) have proven to be useful databases to map landslides in large areas across different land covers and topography. So far, Denmark had no national landslide inventory. Here, we create the first comprehensive national landslide inventory for Denmark derived from a 40 cm resolution DEM from 2015 supported by several 12.5 cm resolution orthophotos. The landslide inventory is created based on a manual expert-based mapping approach, and we implemented a quality control mechanism to assess the completeness of the inventory. Overall, we mapped 3202 landslide polygons in Denmark with a level of completeness of 87 %. The complete landslide inventory is freely available for download at https://doi.org/10.6084/m9.figshare.16965439.v2 (Svennevig and Luetzenburg, 2021) or as a web map (https://data.geus.dk/landskred/, last access: 6 June 2022) for further investigations.
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Osako, L. S. "UPDATING LANDSLIDE INVENTORY MAPS USING HIGH RESOLUTION DIGITAL ORTHOPHOTOS AND DIGITAL SURFACE AND ELEVATION MODELING: THE CASE STUDY OF BRUSQUE CITY, SANTA CATARINA STATE, BRAZIL." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 251–55. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-251-2021.
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Abstract. This study reports the updating of the landslide inventory map of Brusque city, State of Santa Catarina, Southern Brazil. Twenty-six digital orthophotos acquired in 2010 with a ground resolution of 0.4 meters were analyzed together with shaded relief images obtained by Digital Surface and Digital Elevation modelling with spatial resolution of 1 meter. These remote sensing products were treated, analyzed and visualized in a Geographic Information System – GIS environment. The landslide inventory included a total of 500 landslides, corresponding to a mean density of 1.76 landslides per km2. The total area of landslide occurrences is 0.81 km2, which corresponds to 0.29% of the study area. 0.22 km2 of the total area landslides occur inside the urban perimeter and 0.59 km2 outside Brusque. The geological context and the occurrence of landslides were analyzed together: 277 landslides affect altered metamorphic rocks, 179 landslides granite, and 44 landslides unconsolidated sediments. The updated landslide inventory map showed that 80% of mapped landslides occur in areas of high and moderate susceptibility.
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Nicușor, NECULA, and NICULIȚĂ Mihai. "Landslide reactivation susceptibility modeling in Iași Municipality." Revista de Geomorfologie 19, no. 1 (December 30, 2017): 101–17. http://dx.doi.org/10.21094/rg.2017.021.
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Iași Municipality as other urban areas around the world has a long history of landslide activity which needs to be studied considering the urban sprawl. We performed a first landslide susceptibility modeling for Iași Municipality based on the AHP method using weights given by expert judgements regarding the influence of preparatory and conditional landslides factors (slope, ruggedness, lithology, historic landslide density and hydrogeology) and weights given by the historic landslide density over the factors. The landslide inventory was performed based on LiDAR data and aerial imagery using the geomorphological mapping of landslide elements. Using the landslide probability density function we have shown that the landslide inventory is valid and we have selected 411 landslides considered recent to be used for the validation. The resulted susceptibility show that the most susceptible to landslide reactivations are the hillslopes of cuesta hills with relict and old landslides, especially at the main scarp. Recent landslides are mainly scarp slumps or landslide body translational reactivations. The validation showed that almost 70% of recent landslides are located on high susceptibility areas. Future work to improve the susceptibility and extend it to hazard modeling is needed considering the long history of landslide reactivations from Iași Municipality and the slow evolution of old landslides like in Țicău neighborhoods.
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Rabby, Yasin Wahid, and Yingkui Li. "Landslide Inventory (2001–2017) of Chittagong Hilly Areas, Bangladesh." Data 5, no. 1 (December 25, 2019): 4. http://dx.doi.org/10.3390/data5010004.
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Landslides are a frequent natural hazard in Chittagong Hilly Areas (CHA), Bangladesh, which causes the loss of lives and damage to the economy. Despite this, an official landslide inventory is still lacking in this area. In this paper, we present a landslide inventory of this area prepared using the visual interpretation of Google Earth images (Google Earth Mapping), field mapping, and a literature search. We mapped 730 landslides that occurred from January 2001 to March 2017. Different landslide attributes including type, size, distribution, state, water content, and triggers are presented in the dataset. In this area, slide and flow were the two dominant types of landslides. Out of the five districts (Bandarban, Chittagong, Cox’s Bazar, Khagrachari, and Rangamati), most (55%) of the landslides occurred in the Chittagong and Rangamati districts. About 45% of the landslides were small (<100 m2) in size, while the maximum size of the detected landslides was 85202 m2. This dataset will help to understand the characteristics of landslides in CHA and provide useful guidance for policy implementation.
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Huang, Yuandong, Chong Xu, Lei Li, Xiangli He, Jia Cheng, Xiwei Xu, Junlei Li, and Xujiao Zhang. "Inventory and Spatial Distribution of Ancient Landslides in Hualong County, China." Land 12, no. 1 (December 31, 2022): 136. http://dx.doi.org/10.3390/land12010136.
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The establishment of a regional historical landslide inventory plays an indispensable role in landslide assessment and prevention. In this study, based on the Google Earth platform, an inventory of ancient landslides in Hualong County, Qinghai Province was established. The inventory includes 3517 ancient landslides with individual areas ranging from 2354.6 m2 to 12.44 km2. The dominant characteristics include an elevation of 2600–2800 m, slope of 10–20°, aspects SW, W, and NW, mudstone and sandstone of Paleoproterozoic, Neoproterozoic and Quaternary loess, 8–10 km from faults, 0–1 km from rivers, cultivated and grassland types, NDVI of 0.25–0.3, and an average precipitation in the range of 480–500 mm. In addition, the geometric analysis of landslides shows that the average height and length of ancient landslides in the study area are 151.92 m and 429.52 m, respectively. The power law relationship between the two is L= 0.41 × H1.37. The ancient landslide inventory of this study exhibits an integrated pattern of the development characteristics and spatial distribution of landslides in the Tibetan Plateau and the upper Yellow River basin, as well as providing a significant reference for subsequent landslide susceptibility mapping in the area.
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Hao, Lina, Cees van Westen, Tapas Ranjan Martha, Pankaj Jaiswal, and Brian G. McAdoo. "Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis." Earth System Science Data 12, no. 4 (November 16, 2020): 2899–918. http://dx.doi.org/10.5194/essd-12-2899-2020.
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Abstract. Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.
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Kien, Nguyen Trung, The Viet Tran, Vy Thi Hong Lien, Pham Le Hoang Linh, and Nguyen Quoc Thanh. "Landslide Susceptibility Mapping Based on the Combination of Bivariate Statistics and Modified Analytic Hierarchy Process Methods: A Case Study of Tinh Tuc Town, Nguyen Binh District, Cao Bang Province, Vietnam." Journal of Disaster Research 16, no. 4 (June 1, 2021): 521–28. http://dx.doi.org/10.20965/jdr.2021.p0521.
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Tinh Tuc town, Cao Bang province, Vietnam is prone to landslides due to the complexity of its climatic, geological, and geomorphological conditions. In this study, in order to produce a landslide susceptibility map, the modified analytical hierarchy process and landslide susceptibility analysis methods were used together with the layers, including: landslide inventory, slope, weathering crust, water storage, geology, land use, and distance from the road. In the study area, 98% of landslides occurred in highly or completely weathered units. Geology, land use, and water storage data layers were found to be important factors that are closely related with the occurrence of landslides. Although the weight of the “distance from the road” factor has a low value, the weight of layer “<100 m” has a high value. Therefore, the landslide susceptibility index very high is concentrated along the roads. For the validation of the predicted result, the landslide susceptibility map was compared with the landslide inventory map containing 47 landslides. The outcome shows that about 90% of these landslides fall into very high susceptibility zones.
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Xu, C., J. B. H. Shyu, and X. W. Xu. "Landslides triggered by the 12 January 2010 Mw 7.0 Port-au-Prince, Haiti, earthquake: visual interpretation, inventory compiling and spatial distribution statistical analysis." Natural Hazards and Earth System Sciences Discussions 2, no. 2 (February 10, 2014): 1259–331. http://dx.doi.org/10.5194/nhessd-2-1259-2014.
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Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and their erosion thicknesses with topographic factors, seismic parameters, and their distance from roads. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolutions satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various landslide controlling parameters. These controlling parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons of any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to update on the basis of the abundant and more complete co-seismic landslide inventories recently available.
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Hurst, M. D., M. A. Ellis, K. R. Royse, K. A. Lee, and K. Freeborough. "Controls on the magnitude-frequency scaling of an inventory of secular landslides." Earth Surface Dynamics 1, no. 1 (December 11, 2013): 67–78. http://dx.doi.org/10.5194/esurf-1-67-2013.
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Abstract. Linking landslide size and frequency is important at both human and geological timescales for quantifying both landslide hazards and the effectiveness of landslides in the removal of sediment from evolving landscapes. The statistical behaviour of the magnitude-frequency of landslide inventories is usually compiled following a particular triggering event such as an earthquake or storm, and their statistical behaviour is often characterised by a power-law relationship with a small landslide rollover. The occurrence of landslides is expected to be influenced by the material properties of rock and/or regolith in which failure occurs. Here we explore the statistical behaviour and the controls of a secular landslide inventory (SLI) (i.e. events occurring over an indefinite geological time period) consisting of mapped landslide deposits and their underlying lithology (bedrock or superficial) across the United Kingdom. The magnitude-frequency distribution of this secular inventory exhibits an inflected power-law relationship, well approximated by either an inverse gamma or double Pareto model. The scaling exponent for the power-law scaling of medium to large landslides is α = −1.71 ± 0.02. The small-event rollover occurs at a significantly higher magnitude (1.0–7.0 × 10−3 km2) than observed in single-event landslide records (~ 4 × 10−3 km2). We interpret this as evidence of landscape annealing, from which we infer that the SLI underestimates the frequency of small landslides. This is supported by a subset of data where a complete landslide inventory was recently mapped. Large landslides also appear to be under-represented relative to model predictions. There are several possible reasons for this, including an incomplete data set, an incomplete landscape (i.e. relatively steep slopes are under-represented), and/or temporal transience in landslide activity during emergence from the last glacial maximum toward a generally more stable late-Holocene state. The proposed process of landscape annealing and the possibility of a transient hillslope response have the consequence that it is not possible to use the statistical properties of the current SLI database to rigorously constrain probabilities of future landslides in the UK.
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Shao, Xiaoyi, Siyuan Ma, Chong Xu, Lingling Shen, and Yongkun Lu. "Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China." Sustainability 12, no. 6 (March 20, 2020): 2433. http://dx.doi.org/10.3390/su12062433.
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Inventorying landslides in mountainous areas is of great importance for prevention of geologic hazards. This study aimed to establish a detailed landslide inventory of Baoshan City, Yunnan Province, China, based on a large set of high-resolution satellite images from Google Earth. The landslides of this region were divided into two groups, i.e., recent landslides and old landslides. The spatial distribution and geometric characteristics of the two kinds of landslides were analyzed, respectively. Results show that 2427 landslides are present in the study area, including 2144 recent landslides and 283 old landslides with a total area of 7.2 km2 and 97.6 km2, respectively. The recent landslides occurred primarily at steep slopes with higher elevation, while old landslides took place at gentle terrains. For the slope position, most landslides, whether old or recent, cluster near ridges. The lower boundary of the recent landslides is far away from the valley, while the accumulation area of the old landslide is closer to the valley. The H/L (height to length) ratios are basically the same for all landslides, ranging from 0.2 to 0.5. Old landslides have larger mobility, as their travel distances are longer than recent landslides at the same height. The results would be helpful for further understanding the development and spatial distribution of the landslides in Southwest China, and also provide essential support for the subsequent landslide susceptibility mapping and geologic hazard assessment in this area.
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Ardizzone, F., M. Cardinali, M. Galli, F. Guzzetti, and P. Reichenbach. "Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar." Natural Hazards and Earth System Sciences 7, no. 6 (November 6, 2007): 637–50. http://dx.doi.org/10.5194/nhess-7-637-2007.
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Abstract. A high resolution Digital Elevation Model with a ground resolution of 2 m×2 m (DEM2) was obtained for the Collazzone area, central Umbria, through weighted linear interpolation of elevation points acquired by Airborne Lidar Swath Mapping. Acquisition of the elevation data was performed on 3 May 2004, following a rainfall period that resulted in numerous landslides. A reconnaissance field survey conducted immediately after the rainfall period allowed mapping 70 landslides in the study area, for a total landslide area of 2.7×105 m2. Topographic derivative maps obtained from the DEM2 were used to update the reconnaissance landslide inventory map in 22 selected sub-areas. The revised inventory map shows 27% more landslides and 39% less total landslide area, corresponding to a smaller average landslide size. Discrepancies between the reconnaissance and the revised inventory maps were attributed to mapping errors and imprecision chiefly in the reconnaissance field inventory. Landslides identified exploiting the Lidar elevation data matched the local topography more accurately than the same landslides mapped using the existing topographic maps. Reasons for the difference include an incomplete or inaccurate view of the landslides in the field, an unfaithful representation of topography in the based maps, and the limited time available to map the landslides in the field. The high resolution DEM2 was compared to a coarser resolution (10 m×10 m) DEM10 to establish how well the two DEMs captured the topographic signature of landslides. Results indicate that the improved topographic information provided by DEM2 was significant in identifying recent rainfall-induced landslides, and was less significant in improving the representation of stable slopes.
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Podolszki, Laszlo, Tomislav Kurečić, Luke Bateson, and Kristian Svennevig. "Remote landslide mapping, field validation and model development – An example from Kravarsko, Croatia." Geologia Croatica 75, no. 1 (February 28, 2022): 67–82. http://dx.doi.org/10.4154/gc.2022.01.
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The Kravarsko settlement area, in northern Croatia, has multiple landslides and damage to buildings and infrastructure caused by landslides. However, actual landslide investigation data for the wider Kravarsko area (pilot area PA1) is relatively sparse and no landslide inventory or typical landslide model exists. The aim of this research was to develop such a landslide inventory by integrating new approaches in geohazard research such as remote landslide mapping from high resolution digital elevation models (DEMs) and current and historical aerial images with existing and new geological data related to landslides. The conclusion is that detailed DEMs are more than adequate for the development of reliable landslide inventories but field checks are still necessary to account for the specific set of natural and man-made conditions found in the research area. The landslide inventory developed for Kravarsko has been field validated in a smaller validation area (VA1) and a typical simplified landslide model for PA1/VA1 was developed. Within the model, sliding is interpreted as complex with multiple generations of sliding and multiple sliding surfaces. Based on the analysis undertaken and the available field data, around 10-20% of urban structures are endangered in the Kravarsko area and anthropogenic activity was determined as an important landslide triggering factor for landslide activation or reactivation. Still the question remains of how to quantify the anthropogenic influence? The developed landslide inventory for PA1/VA1 could be used for local urban planning/development and endangerment assessment/evaluation.
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Subiaya, Bashir, and T. Ramkumar. "A multi-temporal landslide inventory and hazard zonation using relative effect method along the Mughal road Shopian, India." Disaster Advances 14, no. 7 (June 25, 2021): 42–51. http://dx.doi.org/10.25303/147da4221.
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Landslide inventory and thematic data are of utmost importance in the domain of landslide hazard mapping. The union territory of Jammu and Kashmir, India surrounded by the Himalayan and the Pir-Panjal mountain range is prone to landslides and has already caused havoc at many places. The present study aims to provide the landslide inventory of the Mughal Road, Shopian, which lies in the Pir Panjal range of Kashmir valley. Multidate satellite data of the years 2008 to 2020 are utilized to create an inventory of landslides in this area.The use of high-resolution satellite imagery made it possible to delineate the shallow as well as the deep landslides along the roadside where they occur frequently. To understand the landslide causes, a statistical technique, relative effect method has been implemented in this study. This method helped in mapping the hazard zone areas. The relative effect of each causative factor on landslides is determined by calculating the ratio of coverage and slide which were analyzed in GIS environment. The resulting landslide hazard zone map has been classified as very low, low, moderate, high and very high zones. Out of the total area, 12.62% is critical to landslides, 21.45% is highly prone and 24.84% is moderately prone while 21.94% is low and 19.13% is very low prone to landslides. The outcome of this susceptibility modeling will be beneficial for handling and monitoring the forthcoming landslides as well as the fortification of the general public and environmental hazards of the study area. It will also help the planners in the development around the study area.
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Wang, H. B., B. Zhou, S. R. Wu, J. S. Shi, and B. Li. "Characteristic analysis of large-scale loess landslides: a case study in Baoji City of Loess Plateau of Northwest China." Natural Hazards and Earth System Sciences 11, no. 7 (July 5, 2011): 1829–37. http://dx.doi.org/10.5194/nhess-11-1829-2011.
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Abstract. Landslides are one of the most common geologic hazards in the Loess Plateau of northwest China, especially with some of the highest landslide densities found in Shaanxi and adjacent provinces. Prior to assessing the landslide hazard, a detailed landslide inventory map is fundamental. This study documents the landslides on the northwest Loess Plateau with high accuracy using high-resolution Quickbird imagery for landslide inventory mapping in the Changshou valley of Baoji city. By far the majority of landslides are in loess, representing small-scale planar sliding. Most of the large-scale landslides involve loess and bedrock, and the failure planes occurred either along the contacts between fluvial deposits and Neogene argillites, or partially within the bedrock. In the sliding zones of a large scale landslide, linear striations and fractures of the soils were clearly developed, clay minerals were oriented in the same direction and microorganism growths were present. From the analysis of microstructure of sliding soils, it is concluded that the Zhuyuan landslide can be reactivated if either new or recurring water seepage is caused in the sliding surface. It can be concluded that most landslides are attributed to the undercutting of the slope associated with gullying, and numerous ancillary factors including bedrock-loess interface, slope steepness, vegetation cover and land utilization.
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Li, Lei, Chong Xu, Xiwei Xu, Zhongjian Zhang, and Jia Cheng. "Inventory and Distribution Characteristics of Large-Scale Landslides in Baoji City, Shaanxi Province, China." ISPRS International Journal of Geo-Information 11, no. 1 (December 29, 2021): 10. http://dx.doi.org/10.3390/ijgi11010010.
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Inventories of historical landslides play an important role in the assessment of natural hazards. In this study, we used high-resolution satellite imagery from Google Earth to interpret large landslides in Baoji city, Shaanxi Province on the southwestern edge of the Loess Plateau. Then, a comprehensive and detailed map of the landslide distribution in this area was prepared in conjunction with the historical literature, which includes 3440 landslides. On this basis, eight variables, including elevation, slope, aspect, slope position, distance to the fault, land cover, lithology and distance to the stream were selected to examine their influence on the landslides in the study area. Landslide number density (LND) and landslide area percentage (LAP) were used as evaluation indicators to analyze the spatial distribution characteristics of the landslides. The results show that most of the landslides are situated at elevations from 500 to 1400 m. The LND and LAP reach their peaks at slopes of 10–20°. Slopes facing WNW and NW directions, and middle and lower slopes are more prone to sliding with higher LND and LAP. LND and LAP show a decreasing trend as the distance to the fault or stream increases, followed by a slow rise. Landslides occur primarily in the areas covered by crops. Regarding lithology, the regions covered by the Quaternary loess and Cretaceous gravels are the main areas where landslides occur. The results would be helpful for further understanding the developmental characteristics and spatial distribution of landslides on the Loess Plateau, and also provide a support to subsequent landslide susceptibility mapping in this region.
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Ubaidulloev, Akmal, Hu Kaiheng, Manuchekhr Rustamov, and Makhvash Kurbanova. "Landslide Inventory along a National Highway Corridor in the Hissar-Allay Mountains, Central Tajikistan." GeoHazards 2, no. 3 (August 9, 2021): 212–27. http://dx.doi.org/10.3390/geohazards2030012.
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An increasing amount of landslides leading to significant human and economic consequences is a primary concern for the government of Tajikistan and local authorities. Based on the Committee on Emergency Situations data, from 1996 to 2018, there were 3460 emergencies and more than 1000 fatalities because of earthquake-triggered and rainfall-induced landslides in the region. In addition, landslides caused severe damage to houses and infrastructure facilities due to the population’s lack of landslide hazard knowledge. Therefore, current research focuses on developing a regional-scale landslide inventory map in the Hissar–Allay region, central Tajikistan, where the population density is much higher than at other mountainous territories. In recent decades, the enhancements in geographic information systems, the open access to high-resolution remote sensing data, and an extensive field survey allowed us to identify 922 landslides possible along the highway corridor in the Hissar–Allay region. Based on Varnes’s system, these landslides are classified into four categories: debris flows, rockfalls, shallow landslides, and complex (deep-seated) landslides, considering landslides morphology, geology, deformation of slopes, degree and aspect of slopes, and weathered and disintegrated zones on slopes in the study area. The results show that 8.24% of the total study area is affected by landslides. Along the highway corridor in the Hissar–Allay region there are 96 bodies of deep-seated landslides and 216 rockfall catchments, 273 debris flow catchments, and 313 shallow landslides. Thus, shallow landslides are the most frequent type of movement. In addition, landslide frequency-area distribution analysis shows that shallow landslides are frequent with an area of 1.88E+04 m2; most frequent debris flow channels have a place of 5.58E+05 m2; rockfalls, for its part, are rife with an area of 1.50E+05 m2, and frequent complex landslides have an area of 4.70E+06 m2. Furthermore, it was found out that slopes consist of Silurian formation comprise shales, pebbles, sands, loams, and limestones, metamorphic clays are exposed to landslides more than other geological formations because of the layered structure and their broad spatial distribution in the study area. As the first applied research to compile a landslide inventory map in the Hissar–Allay region on the regional scale, our study provides a sound basis for future explorations of landslide susceptibility, hazard, and risk assessment for this region.
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Li, Lei, Chong Xu, Zhiqiang Yang, Zhongjian Zhang, and Mingsheng Lv. "An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China." Data 7, no. 8 (August 15, 2022): 114. http://dx.doi.org/10.3390/data7080114.
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Landslides are a typical geological hazard that endangers people’s lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it is urgent to compile a complete inventory of landslides in a specific region. The investigation object of this study is Baoji City, Shaanxi Province, China. Using the multi-temporal high-resolution remote sensing images from Google Earth, we preliminarily completed the cataloging of large-scale (area > 5000 m2) landslides in the study area through visual interpretation. The inventory was subsequently compared with the existing literature and hazard records for improvement and supplement. We identified 3422 landslides with a total area of 360.7 km2 and an average area of 105,400 m2 for each individual landslide. The largest landslide had an area of 1.71 km2, while the smallest one was 6042 m2. In previous studies, we analyzed these data without describing the data sources in detail. We now provide a shared dataset of each landslide in shp format, containing geographic location, boundary information, etc. The dataset is significantly useful for understanding the distribution characteristics of large-scale landslides in this region. Moreover, it can serve as basic data for the study of paleolandslide resurrection.
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Lee, Sunmin, Won-Kyung Baek, Hyung-Sup Jung, and Saro Lee. "Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon." Applied Sciences 10, no. 22 (November 19, 2020): 8189. http://dx.doi.org/10.3390/app10228189.
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In recent years, the incidence of localized heavy rainfall has increased as abnormal weather events occur more frequently. In densely populated urban areas, this type of heavy rain can cause extreme landslide damage, so that it is necessary to estimate and analyze the susceptibility of future landslides. In this regard, deep learning (DL) methodologies have been used to identify areas prone to landslides recently. Therefore, in this study, DL methodologies, including a deep neural network (DNN), kernel-based DNN, and convolutional neural network (CNN) were used to identify areas where landslides could occur. As a detailed step for this purpose, landslide occurrence was first determined as landslide inventory through aerial photographs with comparative analysis using field survey data; a training set was built for model training through oversampling based on the landslide inventory. A total of 17 landslide influencing variables that influence the frequency of landslides by topography and geomorphology, as well as soil and forest variables, were selected to establish a landslide inventory. Then models were built using DNN, kernel-based DNN, and CNN models, and the susceptibility of landslides in the study area was determined. Model performance was evaluated through the average precision (AP) score and root mean square error (RMSE) for each of the three models. Finally, DNN, kernel-based DNN, and CNN models showed performances of 99.45%, 99.44%, and 99.41%, and RMSE values of 0.1694, 0.1806, and 0.1747, respectively. As a result, all three models showed similar performance, indicating excellent predictive ability of the models developed in this study. The information of landslides occurring in urban areas, which cause a great damage even with a small number of occurrences, can provide a basis for reference to the government and local authorities for urban landslide management.
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Guzzetti, F., M. Galli, P. Reichenbach, F. Ardizzone, and M. Cardinali. "Landslide hazard assessment in the Collazzone area, Umbria, Central Italy." Natural Hazards and Earth System Sciences 6, no. 1 (January 31, 2006): 115–31. http://dx.doi.org/10.5194/nhess-6-115-2006.
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Abstract. We present the results of the application of a recently proposed model to determine landslide hazard. The model predicts where landslides will occur, how frequently they will occur, and how large they will be in a given area. For the Collazzone area, in the central Italian Apennines, we prepared a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs taken between 1941 and 1997 and field surveys conducted in the period between 1998 and 2004. We then partitioned the 79 square kilometres study area into 894 slope units, and obtained the probability of spatial occurrence of landslides by discriminant analysis of thematic variables, including morphology, lithology, structure and land use. For each slope unit, we computed the expected landslide recurrence by dividing the total number of landslide events inventoried in the terrain unit by the time span of the investigated period. Assuming landslide recurrence was constant, and adopting a Poisson probability model, we determined the exceedance probability of having one or more landslides in each slope unit, for different periods. We obtained the probability of landslide size, a proxy for landslide magnitude, by analysing the frequency-area statistics of landslides, obtained from the multi-temporal inventory map. Lastly, assuming independence, we determined landslide hazard for each slope unit as the joint probability of landslide size, of landslide temporal occurrence, and of landslide spatial occurrence.
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Jia, Hongying, Yingjie Wang, Daqing Ge, Yunkai Deng, and Robert Wang. "InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation—A Case of Xiaojiang River Basin, China." Remote Sensing 14, no. 7 (April 6, 2022): 1759. http://dx.doi.org/10.3390/rs14071759.
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Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting.
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Patton, A. I., S. R. Rathburn, D. Capps, R. A. Brown, and J. S. Singleton. "Lithologic, geomorphic, and permafrost controls on recent landsliding in the Alaska Range." Geosphere 16, no. 6 (November 2, 2020): 1479–94. http://dx.doi.org/10.1130/ges02256.1.
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Abstract Because landslide regimes are likely to change in response to climate change in upcoming decades, the need for mechanistic understanding of landslide initiation and up-to-date landslide inventory data is greater than ever. We conducted surficial geologic mapping and compiled a comprehensive landslide inventory of the Denali National Park road corridor to identify geologic and geomorphic controls on landslide initiation in the Alaska Range. The supplemental geologic map refines and improves the resolution of mapping in the study area and adds emphasis on surficial units, distinguishing multiple glacial deposits, hillslope deposits, landslides, and alluvial units that were previously grouped. Results indicate that slope angle, lithology, and thawing ice-rich permafrost exert first-order controls on landslide occurrence. The majority (84%) of inventoried landslides are <0.01 km2 in area and occur most frequently on slopes with a bimodal distribution of slope angles with peaks at 18° and 28°. Of the 85 mapped landslides, a disproportionate number occurred in unconsolidated sediments and in felsic volcanic rocks. Weathering of feldspar within volcanic rocks and subsequent interactions with groundwater produced clay minerals that promote landslide initiation by impeding subsurface conductivity and reducing shear strength. Landslides also preferentially initiated within permafrost, where modeled mean decadal ground temperature is −0.2 ± 0.04 °C on average, and active layer thickness is ∼1 m. Landslides that initiated within permafrost occurred on slope angles ∼7° lower than landslides on seasonally thawed hillslopes. The bimodal distribution of slope angles indicates that there are two primary drivers of landslide failure within discontinuous permafrost zones: (1) atmospheric events (snowmelt or rainfall) that saturate the subsurface, as is commonly observed in temperate settings, and (2) shallow-angle landslides (<20° slopes) in permafrost demonstrate that permafrost and ice thaw are also important triggering mechanisms in the study region. Melting permafrost reduces substrate shear strength by lowering cohesion and friction along ice boundaries. Increased permafrost degradation associated with climate change brings heightened focus to low-angle slopes regionally as well as in high-latitude areas worldwide. Areas normally considered of low landslide potential will be more susceptible to shallow-angle landslides in the future. Our landslide inventory and analyses also suggest that landslides throughout the Alaska Range and similar climatic zones are most likely to occur where low-cohesion unconsolidated material is available or where alteration of volcanic rocks produces sufficient clay content to reduce rock and/or sediment strength. Permafrost thaw is likely to exacerbate slope instability in these materials and expand areas impacted by landslides.
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Kubwimana, Desire, Lahsen Ait Brahim, and Abdellah Abdelouafi. "A new approach in the development and analysis of the landslide susceptibility map of the hillslopes of Bujumbura, Burundi." EUREKA: Physics and Engineering, no. 3 (May 27, 2021): 26–34. http://dx.doi.org/10.21303/2461-4262.2021.001724.
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As in other hilly and mountainous regions of the world, the hillslopes of Bujumbura are prone to landslides. In this area, landslides impact human lives and infrastructures. Despite the high landslide-induced damages, slope instabilities are less investigated. The aim of this research is to assess the landslide susceptibility using a probabilistic/statistical data modeling approach for predicting the initiation of future landslides. A spatial landslide inventory with their physical characteristics through interpretation of high-resolution optic imageries/aerial photos and intensive fieldwork are carried out. Base on in-depth field knowledge and green literature, let’s select potential landslide conditioning factors. A landslide inventory map with 568 landslides is produced. Out of the total of 568 landslide sites, 50 % of the data taken before the 2000s is used for training and the remaining 50 % (post-2000 events) were used for validation purposes. A landslide susceptibility map with an efficiency of 76 % to predict future slope failures is generated. The main landslides controlling factors in ascendant order are the density of drainage networks, the land use/cover, the lithology, the fault density, the slope angle, the curvature, the elevation, and the slope aspect. The causes of landslides support former regional studies which state that in the region, landslides are related to the geology with the high rapid weathering process in tropical environments, topography, and geodynamics. The susceptibility map will be a powerful decision-making tool for drawing up appropriate development plans in the hillslopes of Bujumbura with high demographic exposure. Such an approach will make it possible to mitigate the socio-economic impacts due to these land instabilities
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Nasiah, Nasiah, and Ichsan Invanni. "Landslide Suceptibility Zonation in South Sulawesi." Forum Geografi 27, no. 2 (December 10, 2013): 189. http://dx.doi.org/10.23917/forgeo.v27i2.2376.
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Landslide Hazard Zonationin South Sulawesi. Landslides are natural disasters that can cause substantial loss in the form of life and properties. Therefore, it is necessary to inventory landslide-vulnerable areas. A weighted summation model (Dibyosaputro, 1998) was applied to determine the landslide-vulnerable areas in the Geographic Information Systems (GIS). Factors that trigger the landslides are geology (rock properties, stratigraphy, structural geology, weathering level and earthquake), climate (rainfall), soil (solum thickness), topography (slope), vegetation (vegetation density) and human (land use); Siagian & Sugalan (in Sutikno, 1991) in combination with Dibyosaputro (1998). There are five classes of landslide vulnerability i.e. invulnerable, fairly vulnerable, quite vulnerable, vulnerable, and very vulnerable. In general, South Sulawesi is quite vulnerable to landslides, but there are three regencies very vulnerable for landslides; Luwu, Northern Luwu and Northern Toraja.Keyword : landslide, South Sulawesi.
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Gulam, Vlatko, Iris Bostjančić, Nina Hećej, Marina Filipović, and Radovan Filjak. "Preliminary analysis of a LiDAR-based landslide inventory in the area of Samobor, Croatia." Geologia Croatica 75, no. 1 (February 28, 2022): 51–66. http://dx.doi.org/10.4154/gc.2022.12.
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The paper presents an analysis of the LiDAR-based landslide inventory for the area near Samobor, in northwestern Croatia with two main objectives: i) to define the geological units (obtained from Basic Geological Map of Croatia) most susceptible to landslides, and ii) to analyse the limitations of the Basic Geological Map and its applicability in landslide susceptibility map design. Within the study area of 63.8 km2, 874 landslide polygons were manually outlined, covering an area of 2.15 km2. The landslide outline confidence level, landslide index and the relief energy map were used to analyse the landslide susceptibility of a particular geological unit. By that, units in the same state of stress, i.e., in the same relief energy group were compared. This preliminary analysis has shown that the geological units Pl,Q, M3 1,2, and 1M3 1 are the most susceptible to landslides and that older geological units, Pc and K1,2, are also prone to landslides. Still, landslides within those older units can be considered as old and inactive. As for the limitations of the Basic Geological Map of Croatia, three things emerged, namely scale, the geological unit defining approach, and the neglect of regolith. Despite the limitations presented, the usability of the Basic Geological Map of Croatia in the development of small-scale landslide susceptibility maps is emphasized. However, instructions that should attribute engineering geological features to the geological units outlined in the Basic Geological Map should be prepared in the near future.
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Loche, Marco, Luigi Lombardo, Tolga Gorum, Hakan Tanyas, and Gianvito Scaringi. "Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey." Remote Sensing 14, no. 6 (March 9, 2022): 1321. http://dx.doi.org/10.3390/rs14061321.
Full textAbstract:
To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of landslides more difficult. In some cases, landslide inventories report information on the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis. Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be related to different triggers. To do so, we built two landslide susceptibility models and observed that the spatial patterns of susceptibility were completely distinct. We found that these patterns were correlated with specific controlling factors, suggesting that active landslides are regulated by current rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The importance of this result resides in that we obtained it with a purely data-driven approach, and this was possible because the active/relict landslide classification in the inventory was accurate.
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Schlögel, R., J. P. Malet, A. Remaître, P. Reichenbach, and C. Doubre. "Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study." Natural Hazards and Earth System Sciences Discussions 3, no. 3 (March 30, 2015): 2051–98. http://dx.doi.org/10.5194/nhessd-3-2051-2015.
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Abstract. We propose a methodology (1) to prepare a multi-date landslide inventory for a mountainous area affected by several landslide types with different degrees of activity, and (2) to estimate the temporal occurrence and the intensity of the landslides through the analysis of morphological indicators. The inventory, covering the period 1956–2010, is constructed for the middle section of the Ubaye valley (South French Alps) based on the analysis of multi-source documents (geomorphological maps, historical reports of landslide events, field surveys, series of orthophotographs and SAR satellite images). The uncertainties in the interpretation of the documents and the landslide morphological features are taken into account in relation to the scale of the source documents. Several morphological indicators are calculated to describe quantitatively the evolution of the landslides (length, area, relative elevation, runout distance). Frequency-area density functions are calculated to estimate the changes in the landslide distributions. A Poisson model is used to estimate the probability of reactivation of the observed landslides. The proposed multi-date inventory and the associated statistics give additional information to the event catalogue managed by local authorities.
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Ali, M. Z., H. J. Chu, S. Ullah, M. Shafique, and A. Ali. "UTILIZATION OF FINE RESOLUTION SATELLITE DATA FOR LANDSLIDE SUSCEPTIBILITY MODELLING: A CASE STUDY OF KASHMIR EARTHQUAKE INDUCED LANDSLIDES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 20, 2019): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-25-2019.
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<p><strong>Abstract.</strong> The 2005 Kashmir earthquake has triggered thousands of landslides which devastated most of the livelihood and other infrastructure in the area. Landslide inventory and subsequently landslide susceptibility mapping is one of the main prerequisite for taking mitigation measure against landslide effects. This study has focused on developing most updated and realistic landslide inventory and Susceptibility mapping. The high resolution data of Worldveiw-2 having spatial resolution of 0.4 m is used for landslide inventory. Support Vector Machine (SVM) classifier was used for landslide inventory developing. Total 51460 number of landslides were classified using semi-automatic technique with covering area of 265 Km<sup>2</sup>, smallest landslide mapped is covering area of 2.01 m<sup>2</sup> and the maximum covered area of single landslide is 3.01 Km<sup>2</sup>. Nine influential causative factors are used for landslide susceptibility mapping. Those causative factors include slope, aspect, profile curvature, elevation, distance from fault lines, distance from streams and geology. Logistic regression model was used for the Landslides susceptibility modelling. From model the highest coefficient was assigned to geology which shows that the geology has higher influence in the area. For landslide susceptibility mapping the 70 % of the data was used and 30% is used for the validation of the model. The prediction accuracy of the model in this study is 92 % using validation data. This landslide susceptibility map can be used for land use planning and also for the mitigation measure during any disaster.</p>
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Hurst, M. D., M. A. Ellis, K. R. Royse, K. A. Lee, and K. Freeborough. "Controls on the magnitude-frequency scaling of an inventory of secular landslides." Earth Surface Dynamics Discussions 1, no. 1 (July 1, 2013): 113–39. http://dx.doi.org/10.5194/esurfd-1-113-2013.
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Abstract. Linking landslide size and frequency is important at both human and geological time-scales for quantifying both landslide hazards and the effectiveness of landslides in the removal of sediment from evolving landscapes. Landslide inventories are usually compiled following a particular triggering event such as an earthquake or storm, and their statistical behavior is typically characterized by an inflected power-law relationship. The occurrence of landslides is expected to be influenced by the material properties of rock and/or regolith in which failure occurs. Here we explore the statistical behavior and the controls of a secular landslide inventory (SLI) (i.e. events occurring over an indefinite time period) consisting of mapped landslide deposits and their underlying lithology (bedrock or superficial) across the United Kingdom. The magnitude-frequency distribution of this secular inventory exhibits an inflected power law relationship, well approximated by an inverse Gamma or double Pareto model. The scaling exponent for the power-law relationship is α = −1.76. The small-event rollover occurs at a significantly higher magnitude than observed in single-event landslide records, which we interpret as evidence of “landscape annealing” at these relatively short length-scales, noting the corollary that a secular dataset will tend to underestimate the frequency of small landslides. This is supported by a subset of data where a complete landslide inventory was recently mapped. Large landslides also appear to be under-represented relative to model predictions, which we interpret as a non-linear or transient landscape response as the UK emerged from the last glacial maximum and through relatively volatile conditions toward a generally more stable late Holocene climate.
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Ramos-Bernal, Rocío, René Vázquez-Jiménez, Raúl Romero-Calcerrada, Patricia Arrogante-Funes, and Carlos Novillo. "Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery." Remote Sensing 10, no. 12 (December 8, 2018): 1987. http://dx.doi.org/10.3390/rs10121987.
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Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.
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Guzzetti, F., P. Reichenbach, M. Cardinali, F. Ardizzone, and M. Galli. "The impact of landslides in the Umbria region, central Italy." Natural Hazards and Earth System Sciences 3, no. 5 (October 31, 2003): 469–86. http://dx.doi.org/10.5194/nhess-3-469-2003.
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Abstract. The Umbria Region of Central Italy has a long history of mass movements. Landslides range from fast moving rock falls and debris flows, most abundant in mountain areas, to slow moving complex failures extending up to several hectares in the hilly part of the Region. Despite landslides occurring every year in Umbria, their impact remains largely unknown. We present an estimate of the impact of slope failures in the Umbria region based on the analysis of a catalogue of historical information on landslide events, a recent and detailed regional landslide inventory map, and three event inventories prepared after major landslide triggering events. Emphasis is given to the impact of landslides on the population, the transportation network, and the built-up areas. Analysis of the available historical information reveals that 1488 landslide events occurred at 1292 sites in Umbria between 1917 and 2001. In the same period 16 people died or were missing and 31 people were injured by slope movements. Roads and railways were damaged by slope failures at 661 sites, and 281 built-up areas suffered landslide damage. Three event inventories showing landslides triggered by high intensity rainfall events in the period 1937–1941, rapid snow melting in January 1997, and earthquakes in September–October 1997, indicate the type, abundance and distribution of damage to the population, the built-up areas and the transportation network caused by typical landslide-triggering events. Analysis of a geomorphological landslide inventory map reveals that in some of the municipalities in the region total landslide area exceeds 25%. Of the more than 45 700 landslide areas shown in the geomorphological inventory map, 4115 intersect a road or railway, and 6119 intersect a built-up area. In these areas slope failures can be expected during future landslide triggering events.
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Nasir, N. S., M. F. Abdul Khanan, S. H. Othman, M. Z. Abdul Rahman, K. A. Razak, M. R. Mohd Salleh, H. A. Umar, and A. N. Abdul Razak. "ASSIMILATING GEOSPATIAL METAMODEL AND INVENTORY MAPPING FOR NON-STRUCTURAL MITIGATION OF LANDSLIDE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 (October 30, 2018): 217–28. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-217-2018.
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<p><strong>Abstract.</strong> In Malaysia, issues related to disaster management are always given attention in society and by the responsible parties. However, in general, citizen do not think of the consequential impact of disaster due to less of knowledge regarding the early phase in disaster management. Therefore, citizen in those areas will be more vulnerable to landslide as the citizen face difficulties in identifying specific areas with the tendency of landslides. This paper presents a geospatial metamodel approach for non-structural mitigation of landslide using data from airborne LiDAR and aerial photograph. Disaster management metamodel with geospatial element combines activity for managing disaster along with geospatial database that makes it handy for appreciating the metamodel. On the other hand, the digital terrain model (DTM) from LiDAR and aerial photograph is required to produce landslide inventory mapping. The case study area is located in Kundasang, Sabah, where landslides occur frequently. In order to get better visual in identifying landslides in the study area, three types of data are required to carry out image interpretation. The three types of data are hillshade, topographic openness and colour composite. The result of the landslide inventory map shows that there are five types of landslide, which is debris flow, debris fall, mud flow, deep-seated landslide and shallow landslide. Finally, the result of landslide inventory map will be integrated into the developed metamodel for presentation to the users. This landslide inventory map is used as a non-structural mitigation step in one of disaster management phases that is suitable to prepare and use in mitigating the landslide hazard impact.</p>
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Ma, Siyuan, Xiaoyi Shao, and Chong Xu. "Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China." Remote Sensing 14, no. 17 (August 29, 2022): 4257. http://dx.doi.org/10.3390/rs14174257.
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Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous and hilly area; hence, characterizing the distribution pattern and effective susceptibility mapping for rainfall-induced landslides are regarded as important and necessary measures to remediate the damage and loss resulting from landslides. From 10 June 2019 to 13 June 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province; this event triggered extensive landslide disasters in the villages of Longchuan County. Based on high-resolution satellite images, a landslide inventory of the affected area was compiled, comprising a total of 667 rainfall-induced landslides over an area of 108 km2. These landslides consisted of a large number of shallow landslides with a few flowslides, rockfalls, and debris flows, and the majority of them occurred in Mibei and Yanhua villages. The inventory was used to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to slope angle, TWI, and road density. The landslide area density (LAD) increased with the increase in the above three influencing factors and was described by an exponential or linear relationship. In addition, southeast and south aspect hillslopes were more prone to collapse than the northwest–north aspect ones because of the influence of the summer southeast monsoon. A new open-source tool named MAT.TRIGRS(V1.0) was adopted to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distribution, and most areas with high susceptibility were located on both sides of the river valley; that is, the areas with relatively steep slopes. The slope stability changes in different periods revealed that the onset of heavy rain on 10 June 2019 was the main triggering factor of these group‑occurring landslides, and the subsequent rainfall with low intensity had little impact on slope stability.
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Xu, C., J. B. H. Shyu, and X. Xu. "Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, <i>M</i><sub>w</sub> = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis." Natural Hazards and Earth System Sciences 14, no. 7 (July 21, 2014): 1789–818. http://dx.doi.org/10.5194/nhess-14-1789-2014.
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Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw= 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and the thicknesses of their erosion with topographic, geologic, and seismic parameters. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolution satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various environmental parameters. These parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons for any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to be updated on the basis of the abundant and more complete co-seismic landslide inventories recently available.
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Perera, E. N. C., A. M. C. T. Gunaratne, and S. B. D. Samarasinghe. "Participatory Landslide Inventory (PLI): An Online Tool for the Development of a Landslide Inventory." Complexity 2022 (February 8, 2022): 1–10. http://dx.doi.org/10.1155/2022/2659203.
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A landslide inventory is a detailed register of the spatial distribution, geometry, and attributes of landslides and is essential for landslide hazard analysis, risk management, regional planning, and land use management and development, especially in landslide-prone regions. However, the development of a national landslide inventory is time-consuming and costly. Accordingly, most developing countries, including Sri Lanka, have basic landslide databases, which identify the location, date, and time of occurrence on a point map. This study, therefore, aimed to introduce a new method to report landslide information via a mobile application based on a participatory approach with information recorded in a web portal called the participatory landslide inventory (PLI). Twenty-one landslide site locations and their attributes were captured on the PLI web portal using the PLI mobile app. The system administrator then demarcated the landslide boundaries and performed geometrical calculations for each landslide to complete the inventory. Finally, the landslide information was published through the PLI web portal. The PLI is an effective and efficient tool for developing a landslide inventory economically.
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Ardizzone, Francesca, Francesco Bucci, Mauro Cardinali, Federica Fiorucci, Luca Pisano, Michele Santangelo, and Veronica Zumpano. "Geomorphological landslide inventory map of the Daunia Apennines, southern Italy." Earth System Science Data 15, no. 2 (February 14, 2023): 753–67. http://dx.doi.org/10.5194/essd-15-753-2023.
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Abstract. 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, and 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 1460 km2 area in the Daunia Apennines, the north-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.org/10.1594/PANGAEA.942427 (Cardinali et al., 2022).
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Pollak, Davor, Nina Hećej, and Anita Grizelj. "Landslide inventory and characteristics, based on LiDAR scanning and optimised field investigations in the Kutina area, Croatia." Geologia Croatica 75, no. 1 (February 28, 2022): 83–99. http://dx.doi.org/10.4154/gc.2022.02.
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This paper presents the preliminary results of analyses of landsliding processes derived from detailed LiDAR (Light Detection and Ranging) scans supported by field prospection on the south-western slopes of Mt. Moslavačka gora, in the wider Kutina area. This area is known for frequent landslides, but dedicated regional landslide research has not been previously undertaken. High resolution LiDAR scanning and orthophoto imaging enabled the production of a reliable landslide inventory, but also enabled research on landslide properties and the morphology of the area. Field mapping and prospection, sampling and borehole coring assisted in the collection of information about the material characteristics and specific features of typical landslides. In the research area, which covers more than 71 km2, more than 1200 very small landslides were detected. The majority of landslides were discovered in just several geological units indicating their high susceptibility: Pleistocene silts and sands with clayey interlayers, followed by M2 silty sands and gravels, and M7 sands. Nearly half of the landslides are estimated to be of recent and younger age, while other landslides may be considered as being historical implying a “long tradition” of landslide events in the research area. Preliminary terrain surface roughness analysis also supported the conclusion that the inventory contains landslides of several historical generations which are still detectable. In addition to slides (1123), this research also discovered numerous earthflow processes (143), which are more frequent in the predominantly sandy units. The landslides in this area are largely located on the banks of the gullies and are directly related to the action of water. Regarding that situation and the engineering properties of the encountered geological units, four types of bank instabilities can be differentiated: slides on top of rock masses; slides in firm soil mixtures; landslides in sands; landslides in predominantly coherent soil complexes.
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Pyrgiotis, L., G. Koukis, and N. Sabatakakis. "Rainfall and landslides in Karditsa county (Greece): a statistical approach." Bulletin of the Geological Society of Greece 40, no. 4 (January 1, 2007): 1722. http://dx.doi.org/10.12681/bgsg.17099.
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The mountainous area of Karditsa County, being geologically a representative sample of Olonos- Pindos geotectonic zone, is characterized by rainfall- induced landslides on steep slopes of high relief. After a systematic data inventory concerning landslides and their quantitative expression, the correlation between landslide occurrence and rainfall is investigated. Following the analyses performed a close interrelation between landslides and precipitation is established. Finally, a characteristic example of rainfall- induced landslide in the wider area of the county is given
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Wang, H. B., J. W. Li, B. Zhou, Z. Q. Yuan, and Y. P. Chen. "Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility." Natural Hazards and Earth System Sciences Discussions 1, no. 2 (March 4, 2013): 353–88. http://dx.doi.org/10.5194/nhessd-1-353-2013.
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Abstract. In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100–150 m with slope angles from 135°–225° and 40°–60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence is 93.02%, whereas units without landslide occurrence are predicted with an accuracy of 81.13%. To sum up, the verification shows satisfactory agreement with an accuracy of 86.46% between the susceptibility map and the landslide locations. In the landslide susceptibility assessment, ten new slopes were predicted to show potential for failure, which can be confirmed by the engineering geological conditions of these slopes. It was also observed that some disadvantages could be overcome in the application of the neural networks with back propagation, for example, the low convergence rate and local minimum, after the network was optimized using genetic algorithms. To conclude, neural networks with back propagation that are optimized by genetic algorithms are an effective method to predict landslide susceptibility with high accuracy.
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Santangelo, M., I. Marchesini, F. Bucci, M. Cardinali, F. Fiorucci, and F. Guzzetti. "An approach to reduce mapping errors in the production of landslide inventory maps." Natural Hazards and Earth System Sciences Discussions 3, no. 7 (July 1, 2015): 4189–229. http://dx.doi.org/10.5194/nhessd-3-4189-2015.
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Abstract. Landslide inventory maps (LIMs) show where landslides have occurred in an area, and provide information useful to different types of landslide studies, including susceptibility and hazard modelling and validation, risk assessment, erosion analyses, and to evaluate relationships between landslides and geological settings. Despite recent technological advancements, visual interpretation of aerial photographs (API) remains the most common method to prepare LIMs. In this work, we present a new semi-automatic procedure that exploits GIS technology for the digitalization of landslide data obtained through API. To test the procedure, and to compare it to a consolidated landslide mapping method, we prepared two LIMs starting from the same set of landslide API data, which were digitalized (a) manually adopting a consolidated visual transfer method, and (b) adopting our new semi-automatic procedure. Results indicate that the new semi-automatic procedure is more efficient and results in a more accurate LIM. With the new procedure, the landslide positional error decreases with increasing landslide size following a power-law. We expect that our work will help adopt standards for transferring landslide information from the aerial photographs to a digital landslide map, contributing to the production of accurate landslide maps.
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Zhang, Weiheng, Yueren Xu, Xinyi Guo, Wenqiao Li, Peng Du, and Qinjian Tian. "Distribution and Characteristics of Damming Landslides Triggered by 1920 M~8 Haiyuan Earthquake, NW China." Remote Sensing 14, no. 10 (May 18, 2022): 2427. http://dx.doi.org/10.3390/rs14102427.
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Earthquake-triggered damming landslides threaten downstream residents and affect the regional landscape by disrupting water and sediment fluxes. Therefore, it is essential to study the distribution characteristics and distinctive controlling factors of earthquake-triggered damming landslides to provide a reference for treating landslide dams caused by damming landslides. This study uses the 1920 M~8 Haiyuan earthquake-triggered landslides as an example to study the characteristics and topographic effects of damming landslides in the Loess Plateau in Northwestern China. A detailed Haiyuan-earthquake-triggered damming landslide inventory was established. The statistics of terrain, geology, seismic factors, and information gain rankings were used to quantify the significance of the controlling factors. The aspect ratio, equivalent coefficient of friction, area, and slope position was calculated. Damming landslides’ distinctive geomorphic and morphological characteristics were summarized through comparisons with non-damming landslides. The results showed that damming landslides were concentrated in areas with thick loess sediment, low relief, and close proximity to a river. Loess thickness was the most critical control factor among them. Damming landslides have the geomorphological characteristics of a large ratio of length to width (L/W), a low ratio of height to length (H/L), large scales, and entire-slope failure. Moreover, damming landslides can transform the topography of the Loess Plateau through their long-term effects. These findings highlight the characteristics of damming landslides in the Loess Plateau and supplement the global landslide dam inventory. They provide a reference for assisting in earthquake-triggered damming landslides treatments in the Loess Plateau.
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Niculiţǎ, Mihai. "Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs." Natural Hazards and Earth System Sciences 16, no. 8 (August 30, 2016): 2021–30. http://dx.doi.org/10.5194/nhess-16-2021-2016.
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Abstract. The morphology of landslides is influenced by the slide/flow of the material downslope. Usually, the distance of the movement of the material is greater than the width of the displaced material (especially for flows, but also the majority of slides); the resulting landslides have a greater length than width. In some specific geomorphologic environments (monoclinic regions, with cuesta landforms type) or as is the case for some types of landslides (translational slides, bank failures, complex landslides), for the majority of landslides, the distance of the movement of the displaced material can be smaller than its width; thus the landslides have a smaller length than width. When working with landslide inventories containing both types of landslides presented above, the analysis of the length and width of the landslides computed using usual geographic information system techniques (like bounding boxes) can be flawed. To overcome this flaw, I present an algorithm which uses both the geometry of the landslide polygon minimum oriented bounding box and a digital elevation model of the landslide topography for identifying the long vs. wide landslides. I tested the proposed algorithm for a landslide inventory which covers 131.1 km2 of the Moldavian Plateau, eastern Romania. This inventory contains 1327 landslides, of which 518 were manually classified as long and 809 as wide. In a first step, the difference in elevation of the length and width of the minimum oriented bounding box is used to separate long landslides from wide landslides (long landslides having the greatest elevation difference along the length of the bounding box). In a second step, the long landslides are checked as to whether their length is greater than the length of flow downslope (estimated with a flow-routing algorithm), in which case the landslide is classified as wide. By using this approach, the area under the Receiver Operating Characteristic curve value for the classification of the long vs. wide landslides is 87.8 %. An intensive review of the misclassified cases and the challenges of the proposed algorithm is made, and discussions are included about the prospects of improving the approach with further steps, to reduce the number of misclassifications.
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Jaiswal, P., C. J. van Westen, and V. Jetten. "Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India." Natural Hazards and Earth System Sciences 11, no. 6 (June 21, 2011): 1723–43. http://dx.doi.org/10.5194/nhess-11-1723-2011.
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Abstract. A quantitative procedure for estimating landslide risk to life and property is presented and applied in a mountainous area in the Nilgiri hills of southern India. Risk is estimated for elements at risk located in both initiation zones and run-out paths of potential landslides. Loss of life is expressed as individual risk and as societal risk using F-N curves, whereas the direct loss of properties is expressed in monetary terms. An inventory of 1084 landslides was prepared from historical records available for the period between 1987 and 2009. A substantially complete inventory was obtained for landslides on cut slopes (1042 landslides), while for natural slopes information on only 42 landslides was available. Most landslides were shallow translational debris slides and debris flowslides triggered by rainfall. On natural slopes most landslides occurred as first-time failures. For landslide hazard assessment the following information was derived: (1) landslides on natural slopes grouped into three landslide magnitude classes, based on landslide volumes, (2) the number of future landslides on natural slopes, obtained by establishing a relationship between the number of landslides on natural slopes and cut slopes for different return periods using a Gumbel distribution model, (3) landslide susceptible zones, obtained using a logistic regression model, and (4) distribution of landslides in the susceptible zones, obtained from the model fitting performance (success rate curve). The run-out distance of landslides was assessed empirically using landslide volumes, and the vulnerability of elements at risk was subjectively assessed based on limited historic incidents. Direct specific risk was estimated individually for tea/coffee and horticulture plantations, transport infrastructures, buildings, and people both in initiation and run-out areas. Risks were calculated by considering the minimum, average, and maximum landslide volumes in each magnitude class and the corresponding minimum, average, and maximum run-out distances and vulnerability values, thus obtaining a range of risk values per return period. The results indicate that the total annual minimum, average, and maximum losses are about US$ 44 000, US$ 136 000 and US$ 268 000, respectively. The maximum risk to population varies from 2.1 × 10−1 for one or more lives lost to 6.0 × 10−2 yr−1 for 100 or more lives lost. The obtained results will provide a basis for planning risk reduction strategies in the Nilgiri area.
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Ullah, Md Sofi. "Geospatial Modeling of Landslide Vulnerability and Simulating Spatial Correlation with Associated Factors in Bandarban District." Dhaka University Journal of Earth and Environmental Sciences 8, no. 2 (January 30, 2021): 51–66. http://dx.doi.org/10.3329/dujees.v8i2.54839.
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The present study aims at identifying and predicting landslide vulnerable areas in Bandarban District of Chittagong Hill Tracts (CHT) using weighted overlaying of the multiple geospatial layers to determine landslide hazard areas. The historical landslide inventory map was prepared using Google Earth image and through PRA technique. Then ten landslide triggering factors including landuse, rainfall, slope, elevation, cut-fill, soil types, geology, distance to rivers, roads and stream orders, population density, income, education of the inhabitants were chosen as effective factors on a landslide in the study area. Subsequently, the landslide vulnerability map was constructed using the weighted overlay model in Geographic Information System (GIS). Bandarban District has 348 landslides vulnerable locations. Among them, 6 are extremely vulnerable and 342 are highly vulnerable to landslides. Model results show that the Upazila Ruma and Thanchi are extremely vulnerable to landslides. About 91 percent of the landslides will occur within 10 degrees of slope, about 65 percent will occur within 50 meters elevation. The model shows that there is a strong relationship between landslides and physical, economic and social variables.
The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(2), 2019, P 51-56
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Robbins, J. C., and M. G. Petterson. "Landslide inventory development in a data sparse region: spatial and temporal characteristics of landslides in Papua New Guinea." Natural Hazards and Earth System Sciences Discussions 3, no. 8 (August 17, 2015): 4871–917. http://dx.doi.org/10.5194/nhessd-3-4871-2015.
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Abstract. In Papua New Guinea (PNG) earthquakes and rainfall events form the dominant trigger mechanisms capable of generating many landslides. Large volume and high density landsliding can result in significant socio-economic impacts, which are felt particularly strongly in the largely subsistence-orientated communities which reside in the most susceptible areas of the country. As PNG has undergone rapid development and increased external investment from mining and other companies, population and settled areas have increased, hence the potential for damage from landslides has also increased. Information on the spatial and temporal distribution of landslides, at a regional-scale, is critical for developing landslide hazard maps and for planning, sustainable development and decision making. This study describes the methods used to produce the first, country-wide landslide inventory for PNG and analyses of landslide events which occurred between 1970 and 2013. The findings illustrate that there is a strong climatic control on landslide-triggering events and that the majority (~ 61 %) of landslides in the PNG landslide inventory are initiated by rainfall related triggers. There is also large year to year variability in the annual occurrence of landslide events and this is related to the phase of El Niño Southern Oscillation (ENSO) and mesoscale rainfall variability. Landslide-triggering events occur during the north-westerly monsoon season during all phases of ENSO, but less landslide-triggering events are observed during drier season months (May to October) during El Niño phases, than either La Niña or ENSO neutral periods. This analysis has identified landslide hazard hotspots and relationships between landslide occurrence and rainfall climatology and this information can prove to be very valuable in the assessment of trends and future behaviour, which can be useful for policy makers and planners.
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Emberson, Robert, Dalia B. Kirschbaum, Pukar Amatya, Hakan Tanyas, and Odin Marc. "Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories." Natural Hazards and Earth System Sciences 22, no. 3 (April 1, 2022): 1129–49. http://dx.doi.org/10.5194/nhess-22-1129-2022.
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Abstract. Landslides are a key hazard in high-relief areas around the world and pose a risk to populations and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall-induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that the average upstream angle and compound topographic index are strong predictors of landslide scar location, while the local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modeling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.
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Salinas-Jasso, Jorge A., Ricardo A. Salinas-Jasso, Juan C. Montalvo-Arrieta, and Efraín Alva-Niño. "Inventario de movimientos en masa en el sector sur de la Saliente de Monterrey. Caso de estudio: cañón Santa Rosa, Nuevo León, noreste de México." Revista Mexicana de Ciencias Geológicas 34, no. 3 (November 29, 2017): 182. http://dx.doi.org/10.22201/cgeo.20072902e.2017.3.459.
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We present a landslide inventory for the Santa Rosa Canyon in the Monterrey Salient, between Linares and Iturbide in Nuevo León state. A total of 429 landslides were documented from field investigation, analysis and interpretation of satellite imagery and historical data from Google Earth platform for the last 30 years. Falls, slides and flows are the most typical landslides, mainly related to extraordinary rainfalls from hurricanes and tropical storms. Moreover, an overall increase in seismicity in northeastern Mexico may indicate this activity as the second most important factor triggering slope instabilities. This inventory could be used in detailed landslide risk assessment studies in the region, and the methodology may be extrapolated to neighboring areas with recurrent landslides and a lack of scientific studies.
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Mohd Salleh, Mohd Radhie, Muhammad Zulkarnain Abd Rahman, Zamri Ismail, Mohd Faisal Abdul Khanan, Huey Tam Tze, Ismaila Usman Kaoje, Mohamad Jahidi Osman, and Mohd Asraff Asmadi. "SUPPORT VECTOR MACHINE FOR LANDSLIDE ACTIVITY IDENTIFICATION BASED ON VEGETATION ANOMALIES INDICATOR." Journal of Information System and Technology Management 7, no. 25 (March 7, 2022): 148–58. http://dx.doi.org/10.35631/jistm.725012.
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Landslide activity identification is critical for landslide inventory mapping. A detailed landslide inventory map is highly required for various purposes such as landslide susceptibility, hazard, and risk assessments. This paper proposes a novel approach based on vegetation anomalies indicator (VAI) and applying machine learning method namely support vector machine (SVM) to identify status of natural-terrain landslides. First, high resolution airborne LiDAR data and satellite imagery were used to derive landslide-related VAIs, including tree height irregularities, canopy gap, density of different layer of vegetation, vegetation type, vegetation indices, root strength index (RSI), and distribution of water-loving trees. Then, SVM is utilized with different setting of parameter using grid search optimization. SVM Radial Basis Function (RBF) recorded the best optimal pair value with 0.062 and 0.092 misclassification rate for deep seated and shallow translational landslide, respectively. For landslide activity classification, SVM RBF recorded the best accuracy value for both deep seated and shallow translational landslides with 86.0 and 71.3, respectively. Overall, VAIs have great potential in tackling the landslide activity identification problem especially in tropical vegetated area.
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Qin, Y., P. Lu, and Z. Li. "LANDSLIDE INVENTORY MAPPING FROM BITEMPORAL 10 m SENTINEL-2 IMAGES USING CHANGE DETECTION BASED MARKOV RANDOM FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1447–52. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1447-2018.
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Landslide inventory mapping is essential for hazard assessment and mitigation. In most previous studies, landslide mapping was achieved by visual interpretation of aerial photos and remote sensing images. However, such method is labor-intensive and time-consuming, especially over large areas. Although a number of semi-automatic landslide mapping methods have been proposed over the past few years, limitations remain in terms of their applicability over different study areas and data, and there is large room for improvement in terms of the accuracy and automation degree. For these reasons, we developed a change detection-based Markov Random Field (CDMRF) method for landslide inventory mapping. The proposed method mainly includes two steps: 1) change detection-based multi-threshold for training samples generation and 2) MRF for landslide inventory mapping. Compared with the previous methods, the proposed method in this study has three advantages: 1) it combines multiple image difference techniques with multi-threshold method to generate reliable training samples; 2) it takes the spectral characteristics of landslides into account; and 3) it is highly automatic with little parameter tuning. The proposed method was applied for regional landslides mapping from 10&thinsp;m Sentinel-2 images in Western China. Results corroborated the effectiveness and applicability of the proposed method especially the capability of rapid landslide mapping. Some directions for future research are offered. This study to our knowledge is the first attempt to map landslides from free and medium resolution satellite (i.e., Sentinel-2) images in China.