Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-2929-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-2929-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Sep 2022
Research article |
| 06 Sep 2022
| 06 Sep 2022
Terrain visibility impact on the preparation of landslide inventories: a practical example in Darjeeling district (India)
Txomin Bornaetxea
CORRESPONDING AUTHOR
Euskal Herriko Unibertsitatea (UPV/EHU), Barrio Sarriena s/n, 48940
Leioa, Spain
Ivan Marchesini
CNR-IRPI, via Madonna Alta 126, 06128 Perugia, Italy
Sumit Kumar
Geohazard Research and Management Centre, Geological Survey of India, Kolkata, India
Rabisankar Karmakar
Geohazard Research and Management Centre, Geological Survey of India, Kolkata, India
Alessandro Mondini
CNR-IRPI, via Madonna Alta 126, 06128 Perugia, Italy
Related authors
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Lorenzo Nava, Alessandro Mondini, Kushanav Bhuyan, Chengyong Fang, Oriol Monserrat, Alessandro Novellino, and Filippo Catani
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-230, https://doi.org/10.5194/gmd-2024-230, 2025
Preprint under review for GMD
Short summary
Short summary
This paper presents a new framework for landslide detection using radar and deep learning, informed by data from 73000 landslides across diverse regions in the world. The method showed high accuracy and rapid response potential regardless of weather and illumination conditions. By overcoming the limits of optical satellite imagery, it offers a powerful tool for global landslide detection, benefiting disaster management and advancing methods for monitoring hazardous terrains.
Francesco Bucci, Michele Santangelo, Lorenzo Fongo, Massimiliano Alvioli, Mauro Cardinali, Laura Melelli, and Ivan Marchesini
Earth Syst. Sci. Data, 14, 4129–4151, https://doi.org/10.5194/essd-14-4129-2022, https://doi.org/10.5194/essd-14-4129-2022, 2022
Short summary
Short summary
The paper describes a new lithological map of Italy at a scale of 1 : 100 000 obtained from classification of a digital database following compositional and geomechanical criteria. The map represents the national distribution of the lithological classes at high resolution. The outcomes of this study can be relevant for a wide range of applications, including statistical and physically based modelling of slope stability assessment and other geoenvironmental studies.
Angelica Tarpanelli, Alessandro C. Mondini, and Stefania Camici
Nat. Hazards Earth Syst. Sci., 22, 2473–2489, https://doi.org/10.5194/nhess-22-2473-2022, https://doi.org/10.5194/nhess-22-2473-2022, 2022
Short summary
Short summary
We analysed 10 years of river discharge data from almost 2000 sites in Europe, and we extracted flood events, as proxies of flood inundations, based on the overpasses of Sentinel-1 and Sentinel-2 satellites to derive the percentage of potential inundation events that they were able to observe. Results show that on average 58 % of flood events are potentially observable by Sentinel-1 and only 28 % by Sentinel-2 due to the obstacle of cloud coverage.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Andrea Manconi, Alessandro C. Mondini, and the AlpArray working group
Nat. Hazards Earth Syst. Sci., 22, 1655–1664, https://doi.org/10.5194/nhess-22-1655-2022, https://doi.org/10.5194/nhess-22-1655-2022, 2022
Short summary
Short summary
Information on when, where, and how landslide events occur is the key to building complete catalogues and performing accurate hazard assessments. Here we show a procedure that allows us to benefit from the increased density of seismic sensors installed on ground for earthquake monitoring and from the unprecedented availability of satellite radar data. We show how the procedure works on a recent sequence of landslides that occurred at Piz Cengalo (Swiss Alps) in 2017.
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
Short summary
Short summary
In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
Cited articles
Bera, S., Guru, B., and Ramesh, V.: Evaluation of landslide susceptibility
models: A comparative study on the part of Western Ghat Region, India,
Remote Sens. Appl. Soc. Environ., 13, 39–52,
https://doi.org/10.1016/j.rsase.2018.10.010, 2019.
Bornaetxea, T. and Marchesini, I.: r.survey: a tool for calculating
visibility of variable-size objects based on orientation, Int. J. Geogr.
Inf. Sci., 36,
429–452, https://doi.org/10.1080/13658816.2021.1942476, 2021.
Bornaetxea, T., Rossi, M., Marchesini, I., and Alvioli, M.: Effective surveyed area and its role in statistical landslide susceptibility assessments, Nat. Hazards Earth Syst. Sci., 18, 2455–2469, https://doi.org/10.5194/nhess-18-2455-2018, 2018.
Brenning, A., Schwinn, M., Ruiz-Páez, A. P., and Muenchow, J.: Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province, Nat. Hazards Earth Syst. Sci., 15, 45–57, https://doi.org/10.5194/nhess-15-45-2015, 2015.
Cascini, L.: Applicability of landslide susceptibility and hazard zoning at
different scales, Eng. Geol., 102, 164–177,
https://doi.org/10.1016/j.enggeo.2008.03.016, 2008.
Chaparro-Cordón, J. L., Rodríguez-Castiblanco, E .A., Rangel-Flórez, M. S., García-Delgado, H., and Medina-Bello, E.: Statistical description of some landslide inventories from Colombian Andes: study cases in Mocoa, Villavicencio, Popayán, and Cajamarca, SCG-XIII International Symposium on Landslides 2020, Cartajena, Colombia, 15–19 June 2020, https://doi.org/10.13140/RG.2.2.17237.04327, 2020.
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.-P.,
Fotopoulou, S., Catani, F., van Den Eeckhaut, M., Mavrouli, O., Agliardi,
F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V.,
Hervás, J., and Smith, J. T.: Recommendations for the quantitative
analysis of landslide risk, B. Eng. Geol. Environ., 73, 209–263,
https://doi.org/10.1007/s10064-013-0538-8, 2014.
Domingo-Santos, J. M., de Villarán, R. F., Rapp-Arrarás, Í., and
de Provens, E. C.-P.: The visual exposure in forest and rural landscapes: An
algorithm and a GIS tool, Landscape Urban Plan., 101, 52–58,
https://doi.org/10.1016/j.landurbplan.2010.11.018, 2011.
Donnini, M., Napolitano, E., Salvati, P., Ardizzone, F., Bucci, F.,
Fiorucci, F., Santangelo, M., Cardinali, M., and Guzzetti, F.: Impact of
event landslides on road networks: a statistical analysis of two Italian
case studies, Landslides, 14, 1521–1535,
https://doi.org/10.1007/s10346-017-0829-4, 2017.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W.
Z.: Guidelines for landslide susceptibility, hazard and risk zoning for
land-use planning, Eng. Geol., 102, 99–111,
https://doi.org/10.1016/j.enggeo.2008.03.014, 2008.
Fiorucci, F., Giordan, D., Santangelo, M., Dutto, F., Rossi, M., and Guzzetti, F.: Criteria for the optimal selection of remote sensing optical images to map event landslides, Nat. Hazards Earth Syst. Sci., 18, 405–417, https://doi.org/10.5194/nhess-18-405-2018, 2018.
Fontani, F.: Application of the Fisher's “Horizon Viewshed” to a proposed
power transmission line in Nozzano (Italy), T. GIS, 21,
835–843, https://doi.org/10.1111/tgis.12260, 2017.
Fressard, M., Thiery, Y., and Maquaire, O.: Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France), Nat. Hazards Earth Syst. Sci., 14, 569–588, https://doi.org/10.5194/nhess-14-569-2014, 2014.
Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., and Reichenbach, P.:
Comparing landslide inventory maps, Geomorphology, 94, 268–289,
https://doi.org/10.1016/j.geomorph.2006.09.023, 2008.
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate,
Earth-Sci. Rev., 162, 227–252,
https://doi.org/10.1016/j.earscirev.2016.08.011, 2016.
Ghorbanzadeh, O., Meena, S. R., Blaschke, T., and Aryal, J.: UAV-Based Slope
Failure Detection Using Deep-Learning Convolutional Neural Networks, Remote
Sens., 11, 2046, https://doi.org/10.3390/rs11172046, 2019.
Giordan, D., Hayakawa, Y., Nex, F., Remondino, F., and Tarolli, P.: Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management, Nat. Hazards Earth Syst. Sci., 18, 1079–1096, https://doi.org/10.5194/nhess-18-1079-2018, 2018.
Govierno Vasco: Modelo Digital del Terreno (MDT) remuestreado de 5 m de la Comunidad Autónoma del País Vasco, Año 2016, GEOEUSKADI [data set], https://www.geo.euskadi.eus/modelo-digital-del-terreno-mdt-remuestreado-de-5m-de-la-comunidad-autonoma-del-pais-vasco-ano-2016/webgeo00-dataset/es/ (last access: May 2020), 2016.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide
hazard evaluation: a review of current techniques and their application in a
multi-scale study, Central Italy, Geomorphology, 31, 181–216,
https://doi.org/10.1016/S0169-555X(99)00078-1, 1999.
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., and Galli, M.:
Estimating the quality of landslide susceptibility models, Geomorphology,
81, 166–184, https://doi.org/10.1016/j.geomorph.2006.04.007, 2006.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M.,
and Chang, K.-T.: Landslide inventory maps: New tools for an old problem,
Earth-Sci. Rev., 112, 42–66,
https://doi.org/10.1016/j.earscirev.2012.02.001, 2012.
Hao, L., Rajaneesh A., van Westen, C., Sajinkumar K. S., Martha, T. R., Jaiswal, P., and McAdoo, B. G.: Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis, Earth Syst. Sci. Data, 12, 2899–2918, https://doi.org/10.5194/essd-12-2899-2020, 2020.
Healey, C. G. and Sawant, A. P.: On the limits of resolution and visual
angle in visualization, ACM T. Appl. Percept., 9, 1–21,
https://doi.org/10.1145/2355598.2355603, 2012.
Hussain, G., Singh, Y., Singh, K., and Bhat, G. M.: Landslide susceptibility
mapping along national highway-1 in Jammu and Kashmir State (India), Innov.
Infrastruct. Solut., 4, 59, https://doi.org/10.1007/s41062-019-0245-9, 2019.
Jacobs, L., Kervyn, M., Reichenbach, P., Rossi, M., Marchesini, I., Alvioli,
M., and Dewitte, O.: Regional susceptibility assessments with heterogeneous
landslide information: Slope unit- vs. pixel-based approach, Geomorphology,
356, 107084, https://doi.org/10.1016/j.geomorph.2020.107084, 2020.
Knevels, R., Petschko, H., Proske, H., Leopold, P., Maraun, D., and
Brenning, A.: Event-Based Landslide Modeling in the Styrian Basin, Austria:
Accounting for Time-Varying Rainfall and Land Cover, Geosciences, 10, 217,
https://doi.org/10.3390/geosciences10060217, 2020.
Lee, S., Jang, J., Kim, Y., Cho, N., and Lee, M.-J.: Susceptibility Analysis
of the Mt. Umyeon Landslide Area Using a Physical Slope Model and
Probabilistic Method, Remote Sens., 12, 2663,
https://doi.org/10.3390/rs12162663, 2020.
Lima, P., Steger, S., and Glade, T.: Counteracting flawed landslide data in
statistically based landslide susceptibility modelling for very large areas:
a national-scale assessment for Austria, Landslides, 18, 3531–3546,
https://doi.org/10.1007/s10346-021-01693-7, 2021.
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.:
Landslide inventories and their statistical properties, Earth Surf. Proc.
Land., 29, 687–711, https://doi.org/10.1002/esp.1064, 2004.
Marchesini, I. and Bornaetxea, T.: r.survey.py, Zenodo [code],
https://doi.org/10.5281/zenodo.3993140, 2022.
Martha, T. R., Roy, P., Jain, N., Khanna, K., Mrinalni, K., Kumar, K. V.,
and Rao, P. V. N.: Geospatial landslide inventory of India – an insight into
occurrence and exposure on a national scale, Landslides, 18, 2125–2141,
https://doi.org/10.1007/s10346-021-01645-1, 2021.
McAdoo, B. G., Quak, M., Gnyawali, K. R., Adhikari, B. R., Devkota, S., Rajbhandari, P. L., and Sudmeier-Rieux, K.: Roads and landslides in Nepal: how development affects environmental risk, Nat. Hazards Earth Syst. Sci., 18, 3203–3210, https://doi.org/10.5194/nhess-18-3203-2018, 2018.
Meena, S. R., Mishra, B. K., and Tavakkoli Piralilou, S.: A Hybrid Spatial
Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in
Kullu Valley, Himalayas, Geosciences, 9, 156,
https://doi.org/10.3390/geosciences9040156, 2019.
Melzner, S., Rossi, M., and Guzzetti, F.: Impact of mapping strategies on
rockfall frequency-size distributions, Eng. Geol., 272, 105639,
https://doi.org/10.1016/j.enggeo.2020.105639, 2020.
Meneses, B. M., Pereira, S., and Reis, E.: Effects of different land use and land cover data on the landslide susceptibility zonation of road networks, Nat. Hazards Earth Syst. Sci., 19, 471–487, https://doi.org/10.5194/nhess-19-471-2019, 2019.
Mondini, A. C., Viero, A., Cavalli, M., Marchi, L., Herrera, G., and Guzzetti, F.: Comparison of event landslide inventories: the Pogliaschina catchment test case, Italy, Nat. Hazards Earth Syst. Sci., 14, 1749–1759, https://doi.org/10.5194/nhess-14-1749-2014, 2014.
Nicu, I. C., Lombardo, L., and Rubensdotter, L.: Preliminary assessment of
thaw slump hazard to Arctic cultural heritage in Nordenskiöld Land,
Svalbard, Landslides, 18, 2935–2947,
https://doi.org/10.1007/s10346-021-01684-8, 2021.
Park, J.-Y., Lee, S.-R., Lee, D.-H., Kim, Y.-T., and Lee, J.-S.: A
regional-scale landslide early warning methodology applying statistical and
physically based approaches in sequence, Eng. Geol., 260, 105193,
https://doi.org/10.1016/j.enggeo.2019.105193, 2019.
Piacentini, D., Troiani, F., Daniele, G., and Pizziolo, M.: Historical
geospatial database for landslide analysis: the Catalogue of Landslide
OCcurrences in the Emilia-Romagna Region (CLOCkER), Landslides, 15,
811–822, https://doi.org/10.1007/s10346-018-0962-8, 2018.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A
review of statistically-based landslide susceptibility models, Earth-Sci.
Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Roberts, S., Jones, J. N., and Boulton, S. J.: Characteristics of landslide
path dependency revealed through multiple resolution landslide inventories
in the Nepal Himalaya, Geomorphology, 390, 107868,
https://doi.org/10.1016/j.geomorph.2021.107868, 2021.
Rohan, T. and Shelef, E.: Analysis of 311 based Landslide Inventories for Landslide Susceptibility Mapping, AGU Fall Meeting 2019, Abstract 33, 9–13 Decembre 2019.
Santangelo, M., Marchesini, I., Bucci, F., Cardinali, M., Fiorucci, F., and Guzzetti, F.: An approach to reduce mapping errors in the production of landslide inventory maps, Nat. Hazards Earth Syst. Sci., 15, 2111–2126, https://doi.org/10.5194/nhess-15-2111-2015, 2015.
Sidle, R. C. and Ziegler, A. D.: The dilemma of mountain roads, Nat.
Geosci., 5, 437–438, https://doi.org/10.1038/ngeo1512, 2012.
Sidle, R. C., Ghestem, M., and Stokes, A.: Epic landslide erosion from mountain roads in Yunnan, China – challenges for sustainable development, Nat. Hazards Earth Syst. Sci., 14, 3093–3104, https://doi.org/10.5194/nhess-14-3093-2014, 2014.
Stark, C. P. and Hovius, N.: The characterization of landslide size
distributions, Geophys. Res. Lett., 28, 1091–1094,
https://doi.org/10.1029/2000GL008527, 2001.
Steger, S., Brenning, A., Bell, R., Petschko, H., and Glade, T.: Exploring
discrepancies between quantitative validation results and the geomorphic
plausibility of statistical landslide susceptibility maps, Geomorphology,
262, 8–23, https://doi.org/10.1016/j.geomorph.2016.03.015, 2016a.
Steger, S., Brenning, A., Bell, R., and Glade, T.: The propagation of inventory-based positional errors into statistical landslide susceptibility models, Nat. Hazards Earth Syst. Sci., 16, 2729–2745, https://doi.org/10.5194/nhess-16-2729-2016, 2016b.
Steger, S., Brenning, A., Bell, R., and Glade, T.: The influence of
systematically incomplete shallow landslide inventories on statistical
susceptibility models and suggestions for improvements, Landslides, 14,
1767–1781, https://doi.org/10.1007/s10346-017-0820-0, 2017.
Steger, S., Mair, V., Kofler, C., Pittore, M., Zebisch, M., and
Schneiderbauer, S.: Correlation does not imply geomorphic causation in
data-driven landslide susceptibility modelling – Benefits of exploring
landslide data collection effects, Sci. Total Environ., 776, 145935,
https://doi.org/10.1016/j.scitotenv.2021.145935, 2021.
Tanyaş, H. and Lombardo, L.: Completeness Index for Earthquake-Induced
Landslide Inventories, Eng. Geol., 264, 105331,
https://doi.org/10.1016/j.enggeo.2019.105331, 2020.
Tanyaş, H., Westen, C. J. van, Allstadt, K. E., and Jibson, R. W.:
Factors controlling landslide frequency–area distributions, Earth Surf.
Proc. Land., 44, 900–917, https://doi.org/10.1002/esp.4543, 2019.
Tanyaş, H., Görüm, T., Kirschbaum, D., and Lombardo, L.: Could
road constructions be more hazardous than an earthquake in terms of mass
movement?, Natural Hazards, 112, 639–663,
https://doi.org/10.1007/s11069-021-05199-2, 2022.
Taylor, F. E., Tarolli, P., and Malamud, B. D.: Preface: Landslide–transport network interactions, Nat. Hazards Earth Syst. Sci., 20, 2585–2590, https://doi.org/10.5194/nhess-20-2585-2020, 2020.
Tekin, S.: Completeness of landslide inventory and landslide susceptibility
mapping using logistic regression method in Ceyhan Watershed (southern
Turkey), Arab. J. Geosci., 14, 1706,
https://doi.org/10.1007/s12517-021-07583-5, 2021.
Trigila, A., Iadanza, C., and Spizzichino, D.: Quality assessment of the
Italian Landslide Inventory using GIS processing, Landslides, 7, 455–470,
2010.
Ubaidulloev, A., Kaiheng, H., Rustamov, M., and Kurbanova, M.: Landslide
Inventory along a National Highway Corridor in the Hissar-Allay Mountains,
Central Tajikistan, GeoHazards, 2, 212–227,
https://doi.org/10.3390/geohazards2030012, 2021.
van Den Eeckhaut, M. and Hervás, J.: State of the art of national
landslide databases in Europe and their potential for assessing landslide
susceptibility, hazard and risk, Geomorphology, 139–140, 545–558,
https://doi.org/10.1016/j.geomorph.2011.12.006, 2012.
van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for
landslide susceptibility, hazard, and vulnerability assessment: An overview,
Eng. Geol., 102, 112–131, https://doi.org/10.1016/j.enggeo.2008.03.010,
2008.
Voumard, J., Derron, M.-H., and Jaboyedoff, M.: Natural hazard events affecting transportation networks in Switzerland from 2012 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2093–2109, https://doi.org/10.5194/nhess-18-2093-2018, 2018.
Zhang, T., Han, L., Han, J., Li, X., Zhang, H., and Wang, H.: Assessment of
Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with
Kernel Logistic Regression Model, Entropy, 21, 218,
https://doi.org/10.3390/e21020218, 2019.
Short summary
One cannot know if there is a landslide or not in an area that one has not observed. This is an obvious statement, but when landslide inventories are obtained by field observation, this fact is seldom taken into account. Since fieldwork campaigns are often done following the roads, we present a methodology to estimate the visibility of the terrain from the roads, and we demonstrate that fieldwork-based inventories are underestimating landslide density in less visible areas.
One cannot know if there is a landslide or not in an area that one has not observed. This is an...
Altmetrics
Final-revised paper
Preprint