Articles | Volume 26, issue 3
https://doi.org/10.5194/nhess-26-1375-2026
© Author(s) 2026. 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-26-1375-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Topographic profile and morphology analysis of shallow landslides inside and outside of forests with a semi-automatic mapping approach and bi-temporal airborne laser scanning data
Department of Geography, University of Innsbruck, Innsbruck, Austria
Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Innsbruck, Austria
Thomas Zieher
Department of Natural Hazards, Austrian Research Centre for Forests (BFW), Innsbruck, Austria
Barbara Schneider-Muntau
Department of Infrastructure, University of Innsbruck, Innsbruck, Austria
Frank Perzl
Department of Natural Hazards, Austrian Research Centre for Forests (BFW), Innsbruck, Austria
Marc Adams
Department of Natural Hazards, Austrian Research Centre for Forests (BFW), Innsbruck, Austria
Martin Rutzinger
Department of Geography, University of Innsbruck, Innsbruck, Austria
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Cited articles
Adams, M. S., Fromm, R., and Lechner, V.: High-resolution debris flow volume mapping with unmanned aerial systems (UAS) and photogrammetric techniques, Int. Arch. Photogramm., XLI-B1, 749–755, https://doi.org/10.5194/isprsarchives-XLI-B1-749-2016, 2016.
Adams, R. and Bischof, L.: Seeded region growing, IEEE T. Pattern Anal., 16, 641–647, https://doi.org/10.1109/34.295913, 1994.
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA, 2623–2631, https://doi.org/10.1145/3292500.3330701, 2019.
Ardizzone, F., Cardinali, M., Galli, M., Guzzetti, F., and Reichenbach, P.: Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar, Nat. Hazards Earth Syst. Sci., 7, 637–650, https://doi.org/10.5194/nhess-7-637-2007, 2007.
Bechtel, B., Ringeler, A., and Böhner, J.: Segmentation for Object Extraction of Trees using MATLAB and SAGA, in: SAGA – Seconds Out, edited by: Böhner, J., Blaschke, T., and Montanarella, L., ISSN 1866-170X, 2008.
Brardinoni, F. and Church, M.: Representing the landslide magnitude–frequency relation: Capilano River basin, British Columbia, Earth Surf. Proc. Land., 29, 115–124, https://doi.org/10.1002/esp.1029, 2004.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Cardinali, M., Galli, M., Guzzetti, F., Ardizzone, F., Reichenbach, P., and Bartoccini, P.: Rainfall induced landslides in December 2004 in south-western Umbria, central Italy: types, extent, damage and risk assessment, Nat. Hazards Earth Syst. Sci., 6, 237–260, https://doi.org/10.5194/nhess-6-237-2006, 2006.
Cohen, D. and Schwarz, M.: Tree-root control of shallow landslides, Earth Surf. Dynam., 5, 451–477, https://doi.org/10.5194/esurf-5-451-2017, 2017.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Copernicus Land Monitoring Service: European Digital Elevation Model (EU-DEM), European Environment Agency, https://sdi.eea.europa.eu/catalogue/srv/api/records/d08852bc-7b5f-4835-a776-08362e2fbf4b (last access: 12 March 2026), 2016.
de Vugt, L., Zieher, T., Schneider-Muntau, B., Moreno, M., Steger, S., and Rutzinger, M.: Spatial transferability of the physically based model TRIGRS using parameter ensembles, Earth Surf. Proc. Land., 49, 1330–1347, https://doi.org/10.1002/esp.5770, 2024.
de Vugt, L., Zieher, T., Schneider-Muntau, B., Adams, M., Perzl, F., and Rutzinger, M.: SLUF and LEAF project datasets and code, Zenodo [data set], https://doi.org/10.5281/zenodo.18804682, 2026.
Emberson, R., Kirschbaum, D. B., Amatya, P., Tanyas, H., and Marc, O.: Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories, Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, 2022.
Eysn, L., Hollaus, M., Schadauer, K., and Pfeifer, N.: Forest Delineation Based on Airborne LIDAR Data, Remote Sens.-Basel, 4, 762–783, https://doi.org/10.3390/rs4030762, 2012.
Fiorucci, F., Cardinali, M., Carlà, R., Rossi, M., Mondini, A. C., Santurri, L., Ardizzone, F., and Guzzetti, F.: Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images, Geomorphology, 129, 59–70, https://doi.org/10.1016/j.geomorph.2011.01.013, 2011.
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.
GeoSphere Austria: INCA hourly data [data set], https://doi.org/10.60669/6akt-5p05, 2015.
GeoSphere Austria: Geodaten zu GEOFAST – Blatt 147 Axams (1:50.000), Tethys RDR, Geologische Bundesanstalt (GBA) [data set], https://doi.org/10.24341/tethys.141, 2021.
Gonzalez-Ollauri, A. and Mickovski, S. B.: Hydrological effect of vegetation against rainfall-induced landslides, J. Hydrol., 549, 374–387, https://doi.org/10.1016/j.jhydrol.2017.04.014, 2017.
GRASS Development Team: Geographic Resources Analysis Support System (GRASS GIS) Software, Version 8.4, Open Source Geospatial Foundation, USA, https://doi.org/10.5281/zenodo.5176030, 2024.
Greco, R., Marino, P., and Bogaard, T. A.: Recent advancements of landslide hydrology, WIREs Water, 10, e1675, https://doi.org/10.1002/wat2.1675, 2023.
Guzzetti, F.: Invited perspectives: Landslide populations – can they be predicted?, Nat. Hazards Earth Syst. Sci., 21, 1467–1471, https://doi.org/10.5194/nhess-21-1467-2021, 2021.
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.
Jenner, A.: Ereignisdokumentation Seigesbach nach dem Hochwasserereignis in Sellrain/ Tirol am 7.6.2015, in: Tagungsband 17. Geoforum Umhausen, 17. Geoforum Umhausen, 101–110, https://atnastablobgeoforumarc01.blob.core.windows.net/geoforumarchive001/Tagungsband 17 Geoforum Umhausen 2015.pdf (last access: 12 March 2026), 2015.
Koyanagi, K., Gomi, T., and Sidle, R. C.: Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake, Earth Surf. Proc. Land., 45, 893–904, https://doi.org/10.1002/esp.4781, 2020.
Lagger, M.: Sellraintal Juni 2015: Hangexplosionen und Muren – Eine Bestandsaufnahme, in: Tagungsband 17. Geoforum Umhausen, 17. Geoforum Umhausen, 4–11, 2015.
Lague, D., Brodu, N., and Leroux, J.: Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z), ISPRS J. Photogramm., 82, 10–26, https://doi.org/10.1016/j.isprsjprs.2013.04.009, 2013.
Land Tirol: Waldtypisierung Tirol – Waldstypen, https://maps.tirol.gv.at (last access: 25 April 2025), 2014.
Land Tirol: Gewässernetz Tirol, https://maps.tirol.gv.at, (last access: 25 April 2025), 2023.
Land Tirol: Laser- und Luftbildatlas Tirol, https://lba.tirol.gv.at, last access: 3 November 2025.
Li, P., Li, D., Hu, J., Fassnacht, F. E., Latifi, H., Yao, W., Gao, J., Chan, F. K. S., Dang, T., and Tang, F.: Improving the application of UAV-LiDAR for erosion monitoring through accounting for uncertainty in DEM of difference, CATENA, 234, 107534, https://doi.org/10.1016/j.catena.2023.107534, 2024.
LIS Pro 3D: Point cloud processing software. Version 2024.03 [online], https://lispro3d.com, last access: 3 July 2025.
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.
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.
Moos, C., Bebi, P., Graf, F., Mattli, J., Rickli, C., and Schwarz, M.: How does forest structure affect root reinforcement and susceptibility to shallow landslides?, Earth Surf. Proc. Land., 41, 951–960, https://doi.org/10.1002/esp.3887, 2016.
Moreno, M., Lombardo, L., Steger, S., de Vugt, L., Zieher, T., Crespi, A., Marra, F., van Westen, C., and Opitz, T.: Functional Regression for Space-Time Prediction of Precipitation-Induced Shallow Landslides in South Tyrol, Italy, J. Geophys. Res.-Earth, 130, e2024JF008219, https://doi.org/10.1029/2024JF008219, 2025.
Moser, M.: GEOFAST – Zusammenstellung ausgewählter Archivunterlagen der Geologischen Bundesanstalt 1:50.000 – 147 Axams (1:50.000), Geologische Bundesanstalt, GEOFAST 147, 2011.
ÖVDAT: Intermodales Verkehrsreferenzsystem Österreich (GIP.at), https://www.gip.gv.at (last access: 25 April 2025), 2022.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Petschko, H., Bell, R., and Glade, T.: Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling, Landslides, 13, 857–872, https://doi.org/10.1007/s10346-015-0622-1, 2016.
Preti, F.: Forest protection and protection forest: Tree root degradation over hydrological shallow landslides triggering, Ecol. Eng., 61, 633–645, https://doi.org/10.1016/j.ecoleng.2012.11.009, 2013.
Razak, K. A., Straatsma, M. W., van Westen, C. J., Malet, J.-P., and de Jong, S. M.: Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization, Geomorphology, 126, 186–200, https://doi.org/10.1016/j.geomorph.2010.11.003, 2011.
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.
Rickli, C. and Graf, F.: Effects of forests on shallow landslides – case studies in Switzerland, For. Snow Landsc. Res., 82, 33–44, 2009.
Roering, J. J., Schmidt, K. M., Stock, J. D., Dietrich, W. E., and Montgomery, D. R.: Shallow landsliding, root reinforcement, and the spatial distribution of trees in the Oregon Coast Range, Can. Geotech. J., 40, 237–253, https://doi.org/10.1139/t02-113, 2003.
Schaller, C., Dorren, L., Schwarz, M., Moos, C., Seijmonsbergen, A. C., and van Loon, E. E.: Predicting the thickness of shallow landslides in Switzerland using machine learning, Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025, 2025.
Schmaltz, E. M., Steger, S., and Glade, T.: The influence of forest cover on landslide occurrence explored with spatio-temporal information, Geomorphology, 290, 250–264, https://doi.org/10.1016/j.geomorph.2017.04.024, 2017.
Schmidt, K. M., Roering, J. J., Stock, J. D., Dietrich, W. E., Montgomery, D. R., and Schaub, T.: The variability of root cohesion as an influence on shallow landslide susceptibility in the Oregon Coast Range, Can. Geotech. J., 38, 995–1024, https://doi.org/10.1139/t01-031, 2001.
Schwarz, M., Cohen, D., and Or, D.: Root-soil mechanical interactions during pullout and failure of root bundles, J. Geophys. Res.-Earth, 115, https://doi.org/10.1029/2009JF001603, 2010.
Seabold, S. and Perktold, J.: statsmodels: Econometric and statistical modeling with python, in: Proceedings of the 9th Python in Science Conference, SciPy 2010, Austin, United States, 28 June–3 July 2010, 92–96, https://doi.org/10.25080/Majora-92bf1922-011, 2010.
Taylor, F. E., Malamud, B. D., Witt, A., and Guzzetti, F.: Landslide shape, ellipticity and length-to-width ratios, Earth Surf. Proc. Land., 43, 3164–3189, https://doi.org/10.1002/esp.4479, 2018.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and van Mulbregt, P.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
Wehr, A. and Lohr, U.: Airborne laser scanning – an introduction and overview, ISPRS J. Photogramm., 54, 68–82, https://doi.org/10.1016/S0924-2716(99)00011-8, 1999.
Welch, B. L.: On the Comparison of Several Mean Values: An Alternative Approach, Biometrika, 38, 330–336, https://doi.org/10.2307/2332579, 1951.
Zieher, T., Perzl, F., Rössel, M., Rutzinger, M., Meißl, G., Markart, G., and Geitner, C.: A multi-annual landslide inventory for the assessment of shallow landslide susceptibility – Two test cases in Vorarlberg, Austria, Geomorphology, 259, 40–54, https://doi.org/10.1016/j.geomorph.2016.02.008, 2016.
Short summary
We performed an analysis on semi-automatically mapped shallow landslide scarps and forest cover in the Sellrain valley, Tyrol (Austria), to investigate how the morphology and topographic profiles of landslides are affected by the forest. The results show that landslides located in dense forest cover occurred on steeper slopes and were deeper than others. The results also show that the use of forest stand density parameters, such as tree spacing, enhanced the found differences in the study area.
We performed an analysis on semi-automatically mapped shallow landslide scarps and forest cover...
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