Abstract
Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abellan A, Derron MH, Jaboyedoff M (2016) Use of 3D points clouds in geohazards. Special issue: current challenges and future trends. Remote Sens 8(2):130. https://doi.org/10.3390/rs8020130
Achour Y, Pourghasemi HR (2019) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front 11(3):871–883. https://doi.org/10.1016/j.gsf.2019.10.001
Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Anders NS, Seijmonsbergen AC, Bouten W (2011) Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens Environ 115(12):2976–2985. https://doi.org/10.1016/j.rse.2011.05.007
Arabameri A, Pradhan B, Rezaei K, Lee CW (2019) Assessment of landslide susceptibility using statistical-and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sens 11(9):999. https://doi.org/10.3390/rs11090999
Ardizzone F, Cardinali M, Galli M, Guzzetti F, Reichenbach P (2007) Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar. Nat Hazards Earth Syst Sci 7(6):637–650
Ayoub F, Leprince S, Avouac JP (2009) Co-registration and correlation of aerial photographs for ground deformation measurements. ISPRS J Photogramm Remote Sens 64(6):551–560. https://doi.org/10.1016/j.isprsjprs.2009.03.005
Babkina EA, Leibman MO, Dvornikov YA, Fakashchuk NY, Khairullin RR, Khomutov AV (2019) Activation of cryogenic processes in Central Yamal as a result of regional and local change in climate and thermal state of permafrost. Russ Meteorol Hydrol 44(4):283–290. https://doi.org/10.3103/S1068373919040083
Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora river basin case study, Italy. Math Geosci 44:47–70. https://doi.org/10.1007/s11004-011-9379-9
Bamler R, Hartl P (1998) Synthetic aperture radar interferometry. Inverse Prob 14(4):R1
Barbarella M, Fiani M, Lugli A (2017) Uncertainty in terrestrial laser scanner surveys of landslides. Remote Sens 9(2):113. https://doi.org/10.3390/rs9020113
Barboux C, Delaloye R, Lambiel C, Strozzi T, Collet C, Raetzo H (2013) Surveying the activity of landslides and rock glaciers above the tree line with InSAR. In: Graf C (ed) Mattertal-ein Tal in Bewegung. Jahrestagung der Schweizerischen Geomorphologischen Gesellschaft 29. Juni–1. Juli 2011, St. Niklaus, Birmensdorf, Eidg. Forschungsanstalt WSL, pp 7–19
Barboux C, Delaloye R, Lambiel C (2014) Inventorying slope movements in an Alpine environment using DInSAR. Earth Surf Proc Land 39(15):2087–2099. https://doi.org/10.1002/esp.3603
Barla G, Antolini F, Barla M, Mensi E, Piovano G (2010) Monitoring of the Beauregard landslide (Aosta Valley, Italy) using advanced and conventional techniques. Eng Geol 116(3–4):218–235. https://doi.org/10.1016/j.enggeo.2010.09.004
Bartsch A, Höfler A, Kroisleitner C, Trofaier AM (2016) Land cover mapping in northern high latitude permafrost regions with satellite data: achievements and remaining challenges. Remote Sens 8:979. https://doi.org/10.3390/rs8120979
Bartsch A, Leibman M, Strozzi T, Khomutov A, Widhalm B, Babkina E et al (2019) Seasonal progression of ground displacement identified with satellite radar interferometry and the impact of unusually warm conditions on permafrost at the Yamal Peninsula in 2016. Remote Sens 11(16):1865. https://doi.org/10.3390/rs11161865
Behling R, Roessner S, Kaufmann H, Kleinschmit B (2014) Automated spatiotemporal landslide mapping over large areas using rapideye time series data. Remote Sens 6(9):8026–8055. https://doi.org/10.3390/rs6098026
Bell R, Petschko H, Röhrs M, Dix A (2012) Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models. Geografiska Annaler Ser A Phys Geogr 94(1):135–156. https://doi.org/10.1111/j.1468-0459.2012.00454.x
Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens 40(11):2375–2383. https://doi.org/10.1109/TGRS.2002.803792
Biass S, Orr TR, Houghton BF, Ratrick MR, James M, Turner N (2019) Insights into Pāhoehoe lava emplacement using visible and thermal structure-from-motion photogrammetry. J Geophys Res Solid Earth 124(6):5678–5695. https://doi.org/10.1029/2019JB017444
Bitelli G, Dubbini M, Zanutta A (2004) Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies. Int Arch Photogramm Remote Sens Spatial Inf Sci 35(B5):246–251
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Brun F, Buri P, Miles E, Wagnon P, Steiner J, Berthier E, Ragettli S, Kraaijenbrink P, Immerzeel W, Pellicciotti F (2016) Quantifying volume loss from ice cliffs on debris-covered glaciers using high-resolution terrestrial and aerial photogrammetry. J Glaciol 62(234):684–695. https://doi.org/10.1017/jog.2016.54
Brunsden D (1993) Mass movement; the research frontier and beyond: a geomorphological approach. Geomorphology 7(1–3):85–128
Bui DT, Hoang ND, Nguyen H, Tran XL (2019) Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: a case study in Lang Son Province, Vietnam. Adv Eng Inf 42:100978. https://doi.org/10.1016/j.aei.2019.100978
Bui DT, Tsangaratos P, Nguyen VT, Van Liem N, Trinh PT (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188:104426. https://doi.org/10.1016/j.catena.2019.104426
Bunn MD, Leshchinsky BA, Olsen MJ, Booth A (2019) A simplified, object-based framework for efficient landslide inventorying using LIDAR digital elevation model derivatives. Remote Sens 11(3):303. https://doi.org/10.3390/rs11030303
Burns WJ, Madin I (2009) Protocol for inventory mapping of landslide deposits from light detection and ranging (LiDAR) imagery. Oregon Department of Geology and Mineral Industries, Portland, pp 1–30
Burns WJ, Duplantis S, Jones CB, English JT (2012) Lidar data and landslide inventory maps of the North Fork Siuslaw River and Big Elk Creek watersheds. Lane, Lincoln, and Benton Counties, Oregon
Carlà T, Tofani V, Lombardi L, Raspini F, Bianchini S, Bertolo D et al (2019) Combination of GNSS, satellite InSAR, and GBInSAR remote sensing monitoring to improve the understanding of a large landslide in high alpine environment. Geomorphology 335:62–75. https://doi.org/10.1016/j.geomorph.2019.03.014
Carr BB, Clarke BA, Arrowsmith JR, Vanderkluysen L, Eko Dhanu B (2018) The emplacement of the active lava flow at Sinabung Volcano, Sumatra, Indonesia, documented by structure-from-motion photogrammetry. J Volcanol Geoth Res 382:164–172. https://doi.org/10.1016/j.jvolgeores.2018.02.004
Casagli N, Cigna F, Bianchini S, Hölbling D, Füreder P, Righini G et al (2016) Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER. Remote Sens Appl Soc Environ 4:92–108. https://doi.org/10.1016/j.rsase.2016.07.001
Casagli N, Frodella W, Morelli S, Tofani V, Ciampalini A, Intrieri E et al (2017) Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning. Geoenviron Disasters 4(9):1–23. https://doi.org/10.1186/s40677-017-0073-1
Casson B, Baratoux D, Delacourt D, Allemand P (2003) Seventeen years of the “La Clapière” landslide evolution analysed from ortho-rectified aerial photographs. Eng Geol 68(1–2):123–139
Casson B, Delacourt C, Allemand P (2005) Contribution of multi-temporal remote sensing images to characterize landslide slip surface? Application to the La Clapière landslide (France). NHESS 5:425–437
Catani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2(4):329–342. https://doi.org/10.1007/s10346-005-0021-0
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Exploring model sensitivity issues across different scales in landslide susceptibility. NHESD 1(2):583–623
Chanut MA, Kasperski J, Dubois L, Dauphin S, Duranthon JP (2017) Quantification des déplacements 3D par la méthode PLaS: application au glissement du Chambon (Isère). Rev Fr Géotech 150(4):1–14. https://doi.org/10.1051/geotech/2017009
Chen W, Li X, Wang Y, Chen G, Liu S (2014) Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the Three Gorges, China. Remote Sens Environ 152:291–301. https://doi.org/10.1016/j.rse.2014.07.004
Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017) Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327. https://doi.org/10.1016/j.geoderma.2017.06.020
Chigira M, Duan F, Yagi H, Furuya T (2004) Using an airborne laser scanner for the identification of shallow landslides and susceptibility assessment in an area of ignimbrite overlain by permeable pyroclastics. Landslides 1(3):203–209. https://doi.org/10.1007/s10346-004-0029-x
Chigira M, Wu X, Inokuchi T, Wang G (2010) Landslides induced by the 2008 Wenchuan earthquake, Sichuan, China. Geomorphology 118(3–4):225–238. https://doi.org/10.1016/j.geomorph.2010.01.003
Chunhui Z, Bing G, Lejun Z, Xiaoqing W (2018) Classification of Hyperspectral Imagery based on spectral gradient, SVM and spatial random forest. Infrared Phys Technol 95:61–69. https://doi.org/10.1016/j.infrared.2018.10.012
Ciampalini A, Raspini F, Bianchini S, Frodella W, Bardi F, Lagomarsino D et al (2015) Remote sensing as tool for development of landslide databases: the case of the Messina Province (Italy) geodatabase. Geomorphology 249:103–118. https://doi.org/10.1016/j.geomorph.2015.01.029
Colesanti C, Wasowski J (2006) Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Eng Geol 88(3–4):173–199. https://doi.org/10.1016/j.enggeo.2006.09.013
Comert R, Avdan U, Gorum T, Nefeslioglu HA (2019) Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data. Eng Geol 260:105264. https://doi.org/10.1016/j.enggeo.2019.105264
Conner JC, Olsen MJ (2014) Automated quantification of distributed landslide movement using circular tree trunks extracted from terrestrial laser scan data. Comput Geosci 67:31–39. https://doi.org/10.1016/j.cageo.2014.02.007
Corsini A, Farina P, Antonello G, Barbieri M, Casagli N, Coren F et al (2006) Space-borne and ground-based SAR interferometry as tools for landslide hazard management in civil protection. Int J Remote Sens 27(12):2351–2369. https://doi.org/10.1080/01431160600554405
Corsini A, Berti M, Monni A, Pizziolo M, Bonacini F, Cervi F et al (2013) Rapid assessment of landslide activity in Emilia Romagna using GB-InSAR short surveys. Landslide Sci Pract. https://doi.org/10.1007/978-3-642-31445-2_51
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–290
Costa S, Maquaire O, Letortu P, Thirard G, Compain V, Roulland T et al (2019) Sedimentary Coastal cliffs of Normandy: modalities and quantification of retreat. J Coast Res 88(SI):46–60. https://doi.org/10.2112/SI88-005.1
Crippen RE (1992) Measurement of subresolution terrain displacements using SPOT panchromatic imagery. Report 15(1):56–61
Crosetto M, Monserrat O, Luzi G, Cuevas-González M, Devanthéry N (2014) A non interferometric procedure for deformation measurement using GB-SAR imagery. IEEE Geosci Remote Sens Lett 11(1):34–38. https://doi.org/10.1109/LGRS.2013.2245098
Crosetto M, Monserrat O, Cuevas-González M, Devanthéry N, Crippa B (2016) Persistent scatterer interferometry: a review. ISPRS J Photogramm Remote Sens 115:78–89. https://doi.org/10.1016/j.isprsjprs.2015.10.011
Dai K, Li Z, Tomás R, Liu G, Yu B, Wang X, Cheng H, Chen J, Stockamp J (2016) Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry. Remote Sens Environ 186:501–513. https://doi.org/10.1016/j.rse.2016.09.009
De Michele M, Briole P (2007) Deformation between 1989 and 1997 at Piton de la Fournaise volcano retrieved from correlation of panchromatic airborne images. Geophys J Int 169(1):357–364. https://doi.org/10.1111/j.1365-246X.2006.03307.x
De Michele M, Raucoules D, De Sigoyer J, Pubellier M, Chamot-Rooke N (2010) Three-dimensional surface displacement of the 2008 May 12 Sichuan earthquake (China) derived from Synthetic Aperture Radar: evidence for rupture on a blind thrust. Geophys J Int 183(3):1097–1103. https://doi.org/10.1111/j.1365-246X.2010.04807.x
Debella-Gilo M, Kääb A (2011) Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation. Remote Sens Environ 115(1):130–142. https://doi.org/10.1016/j.rse.2010.08.012
Delacourt C, Allemand P, Casson B, Vadon H (2004) Velocity field of the “La Clapière” landslide measured by the correlation of aerial and QuickBird satellite images. Geophys Res Lett 31(15):1–5. https://doi.org/10.1029/2004GL020193
Delacourt C, Allemand P, Berthier E, Raucoules D, Casson B, Grandjean P, Pambrun C, Varel E (2007) Remote-sensing techniques for analysing landslide kinematics: a review. Bulletin de la Société Géologique de France 178(2):89–100. https://doi.org/10.2113/gssgfbull.178.2.89
Delacourt C, Raucoules D, Le Mouélic S, Carnec C, Feurer D, Allemand P, Cruchet M (2009) Observation of a large landslide on La Reunion Island using differential SAR interferometry (JERS and Radarsat) and correlation of optical (Spot5 and Aerial) images. Sensors 9(1):616–630. https://doi.org/10.3390/s90100616
Di Stefano C, Palmeri V, Pampalone V (2019) An automatic approach for rill network extraction to measure rill erosion by terrestrial and low-cost unmanned aerial vehicle photogrammetry. Hydrol Process 33(13):1883–1895. https://doi.org/10.1002/hyp.13444
Du J, Glade T, Woldai T, Chai B, Zeng B (2020) Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Eng Geol. https://doi.org/10.1016/j.enggeo.2020.105572
Echelard T, Krysiecki JM, Gay M, Schoeneich P (2013) Détection des mouvements de glaciers rocheux dans les Alpes françaises par interférométrie radar différentielle (D-InSAR) dérivée des archives satellitaires ERS (European Remote Sensing). Géomorphologie Relief Processus Environnement 19(3):231–242. https://doi.org/10.4000/geomorphologie.10264
Fan X, Xu Q, Scaringi G, Dai L, Li W, Dong X, Zhu X, Pei X, Dai K, Havenith HB (2017) Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China. Landslides 14(6):2129–2146. https://doi.org/10.1007/s10346-017-0907-7
Fan X, Domènech G, Scaringi G, Huang R, Xu Q, Hales TC et al (2018) Spatio-temporal evolution of mass wasting after the 2008 Mw 7.9 Wenchuan earthquake revealed by a detailed multi-temporal inventory. Landslides 15(12):2325–2341. https://doi.org/10.1007/s10346-018-1054-5
Fang Z, Wang Y, Peng L, Hong H (2020) Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Comput Geosci. https://doi.org/10.1016/j.cageo.2020.104470
Farahmand A, AghaKouchak A (2013) A satellite-based global landslide model. Nat Hazards Earth Syst Sci 13(5):1259–1267. https://escholarship.org/uc/item/0p905918
Fernández T, Pérez J, Cardenal J, Gómez J, Colomo C, Delgado J (2016) Analysis of landslide evolution affecting olive groves using UAV and photogrammetric techniques. Remote Sens 8(10):837. https://doi.org/10.3390/rs8100837
Francioni M, Coggan J, Eyre M, Stead D (2018) A combined field/remote sensing approach for characterizing landslide risk in coastal areas. Int J Appl Earth Obs Geoinf 67:79–95. https://doi.org/10.1016/j.jag.2017.12.016
French HM, Williams P (2017) The periglacial environment, 4th edn. Longman, London. https://doi.org/10.1002/9781119132820
Fressard M, Maquaire O, Thiery Y, Davidson R, Lissak C (2016) Multi-method characterisation of an active landslide: case study in the Pays d’Auge plateau (Normandy, France). Geomorphology 270:22–39. https://doi.org/10.1016/j.geomorph.2016.07.001
Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. Eur Conf Comput Learn Theory. https://doi.org/10.1007/3-540-59119-2_166
Galli M, Ardizzone F, Cardinali M, Guzzetti F, Reichenbach P (2008) Comparing landslide inventory maps. Geomorphology 94(3–4):268–289. https://doi.org/10.1016/j.geomorph.2006.09.023
Gance J, Malet JP, Dewez T, Travelletti J (2014) Target Detection and Tracking of moving objects for characterizing landslide displacements from time-lapse terrestrial optical images. Eng Geol 172:26–40. https://doi.org/10.1016/j.enggeo.2014.01.003
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens 11(2):196. https://doi.org/10.3390/rs11020196
Gilham J, Barlow J, Moore R (2019) Detection and analysis of mass wasting events in chalk sea cliffs using UAV photogrammetry. Eng Geol 250:101–112. https://doi.org/10.1016/j.enggeo.2019.01.013
Girshick R (2015) Fast r-cnn. Proc IEEE Int Conf Comput Vis. https://doi.org/10.1109/ICCV.2015.169
Gomez C, Kennedy B (2018) Capturing volcanic plumes in 3D with UAV-based photogrammetry at Yasur Volcano-Vanuatu. J Volcanol Geoth Res 350:84–88. https://doi.org/10.1016/j.jvolgeores.2017.12.007
Gong M, Zhao J, Liu J, Miao Q, Jiao L (2015) Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans Neural Netw Learn Syst 27(1):125–138. https://doi.org/10.1109/TNNLS.2015.2435783
Görüm T (2019) Landslide recognition and mapping in a mixed forest environment from airborne LiDAR data. Eng Geol 258:105155. https://doi.org/10.1016/j.enggeo.2019.105155
Groos AR, Bertschinger TJ, Kummer CM, Erlwein S, Munz L, Philipp A (2019) The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of Alpine glaciers: a case study from the Kanderfirn (Swiss Alps). Geosciences 9(8):356. https://doi.org/10.3390/geosciences9080356
Gudino-Elizondo N, Biggs TW, Castillo C, Bingner RL et al (2018) Measuring ephemeral gully erosion rates and topographical thresholds in an urban watershed using unmanned aerial systems and structure from motion photogrammetric techniques. Land Degrad Dev 29(6):1896–1905. https://doi.org/10.1002/ldr.2976
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112(1–2):42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
Heindel RC, Chipman JW, Dietrich JT, Virginia RA (2018) Quantifying rates of soil deflation with structure-from-motion photogrammetry in west Greenland. Arct Antarct Alp Res 50(1):S100012. https://doi.org/10.1080/15230430.2017.1415852
Herrera G, Fernández-Merodo JA, Mulas J, Pastor M, Luzi G, Monserrat O (2009) A landslide forecasting model using ground based SAR data: the Portalet case study. Eng Geol 105(3–4):220–230. https://doi.org/10.1016/j.enggeo.2009.02.009
Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. CATENA 133:266–281. https://doi.org/10.1016/j.catena.2015.05.019
Hong H, Pradhan B, Jebur MN, Bui DT, Xu C, Akgun A (2016) Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci 75(1):40. https://doi.org/10.1007/s12665-015-4866-9
Hu Q, Zhou Y, Wang S, Wang F (2020) Machine learning and fractal theory models for landslide susceptibility mapping: case study from the Jinsha River Basin. Geomorphology 351:106975. https://doi.org/10.1016/j.geomorph.2019.106975
Huang RQ, Li AW (2009) Analysis of the geo-hazards triggered by the 12 May 2008 Wenchuan Earthquake, China. Bull Eng Geol Environ 68(3):363–371. https://doi.org/10.1007/s10064-009-0207-0
Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529. https://doi.org/10.1016/j.catena.2018.03.003
Huang F, Cao Z, Guo J, Jiang SH, Li S, Guo Z (2020) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580. https://doi.org/10.1016/j.catena.2020.104580
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11(2):167–194. https://doi.org/10.1007/s10346-013-0436-y
Ingles J, Darrozes J, Soula JC (2006) Effects of the vertical component of ground shaking on earthquake-induced landslide displacements using generalized Newmark analysis. Eng Geol 86(2–3):134–147. https://doi.org/10.1016/j.enggeo.2006.02.018
Irrgang AM, Lantuit H, Gordon RR, Piskor A, Manson GK (2019) Impacts of past and future coastal changes on the Yukon coast: threats for cultural sites, infrastructure, and travel routes. Arct Sci 5(2):107–126. https://doi.org/10.1139/as-2017-0041
Jaboyedoff M, Oppikofer T, Abellán A, Derron MH, Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in landslide investigations: a review. Nat Hazards 61(1):5–28. https://doi.org/10.1007/s11069-010-9634-2
Jaboyedoff M, Del Gaudio V, Derron MH, Grandjean G, Jongmans D (2019) Characterizing and monitoring landslide processes using remote sensing and geophysics. Eng Geol 259:105167
James MR, Chandler JH, Eltner A, Fraser C, Miller PE, Mills JP, Noble T, Robson S, Lane SN (2019) Guidelines on the use of structure-from-motion photogrammetry in geomorphic research. Earth Surf Process Landf 44:2081–2084. https://doi.org/10.1002/esp.4637
Jaud M, Letortu P, Théry C, Grandjean P, Costa S, Maquaire O, Davidson R, Le Dantec N (2019) UAV survey of a coastal cliff face: selection of the best imaging angle. Measurement 139:10–20. https://doi.org/10.1016/j.measurement.2019.02.024
Jiang S, Wen BP, Zhao C, Li RD, Li ZH (2016) Kinematics of a giant slow-moving landslide in Northwest China: constraints from high-resolution remote sensing imagery and GPS monitoring. J Asian Earth Sci 123:34–46. https://doi.org/10.1016/j.jseaes.2016.03.019
Jones MKW, Pollard WH, Jones BM (2019) Rapid initialization of retrogressive thaw slumps in the Canadian high Arctic and their response to climate and terrain factors. Environ Res Lett 14(5):055006. https://doi.org/10.1088/1748-9326/ab12fd
Jorgenson MT, Grosse G (2016) Remote sensing of landscape change in permafrost regions. Permafrost Periglac Process 27(4):324–338. https://doi.org/10.1002/ppp.1914
Jugie M, Gob F, Virmoux C, Brunstein D, Tamisier V, Le Cœur C, Grancher D (2018) Characterizing and quantifying the discontinuous bank erosion of a small low energy river using structure-from-motion photogrammetry and erosion pins. J Hydrol 563:418–434. https://doi.org/10.1016/j.jhydrol.2018.06.019
Kadavi PR, Lee CW, Lee S (2018) Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens 10(8):1252. https://doi.org/10.3390/rs10081252
Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomat Nat Hazards Risk 9(1):49–69. https://doi.org/10.1080/19475705.2017.1407368
Kenner R, Bühler Y, Delaloye R, Ginzler C, Phillips M (2014) Monitoring of high alpine mass movements combining laser scanning with digital airborne photogrammetry. Geomorphology 206:492–504. https://doi.org/10.1016/j.geomorph.2013.10.020
Klinger Y, Michel R, King GCP (2006) Evidence for an earthquake barrier model from Mw ∼ 7.8 Kokoxili (Tibet) earthquake slip-distribution. Earth Planet Sci Lett 242(3–4):354–364. https://doi.org/10.1016/j.epsl.2005.12.003
Ko FW, Lo FL (2018) From landslide susceptibility to landslide frequency: a territory-wide study in Hong Kong. Eng Geol 242:12–22. https://doi.org/10.1016/j.enggeo.2018.05.001
Kokalj Ž, Zakšek K, Oštir K, Pehani P, Čotar K, Somrak M et al (2016) Relief Visualization Toolbox, ver. 2.2.1 Manual. Remote Sens 3(2):398–415
Kromer R, Walton G, Gray B, Lato M, Group R (2019) Development and optimization of an automated fixed-location time-lapse photogrammetric rock slope monitoring system. Remote Sens 11:1890–1908. https://doi.org/10.3390/rs11161890
Kurtz C, Stumpf A, Malet JP, Gançarski P, Puissant A, Passat N (2014) Hierarchical extraction of landslides from multiresolution remotely sensed optical images. ISPRS J Photogramm Remote Sens 87:122–136. https://doi.org/10.1016/j.isprsjprs.2013.11.003
Lacroix P, Berthier E, Maquerhua ET (2015) Earthquake-driven acceleration of slow-moving landslides in the Colca valley, Peru, detected from Pléiades images. Remote Sens Environ 165:148–158. https://doi.org/10.1016/j.rse.2015.05.010
Lacroix P, Bièvre G, Pathier E, Kniess U, Jongmans D (2018) Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sens Environ 215:507–516. https://doi.org/10.1016/j.rse.2018.03.042
Lacroix P, Araujo G, Hollingsworth J, Taipe E (2019) Self-entrainment motion of a slow-moving landslide inferred from landsat-8 time series. J Geophys Res Earth Surf 124(5):1201–1216. https://doi.org/10.1029/2018JF004920
Lanaras C, Bioucas-Dias J, Galliani S, Baltsavias E, Schindler K (2018) Super-resolution of Sentinel-2 images: learning a globally applicable deep neural network. ISPRS J Photogramm Remote Sens 146:305–319. https://doi.org/10.1016/j.isprsjprs.2018.09.018
Lang S, Füreder P, Rogenhofer E (2018) Earth observation for humanitarian operations. In: Yearbook on space policy 2016, Springer, Cham, pp 217–229. https://doi.org/10.1007/978-3-319-72465-2_10
Lantuit H, Pollard WH, Couture N, Fritz M, Schirrmeister L, Meyer H, Hubberten HW (2012) Modern and late Holocene retrogressive thaw slump activity on the Yukon coastal plain and Herschel Island, Yukon Territory, Canada. Permafr Periglac Process 23(1):39–51. https://doi.org/10.1002/ppp.1731
Lantz TC, Kokelj SV, Gergel SE, Henry GH (2009) Relative impacts of disturbance and temperature: persistent changes in microenvironment and vegetation in retrogressive thaw slumps. Glob Change Biol 15(7):1664–1675. https://doi.org/10.1111/j.1365-2486.2009.01917.x
Le Bivic R, Allemand P, Quiquerez A, Delacourt C (2017) Potential and limitation of SPOT-5 ortho-image correlation to investigate the cinematics of landslides: the example of “Mare à Poule d’Eau” (Réunion, France). Remote Sens 9(2):106. https://doi.org/10.3390/rs9020106
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Lei T, Zhang Y, Lv Z, Li S, Liu S, Nandi AK (2019a) Landslide inventory mapping from bitemporal images using deep convolutional neural networks. IEEE Geosci Remote Sens Lett 16(6):982–986. https://doi.org/10.1109/LGRS.2018.2889307
Lei T, Zhang Q, Xue D, Chen T, Meng H, Nandi AK (2019b) End-to-end change detection using a symmetric fully convolutional network for landslide mapping. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 3027–3031. https://doi.org/10.1109/ICASSP.2019.8682802
Leibman MO, Khomutov AV, Gubarkov AA, Dvornikov YA, Mullanurov DR (2015) The research station” Vaskiny Dachi”, Central Yamal, West Siberia, Russia–a review of 25 years of permafrost studies. Fennia-Int J Geogr 193(1):3–30
Letortu P, Costa S, Cador JM, Coinaud C, Cantat O (2015a) Statistical and empirical analyses of the triggers of coastal chalk cliff failure. Earth Surf Process Landf 40:1371–1386. https://doi.org/10.1002/esp.3741
Letortu P, Costa S, Maquaire O, Delacourt C, Augereau E, Davidson R, Suanez S, Nabucet J (2015b) Retreat rates, modalities and agents responsible for erosion along the coastal chalk cliffs of Upper Normandy: the contribution of terrestrial laser scanning. Geomorphology 245:3–14. https://doi.org/10.1016/j.geomorph.2015.05.007
Letortu P, Costa S, Maquaire O, Davidson R (2019) Marine and subaerial controls of coastal chalk cliff erosion in Normandy (France) based on a 7-year laser scanner monitoring. Geomorphology 335:75–91. https://doi.org/10.1016/j.geomorph.2019.03.005
Lewkowicz AG (2007) Dynamics of active-layer detachment failures, Fosheim peninsula, Ellesmere Island, Nunavut, Canada. Permaf Periglac Process 18(1):89–103. https://doi.org/10.1002/ppp.578
Lewkowicz AG, Way RG (2019) Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat Commun 10(1):1329. https://doi.org/10.1038/s41467-019-09314-7
Li X, Wang L, Sung E (2008) AdaBoost with SVM-based component classifiers. Eng Appl Artif Intell 21(5):785–795. https://doi.org/10.1016/j.engappai.2007.07.001
Li X, Muller JP, Fang C, Zhao Y (2011) Measuring displacement field from TerraSAR-X amplitude images by subpixel correlation: an application to the landslide in Shuping. Three Gorges Area Acta Petrologica Sinica 27(12):3843–3850
Li X, Cheng X, Chen W, Chen G, Liu S (2015) Identification of forested landslides using LiDar data, object-based image analysis, and machine learning algorithms. Remote Sens 7(8):9705–9726. https://doi.org/10.3390/rs70809705
Li Z, Shi W, Myint SW, Lu P, Wang Q (2016) Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sens Environ 175:215–230. https://doi.org/10.1016/j.rse.2016.01.003
Lian C, Zeng Z, Yao W, Tang H (2013) Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat Hazards 66(2):759–771. https://doi.org/10.1007/s11069-012-0517-6
Lin CY, Lo HM, Chou WC, Lin WT (2004) Vegetation recovery assessment at the Jou–Jou Mountain landslide area caused by the 921 Earthquake in Central Taiwan. Ecol Model 176(1–2):75–81. https://doi.org/10.1016/j.ecolmodel.2003.12.037
Lissak C, Maquaire O, Malet JP, Bitri A, Samyn K, Grandjean G et al (2014) Airborne and ground-based data sources for characterizing the morpho-structure of a coastal landslide. Geomorphology 217:140–151. https://doi.org/10.1016/j.geomorph.2014.04.019
Liu P, Wei Y, Wang Q, Chen Y, Xie J (2020) Research on post-earthquake landslide extraction algorithm based on improved u-net model. Remote Sens 12(5):894. https://doi.org/10.3390/rs12050894
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
López-Davalillo JG, Monod B, Alvarez-Fernandez MI, Garcia GH et al (2014) Morphology and causes of landslides in Portalet area (Spanish Pyrenees): probabilistic analysis by means of numerical modelling. Eng Fail Anal 36:390–406. https://doi.org/10.1016/j.engfailanal.2013.10.015
Lowe D (1999) Object recognition from local scale-invariant features. In: Proceedings of the international conference of computer vision, Corfu, Greece
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110
Lv Z, Liu T, Kong X, Shi C, Benediktsson JA (2020) Landslide inventory mapping with bitemporal aerial remote sensing images based on the dual-path full convolutional network. IEEE J Sel Top Appl Earth Observ Remote Sens 14(8)
Ma S, Xu C, Chao X, Zhang P, Liang X, Tian Y (2019) Geometric and kinematic features of a landslide in Mabian Sichuan, China, derived from UAV photography. Landslides 16:373–381. https://doi.org/10.1007/s10346-018-1104-z
Mallet C, Bretar F (2009) Full-waveform topographic lidar: state-of-the-art. ISPRS J Photogramm Remote Sens 64(1):1–16. https://doi.org/10.1016/j.isprsjprs.2008.09.007
Marc O, Meunier P, Hovius N (2017) Prediction of the area affected by earthquake-induced landsliding based on seismological parameters. Nat Hazards Earth Syst Sci 17(7):1159–1175. https://doi.org/10.5194/nhess-17-1159-2017
Marteau B, Vericat D, Gibbins C, Batalla RJ, Green DR (2016) Application of structure-from-motion photogrammetry to river restoration. Earth Surf Proc Land 42(3):503–515. https://doi.org/10.1002/esp.4086
McKean J, Roering J (2004) Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology 57(3–4):331–351. https://doi.org/10.1016/S0169-555X(03)00164-8
Medjkane M, Maquaire O, Costa S, Roulland T, Letortu P, Fauchard C, Antoine R, Davidson R (2018) High-resolution monitoring of complex coastal morphology changes: cross-efficiency of SfM and TLS-based survey (Vaches Noires cliffs, Normandy, France). Landslides 15(6):1097–1108. https://doi.org/10.1007/s10346-017-0942-4
Mezaal MR, Pradhan B, Rizeei HM (2018) Improving landslide detection from airborne laser scanning data using optimized Dempster-Shafer. Remote Sens 10(7):1029. https://doi.org/10.3390/rs10071029
Michel R, Avouac JP, Taboury J (1999) Measuring ground displacements from SAR amplitude images: application to the Landers earthquake. Geophys Res Lett 26(7):875–878. https://doi.org/10.1029/1999GL900138
Michoud C, Carrea D, Costa S, Derron MH, Jaboyedoff M, Davidson R, Delacourt C, Letortu P, Maquaire O (2014) Landslide detection and monitoring capability of boat-based mobile laser scanning along Dieppe coastal cliffs Normandy. Landslides 12(2):403–418. https://doi.org/10.1007/s10346-014-0542-5
Mohammady M, Pourghasemi HR, Amiri M (2019) Land subsidence susceptibility assessment using random forest machine learning algorithm. Environ Earth Sci 78(16):503. https://doi.org/10.1007/s12665-019-8518-3
Monserrat O, Crosetto M, Luzi G (2014) A review of ground-based SAR interferometry for deformation measurement. ISPRS J Photogramm Remote Sens 93:40–48. https://doi.org/10.1016/j.isprsjprs.2014.04.001
Moosavi V, Talebi A, Shirmohammadi B (2014) Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology 204:646–656. https://doi.org/10.1016/j.geomorph.2013.09.012
Nichol J, Wong MS (2005a) Detection and interpretation of landslides using satellite images. Land Degrad Dev 16(3):243–255. https://doi.org/10.1002/ldr.648
Nichol J, Wong MS (2005b) Satellite remote sensing for detailed landslide inventories using change detection and image fusion. Int J Remote Sens 26(9):1913–1926. https://doi.org/10.1080/01431160512331314047
Niethammer U, James MR, Rothmund S, Travelletti J, Joswig M (2012) UAV-based remote sensing of the Super-Sauze landslide: evaluation and results. Eng Geol 128:2–11. https://doi.org/10.1016/j.enggeo.2011.03.012
Nitze I (2018 Trends of land surface change from Landsat time-series 1999-2014, Transect T4, Eastern Canada. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA. https://doi.org/10.1594/PANGAEA.884276
Nitze I, Grosse G, Jones BM, Romanovsky VE, Boike J (2018) Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat Commun 9(1):5423. https://doi.org/10.1038/s41467-018-07663-3
Novák D (2014) Local relief model (LRM) toolbox for ArcGIS. Czech Academy of Science, Staré Město
Obu J, Westermann S, Bartsch A, Berdnikov N, Christiansen HH, Dashtseren A et al (2019) Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale. Earth Sci Rev 193:299–316. https://doi.org/10.1016/j.earscirev.2019.04.023
Ouédraogo MM, Degré A, Debouche C, Lisein J (2014) The evaluation of unmanned aerial system-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds. Geomorphology 214:339–355. https://doi.org/10.1016/j.geomorph.2014.02.016
Pakhale GK, Gupta PK (2010) Comparison of advanced pixel based (ANN and SVM) and object-oriented classification approaches using landsat-7 Etm + data. Int J Eng Technol 2(4):245–251
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222. https://doi.org/10.1080/01431160412331269698
Pánek T, Břežný M, Kapustová V, Lenart J, Chalupa V (2019) Large landslides and deep-seated gravitational slope deformations in the Czech Flysch Carpathians: new LiDAR-based inventory. Geomorphology 346:106852. https://doi.org/10.1016/j.geomorph.2019.106852
Paquette M, Rudy AC, Fortier D, Lamoureux SF (2020) Multi-scale site evaluation of a relict active layer detachment in a High Arctic landscape. Geomorphology 359(15):107159. https://doi.org/10.1016/j.geomorph.2020.107159
Pawłuszek K, Borkowski A (2016) Landslides identification using airborne laser scanning data derived topographic terrain attributes and support vector machine classification. In: The international archives of the photogrammetry, remote sensing and spatial information sciences, XXIII ISPRS Congress, vol 8
Pesci A, Teza G, Casula G, Loddo F, De Martino P, Dolce M, Obrizzo F, Pingue F (2011) Multitemporal laser scanner-based observation of the Mt. Vesuvius crater: characterization of overall geometry and recognition of landslide events. ISPRS J Photogramm Remote Sens 66(3):327–336. https://doi.org/10.1016/j.isprsjprs.2010.12.002
Petley DN, Crick WDO, Hart AB (2002) The use of satellite imagery in landslide studies in high mountain areas. In: Proceedings of the 23rd Asian conference on remote sensing (ACRS’2002), Kathmandu. http://www.gisdevelopment.net/aars/acrs/2002/hdm/48.pdf
Petrie G, Toth CK (2008) Introduction to laser ranging, profiling, and scanning. Topographic laser ranging and scanning: principles and processing, pp 1–28
Pham BT, Jaafari A, Prakash I, Bui DT (2019) A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull Eng Geol Env 78(4):2865–2886. https://doi.org/10.1007/s10064-018-1281-y
Piégay H, Arnaud F, Belletti B, Bertrand M, Bizzi S, Carbonneau P et al (2020) Remotely sensed rivers in the Anthropocene: state of the art and prospects. Earth Surf Proc Land 45(1):157–188. https://doi.org/10.1002/esp.4787
Proy C, Tinel C, Fontannaz D (2013) Pleiades in the context of the International Charter “space and major disasters”. In: 2013 IEEE international geoscience and remote sensing symposium-IGARSS, pp 4530–4533. https://doi.org/10.1109/IGARSS.2013.6723843
Rau JY, Jhan JP, Lo CF, Lin YS (2011) Landslide mapping using imagery acquired by a fixed-wing UAV. Int Arch Photogramm Remote Sens Spat Inf Sci 38(1/C22):195–200
Raucoules D, De Michele M, Malet JP, Ulrich P (2013) Time-variable 3D ground displacements from high-resolution synthetic aperture radar (SAR). Application to La Valette landslide (South French Alps). Remote Sens Environ 139:198–204. https://doi.org/10.1016/j.rse.2013.08.006
Razak KA, Santangelo M, Van Westen CJ, Straatsma MW, de Jong SM (2013) Generating an optimal DTM from airborne laser scanning data for landslide mapping in a tropical forest environment. Geomorphology 190:112–125. https://doi.org/10.1016/j.geomorph.2013.02.021
RIEGL Laser Measurement Systems (2014) Datasheet VZ-400 (RIEGL Laser Measurement Systems GmbH). Austria
Roessner S, Wetzel HU, Kaufmann H, Sarnagoev A (2001) Satellite remote sensing for regional assessment of landslide hazard in Kyrgyzstan (Central Asia). In: Proceedings of second symposium on Katastrophenvorsorge, Leipzig, pp 24–25
Rossi G, Tanteri L, Tofani V, Vannocci P, Moretti S, Casagli N (2018) Multitemporal UAV surveys for landslide mapping and characterization. Landslides 15:1045–1052. https://doi.org/10.1007/s10346-018-0978-0
Rossini M, Di Mauro B, Garzonio R, Baccolo G, Cavallini G, Mattavelli M, De Amicis M, Colombo R (2018) Rapid melting dynamics of an alpine glacier with repeated UAV photogrammetry. Geomorphology 304:159–172. https://doi.org/10.1016/j.geomorph.2017.12.039
Roulland T, Maquaire O, Costa S, Compain V, Davidson R, Medjkane M (2019) Dynamique des falaises des Vaches Noires: analyse diachronique historique et récente à l’aide de documents multi-sources (Normandie, France). Géomorphologie Relief Processus Environnement 25(1):37–55. https://doi.org/10.4000/geomorphologie.12989
Rouyet L, Lauknes TR, Christiansen HH, Strand SM, Larsen Y (2019) Seasonal dynamics of a permafrost landscape, Adventdalen, Svalbard, investigated by InSAR. Remote Sens Environ 231:111236. https://doi.org/10.1016/j.rse.2019.111236
Rudy AC, Lamoureux SF, Treitz P, Ewijk KV, Bonnaventure PP, Budkewitsch P (2016) Terrain controls and landscape-scale susceptibility modelling of active-layer detachments, Sabine Peninsula, Melville Island, Nunavut. Permaf Periglac Process 28(1):79–91. https://doi.org/10.1002/ppp.1900
Rudy AC, Lamoureux SF, Treitz P, Short N, Brisco B (2018) Seasonal and multi-year surface displacements measured by DInSAR in a high Arctic permafrost environment. Int J Appl Earth Obs Geoinf 64:51–61. https://doi.org/10.1016/j.jag.2017.09.002
Rusnák M, Sládek J, Pacina J, Kidov A (2018) Monitoring of avulsion channel evolution and river morphology changes using UAV photogrammetry: case study of the gravel bed Ondava river in outer western carpathians. Area 51(3):549–560. https://doi.org/10.1111/area.12508
Salvini R, Francioni M, Riccucci S, Bonciani F, Callegari I (2013) Photogrammetry and laser scanning for analyzing slope stability and rockfall runout along the Domodossola-Iselle railway, the Italian Alps. Geomorphology 185:110–122. https://doi.org/10.1016/j.geomorph.2012.12.020
Sato HP, Harp EL (2009) Interpretation of earthquake-induced landslides triggered by the 12 May 2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using satellite imagery and Google Earth. Landslides 2:153–159. https://doi.org/10.1007/s10346-009-0147-6
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003
Schulz WH (2004) Landslides mapped using LIDAR imagery, Seattle, Washington. US Geological Survey Open-File Report, 1396(11)
Shafique M, van der Meijde M, Khan MA (2016) A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing perspective. J Asian Earth Sci 118:68–80. https://doi.org/10.1016/j.jseaes.2016.01.002
Shan J, Toth CK (2018) Topographic laser ranging and scanning: principles and processing. Taylor & Francis Group CRC Press, Boca Raton
Singleton A, Li Z, Hoey T, Muller JP (2014) Evaluating sub-pixel offset techniques as an alternative to D-InSAR for monitoring episodic landslide movements in vegetated terrain. Remote Sens Environ 147:133–144. https://doi.org/10.1016/j.rse.2014.03.003
Slob S, Hack R (2004) 3D terrestrial laser scanning as a new field measurement and monitoring technique. In: Engineering geology for infrastructure planning in Europe. Springer, Berlin, Heidelberg, pp 179–189. https://doi.org/10.1007/978-3-540-39918-6_22
Song KY, Oh HJ, Choi J, Park I, Lee C, Lee S (2012) Prediction of landslides using ASTER imagery and data mining models. Adv Space Res 49(5):978–993. https://doi.org/10.1016/j.asr.2011.11.035
Stettner S, Beamish AL, Bartsch Heim B, Grosse G, Roth A, Lantuit H (2018) Monitoring inter-and intra-seasonal dynamics of rapidly degrading ice-rich permafrost riverbanks in the Lena Delta with TerraSAR-X time series. Remote Sens 10(1):51. https://doi.org/10.3390/rs10010051
Stumpf A, Kerle N (2011a) Object-oriented mapping of landslides using Random Forests. Remote Sens Environ 115(10):2564–2577. https://doi.org/10.1016/j.rse.2011.05.013
Stumpf A, Kerle N (2011b) Combining random forests and object-oriented analysis for landslide mapping from very high resolution imagery. Proc Environ Sci 3:123–129. https://doi.org/10.1016/j.proenv.2011.02.022
Stumpf A, Malet JP, Allemand P, Ulrich P (2014) Surface reconstruction and landslide displacement measurements with Pléiades satellite images. ISPRS J Photogramm Remote Sens 95:1–12. https://doi.org/10.1016/j.isprsjprs.2014.05.008
Stumpf A, Malet JP, Allemand A, Pierrot-Deseilligny M, Skupinski G (2015) Ground-based multi-view photogrammetry for the monitoring of landslide deformation and erosion. Geomorphology 231:130–145. https://doi.org/10.1016/j.geomorph.2014.10.039
Stumpf A, Malet JP, Delacourt C (2017) Correlation of satellite image time-series for the detection and monitoring of slow-moving landslides. Remote Sens Environ 189:40–55. https://doi.org/10.1016/j.rse.2016.11.007
Sun L, Muller JP (2016) Evaluation of the use of sub-pixel offset tracking techniques to monitor landslides in densely vegetated steeply sloped areas. Remote Sens 8(8):659. https://doi.org/10.3390/rs8080659
Svennevig K (2019) Preliminary landslide mapping in Greenland. Geol Surv Den Greenl Bull. https://doi.org/10.34194/GEUSB-201943-02-07
Tarchi D, Casagli N, Fanti R, Leva DD, Luzi G, Pasuto A et al (2003) Landslide monitoring by using ground-based SAR interferometry: an example of application to the Tessina landslide in Italy. Eng Geol 68(1–2):15–30. https://doi.org/10.1016/S0013-7952(02)00196-5
Telling J, Lyda A, Hartzell P, Glennie C (2017) Review of Earth science research using terrestrial laser scanning. Earth Sci Rev 169:35–68. https://doi.org/10.1016/j.earscirev.2017.04.007
Teza G, Galgaro A, Zaltron N, Genevois R (2007) Terrestrial laser scanner to detect landslide displacement fields: a new approach. Int J Remote Sens 28(16):3425–3446. https://doi.org/10.1080/01431160601024234
Tofani V, Segoni S, Agostini A, Catani F, Casagli N (2013) Use of remote sensing for landslide studies in Europe. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-13-299-2013
Travelletti J, Delacourt C, Allemand P, Malet JP, Schmittbuhl J, Toussaint R, Bastard M (2012) Correlation of multi-temporal ground-based optical images for landslide monitoring: application, potential and limitations. ISPRS J Photogramm Remote Sens 70:39–55. https://doi.org/10.1016/j.isprsjprs.2012.03.007
Travelletti J, Malet JP, Delacourt C (2014) Image-based correlation of laser scanning point cloud time series for landslide monitoring. Int J Appl Earth Obs Geoinf 32:1–18. https://doi.org/10.1016/j.jag.2014.03.022
Tyszkowski S, Cebulski J (2019) Practical aspects of landslides surveys using terrestrial laser scanning in diverse geomorphological terrains: case studies from Polish Carpathians and Lower Vistula Valley. Zeitschrift für Geomorphologie 62(2):107–124. https://doi.org/10.1127/zfg/2019/0500
Valkaniotis S, Papathanassiou G, Ganas A (2018) Mapping an earthquake-induced landslide based on UAV imagery; case study of the 2015 Okeanos landslide, Lefkada, Greece. Eng Geol 245:141–152. https://doi.org/10.1016/j.enggeo.2018.08.010
Van Den Eeckhaut M, Kerle N, Poesen J, Hervás J (2012) Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data. Geomorphology 173:30–42. https://doi.org/10.1016/j.geomorph.2012.05.024
Van Puymbroeck N, Michel R, Binet R, Avouac JP, Taboury J (2000) Measuring earthquakes from optical satellite images. Appl Opt 39(20):3486–3494. https://doi.org/10.1364/AO.39.003486
Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3–4):112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
Voigt S, Giulio-Tonolo F, Lyons J, Kučera J, Jones B, Schneiderhan T et al (2016) Global trends in satellite-based emergency mapping. Science 353(6296):247–252. https://doi.org/10.1126/science.aad8728
Wang D, Jiang Y, Wang W, Wang Y (2016) Bias reduction in sub-pixel image registration based on the anti-symmetric feature. Meas Sci Technol 27(3):035206. https://doi.org/10.1088/0957-0233/27/3/035206
Wang Y, Fang Z, Hong H (2019) Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ 666:975–993. https://doi.org/10.1016/j.scitotenv.2019.02.263
Warrick JA, Ritchie AC, Schmidt KM, Reid ME, Logan J (2019) Characterizing the catastrophic 2017 Mud Creek landslide, California, using repeat structure-from-motion (SfM) photogrammetry. Landslides 16(6):1201–1219. https://doi.org/10.1007/s10346-019-01160-4
Wasowski J, Bovenga F (2014) Investigating landslides and unstable slopes with satellite multi temporal interferometry: current issues and future perspectives. Eng Geol 174:103–138. https://doi.org/10.1016/j.enggeo.2014.03.003
Werner C, Strozzi T, Wiesmann A, Wegmüller U (2008) GAMMA’s portable radar interferometer. In: Proceedings of 13th FIG symposium of deformation measurements and analysis, pp 1–10
Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012) Structure-from-Motion photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300–314. https://doi.org/10.1016/j.geomorph.2012.08.021
Westoby M, Lim M, Hogg MJ, Pound M, Dunlop L, Woodward J (2018) Cost-effective erosion monitoring of coastal cliffs. Coast Eng 138:152–164. https://doi.org/10.1016/j.coastaleng.2018.04.008
Wilkinson MW, Jones RR, Woods CE, Gilment SR, McCaffrey KJW, Kokkalas S, Long JJ (2016) A comparison of terrestrial laser scanning and structure-from-motion photogrammetry as methods for digital outcrop acquisition. Geosphere 12(6):1865–1880. https://doi.org/10.1130/GES01342.1
Xu C (2015) Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: principles and case studies. Geosci Front 6(6):825–836. https://doi.org/10.1016/j.gsf.2014.03.004
Xu C, Xu X, Yao X, Dai F (2014) Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 11(3):441–461. https://doi.org/10.1007/s10346-013-0404-6
Xu Q, Ouyang C, Jiang T, Fan X, Cheng D (2019) DFPENet-geology: a deep learning framework for high precision recognition and segmentation of co-seismic landslides. arXiv:1908.10907
Yamaguchi YS, Tanaka S, Odajima T, Kamai T, Tsuchida S (2003) Detection of a landslide movement as geometric misregistration in image matching of SPOT HRV data of two different dates. Int J Remote Sens 24(18):3523–3534. https://doi.org/10.1080/01431160110111063
Yang X, Chen L (2010) Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides. Int J Appl Earth Obs Geoinf 12(6):487–495. https://doi.org/10.1016/j.jag.2010.05.006
Yang W, Wang M, Shi P (2013) Using MODIS NDVI time series to identify geographic patterns of landslides in vegetated regions. IEEE Geosci Remote Sens Lett 10(4):707–710. https://doi.org/10.1109/LGRS.2012.2219576
Yin Y, Wang F, Sun P (2009) Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China. Landslides 6(2):139–152. https://doi.org/10.1007/s10346-009-0148-5
Yu B, Chen F (2017) A new technique for landslide mapping from a large-scale remote sensed image: a case study of Central Nepal. Comput Geosci 100:115–124. https://doi.org/10.1016/j.cageo.2016.12.007
Zhang S, Zhang LM, Glade T (2014) Characteristics of earthquake-and rain-induced landslides near the epicenter of Wenchuan earthquake. Eng Geol 175:58–73. https://doi.org/10.1016/j.enggeo.2014.03.012
Zhao W, Du S (2016) Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J Photogramm Remote Sens 113:155–165. https://doi.org/10.1016/j.isprsjprs.2016.01.004
Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37. https://doi.org/10.1016/j.cageo.2017.11.019
Zhu XX, Tuia D, Mou L, Xia GS, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sens Mag 5(4):8–36. https://doi.org/10.1109/MGRS.2017.2762307
Zolkos S, Tank SE, Striegl RG, Kokelj SV (2019) Thermokarst effects on carbon dioxide and methane fluxes in streams on the Peel Plateau (NWT, Canada). J Geophys Res Biogeosci 124(7):1781–1798. https://doi.org/10.1029/2019JG005038
Zwieback S, Kokelj SV, Günthe F, Boike J, Grosse G, Hajnsek I (2018) Sub-seasonal thaw slump mass wasting is not consistently energy limited at the landscape scale. Cryosphere 12(2):549–564. https://doi.org/10.3929/ethz-b-000244496
Acknowledgements
This paper is a result of the international workshop on “Natural and man-made hazards monitoring by the Earth Observation missions: current status and scientific gaps” held at the International Space Science Institute (ISSI), Bern, Switzerland, on April 15–18, 2019. We thank all the authors for their contribution in this article with the result of their researches. The repeated surveys in Normandy, are supported by the ANR Project “RICOCHET: multi-RIsk assessment on Coastal territory in a global CHange context” funded by the French Research National Agency (ANR-16-CE03-0008). Permafrost researches benefited from Nunataryuk Project (H2020 Research and Innovation Programme under Grant Agreement No. 773421), ESA DUE GlobPermafrost Project (4000116196/15/I–NB) as well as ESA Climate Change Initiative Project on Permafrost (4000123681/18/I-NB).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lissak, C., Bartsch, A., De Michele, M. et al. Remote Sensing for Assessing Landslides and Associated Hazards. Surv Geophys 41, 1391–1435 (2020). https://doi.org/10.1007/s10712-020-09609-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10712-020-09609-1