Natural Hazards

, Volume 73, Issue 2, pp 137–163 | Cite as

Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data

  • K. E. Joyce
  • S. V. Samsonov
  • S. R. Levick
  • J. Engelbrecht
  • S. Belliss
Review Article


Geological hazards and their effects are often geographically widespread. Consequently, their effective mapping and monitoring is best conducted using satellite and airborne imaging platforms to obtain broad scale, synoptic coverage. With a multitude of hazards and effects, potential data types, and processing techniques, it can be challenging to determine the best approach for mapping and monitoring. It is therefore critical to understand the spatial and temporal effects of any particular hazard on the environment before selecting the most appropriate data type/s and processing techniques to apply. This review is designed to assist the decision-making and selection process when embarking on a hazard mapping or monitoring exercise. It focuses on the application of optical, LiDAR, and synthetic aperture RADAR technologies for the assessment of pre-event risk and post-event damage. Geological hazards of global interest summarized here are landslides and erosion; seismic and tectonic hazards; ground subsidence; and flooding and tsunami.


Remote sensing Geohazards Image processing Data acquisition 


  1. Agliardi F, Crosta GB (2003) High resolution three-dimensional numerical modelling of rockfalls. Int J Rock Mech Min Sci 40:455–471Google Scholar
  2. Al Fugura AK, Billa L, Pradhan B (2011) Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image. Estuar Coast Shelf Sci 95:395–400Google Scholar
  3. Alipour S, Tiampo K, Samsonov S, Gonzalez PJ (2013) Multibaseline PolInSAR using RADARSAT-2 Quad-pol data: improvements in interferometric phase analysis. IEEE Geosci Remote Sens Lett 191:1095–1108Google Scholar
  4. Aly MH, Giardino JR, Klein AG, Zebker HA (2012) InSAR study of shoreline change along the Damietta promontory, Egypt. J Coast Res 284:1263–1269Google Scholar
  5. Anderson K, Croft H (2009) Remote sensing of soil surface properties. Prog Phys Geogr 33:457–473Google Scholar
  6. 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 7:637–650Google Scholar
  7. Arrowsmith JR, Zielke O (2009) Tectonic geomorphology of the San Andreas Fault zone from high resolution topography: an example from the Cholame segment. Geomorphology 113:70–81Google Scholar
  8. Avouac JP, Ayoub F, Leprince S, Konca O, Helmberger DV (2006) The 2005, Mw 7.6 Kashmir earthquake: sub-pixel correlation of ASTER images and seismic waveforms analysis. Earth Planet Sci Lett 249:514–528Google Scholar
  9. Ayoub F, Leprince S, Avouac JP (2009) Co-registration and correlation of aerial photographs for ground deformation measurements. ISPRS J Photogramm Remote Sens 64:551–560Google Scholar
  10. Bahadur KK (2009) Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand. Environ Geol 57:695–705Google Scholar
  11. Baldo M, Bicocchi C, Chiocchini U, Giordan D, Lollino G (2009) LIDAR monitoring of mass wasting processes: the Radicofani landslide, Province of Siena, Central Italy. Geomorphology 105:193–201Google Scholar
  12. Barisin I, Leprince S, Parsons B, Wright T (2009) Surface displacements in the September 2005 Afar rifting event from satellite image matching: asymmetric uplift and faulting. Geophys Res Lett 36:L07301Google Scholar
  13. Barlow J, Franklin SE (2007) Mapping hazardous slope processes using digital data. In: Li J, Zlatanova S, Fabbri AG (eds) Geomatics solutions for disaster management. Springer, Berlin, pp 75–90Google Scholar
  14. Barlow J, Franklin SE, Martin Y (2006) High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes. Photogramm Eng Remote Sens 72:687–692Google Scholar
  15. Beavan J, Samsonov S, Denys P, Palmer N, Denham M (2010) Joint inversion of GPS and InSAR data from 15 July 2009 MW 7.8 Dusky Sound earthquake reveals oblique slip on the Puysegur–Fiordland subduction interface. Geophys J Int 183:1265–1286Google Scholar
  16. Beavan J, Fielding E, Motagh M, Samonsov S, Donnelly N (2011) Fault location and slip distribution of 22 February 2011 Mw 6.3 Christchurch, New Zealand, earthquake from geodetic data. Seismol Res Lett 82:789–799Google Scholar
  17. Bechor NBD, Zebker HA (2006) Measuring two-dimensional movements using a single InSAR pair. Geophys Res Lett 33:L16311Google Scholar
  18. Belliss SE, Pairman D, McNeill SJ (1998) Use of Radarsat data to map Landslide erosion in steep landforms. In: Application development and research opportunity (ADRO) final symposium. MontrealGoogle Scholar
  19. Belward AS, Stibig HJ, Eva H, Rembold F, Bucha T, Hartley A, Beuchle R, Khudhairy D, Michielon M, Mollicone D (2007) Mapping severe damage to land cover following the 2004 Indian Ocean tsunami using moderate spatial resolution satellite imagery. Int J Remote Sens 28:2977–2994Google Scholar
  20. Berardino P, Fornaro G, Lanari R, Sansoti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferometry. IEEE Trans Geosci Remote Sens 41:2375–2583Google Scholar
  21. Blaschke T (2010) Object based image analysis for remote sensing. Isprs J Photogramm Remote Sens 65:2–16Google Scholar
  22. Blaschke TS, Lang S, Hay G (2008) Object-based image analysis. Spatial concepts for knowledge-driven remote sensing applications. Springer, BerlinGoogle Scholar
  23. Bonn F, Dixon R (2005) Monitoring flood extent and forecasting excess runoff risk with Radarsat-1 data. Nat Hazards 35:377–393Google Scholar
  24. Booth AM, Roering JJ, Perron JT (2009) Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology 109:132–147Google Scholar
  25. Bovolo F, Bruzzone L (2007) A split-based approach to unsupervised change detection in large-size multitemporal images: application to tsunami-damage assessment. IEEE Trans Geosci Remote Sens 45:1658–1670Google Scholar
  26. Cakir Z, Akoglu AM, Balabbes S, Ergintac S, Meghraoui M (2005) Creeping along the Ismetpasa section of the North Anatolian fault (Western Turkey): rate and extent from InSAR. Earth Planet Sci Lett 238:225–234Google Scholar
  27. Casadei M, Dietrich W, Miller N (2003) Testing a model for predicting the timing and location of shallow landslide initiation in soil mantled landscapes. Earth Surf Proc Land 28:925–950Google Scholar
  28. Casson B, Delacourt C, Allemand P (2005) Contribution of multi-temporal remote sensing images to characterize landslide slip surface—application to the La Clapiere landslide France). Nat Hazards Earth Syst Sci 5:425–437Google Scholar
  29. Chen Y, Gillieson D (2009) Evaluation of landsat TM vegetation indices for estimating vegetation cover on semi-arid rangelands: a case study from Australia. Can J Remote Sens 35:435–446Google Scholar
  30. Chen R-F, Chang K-J, Angelier J, Chan Y-C, Deffontaines B, Lee C-T, Lin M-L (2006) Topographical changes revealed by high-resolution airborne LiDAR data: the 1999 Tsaoling landslide induced by the Chi-Chi earthquake. Eng Geol 88:160–172Google Scholar
  31. Chen F, Lin H, Li Z, Chen Q, Zhou J (2012) Interaction between permafrost and infrastructure along the Qinghai-Tibet Railway detected via jointly analysis of C- and L-band small baseline SAR interferometry. Remote Sens Environ 123:532–540Google Scholar
  32. Cheng KS, Wei C, Chang SC (2004) Locating landslides using multi-temporal satellite images. Adv Space Res 33:296–301Google Scholar
  33. Cheung S, Slatton KC, Cho H, Dean RG (2011) Multiscale parameterization of LIDAR elevations for reducing complexity in hydraulic models of coastal urban areas. J Appl Remote Sens 5:053508Google Scholar
  34. Cigna F, Bianchini S, Casagli N (2012a) How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): the PSI-based matrix approach. Landslides 10:267–283Google Scholar
  35. Cigna F, Osmanoğlu B, Cabral-Cano E, Dixon TH, Ávila-Olivera JA, Garduño-Monroy VH, DeMets C, Wdowinski S (2012b) Monitoring land subsidence and its induced geological hazard with Synthetic Aperture Radar Interferometry: a case study in Morelia, Mexico. Remote Sens Environ 117:146–161Google Scholar
  36. Cloude SR, Pottier E (1996) A review of target decomposition theorems in radar polarimetry. IEEE Trans Geosci Remote Sens 34:498–518Google Scholar
  37. Corsini A, Cervi F, Daehne A, Ronchetti F, Borgatti L (2009) Coupling geomorphic field observation and LIDAR derivatives to map complex landslides. In: Malet J, Remaitre A, Bogaard T (eds) Landslides processes—from geomorphologic mapping to dynamic modeling, proceedings of the landslide processes conference. StrasbourgGoogle Scholar
  38. Crosetto M, Gili JA, Monserrat O, Cuevas-González M, Corominas J, Serral D (2013) Interferometric SAR monitoring of the Vallcebre landslide (Spain) using corner reflectors. Nat Hazards Earth Syst Sci 13:923–933Google Scholar
  39. Cunningham D, Grebby S, Tansey K, Gosar A, Kastelic V (2006) Application of airborne LiDAR to mapping seismogenic faults in forested mountainous terrain, southeastern Alps, Slovenia. Geophys Res Lett 33:L20308Google Scholar
  40. Dietrich W, Bellugi D, De Asua RR (2001) Validation of the shallow landslide model, SHALSTAB, for forest management. Water Sci Appl 2:195–227Google Scholar
  41. Dinger JS, Zourarakis DP, Currens JC (2006) Spectral enhancement and automated extraction from Kentucky’s NAIP imagery of potential sinkhole features, Trigg County, Kentucky, USA- Initial Investigations. Environ Inform Archiv 4:312–323Google Scholar
  42. Domakinis C, Oikonomidis D, Astaras T (2008) Landslide mapping in the coastal area between the Strymonic Gulf and Kavala (Macedonia, Greece) with the aid of remote sensing and geographical information systems. Int J Remote Sens 29:6893Google Scholar
  43. Dragut L, Blaschke T (2006) Automated classification of landform elements using object-based image analysis. Geomorphology 81:330–344Google Scholar
  44. Dymond JR, Ausseil AG, Shepherd JD, Buettner L (2006) Validation of a region-wide model of landslide susceptibility in the Manawatu–Wanganui region of New Zealand. Geomorphology 74:70–79Google Scholar
  45. Eeckhaut MVD, Poesen J, Verstraeten G, Vanacker V, Nyssen J, Moeyersons J, Beek LPH, Vandekerckhove L (2007) Use of LIDAR-derived images for mapping old landslides under forest. Earth Surf Proc Land 32:754–769Google Scholar
  46. Elmahdy SI, Mostafa MM (2013) Natural hazards susceptibility mapping in Kuala Lumpur, Malaysia: an assessment using remote sensing and geographic information system (GIS). Geomat Nat Hazards Risk 4:71–91Google Scholar
  47. Engelbrecht J, Musekiwa C, Kemp J, Inggs MR (2013) Parameters affecting interferometric coherence—the case of a dynamic agricultural region. In: IEEE transactions on geoscience and remote sensing, pp 1–1Google Scholar
  48. Engelkemeir RM, Khan SD (2008) Lidar mapping of faults in Houston, Texas, USA. Geosphere 4:170Google Scholar
  49. Ferretti A, Prati C, Rocca F (2001) Permanent scatterers in SAR interferometry. IEEE Trans Geosci Remote Sens 39:8–20Google Scholar
  50. Ferretti A, Novali F, Burgmann R, Hilley G, Prati C (2004) InSAR permanent scatterer analysis reveals ups and downs in San Francisco Bay area. EOS Trans AGU 85(34):317–324Google Scholar
  51. Frank CL, Weiwei Z, Kingkarn S (2011) Observation of tsunami radiation at Tohoku by remote sensing. Sci Tsunami Hazards 30:223–232Google Scholar
  52. Freeman A, Durden SL (1998) A three-component scattering model for polarimetric SAR data. IEEE Trans Geosci Remote Sens 36:963–973Google Scholar
  53. French J (2003) Airborne LiDAR in support of geomorphological and hydraulic modelling. Earth Surf Proc Land 28:321–335Google Scholar
  54. Fuller IC, Heerdegen RG (2005) The February 2004 floods in the Manawatu, New Zealand: hydrological significance and impact on channel morphology. J Hydrol (New Zealand) 44:75–90Google Scholar
  55. Garay MJ, Diner DJ (2007) Multi-angle Imaging SpectroRadiometer (MISR) time-lapse imagery of tsunami waves from the 26 December 2004 Sumatra–Andaman earthquake. Remote Sens Environ 107:256–263Google Scholar
  56. Geudtner D, Winter R, Vachon P (1996) Flood monitoring using ERS-1 SAR interferometry coherence maps. Int Geosci Remote Sens Symp 2:966–968Google Scholar
  57. Gillespie TW, Chu J, Frankenberg E, Thomas D (2007) Assessment and prediction of natural hazards from satellite imagery. Prog Phys Geogr 31:459–470Google Scholar
  58. Godt JW, Baum RL, Savage WZ, Salciarini D, Schulz WH, Harp EL (2008) Transient deterministic shallow landslide modeling: requirements for susceptibility and hazard assessments in a GIS framework. Eng Geol 102:214–226Google Scholar
  59. Goff J, Lane E, Arnold J (2009) The tsunami geomorphology of coastal dunes. Nat Hazards Earth Syst Sci 9:847–854Google Scholar
  60. Gourmelen N, Amelung F, Casu F, Manzo M, Lanari R (2007) Mining-related ground deformation in Crescent Valley, Nevada: implications for sparse GPS networks. Geophys Res Lett 34Google Scholar
  61. Gower J (2007) The 26 December 2004 tsunami measured by satellite altimetry. Int J Remote Sens 28:2897–2913Google Scholar
  62. Gutiérrez F, Cooper A, Johnson K (2008) Identification, prediction, and mitigation of sinkhole hazards in evaporite karst areas. Environ Geol 53:1007–1022Google Scholar
  63. Haneberg WC, Cole WF, Kasali G (2009) High-resolution lidar-based landslide hazard mapping and modeling, UCSF Parnassus Campus, San Francisco, USA. Bull Eng Geol Environ 68:263–276Google Scholar
  64. Harding DJ, Berghoff GS, County K (2000) Fault scarp detection beneath dense vegetation cover: Airborne lidar mapping of the Seattle fault zone, Bainbridge Island. Kitsap Public Utility District, Washington StateGoogle Scholar
  65. Haugerud RA, Harding DJ, Johnson SY, Harless JL, Weaver CS, Sherrod BL (2003) High-resolution lidar topography of the Puget Lowland, Washington. GSA Today 13:4–10Google Scholar
  66. Herrera G, Tomás R, Lopez-Sanchez JM, Delgado J, Mallorqui JJ, Duque S, Mulas J (2007) Advanced DInSAR analysis on mining areas: La Union case study (Murcia, SE Spain). Eng Geol 90:148–159Google Scholar
  67. Hervas J, Barredo JI, Rosin PL, Pasuto A, Mantovani F, Silvano S (2003) Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy. Geomorphology 54:63–75Google Scholar
  68. Hilley GE, Burgmann R, Ferretti A, Novali F, Rocca F (2004) Dynamics of slow-moving landslides from permanent scatterer analysis. Science 304:1952–1955Google Scholar
  69. Hong S-H, Wdowinski S, Kim S-W, Won J-S (2010) Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR). Remote Sens Environ 114:2436–2447Google Scholar
  70. Hooper A, Zebker H, Segail P, Kampes B (2004) A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophys Res Lett 31:L23611. doi:10.21029/22004GL021737 Google Scholar
  71. Hooper A, Bekaert D, Spaans K, Arıkan M (2012) Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics 514–517:1–13Google Scholar
  72. Horritt M, Bates P (2001) Effects of spatial resolution on a raster based model of flood flow. J Hydrol 253:239–249Google Scholar
  73. Imhoff ML, Vermillion C, Story MH, Choudhury AM, Gafoor A, Polcyn F (1987) Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat data. Photogramm Eng Remote Sens 53:405–413Google Scholar
  74. Jaboyedoff M, Oppikofer T, Abellán A, Derron M-H, Loye A, Metzger R, Pedrazzini A (2010) Use of LIDAR in landslide investigations: a review. Nat Hazards 61:5–28Google Scholar
  75. Jones AF, Brewer PA, Johnstone E, Macklin MG (2007) High resolution interpretative geomorphological mapping of river valley environments using airborne LiDAR data. Earth Surf Proc Land 32:1574–1592Google Scholar
  76. Joyce KE, Glassey PJ, Dellow GD (2008) Methods for mapping landslides in New Zealand using satellite optical remote sensing. In: 14th Australasian remote sensing and photogrammetry conference, Darwin, AustraliaGoogle Scholar
  77. Joyce KE, Belliss S, Samsonov S, McNeill S, Glassey PJ (2009a) A review of the status of satellite remote sensing image processing techniques for mapping natural hazards and disasters. Prog Phys Geogr 33:183–207Google Scholar
  78. Joyce KE, Samsonov S, Manville V, Jongens R, Graettinger A, Cronin S (2009b) Remote sensing data types and techniques for lahar path detection: a case study at Mt Ruapehu, New Zealand. Remote Sens Environ 113:1778–1786Google Scholar
  79. Joyce KE, Dellow GD, Glassey PJ (2009b) Using remote sensing and spatial analysis to understand landslide distribution and dynamics in New Zealand. In: IEEE international geoscience and remote sensing symposium. Cape Town, South AfricaGoogle Scholar
  80. Joyce KE, Wright KC, Samonsov SV, Ambrosia VG (2009d) Remote sensing and the disaster management cycle. In: Jedlovec G (ed) Advances in geoscience and remote sensing. In-Tech Publishing, Vienna, pp 317–346Google Scholar
  81. Kaab A (2002) Monitoring high-mountain terrain deformation from repeated air- and spaceborne optical data: examples using digital aerial imagery and ASTER data. Isprs J Photogramm Remote Sens 57:39–52Google Scholar
  82. Kemeny J, Turner K (2008) Ground-based LiDAR rock slope mapping and assessment. In: US Department of Transporation, Lakewood, ColoradoGoogle Scholar
  83. Kim S-W, Wdowinski S, Amelung F, Dixon TH, Won J-S (2013) Interferometric coherence analysis of the Everglades Wetlands, South Florida. In: IEEE transactions on geoscience and remote sensing (in press)Google Scholar
  84. Klemas V (2011) Remote sensing techniques for studying coastal ecosystems: an overview. J Coast Res 27:2–17Google Scholar
  85. Klemas V (2012) Remote sensing of coastal and ocean currents: an overview. J Coast Res 282:576–586Google Scholar
  86. Klimchouk A (2002) Subsidence hazards in different types of karst: evolutionary and speleogenetic approach. Int J Speleol 31:5–18Google Scholar
  87. Kondo H, Toda S, Okumura K, Takada K, Chiba T (2008) A fault scarp in an urban area identified by LiDAR survey: a case study on the Itoigawa–Shizuoka Tectonic Line, central Japan. Geomorphology 101:731–739Google Scholar
  88. Kouchi K, Yamazaki F (2007) Characteristics of tsunami-affected areas in moderate-resolution satellite images. Geosci Remote Sens IEEE Trans 45:1650–1657Google Scholar
  89. Kumar KV, Martha TR, Roy PS (2006) Mapping damage in the Jammu and Kashmir caused by 8 October 2005 M-w 7.3 earthquake from the Cartosat-1 and Resourcesat-1 imagery. Int J Remote Sens 27:4449–4459Google Scholar
  90. Kumar A, Chingkhei RK, Dolendro T (2007) Tsunami damage assessment: a case study in Car Nicobar Island, India. Int J Remote Sens 28:2937–2959Google Scholar
  91. Lahousse T, Chang KT, Lin YH (2011) Landslide mapping with multi-scale object-based image analysis—a case study in the Baichi watershed, Taiwan. Nat Hazards Earth Syst Sci 11:2715–2726Google Scholar
  92. Lee JS, Pottier E (2009) Polarimetric radar imaging from basics to applications. CRC Press, Boca RatonGoogle Scholar
  93. Leprince S, Ayoub F, Klingert Y, Avouac JP (2007a) Co-registration of optically sensed images and correlation (COSI-Corr): an operational methodology for ground deformation measurements. In: IEEE, pp. 1943–1946Google Scholar
  94. Leprince S, Barbot S, Ayoub F, Avouac JP (2007b) Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. Geosci Remote Sens IEEE Trans 45:1529–1558Google Scholar
  95. Leprince S, Berthier E, Ayoub F, Delacourt C, Avouac J (2008) Monitoring Earth surface dynamics with optical imagery. EOS Trans Am Geophys Union 89:10Google Scholar
  96. Lewis AJ, Henderson FM, Holcomb DW (1998) Radar fundamentals: the geoscience perspective. In: Henderson FM, Lewis AJ (eds) Principles and applications of imaging radar. Wiley, New York, pp 131–181Google Scholar
  97. Li MC, Cheng L, Gong JY, Liu YX, Chen ZJ, Li FX, Chen G, Chen D, Song XG (2008) Post-earthquake assessment of building damage degree using LiDAR data and imagery. Sci China Ser E: Technol Sci 51:133–143Google Scholar
  98. Liu JG, Lee H, Pearson T (2004a) Detection of rapid erosion in SE Spain using ERS SAR interferometric coherence imagery. Proc SPIE Remote Sens Earth Sci Ocean Sea Ice Appl 3868:525–535Google Scholar
  99. Liu JG, Mason P, Hilton F, Lee H (2004b) Detection of rapid erosion in SE Spain: a GIS approach based on ERS SAR coherence imagery. Photogramm Eng Remote Sens 70:1179–1185Google Scholar
  100. Lu P, Stumpf A, Kerle N, Casagli N (2011) Object-oriented change detection for landslide rapid mapping. IEEE Geosci Remote Sens Lett 8:701–705Google Scholar
  101. Manyatsi AM, Ntshangase N (2008) Mapping of soil erosion using remotely sensed data in Zombodze South, Swaziland. Phys Chem Earth 33:800–806Google Scholar
  102. Marks K, Bates P (2000) Integration of high-resolution topographic data with floodplain flow models. Hydrol Process 14:2109–2122Google Scholar
  103. Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar KV (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116:24–36Google Scholar
  104. Martha TR, Kerle N, van Westen CJ, Jetten V, Kumar KV (2011) Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. Geosci Remote Sens IEEE Trans 49:4928–4943Google Scholar
  105. Mason DC, Cobby DM, Horritt MS, Bates PD (2003) Floodplain friction parameterization in two-dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry. Hydrol Process 17:1711–1732Google Scholar
  106. Massonnet D, Feigl KL (1998) Radar interferometry and its application to changes in the Earth's surface. Rev Geophys 36(4):441–500. doi:10.1029/97RG03139 Google Scholar
  107. Mazzotti S, Lambert A, Van der Koij M, Mainville A (2009) Impact of anthropogenic subsidence on relative sea-level rise in the Fraser River delta. Geology 37:771–774Google Scholar
  108. McKean J, Roering J (2004) Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology 57:331Google Scholar
  109. Mondini AC, Guzzetti F, Reichenbach P, Rossi M, Cardinali M, Ardizzone F (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115:1743–1757Google Scholar
  110. Moreira A, Prats-Iraola P, Younis M, Krieger G, Hajnsek I, Papathanassiou KP (2013) A tutorial on synthetic aperture radar. IEEE Geosci Remote Sens Mag 1:6–43Google Scholar
  111. Navarro-Sanchez VD, Lopez-Sanchez JM, Vicente-Guijalba F (2010) A contribution of polarimetry to satellite differential SAR interferometry: increasing the number of pixel candidates. IEEE Geosci Remote Sens Lett 7:276–280Google Scholar
  112. Nazarenko DM, Martenson D, Rossignol S, Staples G (1995) RADARSAT image characteristics and application requirements. In: Record of the IEEE 1995 international radar conference, pp 351–355Google Scholar
  113. Nichol J, Wong MS (2005a) Detection and interpretation of landslides using satellite images. Land Degrad Dev 16:243–255Google Scholar
  114. Nichol J, Wong MS (2005b) Satellite remote sensing for detailed landslide inventories using change detection and image fusion. Int J Remote Sens 26:1913–1926Google Scholar
  115. Nichol JE, Shaker A, Wong MS (2006) Application of high-resolution stereo satellite images to detailed landslide hazard assessment. Geomorphology 76:68–75Google Scholar
  116. Nof RN, Baer G, Ziv A, Raz E, Atzori S, Salvi S (2013) Sinkhole precursors along the Dead Sea, Israel, revealed by SAR interferometry. Geology. doi:10.1130/G34505.1
  117. O’Grady D, Leblanc M (2014) Radar mapping of broad-scale inundation: challenges and opportunities in Australia. Stoch Environ Res Risk Assess 28(1):29–38Google Scholar
  118. Oberstadler R, Honsch H, Hutch D (1997) Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: a case study in Germany. Hydrol Process 11:1415–1425Google Scholar
  119. Ormsby JP, Blanchard BJ, Blanchard AJ (1985) Detection of lowland flooding using active microwave systems. Photogramm Eng Remote Sens 51:317–328Google Scholar
  120. Ostir K, Veljanovski T, Podobnikar T, Stancic Z (2003) Application of satellite remote sensing in natural hazard management: the Mount Mangart landslide case study. Int J Remote Sens 24:3983–4002Google Scholar
  121. Ostrowski JA, Cheng P (2000) DEM extraction from stereo SAR satellite imagery. In: Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International, vol 5. pp 2176–2178Google Scholar
  122. Ouzounov D, Freund F (2004) Mid-infrared emission prior to strong earthquakes analyzed by remote sensing data. Adv Space Res 33:268–273Google Scholar
  123. Pairman D, Belliss SE, McNeill SJ (1997) Terrain influences on SAR backscatter around Mt. Taranaki, New Zealand. IEEE Trans Geosci Remote Sens 35:924–932Google Scholar
  124. Papathanassiou KP, Cloude SR (2001) Single-baseline polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 39:2352–2363Google Scholar
  125. Perlock PA, González PJ, Tiampo KF, Rodríguez-Velasco G, Samsonov S, Fernández J (2008) Time evolution of deformation using time series of differential interferograms: application to La Palma Island (Canary Islands). Pure appl Geophys 165:1531–1554Google Scholar
  126. Prasannakumar V, Shiny R, Geetha N, Vijith H (2011) Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India. Environ Earth Sci 64(4):965–972Google Scholar
  127. Prentice C, Mann P, Crone A, Gold R, Hudnut K, Briggs R, Koehler R, Jean P (2010) Seismic hazard of the Enriquillo–Plantain Garden fault in Haiti inferred from palaeoseismology. Nat Geosci 3:789–793Google Scholar
  128. Quan S, Kvitek RG, Smith DP, Griggs GB (2013) Using vessel-based LIDAR to quantify coastal erosion during El Niño and Inter-El Niño Periods in Monterey Bay, California. J Coast Res 288:555–565Google Scholar
  129. Quigley M, Villamor P, Furlong K, Beavan J, Van Dissen R, Litchfield N, Stahl T, Duffy B, Bilderback E, Noble D (2010) Previously unknown fault shakes New Zealand’s South Island. EOS Trans 91:469–470Google Scholar
  130. Quigley M, Van Dissen R, Litchfield N, Villamor P, Duffy B, Barrell D, Furlong K, Stahl T, Bilderback E, Noble D (2011) Surface rupture during the 2010 Mw 7.1 Darfield (Canterbury) earthquake: implications for fault rupture dynamics and seismic-hazard analysis. Geology 40:55–58Google Scholar
  131. Rathje EM, Adams BJ (2008) The role of remote sensing in earthquake science and engineering: opportunities and challenges. Earthq Spectra 24:471Google Scholar
  132. Rau JY, Chen LC, Liu JK, Wu TH (2007) Dynamics monitoring and disaster assessment for watershed management using time-series satellite images. IEEE Trans Geosci Remote Sens 45:1641–1649Google Scholar
  133. Reutebuch SE, McGaughey RJ, Andersen HE, Carson WW (2003) Accuracy of a high-resolution lidar terrain model under a conifer forest canopy. Can J Remote Sens 29:527–535Google Scholar
  134. Richter A, Faust D, Mass HG (2013) Dune cliff erosion and beach width change at the northern and southern spits of Sylt detected with multi-temporal Lidar. CATENA 103:103–111Google Scholar
  135. Ritchie JC, Grissinger EH, Murphey JB, Garbrecht JD (1994) Measuring channel and gully cross-sections with an airborne laser altimeter. Hydrol Process 8:237–243Google Scholar
  136. Roemer H, Kaiser G, Sterr H, Ludwig R (2010) Using remote sensing to assess tsunami-induced impacts on coastal forest ecosystems at the Andaman Sea coast of Thailand. Nat Hazards Earth Syst Sci 10:729–745Google Scholar
  137. Rosin PL, Hervas J (2005) Remote sensing image thresholding methods for determining landslide activity. Int J Remote Sens 26:1075–1092Google Scholar
  138. Rott H, Nagler T (2006) The contribution of radar interferometry to the assessment of landslide hazards. Adv Space Res 37:710–719Google Scholar
  139. Samsonov S, d’Oreye N (2012) Multidimensional time series analysis of ground deformation from multiple InSAR data sets applied to Virunga Volcanic Province. Geophys J Int 19:1095–1108Google Scholar
  140. Samsonov S, Tiampo K (2011) Polarization phase difference analysis for selection of persistent scatterers in SAR interferometry. IEEE Geosci Remote Sens Lett 8:331–335Google Scholar
  141. Samsonov S, Tiampo K, González PJ, Manville V, Jolly G (2010) Ground deformation occurring in the city of Auckland, New Zealand, and observed by Envisat interferometric synthetic aperture radar during 2003–2007. J Geophys Res 115Google Scholar
  142. Samsonov S, Beavan J, Gonzalez PJ, Tiampo K, Fernandez J (2011a) Ground deformation in the Taupo Volcanic Zone, New Zealand, observed by ALOS PALSAR interferometry. Geophys J Int 187:147–160Google Scholar
  143. Samsonov S, van der Kooij M, Tiampo K (2011b) A simultaneous inversion for deformation rates and topographic errors of DInSAR data utilizing linear least square inversion technique. Comput Geosci 37:1083–1091Google Scholar
  144. Samsonov S, Gonzalez PJ, Tiampo K, d’Oreye N (2013) Spatio-temporal analysis of ground deformation occurring near Rice Lake, Saskatchewan, and observed by Radarsat-2 DInSAR during 2008–2011. Can J Remote Sens 39:27–33Google Scholar
  145. Sansosti E, Casu F, Manzo M, Lanari R (2010) Space-borne radar interferometry techniques for the generation of deformation time series: an advanced tool for Earth’s surface displacement analysis. Geophys Res Lett 37:L20305. doi:10.1029/2010GL044379
  146. Schumann G, Matgen P, Cutler MEJ, Black A, Hoffmann L, Pfister L (2008) Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM. ISPRS J Photogramm Remote Sens 63:283–296Google Scholar
  147. Seale LD, Florea LJ, Vacher H, Brinkmann R (2008) Using ALSM to map sinkholes in the urbanized covered karst of Pinellas County, Florida—1, methodological considerations. Environ Geol 54:995–1005Google Scholar
  148. Singhroy V (1995) SAR integrated techniques for geohazard assessment. Adv Space Res 15:67–78Google Scholar
  149. Singhroy V, Molch K (2004) Characterizing and monitoring rockslides from SAR techniques. Adv Space Res 33:290–295Google Scholar
  150. Singhroy V, Mattar KE, Gray AL (1998) Landslide characterisation in Canada using interferometric SAR and combined SAR and TM images. Adv Space Res 21:465–476Google Scholar
  151. Strozzi T, Luckman A, Murray T, Wegmuller U, Werner C (2002) Glacier motion estimation using SAR offset-tracking procedures. IEEE Trans Geosci Remote Sens 40Google Scholar
  152. Strozzi T, Wegmuller U, Werner CL, Wiesmann A, Spreckels V (2003) JERS SAR interferometry for land subsidence monitoring. IEEE Trans Geosci Remote Sens 41(7):1702–1708. doi:10.1109/TGRS.2003.813273 Google Scholar
  153. Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using Random Forests. Remote Sens Environ 115:2564–2577Google Scholar
  154. Tarolli P, Sofia G, Dalla Fontana G (2010) Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion. Nat Hazards. doi:10.1007/s11069-11010-19695-11062
  155. Thoma DP, Gupta SC, Bauer ME, Kirchoff CE (2005) Airborne laser scanning for riverbank erosion assessment. Remote Sens Environ 95:493–501Google Scholar
  156. Tiampo KF, González PJ, Samsonov SS (2013) Results for aseismic creep on the Hayward fault using polarization persistent scatterer InSAR. Earth Planet Sci Lett 367:157–165Google Scholar
  157. Tralli DM, Blom RG, Zlotnicki V, Donnellan A, Evans DL (2005) Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. Isprs J Photogramm Remote Sens 59:185–198Google Scholar
  158. Trevisani S, Cavalli M, Marchi L (2009) Variogram maps from LiDAR data as fingerprints of surface morphology on scree slopes. Nat Hazards Earth Syst Sci 9:129–133Google Scholar
  159. Tsutsui K, Rokugawa S, Nakagawa H, Miyazaki S, Cheng CT, Shiraishi T, Yang SD (2007) Detection and volume estimation of large-scale landslides based on elevation-change analysis using DEMs extracted from high-resolution satellite stereo imagery. IEEE Trans Geosci Remote Sens 45:1681–1696Google Scholar
  160. Vacher H, Seale LD, Florea LJ, Brinkmann R (2008) Using ALSM to map sinkholes in the urbanized covered karst of Pinellas County, Florida—2. Accuracy statistics. Environ Geol 54:1007–1015Google Scholar
  161. Van Den Eeckhaut M, Hervás J (2012) State of the art of national landslide databases in Europe and their potential for assessing landslide susceptibility, hazard and risk. Geomorphology 139–140:545–558Google Scholar
  162. 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 173–174:30–42Google Scholar
  163. van Zyl JJ (1989) Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Trans Geosci Remote Sens 27:36–45Google Scholar
  164. Voigt S, Kemper T, Riedlinger T, Kiefl R, Scholte K, Mehl H (2007) Satellite image analysis for disaster and crisis-management support. IEEE Trans Geosci Remote Sens 45:1520–1528Google Scholar
  165. Waite WP, MacDonald HC (1971) Vegetation penetration with K-band radars. IEEE Trans Geosci Remote Sens GE-9:147–155Google Scholar
  166. Waltham T, Bell FG, Culshaw M (2005) Sinkholes and subsidence. Springer, BerlinGoogle Scholar
  167. Wan S, Lei TS, Chou TY (2013) Optimized object-based image classification: development of landslide knowledge decision support system. Arabian J Geosci. doi:10.1007/s12517-013-0952-z
  168. Warren WM, Wielchowsky CC (1973) Aerial remote sensing of carbonate terranes in Shelby County, Alabama. Ground Water 11:14–26Google Scholar
  169. Webster TL, Forbes DL, Dickie S, Shreenan R (2004) Using topographic lidar to map flood risk from storm-surge events for Charlottetown, Prince Edward Island, Canada. Can J Remote Sens 30:64–76Google Scholar
  170. Wegmüller U, Werner CL (1996) Land applications using ERS-1/2 Tandem data. In: ‘Fringe 96’ workshop on ERS SAR interferometry. ZurichGoogle Scholar
  171. Wegmüller U, Werner CL, Nüesch D, Borgeaud M (1995) Land-surface analysis using ERS-1 SAR interferometry. ESA Bull 81:30–37Google Scholar
  172. Whitworth MCZ, Giles DP, Murphy W (2005) Airborne remote sensing for landslide hazard assessment: a case study on the Jurassic escarpment slopes of Worcestershire, UK. Q J Eng Geol Hydrogeol 38:285–300Google Scholar
  173. Wikantika K, Sinaga A, Hadi F, Darmawan S (2007) Quick assessment on identification of damaged building and land-use changes in the post-tsunami disaster with a quick-look image of IKONOS and Quickbird (a case study in Meulaboh City, Aceh). Int J Remote Sens 28:3037–3044Google Scholar
  174. Wöppelmann G, Le Cozannet G, de Michele M, Raucoules D, Cazenave A, Garcin M, Hanson S, Marcos M, Santamaría-Gómez A (2013) Is land subsidence increasing the exposure to sea level rise in Alexandria, Egypt? Geophys Res Lett 40(12):2953–2957Google Scholar
  175. Xu H, Dvorkin J, Nur A (2001) Linking oil production to surface subsidence from satellite radar interferometry. Geophys Res Lett 28:1307–1310Google Scholar
  176. Yang MD, Su TC, Hsu CH, Chang KC, Wu AM (2007) Mapping of the 26 December 2004 tsunami disaster by using FORMOSAT-2 images. Int J Remote Sens 28:3071–3091Google Scholar
  177. Yen J-Y, Chen K-S, Chang C-P, Boerner W-M (2008) Evaluation of earthquake potential and surface deformation by differential interferometry. Remote Sens Environ 112:782–795Google Scholar
  178. Zhou G, Xie M (2009) Coastal 3-D morphological change analysis using LiDAR series data: a case study of Assateague Island National Seashore. J Coast Res 252:435–447Google Scholar
  179. Zielke O, Arrowsmith JR, Ludwig LG, Akçiz SO (2010) Slip in the 1857 and earlier large earthquakes along the Carrizo Plain, San Andreas Fault. Science 327:1119Google Scholar
  180. Zwenzner H, Voigt S (2009) Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrol Earth Syst Sci 13:567–576Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • K. E. Joyce
    • 1
  • S. V. Samsonov
    • 2
  • S. R. Levick
    • 3
    • 6
  • J. Engelbrecht
    • 4
  • S. Belliss
    • 5
  1. 1.Research Institute for Environment and LivelihoodsCharles Darwin UniversityDarwinAustralia
  2. 2.Canada Centre for Mapping and Earth ObservationNatural Resources CanadaOttawaCanada
  3. 3.GNS ScienceLower HuttNew Zealand
  4. 4.Western Cape UnitCouncil for GeoscienceCape TownSouth Africa
  5. 5.Landcare Research NZ LimitedLincolnNew Zealand
  6. 6.Max Planck Institute for BiogeochemistryJenaGermany

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