Pre- and Post-Fire Comparison of Forest Areas in 3D

  • Devrim AkcaEmail author
  • Efstratios Stylianidis
  • Daniela Poli
  • Armin Gruen
  • Orhan Altan
  • Martin Hofer
  • Konstantinos Smagas
  • Victor Sanchez Martin
  • Andreas Walli
  • Elisa Jimeno
  • Alejandro Garcia
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


A satellite processing platform for high resolution forest assessment (FORSAT) was developed. It generates the digital surface models (DSMs) of the forest canopy by advanced processing of the very-high resolution (VHR) optical satellite imagery and automatically matches the pre- and post-fire DSMs for 3D change detection. The FORSAT software system can perform the following tasks: pre-processing, point measurement, orientation, quasi-epipolar image generation, image matching, DSM extraction, orthoimage generation, photogrammetric restitution either in mono-plotting mode or in stereo models, 3D surface matching, co-registration, comparison and change detection. It can thoroughly calculate the planimetric and volumetric changes between the epochs. It supports most of the VHR optical imagery commonly used for civil applications. Capabilities of FORSAT have been tested in two real forest fire cases, where the burned areas are located in Cyprus and Austria. The geometric characteristics of burned forest areas have been identified both in 2D plane and 3D volume dimensions, using pre- and post-fire optical image data from different sensors. The test studies showed that FORSAT is an operational software capable of providing spatial (3D) and temporal (4D) information for monitoring of forest fire areas and sustainable forest management. Beyond the wildfires, it can be used for many other forest information needs.


  1. Abdollahi M, Islam T, Gupta A, Hassan QK (2018) An advanced forest fire danger forecasting system: integration of remote sensing and historical sources of ignition data. Remote Sens 10:923. Scholar
  2. Ackermann F, Hahn M (1991) Image pyramids for digital photogrammetry. In: Ebner H, Fritsch D, Heipke C (eds) Digital photogrammetric systems. Wichmann, Karlsruhe, pp 43–58Google Scholar
  3. Addison P, Oommen T (2018) Utilizing satellite radar remote sensing for burn severity estimation. Int J Appl Earth Obs Geoinf 73:292–299CrossRefGoogle Scholar
  4. Adelabu SA, Adepoju KA, Mofokeng OD (2018) Estimation of fire potential index in mountainous protected region using remote sensing. Geocarto International.
  5. Akca D, Gruen A (2005) Recent advances in least squares 3D surface matching. In: Gruen A, Kahmen H (eds) Proceedings of the optical 3-D measurement techniques VII, Vienna, Austria, 3–5 October 2005, vol. II, pp 197–206Google Scholar
  6. Akca D, Gruen A, Alkis Z, Demir N, Breuckmann B, Erduyan I, Nadir E (2006) 3D modeling of the Weary Herakles statue with a coded structured light system. Int Arch Photogramm Remote Sens Spat Inf Sci 36(5):14–19Google Scholar
  7. Akca D (2007) Least Squares 3D surface matching. Ph.D. thesis, Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, Mitteilungen Nr. 92, p 78.
  8. Akca D, Gruen A (2007) Generalized Least Squares multiple 3D surface matching. Int Archives Photogramm Remote Sens Spat Inf Sci 36(3/W52):1–7Google Scholar
  9. Akca D, Remondino F, Novàk D, Hanusch T, Schrotter G, Gruen A (2007) Performance evaluation of a coded structured light system for cultural heritage applications. Proc. of SPIE-IS&T Electronic Imaging, Videometrics IX, San Jose, California, January 29–30. SPIE 6491:64910V-1–12Google Scholar
  10. Akca D (2010) Co-registration of surfaces by 3D Least Squares matching. Photogramm Eng Remote Sens 76(3):307–318CrossRefGoogle Scholar
  11. Akca D, Freeman M, Sargent I, Gruen A (2010) Quality assessment of 3D building data. Photogram Rec 25(132):339–355CrossRefGoogle Scholar
  12. Akca D (2012) 3D modeling of cultural heritage objects with a structured light system. Mediterr Archaeol Archaeom 12(1):139–152Google Scholar
  13. Akca D, Seybold HJ (2016) Monitoring of a laboratory-scale inland-delta formation using a structured-light system. Photogram Rec 31(154):121–142CrossRefGoogle Scholar
  14. Akca D, Stylianidis E, Smagas K, Hofer M, Poli D, Gruen A, Martin VS, Altan O, Walli A, Jimeno E, Garcia A (2016) Volumetric forest change detection through VHR satellite imagery. Int Archives Photogramm Remote Sens Spat Inf Sci 41(B8):1213–1220CrossRefGoogle Scholar
  15. Almeida-Filho R, Rosenqvist A, Shimabukuro YE, dos Santos JR (2005) Evaluation and perspectives of using multitemporal L-band SAR data to monitor deforestation in the Brazilian Amazonia. IEEE Geosci Remote Sens Lett 2(4):409–412CrossRefGoogle Scholar
  16. Almeida-Filho R, Rosenqvist A, Shimabukuro YE, Silva-Gomez R (2007) Detection deforestation with multitemporal L-band SAR imagery: a case study in western Brazilian Amazonia. Int J Remote Sens 28(6):1383–1390CrossRefGoogle Scholar
  17. Almeida-Filho R, Shimabukuro YE, Rosenqvist A, Sanchez GA (2009) Using dual-polarized ALOS PALSAR data for detecting new fronts of deforestation in the Brazilian Amazonia. Int J Remote Sens 30(14):3735–3743CrossRefGoogle Scholar
  18. Altan O, Backhaus R, Boccardo P, van Manen N, Tonolo FG, Trinder J, Zlatanova S (2013). The value of geoinformation for disaster and risk management (VALID), Joint Board of Geospatial Information Society (JB GIS), Copenhagen, ISBN 97887-90907-88-4Google Scholar
  19. Alves DS (2002) Space-time dynamics of deforestation in Brazilian Amazonia. Int J Remote Sens 23(14):2903–2908CrossRefGoogle Scholar
  20. Anderson LO, Shimabukuro YE, Defries RS, Morton D (2005) Assessment of deforestation in near real time over the Brazilian Amazon using multitemporal fraction images derived from Terra MODIS. IEEE Geosci Remote Sens Lett 2(3):315–318CrossRefGoogle Scholar
  21. Baillarin F, Souza C, Gonzales G (2008) Use of Formosat-2 satellite imagery to detect near real time deforestation in Amazonia. IEEE International Geoscience & Remote Sensing Symposium (IGARSS’2008).
  22. Baltsavias E, Kocaman S, Akca D, Wolff K (2007) Geometric and radiometric investigations of Cartosat-1 Data. ISPRS Workshop on high resolution earth imaging for geospatial information, Hannover, Germany, 29 May–1 June 2007Google Scholar
  23. Bodart C, Eva H, Beuchle R et al (2011) Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics. ISPRS J Photogramm Remote Sens 66:555–563CrossRefGoogle Scholar
  24. Burnett JD, Wing MG (2018) A low-cost near-infrared digital camera for fire detection and monitoring. Int J Remote Sens 39(3):741–753CrossRefGoogle Scholar
  25. Cabral AIR, Silva S, Silva PC, Vanneschi L, Vasconcelos MJ (2018) Burned are estimations derived from Landsat ETM+ and OLI data: comparing Genetic Programming with Maximum Likelihood and classification and regression trees. ISPRS J Photogramm Remote Sens 142:94–105CrossRefGoogle Scholar
  26. Cailliez F (1992) Forest volume estimation and yield prediction. FAO For Paper 22(1):98Google Scholar
  27. Camaro W, Steffenino S, Vigna R (2013) Fire risk mapping and fire detection and monitoring. In: The value of Geoinformation for disaster and risk management (VALID), joint board of geospatial information society (JB GIS), Copenhagen, ISBN 97887-90907-88-4Google Scholar
  28. Colson D, Petropoulos GP, Ferentinos KP (2018) Exploring the potential of Sentinels-1 & 2 of the Copernicus Mission in support of rapid and cost-effective wildfire assessment. Int J Appl Earth Obs Geoinf 73:262–276CrossRefGoogle Scholar
  29. Cucchiaro S, Cavalli M, Vericat D, Crema S, Llena M, Beinat A, Marchi L, Cazorzi F (2018) Monitoring topographic changes through 4D-structure-from-motion photogrammetry: Application to a debris-flow channel. Environ Earth Sci 77:632. Scholar
  30. Di Maio Mantovani AC, Setzer AW (1997) Deforestation detection in the Amazon with an AVHRR-based system. Int J Remote Sens 18(2):273–286CrossRefGoogle Scholar
  31. Ebner H, Strunz G (1988) Combined point determination using digital terrain models as control information. Int Archives Photogramm Remote Sens 27(B11/3):578–587Google Scholar
  32. Edwards AC, Russell-Smith J, Maier SW (2018) A comparison and validation of satellite-derived fire severity mapping techniques in fire prone north Australian savannas: extreme fires and tree stem mortality. Remote Sens Environ 206:287–299CrossRefGoogle Scholar
  33. Eva H, Carboni S et al (2010) Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery. ISPRS J Photogramm Remote Sens 65:191–197CrossRefGoogle Scholar
  34. Fernandez-Garcia V, Santamarta M, Fernandez-Manso A, Quintano C, Marcos E, Calvo L (2018) Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery. Remote Sens Environ 206:205–217CrossRefGoogle Scholar
  35. Filizzola C, Corrado R, Marchese F, Mazzeo G, Paciello R, Pergola N, Tramutoli V (2016) RST-FIRES, an exportable algorithm for early-fire detection and monitoring: description, implementation, and field validation in the case of the MSG-SEVIRI sensor. Remote Sens Environ 186:196–216CrossRefGoogle Scholar
  36. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201CrossRefGoogle Scholar
  37. Garcia-Lazaro JR, Moreno-Ruiz JA, Riano D, Arbelo M (2018) Estimation of burned area in the northeastern Siberian Boreal Forests from a long-term data record (LTDR) 1982-2015 time series. Remote Sens 10:940. Scholar
  38. Giglio L, Descloitres J, Justice CO, Kaufman YJ (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87:273–282CrossRefGoogle Scholar
  39. Giglio L, Schroeder W, Justice CO (2016) The collection 6 MODIS active fire detection algorithms and fire products. Remote Sens Environ 178:31–41CrossRefGoogle Scholar
  40. Grodecki J, Dial G (2003) Block Adjustment of High-Resolution Satellite Images Described by Rational Polynomials. Photogramm Eng Remote Sens 69(1):59–68CrossRefGoogle Scholar
  41. Gruen A, Poli D, Zhang L (2004) SPOT-5/HRS stereo images orientation and automated DSM generation. Int Archives Photogramm Remote Sens Spat Inf Sci 35(1):421–432Google Scholar
  42. Gruen A, Akca D (2005) Least squares 3D surface and curve matching. ISPRS J Photogramm Remote Sens 59(3):151–174CrossRefGoogle Scholar
  43. GW website (2018) Insitu ScanEagle UAS helps suppress wildfires. Accessed 09 Oct 2018
  44. Haboudane D, Bahri EM (2008) Deforestation detection and monitoring in cedar forests of the Moroccan Middle-Atlas Mountains. IEEE International Geoscience & Remote Sensing Symposium (IGARSS’2007).
  45. Heipke C, Mayer H, Wiedemann C, Jamet O (1997) Evaluation of automatic road extraction. Int Archives Photogramm Remote Sens 32(3–2W3):47–56Google Scholar
  46. Ichii K, Maruyama M, Yamaguchi Y (2003) Multi-temporal analysis of deforestation in Rondonia state in Brazil using Landsat MSS, ETM+ and NOAA AVHRR imagery and its relationship to changes in the local hydrological environment. Int J Remote Sens 24(22):4467–4479CrossRefGoogle Scholar
  47. Isoguchi O, Shimada M, Uryu Y (2009) A preliminary study on deforestation monitoring in Sumatra island by PALSAR. IEEE International Geoscience & Remote Sensing Symposium (IGARSS’2009).
  48. Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, Roy DP Morisette JT (2002a) An overview of MODIS Land data processing and product status. Remote Sensing of Environment 83:3–15CrossRefGoogle Scholar
  49. Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002b) The MODIS fire products. Remote Sens Environ 83:244–262CrossRefGoogle Scholar
  50. Koch B (2010) Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens 65:581–590CrossRefGoogle Scholar
  51. Krasovskii A, Khabarov N, Pirker J, Kraxner F, Yowargana P, Schepaschenko D, Obersteiner M (2018) Forests 9:437. Scholar
  52. Koltunov A, Ustin SL, Quayle B, Schwind B, Ambrosia VG, Li W (2016) The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm. Remote Sens Environ 184:436–453CrossRefGoogle Scholar
  53. Lee H (2008) Mapping deforestation and age of evergreen trees by applying a binary coding method to time-series Landsat November images. IEEE Trans Geosci Remote Sens 46(11):3926–3936CrossRefGoogle Scholar
  54. Li X, Zhang H, Yang G, Ding Y, Zhao J (2018) Post-fire vegetation succession and surface energy fluxes derived from remote sensing. Remote Sens 10:1000. Scholar
  55. Lin Z, Chen F, Niu Z, Li B, Yu B, Jia H, Zhang M (2018) An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data. Remote Sens Environ 211:376–387CrossRefGoogle Scholar
  56. Mancini LD, Elia M, Barbati A, Salvati L, Corona P, Lafortezza R, Sanesi G (2018) Are wildfires knocking on the built-up areas door? Forests 9:234. Scholar
  57. Mayr MJ, Vanselow KA, Samimi C (2018) Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000–2016) on the controls of fire activity in Namibia from spatial predictive models. Ecol Ind 91:324–337CrossRefGoogle Scholar
  58. McCarley TR, Kolden CA, Vaillant NM, Hudak AT, Smith AMS, Wing BM, Kellogg BS, Kreitler J (2017) Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure. Remote Sens Environ 191:419–432CrossRefGoogle Scholar
  59. McKeown DM, Bulwinkle T, Cochran S, Harvey W, McGlone C, Shufelt JA (2000) Performance evaluation for automatic feature extraction. Int Archives Photogramm Remote Sens 33(B2):379–394Google Scholar
  60. Meng R, Wu J, Schwager KL, Zhao F, Dennison PE, Cook BD, Brewster K, Green TM, Serbin SP (2017) Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem. Remote Sens Environ 191:95–109CrossRefGoogle Scholar
  61. Millington AC, Velez-Liendo XM, Bradley AV (2003) Scale dependence in multitemporal mapping of forest fragmentation in Bolivia: implications for explaining temporal trends in landscape ecology and applications to biodiversity conservation. ISPRS J Photogramm Remote Sens 57:289–299CrossRefGoogle Scholar
  62. Mitchell HL, Chadwick RG (1999) Digital photogrammetric concepts applied to surface deformation studies. Geomatica 53(4):405–414Google Scholar
  63. Mondal P, Southworth J (2010) Protection vs. commercial management: spatial and temporal analysis of land cover changes in the tropical forests of Central India. For Ecol Manage 259:1009–1017CrossRefGoogle Scholar
  64. Mora B, Wulder MA, White JC, Hobart G (2013) Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LiDAR. Remote Sens 5:2308–2326CrossRefGoogle Scholar
  65. Navarro G, Caballero I, Silva G, Parra PC, Vazquez A, Caldeira R (2017) Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. Int J Appl Earth Observ Geoinf 58:97–106CrossRefGoogle Scholar
  66. Nyongesa KW, Vacik H (2018) Fire management in Mount Kenya: a case study of Gathiuru forest station. Forests 9:481. Scholar
  67. Pahari K, Murai S (1999) Modelling for prediction of global deforestation based on the growth of human population. ISPRS J Photogramm Remote Sens 54:317–324CrossRefGoogle Scholar
  68. Pasquarella VJ, Holden CE, Kaufman L, Woodcock CE (2016) From imagery to ecology: leveraging time series of all available Landsat observations to map and monitor ecosystem state & dynamics. Remote Sens Ecol Conserv 2(3):152–170. Scholar
  69. Poli D (2005) Modelling of Spaceborne Linear Array Sensors. Ph.D. thesis, Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, Mitteilungen Nr. 85, p 217Google Scholar
  70. Poli D (2007) A Rigorous Model for Spaceborne Linear Array Sensors. Photogramm Eng Remote Sens 73(2):187–196CrossRefGoogle Scholar
  71. Ramo R, Garcia M, Rodriguez D, Chuvieco E (2018) A data mining approach for global burning area mapping. Int J Appl Earth Observ Geoinf 73:39–51CrossRefGoogle Scholar
  72. Remondino F (2011) Heritage recording and 3D modelling with photogrammetry and 3D scanning. Remote Sensing 3:1104–1138CrossRefGoogle Scholar
  73. Rosenholm D, Torlegard K (1988) Three-dimensional absolute orientation of stereo models using digital elevation models. Photogramm Eng Remote Sens 54(10):1385–1389Google Scholar
  74. Rutzinger M, Rottensteiner F, Pfeifer N (2009) A comparison of evaluation techniques for building extraction from airborne laser scanning. IEEE J Sel Topics Appl Earth Observ Remote Sens 2(1):11–20CrossRefGoogle Scholar
  75. Ryu JH, Han KS, Hong S, Park NW, Lee YW, Cho J (2018) Satellite-based evaluation of the post-fire recovery process from the worst forest case in South Korea. Remote Sens 10:918. Scholar
  76. Santos JR, Mura JC, Paradella WP, Dutra LV, Goncalves FG (2008) Mapping recent deforestation in the Brazilian Amazon using simulated L-band MAPSAR images. Int J Remote Sens 29(16):4879–4884CrossRefGoogle Scholar
  77. Schanz D, Huhn F, Schroeder A (2018) Large-scale volumetric flow measurement of a thermal plume using Lagrangian Particle Tracking (Shake-The-Box). In: Raffel M et al (eds) Particle Image Velocimetry, Springer, 606–610. Scholar
  78. Schroeder W, Oliva P, Giglio L, Csiszar IA (2014) The new VIIRS 375 m active fire detection data product: algorithm description and initial assessment. Remote Sens Environ 143:85–96CrossRefGoogle Scholar
  79. Sefercik UG, Alkan M, Buyuksalih G, Jacobsen K (2013) Generation and validation of high-resolution DEMs from Worldview-2 stereo data. Photogramm Rec 28(144):362–374CrossRefGoogle Scholar
  80. Shufelt JA (1999) Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Trans Pattern Anal Mach Intell 21(4):311–326CrossRefGoogle Scholar
  81. Silva Junior CHL, Aragao LEOC, Fonseca MG, Almeida CT, Vedovato LB, Anderson LO (2018) Deforestation-induced fragmentation increases forest fire occurrence in Central Brazilian Amazonia. Forests 9:305. Scholar
  82. Solberg S, Astrup R, Weydahl DJ (2013) Detection of forest clear-cuts with Shuttle Radar Topography Mission (SRTM) and Tandem-X InSAR data. Remote Sensing 5:5449–5462CrossRefGoogle Scholar
  83. Soto-Berelov M, Jones SD, Clarke E, Reddy S, Gupta V, Felipe MLC (2018) Assessing two large area burnt area products across Australian Southern Forests. Int J Remote Sens 39(3):879–905CrossRefGoogle Scholar
  84. Souza CM, Siqueira JV, Sales MH et al (2013) Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens 5:5493–5513CrossRefGoogle Scholar
  85. Sun P, Zhang Y (2018) A probabilistic method predicting forest fire occurrence combining firebrands and the weather-fuel complex in the northern part of the Daxinganling region. China For 9:428. Scholar
  86. Svancara LK, Scott JM, Loveland TR, Pidgorna AB (2009) Assessing the landscape context and conversion risk of protected areas using satellite data products. Remote Sens Environ 113:1357–1369CrossRefGoogle Scholar
  87. Tao CV, Hu Y (2001) A Comprehensive Study of the Rational Function Model for Photogrammetric Processing. Photogramm Eng Remote Sens 66(12):1477–1485Google Scholar
  88. Tian L, Wang J, Zhou H, Wang J (2018) Automatic detection of forest fire disturbance based on dynamic modelling from MODIS time-series observations. Int J Remote Sens 39(12):3801–3815CrossRefGoogle Scholar
  89. Toschi I, Remondino F, Kellenberger T, Streilein A (2017) A survey of geomatics solutions for the rapid mapping of natural hazards. Photogramm Eng Remote Sens 83(12):843–859CrossRefGoogle Scholar
  90. Toschi I, Allocca M, Remondino F (2018) Geomatics mapping of natural hazards: overview and experiences. Int Archives Photogramm Remote Sens Spat Inf Sci 42(3/W4):505–512Google Scholar
  91. Tucker CJ, Townshend JRG (2000) Strategies for monitoring tropical deforestation using satellite data. Int J Remote Sens 21(6):1461–1471CrossRefGoogle Scholar
  92. Vega SGD, de las Heras J, Moya D (2018) Post-fire regeneration and diversity response to burn severity in pinus halepensis Mill. forests. Forests 9:299. Scholar
  93. Wallis R (1976) An approach to the space variant restoration and enhancement of images. In: Proc of Symposium on Current Mathematical Problems in Image Science, Monterey, CAGoogle Scholar
  94. Wheeler D, Guzder-Williams B, Petersen R, Thau D (2018) Rapid MODIS-based detection of tree cover loss. Int J Appl Earth Obs Geoinf 69:78–87CrossRefGoogle Scholar
  95. Xu C, Manley B, Morgenroth J (2018) Evaluation of modelling approaches in predicting forest volume and stand age for small-scale plantations forests in New Zealand with RapidEye and LiDAR. Int J Appl Earth Observ Geoinf 73:386–396CrossRefGoogle Scholar
  96. Yu B, Chen F, Li B, Wang L, Wu M (2017) Fire risk prediction using remote sensed products: a case of Cambodia. Photogrammetric Engineering and Remote Sensing 83(1):19–25CrossRefGoogle Scholar
  97. Zhang L, Gruen A (2004) Automatic DSM generation from linear array imagery data. Int Archives Photogramm Remote Sens Spat Inf Sci 35(B3):128–133Google Scholar
  98. Zhang L (2005) Automatic Digital Surface Model (DSM) Generation from Linear array Images. Ph.D. thesis, Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland, Mitteilungen Nr.88, p 219. ISBN 3-906467-55-4Google Scholar
  99. Zhang L, Gruen A (2006) Multi-image matching for DSM generation from IKONOS imagery. ISPRS J Photogramm Remote Sens 60:195–211CrossRefGoogle Scholar
  100. Zhang L, Kocaman S, Akca D, Kornus W, Baltsavias E (2006) Test and performance evaluation of DMC images and new methods for their processing. In: Proceedings ISPRS commission I symposium, Paris, 3–6 Jul 2006Google Scholar
  101. Zhang Y, Song C, Band LE, Sun G, Li J (2017) Reanalysis of global terrestrial vegetation trends from MODIS products: browning or greening? Remote Sens Environ 191:145–155CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Devrim Akca
    • 1
    Email author
  • Efstratios Stylianidis
    • 2
  • Daniela Poli
    • 3
  • Armin Gruen
    • 4
  • Orhan Altan
    • 5
  • Martin Hofer
    • 6
  • Konstantinos Smagas
    • 7
  • Victor Sanchez Martin
    • 8
  • Andreas Walli
    • 6
  • Elisa Jimeno
    • 8
  • Alejandro Garcia
    • 8
  1. 1.Isik UniversityIstanbulTurkey
  2. 2.Aristotle University of ThessalonikiThessalonikiGreece
  3. 3.4DiXplorer AGZurichSwitzerland
  4. 4.ETH Zurich, Switzerland/4DiXplorer AGZurichSwitzerland
  5. 5.Istanbul Technical University/Ekinoks Surveying Software Engineering LtdIstanbulTurkey
  6. 6.GeoVille Information Systems GmbHInnsbruckAustria
  7. 7.GeoImaging LtdNicosiaCyprus
  8. 8.Ingeniería Y Soluciones Informáticas S.L.SevilleSpain

Personalised recommendations