Mapping and monitoring of the land use/cover changes in the wider area of Itanos, Crete, using very high resolution EO imagery with specific interest in archaeological sites

Abstract

Archaeological site mapping is important for both understanding the history and protecting the sites from excavation during developmental activities. As archaeological sites are generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study is to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital imagery of two known archaeological sites was used for validation. An object-based image analysis classification was subsequently developed using the five acquired satellite images. Two rule sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images with their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule set versus the 8-band rule set showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved to be challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The high resolution of the satellite data allowed for the accuracy in defining larger archaeological sites, but still there was difficulty in distinguishing smaller areas of interest. The digital image data provided a very good 3D representation for the archaeological sites, assisting as well as in validating the satellite-derived classification maps. To conclude, our study provides further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where the archaelogical features are of an adequate size.

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References

  1. Alexakis, D., et al. (2009). Detection of Neolithic settlements in Thessaly (Greece) through multispectral and hyperspectral satellite imagery. Sensors,9(2), 1167–1187. https://doi.org/10.3390/s90201167.

    Article  Google Scholar 

  2. Banerjee, R., & Srivastava, P. K. (2013). Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing. Land Use Policy,34(2013), 193–203.

    Article  Google Scholar 

  3. Banerjee, R., & Srivastava, P. K. (2014). Remote sensing based identification of painted rock shelter sites: Appraisal using advanced wide field sensor, neural network and field observations. In P. Srivastava, S. Mukherjee, M. Gupta, & T. Islam (Eds.), Remote sensing applications in environmental research. Springer, Cham: Society of Earth Scientists Series.

    Google Scholar 

  4. Banerjee, R., Srivastava, P. K., Pike, A. W. G., & Petropoulos, G. P. (2018). Identification of painted rock-shelter sites using GIS integrated with a Decision Support system and Fuzzy Logic. International Journal of Geo-Information,7, 326–386. https://doi.org/10.3390/ijgi7080326.

    Article  Google Scholar 

  5. Barker, P. (1986). Understanding archaeological excavation (p. 190). London: Batsford.

    Google Scholar 

  6. Bassani, C., et al. (2009). Specific spectral bands for different land cover contexts to improve the efficiency of remote sensing archaeological prospection: The Arpi case study. Journal of Cultural Heritage,10(1), 41–48.

    Article  Google Scholar 

  7. Bernard, M., Decluseau, D., Gabet, L., & Nonin, P. (2012). 3D capabilities of Pleiades satellite. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 XXII ISPRS Congress, 25 August–01 September 2012, Melbourne, Australia.

  8. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing,65(1), 2–16.

    Article  Google Scholar 

  9. Brown, R. A., Petropoulos, G. P., & Ferentinos, K. (2018). Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal’s fires of 2017. Applied Geography,100, 78–89. https://doi.org/10.1016/j.apgeog.2018.10.00.

    Article  Google Scholar 

  10. Brutto, M., & Meli, P. (2012). Computer vision tools for 3D modelling in archaeology. International Journal of Heritage in the Digital Era,1(1), 1–6. https://doi.org/10.1260/2047-4970.1.0.1.

    Article  Google Scholar 

  11. Cetin, M. (2015). Using GIS analysis to assess urban green space in terms of accessibility: case study in Kutahya. International Journal of Sustainable Development and World Ecology,22(5), 420–424.

    Google Scholar 

  12. Cetin, M., Adiguzel, F., Kaya, O., & Sahap, A. (2018). Mapping of bioclimatic comfort for potential planning using GIS in Aydin. Environment, Development and Sustainability,20(1), 361–375. https://doi.org/10.1007/s10668-016-9885-5.

    Article  Google Scholar 

  13. Congalton, R. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment,37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B.

    Article  Google Scholar 

  14. Ehlers, M., Klonus, S., & Astrand, P. J. (2010). Multi-sensor image fusion for pansharpening in remote sensing. International Journal of Image and Data Fusion,1, 25–45. https://doi.org/10.1080/19479830903561985.

    Article  Google Scholar 

  15. Eppelbaum, I., Khesin, B., & ltkis, S. (2010). Archaeological geophysics in arid environments: examples from Israel. Journal of Arid Environments,74(7), 849–860. https://doi.org/10.1016/j.jaridenv.2009.04.018.

    Article  Google Scholar 

  16. Fowler, M. (2004). Archaeology through the keyhole: The serendipity effect of aerial reconnaissance revisited. Interdisciplinary Science Reviews,29, 118–134.

    Google Scholar 

  17. Ghosh, A., & Joshi, P. (2013). Assessment of pan-sharpened very high-resolution WorldView-2 images. International Journal of Remote Sensing,34(23), 8336–8359. https://doi.org/10.1080/01431161.2013.838706.

    Article  Google Scholar 

  18. Gordon, M., Loveland, E., & Cureton, E. (1952). An extended table of Chi square for two degrees of freedom, for use in combining probabilities from independent samples. Psychometrika,17(3), 311–316.

    Article  Google Scholar 

  19. Grossiet, C., Brimblecombe, P., & Harris, L. (2007). Predicting long term freeze-thaw risks on Europe built heritage and archaeological sites in a changing climate. Science of the Total Environment,377(2–3), 81–273.

    Google Scholar 

  20. Hong, Y., et al. (2008). Discussion on UAV remote sensing image capture and disposal. Remote Sensing Technology and Application. Available at: http://en.cnki.com.cn/Article_en/CJFDTOTAL-YGJS200804020.htm [Accessed December 9th, 2018).

  21. Jacobsen, K., & Topan, H. (2015). DEM generation with short base length Pleiades triplet. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-3/W2, 2015 PIA15 + HRIGI15—Joint ISPRS Conference 2015, 25–27 March 2015, Munich, Germany.

  22. Karydas, C., Sekuloska, T., & Silleos, G. (2009). Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete. Environmental Monitoring and Assessment,149(1–4), 19–28.

    Article  Google Scholar 

  23. Kaya, E., Agca, M., Adiguzel, F., & Cetin, M. (2018). Spatial data analysis with R programming for environment. Human and Ecological Risk Assessment: An International Journal. https://doi.org/10.1080/10807039.2018.1470896.

    Article  Google Scholar 

  24. Kersten, T., & Lindstaedt, M. (2012). Image-based low-cost systems for automatic 3D recording and modelling of archaeological finds and objects. In Euro-Mediterranean Conference. EuroMed 2012: Progress in Cultural Heritage Preservation, pp. 1–10 Available at: http://link.springer.com/chapter/10.1007/978-3-642-34234-9_1 [Accessed December, 9th, 2018].

  25. Kim, M., Madden, M., & Warner, T. (2008). Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery. In T. Blaschke, S. Lang, & G. J. Hay (Eds.), Object-based image analysis. Springer, Berlin, Heidelberg: Lecture Notes in Geoinformation and Cartography. https://doi.org/10.1007/978-3-540-77058-9_16.

    Google Scholar 

  26. Klonus, S., & Ehlers, M. (2007). Image fusion using the Ehlers spectral characteristics preservation algorithm. GIScience & Remote Sensing,44(2), 93–116. https://doi.org/10.2747/1548-1603.44.2.93.

    Article  Google Scholar 

  27. Laben, C., & Brower, B. (2000). Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6,011,875. Available at: https://www.google.com/patents/US6011875 [Accessed September 24, 2018).

  28. Laet, V. De, Paulissen, E., & Waelkens, M. (2007). Methods for the extraction of archaeological features from very high-resolution lkonos-2 remote sensing imagery, Hisar (southwest Turkey). Journal of Archaeological Science,34(5), 830–841. https://doi.org/10.1016/j.jas.2006.09.013.

    Article  Google Scholar 

  29. Lafarge, F., Descombes, X., & Zerubia, J., (2006). Automatic 3D building reconstruction from DEMs: An application to PLEIADES simulations. In Proceedings of the ISPRS. Available at: http://www.academia.edu/download/30678958/T07-32.pdf [Accessed September 20, 2018].

  30. Lafarge, F., Descombes, X., & Zerubia, J. (2008). Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling. ISPRS Journal of Photogrammetry and Remote Sensing,63(3), 365–381. https://doi.org/10.1016/j.isprsjprs.2007.09.003.

    Article  Google Scholar 

  31. Lasaponara, R., & Masini, N. (2006). Identification of archaeological buried remains based on the normalized difference vegetation index (NDVI) from Quickbird satellite data. IEEE Geoscience and Remote Sensing Letters,3(3), 325–328.

    Article  Google Scholar 

  32. Lasaponara, R., & Masini, N. (2012). Satellite remote sensing: A new tool for archaeology. Berlin: Springer. ISBN 978-90-481-8800-0.

    Google Scholar 

  33. Lasaponara, R., et al. (2013). Remote sensing in archaeology: An overview. Journal of Aeronautics and Space Technologies,6(1), 7–17.

    Google Scholar 

  34. Manandhar, R., Odeh, I., & Ancev, T. (2009). Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote Sensing, MDPI,1(3), 330–344. https://doi.org/10.3390/rs1030330.

    Article  Google Scholar 

  35. Markogianni, V., Kalivas, D., Petropoulos, G. P., & Dimitriou, E. (2018). An appraisal of the potential of landsat 8 in estimating chlorophyll-a, ammonium concentrations and other water quality indicators. Remote Sensing MDPI,10, 1–22. https://doi.org/10.3390/rs1007101.

    Article  Google Scholar 

  36. Masini, N., Lasaponara, R., & Orefici, G. (2009). Addressing the challenge of detecting archaeological adobe structures in Southern Peru using QuickBird imagery. Journal of Cultural Heritage,10, 24–39. https://doi.org/10.1016/j.culher.2009.10.005.

    Article  Google Scholar 

  37. Matero, F., Fong, K., & Bono, E. (2013). Archaeological site conservation and management An appraisal of recent trends. Archaeological Sites,2(3), 129–142. https://doi.org/10.1179/cma.1998.2.3.129.

    Article  Google Scholar 

  38. Parcak, S. (2009). Satellite remote sensing for archaeology. Handbook, Available at: https://books.google.co.uk/books?hl=en&lr=&id=bMQga28QMIUC&oi=fnd&pg=PP1&dq=EO+technology+archaeology+application+review&ots=LWEmTFUjZe&sig=jFOMGbhmNFYgdUwTSGafTeSTATM [Accessed September 5, 2018].

  39. Pendlebury, J. (1969). The archaeology of Crete, Available at: https://books.google.co.uk/books?hi=en&lr=&id=Yv3MklpeJSQC&o=ifnd&pg=PR23&dq=crete+itanos+minoan&ots=ArFpUKaJ9u&sig=390LOg8HG05JVaZXVxdRQUuvTi4 [Accessed August 25, 2016].

  40. Petropoulos, G. P., Kalaitzidis, C., & Vadrevu, K. P. (2012). Support vector machines and object-based classification for obtaining land use/cover cartography from hyperion hyperspectral imagery. Computers & Geosciences,41, 99–107. https://doi.org/10.1016/j.cageo.2011.08.019.

    Article  Google Scholar 

  41. Petropoulos, G. P., Partsinevelos, P., & Mitraka, Z. (2013). Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal landsat TM imagery. Geocarto International. https://doi.org/10.1080/10106049.2012.706648.

    Article  Google Scholar 

  42. Remondino, F., & El-Hakim, S. (2006). Image-based 3D modelling: A review. The Photogrammetric Record, 21, 269–291. https://doi.org/10.1111/j.1477-9730.2006.00383.x.

    Article  Google Scholar 

  43. Rinaudo, F., Chiabrando, F., & Lingua, A. (2012). Archaeological site monitoring: UAV photogrammetry can be an answer. In ISPRSInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B5, 2012, pp. 583–588.

  44. Rodriguez-Pascua, M., & Perez-Lopez, R. (2011). A comprehensive classification of Earthquake Archaeological Effects (EAE) in archaeoseismology: Application to ancient remains of Roman and Mesoamerican. Quaternary,242(1), 20–30. https://doi.org/10.1016/j.quaint.2011.04.044.

    Article  Google Scholar 

  45. Rowlands, A., & Sarris, A. (2007). Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing. Journal of Archaeological Science,34(5), 795–803. https://doi.org/10.1016/j.jas.2006.06.018.

    Article  Google Scholar 

  46. Schneider, E. (1982). Plunder or excavation? Observations and suggestions on the regulation of ownership and trade in the evidence of cultural patrimony. Syracuse Journal of International Law. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1938574 [Accessed September 19, 2018].

  47. Sdao, F., & Simeone, V. (2007). Mass movements affecting Goddess Mefitis sanctuary in Rossano diVaglio (Basilicata, southern Italy). Journal of Cultural Heritage,8(1), 77–80. https://doi.org/10.1016/j.culher.2006.10.004.

    Article  Google Scholar 

  48. Siart, C., Eitel, B., & Panagiotopoulos, B. (2008). Investigation of past archaeological landscapes using remote sensing and GIS: A multi-method case study from Mount Ida. Crete. Journal of Archaeological Science,35(11), 2918–2926. https://doi.org/10.1016/j.jas.2008.06.006.

    Article  Google Scholar 

  49. Sturdy Colls C., & Colls, K. (2013). Reconstructing a painful past: A non-invasive approach to reconstructing lager norderney in alderney, the channel islands. In E. Ch’ng, V. Gaffney, & H. Chapman (Eds.), Visual heritage in the digital age. Springer, London: Springer Series on Cultural Computing. https://doi.org/10.1007/978-1-4471-5535-5_7.

    Google Scholar 

  50. Thomlinson, J., Bolstad, P., & Cohen, W. (1999). Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products. Remote Sensing of Environment,70(1), 16–28. https://doi.org/10.1016/S0034-4257(99)00055-3.

    Article  Google Scholar 

  51. Tite, M. (2002). Archaeological collections: invasive sampling versus object integrity. Papers from the Institute of Archaeology. Available at:http://www.piajournal.co.uk/articles/10.5334/pia.189/galley/246/download/ [Accessed September 20, 2018].

  52. Tucker, C., Pinzon, J., & Brown, M. (2005). An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing,26(20), 4485–4498. https://doi.org/10.1080/01431160500168686.

    Article  Google Scholar 

  53. Vafidis, A., Economou, N., & Ganiatsos, Y. (2005). Integrated geophysical studies at ancient Itanos (Greece). Journal of Archaeological Science,32(7), 1023–1036. https://doi.org/10.1016/j.jas.2005.02.007.

    Article  Google Scholar 

  54. Vafidis, A., Manakou, M., & Kritikakis, G. (2003). Mapping the ancient port at the archaeological site of ltanos (Greece) using shallow seismic methods. Archaeological Prospection,10(3), 163–173. https://doi.org/10.1002/arp.212.

    Article  Google Scholar 

  55. Whyte, A., Ferentinos, K., & Petropoulos, G. P. (2018). A new synergistic approach for monitoring wetlands using sentinels -1 and 2 data with object-based machine learning algorithms. Environmental Modelling and Software,104, 40–57. https://doi.org/10.1016/j.envsoft.2018.01.02.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank European Space Agency (ESA) and Digital Globe for their prompt provision of satellite imagery. GPP’s contribution to this work has been supported by the EU Marie Curie Project ENViSIon-EO (project contract ID 752094).

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ARB conducted the research described in this study under the supervision and guidance of GPP and GPP together with LT and PKS prepared this manuscript for submission to the journal.

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Correspondence to George P. Petropoulos.

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Dawson, R.A., Petropoulos, G.P., Toulios, L. et al. Mapping and monitoring of the land use/cover changes in the wider area of Itanos, Crete, using very high resolution EO imagery with specific interest in archaeological sites. Environ Dev Sustain 22, 3433–3460 (2020). https://doi.org/10.1007/s10668-019-00353-0

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Keywords

  • 3D modelling
  • Archaeology
  • Land cover mapping
  • OBIA
  • Remote sensing
  • GIS