Advertisement

Greenhouse Detection Using Aerial Orthophoto and Digital Surface Model

  • Salih Celik
  • Dilek Koc-SanEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)

Abstract

Detection of greenhouse areas from remote sensing imagery is important for rural planning, yield estimation and sustainable development. This study is focused on plastic and glass greenhouse detection and discrimination using true color orthophoto and Digital Surface Model (DSM). Initially, the greenhouse areas were detected from true color aerial photograph and the additional normalised Digital Surface Model (nDSM) band using Support Vector Machines (SVM) classification technique. Then, an approach was developed for discriminating plastic and glass greenhouses by utilising the nDSM. The developed approach was implemented in a selected study area from Kumluca district of Antalya, Turkey that includes intensive plastic and glass greenhouses. The obtained results show the effectiveness of the SVM classifier for greenhouse detection with overall accuracy value of 96.15%. In addition, the plastic and glass greenhouses were discriminated efficiently using the developed approach that use nDSM.

Keywords

Plastic and glass greenhouses Orthophotos nDSM SVM 

Notes

Acknowledgements

The authors would like to thank General Command of Mapping (GCM) for providing the data used in this study. The authors are grateful to Mustafa Kaynarca (Geomatics Engineer-ASAT) for his help during the DEM editing.

References

  1. 1.
    Picuno, P., Tortora, A., Capobianco, R.L.: Analysis of plasticulture landscapes in southern Italy through remote sensing and solid modelling techniques. Landscape Urban Plann. 100, 45–56 (2011)CrossRefGoogle Scholar
  2. 2.
    Novelli, A., Tarantino, E.: Combining Ad Hoc spectral indices based on landsat-8 OLI/TIRS sensor data for the detection of plastic cover vineyard. Remote Sens. Lett. 6(12), 933–941 (2015)CrossRefGoogle Scholar
  3. 3.
    Hasituya, Chen, Z., Wang, L., Wu, W., Jiang, Z., Li, H.: Monitoring plastic-mulched farmland by landsat-8 OLI imagery using spectral and textural features. Remote Sens. 8, 1–16 (2016). 353, rs8040353CrossRefGoogle Scholar
  4. 4.
    Novelli, A., Aguilar, M.A., Nemmaoui, A., Aguilar, F.J., Tarantino, E.: Performance evaluation of object based greenhouse detection from sentinel-2 MSI and landsat 8 OLI data: a case study from almeria (Spain). Int. J. Appl. Earth Obs. Geoinf. 52, 403–411 (2016)CrossRefGoogle Scholar
  5. 5.
    Wu, C.F., Deng, J.S., Wang, K., Ma, L.G., Tahmassebi, A.R.S.: Object-based classification approach for greenhouse mapping using landsat-8 imagery. Int J Agric. Biol. Eng. 9(1), 79–88 (2016)Google Scholar
  6. 6.
    Agüera, F., Aguilar, M.A., Aguilar, F.J.: Detecting greenhouse changes from quickbird imagery on the mediterranean coast. Int. J. Remote Sens. 27(21), 4751–4767 (2006)CrossRefGoogle Scholar
  7. 7.
    Sonmez, N.K., Sari, M.: Use of remote sensing and geographic information system technologies for developing greenhouse databases. Turk. J. Agric. For. 30, 413–420 (2006)Google Scholar
  8. 8.
    Agüera, F., Aguilar, F.J., Aguilar, M.A.: Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J. Photogrammetry Remote Sens. 63, 635–646 (2008)CrossRefGoogle Scholar
  9. 9.
    Agüera, F., Liu, J.G.: Automatic greenhouse delineation from quickbird and IKONOS satellite images. Comput. Electr. Agric. 66, 191–200 (2009)CrossRefGoogle Scholar
  10. 10.
    Carjaval, F., Agüera, F., Aguilar, F.J., Aguilar, M.A.: Relationship between atmospheric corrections and training-site strategy with respect to accuracy of greenhouse detection process from very high resolution imagery. Int. J. Rem. Sens. 31(11), 2977–2994 (2010)CrossRefGoogle Scholar
  11. 11.
    Koc-San, D.: Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery. J. Appl. Remote Sens. 7 (2013). 073553-1-20Google Scholar
  12. 12.
    Aguilar, M.A., Bianconi, F., Aguilar, F.J., Fernandez, I.: Object-based greenhouse classification from geoeye-1 and worldview-2 stereo imagery. Remote Sens. 6, 3554–3582 (2014)CrossRefGoogle Scholar
  13. 13.
    Koc-San, D., Sonmez, N.K.: Plastic and glass greenhouses detection and delineation from worldview-2 satellite imagery. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLI-B7, XXIII ISPRS Congress, 12-19 July, pp. 257–262, Prague, Czech Republic (2016)Google Scholar
  14. 14.
    Tarantino, E., Figorito, B.: Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote Sens. 4, 1913–1928 (2012)CrossRefGoogle Scholar
  15. 15.
    Carvajal, F., Crizanto, E., Aguilar, F.J., Agüera, F. Aguilar, M.A.: Greenhouses detection using an artificial neural network with a very high resolution satellite image. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVI, part 2, pp. 37–42, Vienna, Austria (2006)Google Scholar
  16. 16.
    Turkish Statistical Institute (TSI), Vegetable and fruit production for land under protective cover, 1995–2015 (2016). https://biruni.tuik.gov.tr/bitkiselapp/bitkisel.zul
  17. 17.
    Mathur, A., Foody, G.M.: Crop classification by support vector machine with intelligently selected training data for an operational application. Int. J. Rem. Sens. 29(8), 2227–2240 (2008)CrossRefGoogle Scholar
  18. 18.
    Watanachaturaporn, P., Arora, M.K., Varshney, P.K.: Multisource classification using support vector machines: an empirical comparison with decision tree and neural network classifiers. Photogramm. Eng. Rem. Sens. 74(2), 239–246 (2008)CrossRefGoogle Scholar
  19. 19.
    Koc-San, D., Turker, M.: A model-based approach for automatic building database updating from high resolution space imagery. Int. J. Rem. Sens. 33(13), 4193–4218 (2012)CrossRefGoogle Scholar
  20. 20.
    Koc-San, D., Turker, M.: Support vector machines classification for finding building patches from ikonos imagery: the effect of additional bands. J. Appl. Remote Sens. 8 (2014). 083694-1-17Google Scholar
  21. 21.
    Geomatica, P.C.I.: Software User’s Manual. PCI Geomatics Enterprises Inc., Richmond Hill (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Space Sciences and TechnologiesAkdeniz UniversityAntalyaTurkey
  2. 2.Department of City and Regional PlanningAkdeniz UniversityAntalyaTurkey
  3. 3.Remote Sensing Research and Applications CentreAkdeniz UniversityAntalyaTurkey

Personalised recommendations