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Determination of Opium Poppy (Papaver Somniferum) Parcels Using High-Resolution Satellite Imagery

  • Sinan DemirEmail author
  • Levent Başayiğit
Research Article
  • 29 Downloads

Abstract

Narcotic plants contain substances that cause unusual excitation and subsequent depression of the central nervous system. Many narcotic plants contain substances that have medicinal properties and are used primarily as pain relievers. Opium poppy (Papaver somniferum) is one of the most cultivated narcotic plants. The planning of Opium poppy growing is controlled by United Nations and Drugs and Medicines Control Program. This study was conducted to establish a basis for determining the traceability of poppy cultivating areas using remote sensing in the large plain. A very high-resolution QuickBird-2 satellite image was used in the study. The software of ERDAS Imagine and eCognition Developer was performed for image processing and classification. A variety of classification methods were performed on the satellite image. ArcGIS software was used for accuracy assessment, ground control, and map production of the poppy cultivating area. The accuracy of classification methods was compared. The producer–user accuracy was estimated as 97.99% using spectral difference sub-segmentation process of multiresolution segmentation algorithm in object-based classifications method. This algorithm can be a practical approach for determining of opium poppy parcels in the large plain.

Keywords

Opium poppy (Papaver SomniferumPixel-based classification Object-based classification QuickBird-2 High-resolution satellite image 

Notes

Acknowledgements

This work was supported by the Scientific Research Projects Council of Suleyman Demirel University (Grant Number 4103-YL2-14).

References

  1. Aguilar, M. A., Bianconi, F., Aguilar, F. J., & Fernández, I. (2014). Object-based greenhouse classification from GeoEye-1 and WorldView-2 stereo imagery. Remote Sensing, 6(5), 3554–3582.CrossRefGoogle Scholar
  2. Antunes, A. F. B., Lingnau, C., & Centeno, J. A. S. (2003). Object oriented analysis and semantic network for high resolution image classification. Boletim de Ciências Geodésicas, 9(2), 235–238.Google Scholar
  3. Asano, T., Chen, D. Z., Katoh, N., & Tokuyama, T. (1996, January). Polynomial-time solutions to image segmentation. In SODA (Vol. 96, pp. 104–113).Google Scholar
  4. Asner, G. P. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment, 64(3), 234–253.CrossRefGoogle Scholar
  5. Attarzadeh, R., & Momeni, M. (2018). Object-based rule sets and its transferability for building extraction from high resolution satellite imagery. Journal of the Indian Society of Remote Sensing, 46(2), 169–178.CrossRefGoogle Scholar
  6. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of photogrammetry and remote sensing, 58(3–4), 239–258.CrossRefGoogle Scholar
  7. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.CrossRefGoogle Scholar
  8. Campos, N., Lawrence, R., McGlynn, B., & Gardner, K. (2010). Effects of LiDAR-Quickbird fusion on object-oriented classification of mountain resort development. Journal of Applied Remote Sensing, 4(1), 043556.CrossRefGoogle Scholar
  9. Chen, Y., Shi, P., Fung, T., Wang, J., & Li, X. (2007). Object-oriented classification for urban land cover mapping with ASTER imagery. International Journal of Remote Sensing, 28(20), 4645–4651.CrossRefGoogle Scholar
  10. Chuinsiri, S., Blasco, F., Bellan, M. F., & Kergoat, L. (1997). A poppy survey using high resolution remote sensing data. International Journal of Remote Sensing, 18(2), 393–407.CrossRefGoogle Scholar
  11. CNNCC. (2008). Poppy crop monitoring using remote sensing in North Myanmar (interior material). Chinese National Narcotics Control Commission, Beijing.Google Scholar
  12. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton: CRC Press.CrossRefGoogle Scholar
  13. Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205, 338–351.CrossRefGoogle Scholar
  14. Curran, P. J. (1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30(3), 271–278.CrossRefGoogle Scholar
  15. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201.CrossRefGoogle Scholar
  16. Foody, G. M. (2009). Sample size determination for image classification accuracy assessment and comparison. International Journal of Remote Sensing, 30(20), 5273–5291.CrossRefGoogle Scholar
  17. Hay, G. J., & Castilla, G. (2008). Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In Object-based image analysis (pp. 75–89). Springer: Berlin.Google Scholar
  18. Jelsma, M. (2011). The development of international drug control: Lessons learned and strategic challenges of the future. International Drug Policy Consortium.Google Scholar
  19. Jensen, J. R. (1986). Introductory digital image processing: a remote sensing perspective. Prentice-Hall, Englewood Cliffs, New Jersey, USA.Google Scholar
  20. Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective (3rd ed., p. 526). Upper Saddle River: Pearson Prentice Hall.Google Scholar
  21. Jia, K., Wu, B., Tian, Y., Li, Q., & Du, X. (2011). Spectral discrimination of opium poppy using field spectrometry. IEEE Transactions on Geoscience and Remote Sensing, 49(9), 3414.CrossRefGoogle Scholar
  22. Kanade, R., & John, R. (2018). Topographical influence on recent deforestation and degradation in the Sikkim Himalaya in India; Implications for conservation of East Himalayan broadleaf forest. Applied Geography, 92, 85–93.CrossRefGoogle Scholar
  23. Li, D., Ke, Y., Gong, H., & Li, X. (2015). Object-based urban tree species classification using bi-temporal WorldView-2 and WorldView-3 images. Remote Sensing, 7(12), 16917–16937.CrossRefGoogle Scholar
  24. Lillesand, T. M., & Kiefer, R. W. (1987). Remote sensing and image interpretation (2nd ed.). Wiley: Toronto.Google Scholar
  25. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2008). Digital image interpretation and analysis. Remote Sensing and Image Interpretation, 6, 545–581.Google Scholar
  26. Lillesand, T. M., et al. (2014). Remote sensing and image interpretation. New York: Wiley.Google Scholar
  27. Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: An introduction. New York: Wiley.CrossRefGoogle Scholar
  28. MGM. (2017). Annual Precipitation and longest average climate Data of Turkey. Ankara: Turkish State Meteorological Service (MGM).Google Scholar
  29. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161.CrossRefGoogle Scholar
  30. Nakazawa, A., Kim, J. H., Mitani, T., Odagawa, S., Takeda, T., Kobayashi, C., & Kashimura, O. (2012). A study on detecting the poppy field using hyperspectral remote sensing techniques. In IEEE International Geoscience and remote sensing symposium (IGARSS) (pp. 4829–4832). IEEE.Google Scholar
  31. Rao, D. A., & Guha, A. (2018). Potential utility of spectral angle mapper and spectral information divergence methods for mapping lower vindhyan rocks and their accuracy assessment with respect to conventional lithological map in Jharkhand, India. Journal of the Indian Society of Remote Sensing, 46(5), 737–747.CrossRefGoogle Scholar
  32. Richards, J. A. (1999). Remote sensing digital image analysis (Vol. 3). Berlin: Springer.CrossRefGoogle Scholar
  33. Richards, J. A., & Jia, X. (1986a). Remote sensing digital analysis. Berlin: Spring.CrossRefGoogle Scholar
  34. Richards, J. A., & Jia, X. (1986b). Remote sensing digital analysis. Berlin: Spring.CrossRefGoogle Scholar
  35. Simms, D. M., Waine, T. W., Taylor, J. C., & Brewer, T. R. (2016). Image segmentation for improved consistency in image-interpretation of opium poppy. International Journal of Remote Sensing, 37(6), 1243–1256.CrossRefGoogle Scholar
  36. Smits, P. C., Dellepiane, S. G., & Schowengerdt, R. A. (1999). Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20(8), 1461–1486.CrossRefGoogle Scholar
  37. Stehman, S. V. (1997). Estimating standard errors of accuracy assessment statistics under cluster sampling. Remote Sensing of Environment, 60(3), 258–269.CrossRefGoogle Scholar
  38. Taylor, J. C., Waine, T. W., Juniper, G. R., Simms, D. M., & Brewer, T. R. (2010). Survey and monitoring of opium poppy and wheat in Afghanistan: 2003–2009. Remote Sensing Letters, 1(3), 179–185.CrossRefGoogle Scholar
  39. Tian, Y., Wu, B., Zhang, L., Li, Q., Jia, K., & Wen, M. (2011). Opium poppy monitoring with remote sensing in North Myanmar. International Journal of Drug Policy, 22(4), 278–284.CrossRefGoogle Scholar
  40. Trimble (2014).” eCognition Developer 9.0 User Guide.” Trimble Germany GmbH: Munich, Germany.Google Scholar
  41. Tucker, C. J., Newcomb, W. W., & Dregne, H. E. (1994). AVHRR data sets for determination of desert spatial extent. International Journal of Remote Sensing, 15(17), 3547–3565.CrossRefGoogle Scholar
  42. UNODC. (2007). Afghanistan Opium Survey 2007. United National Office on Drugs and Crime.Google Scholar
  43. UNODC. (2008). Monitoreo de cultivos de coca, United Nations Office for Drug and Crime, (pp. 107–110). Bogotá: Colombia.Google Scholar
  44. UNODC. (2009). World Drugs Report 2009. United National Office on Drugs and Crime.Google Scholar
  45. UNODC. (2012). Afghanistan Opium Survey 2012. United Nations Office on Drugs and Crime.Google Scholar
  46. UNODC. (2015). Guidelines for illicit opium and cannabis monitoring in Afghanistan. United Nations Office on Drugs and Crime, Unpublished technical report, contract no. 14.519.Google Scholar
  47. Wan, L., Tang, K., Li, M., Zhong, Y., & Qin, A. K. (2015). Collaborative active and semisupervised learning for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2384–2396.CrossRefGoogle Scholar
  48. Wang, J. J., Zhang, Y., & Bussink, C. (2014). Unsupervised multiple endmember spectral mixture analysis-based detection of opium poppy fields from an EO-1 Hyperion image in Helmand, Afghanistan. Science of the Total Environment, 476, 1–6.Google Scholar
  49. Wong, T. H., Mansor, S. B., Mispan, M. R., Ahmad, N., & Sulaiman, W. N. A. (2003). Feature extraction based on object oriented analysis. In Proceedings of ATC 2003 Conference (Vol. 2021).Google Scholar
  50. Yang, C., Goolsby, J. A., & Everitt, J. H. (2009). Mapping giant reed with QuickBird imagery in the Mexican portion of the Rio Grande Basin. Journal of Applied Remote Sensing, 3(1), 033530.CrossRefGoogle Scholar
  51. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), 799–811.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Soil Science and Plant Nutrition, Faculty of AgricultureSuleyman Demirel UniversityIspartaTurkey

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