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Satellite monitoring of consequences of illegal extraction of amber in Ukraine

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Category: Environmental safety, labour protection

Last Updated on 21 May 2017

Published on 21 May 2017

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Authors:

V.V.Hnatushenko, Dr. Sc. (Tech.), Prof., Oles Honchar Dnipropetrovsk National University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.K.Mozgovyi, Cand. Sc. (Tech.), Oles Honchar Dnipropetrovsk National University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Vasyliev, Oles Honchar Dnipropetrovsk National University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

О.О.Kavats, Cand. Sc. (Tech.), Assoc. Prof., National metallurgical academy of Ukraine, Dnipro, Ukraine

Abstract:

Purpose. The main goal is to detect sites of illegal extraction of amber, to determine the areas of the sites, as well as to evaluate the dynamics and nature of the change of soils, vegetation and water bodies on the affected territories.

Methodology. For the quantitative evaluation of the consequences of illegal extraction of amber, a technology of processing images from satellites Landsat 7 (ETM imaging device) and Landsat 8 (OLI imaging device) is proposed. Image processing includes the following main stages: reduction of the dimensions of data arrays with the use of the Principal Component Analysis (PCA); creation of a differential image for the two first main components of the given territory taken at different times; subsequent threshold binarization of the differential image; morphological filtering of the binary image, vectorization and calculation of geometrical parameters.

Findings. An effective technology of automated detection of illegal amber extraction sites and determination of their areas with the use of satellite images was proposed. Processing of multispectral images of a medium spatial resolution from satellites Landsat 7 (ETM imaging device) and Landsat 8 (OLI imaging device) was carried out with the purpose of determination of the boundaries of the areas affected by the illegal amber extraction and evaluation of their area. Only on the territories selected for the analysis, the area of territories affected by illegal extraction of amber amounted to over 500 hectares. A detailed analysis of changes after the illegal extraction of amber was carried out for certain territories with the use of high spatial resolution satellite images (2002–2015).

Originality. Unlike the known methods of detection of changes on territories using Landsat satellite images taken at different time, which are based on the use of data of panchromatic channels and indexed images using only two spectral channels, the proposed method is capable of more accurate determination of affected areas owing to analysis of all spectral channels with the use of the Principal Component Analysis. Besides, the proposed technology provides a possibility to analyze temporal changes on the affected territories during long observation periods with the use of created vector layers with attributive information.

Practical value. The developed method can be implemented in the form of a web service for regular space monitoring providing unbiased and reliable information as for the scales and severity of consequences of illegal amber extraction. An important advantage of this method is a high degree of automation of satellite image processing and the use of Earth remote sensing data available in free access on the Internet. The potential users of such a web service are state regulating organs, emergency services, the police, forestry and municipal services, environmental organizations, radio and TV companies and other mass media, and also the population living near the territories affected by illegal extraction of amber.

References.

1. Kumar, Sandeep, Kumar, Pardeep, Shekhar and Chander, 2016. Monitoring of land resources and their temporal utilization in Ellenabad block of Sirsa district using remote sensing & GIS techniques. International Journal of Science, Engineering and Technology Research (IJSETR), 5(4), pp. 987–992.

2. Sakti, A.D. and Tsuyuki, S., 2015. Spectral Mixture Analysis of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, pp. 495–501.

3. Hnatushenko, V.V., Hnatushenko, Vik.V., Mozgovyi, D.K. and Vasyliev, V.V., 2016. Satellite technology of the forest fires effects monitoring. Visnyk Natsionalnoho Hirnychoho Universytetu, 1(151), pp. 70–76.

4. Lefebvre, A., Sannier, C. and Corpetti, T., 2016. Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree. Remote Sens., 8, p. 606.

5. Ruelland, D., Tribotte, A., Puech, C. and Dieulin, C., 2011. Comparison of methods for LUCC monitoring over 50 years from aerial photographs and satellite images in a sahelian catchment. Int. J. Remote Sens., 32, pp. 1747–1777.

6. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M. R., Kuemmerle, T., Meyfroidt, P., Mitchard, E.T.A., Reiche, J., Ryan, C.M. and Waske, B., 2016. A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing, 8, p. 70.

7. Hnatushenko, V.V., Hnatushenko, Vik.V., Kavats, O.O. and Shevchenko, V.Yu., 2015. Pansharpening technology of high resolution multispectral and panchromatic satellite images. Visnyk Natsionalnoho Hir­nychoho Universytetu, 4, pp. 91–98.

8. Zhang, H., Lin, H. and Li, Y., 2015. Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification. IEEE Geosci. Remote Sensing. 12, pp. 1061–1065.

9. Hnatushenko, V.V. and Vasyliev, V.V., 2016. Remote sensing image fusion using ICA and optimized wavelet transform. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ‒ ISPRS Archives, XLI-B7, pp. 653–659.

10. Estornell, Javier, Martı-Gavila, Jesus M., Sebastia, M. Teresa and Mengual, Jesus, 2013. Principal component analysis applied to remote sensing. Modelling in Science Education and Learning, 6(2), 7, pp. 83–89.

11. Balaji, T. and Sumathi, M., 2014. Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method. International Conference on Intelligent Computing Applications, pp. 149–153.

12. Rukhsar Firdousi and Shaheen Parveen, 2014. Local Thresholding Techniques in Image Binarization. International Journal Of Engineering And Computer Science, 3(3), pp. 4062–4065.

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2017-05-21  16.25 MB  1260

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