Satellite monitoring of deforestation as a result of mining

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Authors:

V.V.Hnatushenko, Dr. Sc. (Tech.), Prof., Oles Honchar Dnipro National University, Head of the Department of Automated Information Processing Systems, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.">This email address is being protected from spambots. You need JavaScript enabled to view it.

D.K.Mozhovyi, Cand. Sc. (Tech.), Oles Honchar Dnipro National University, Associate Professor of the Electronics Telecommunications Department, Dnipro, Ukraine.

V.V.Vasyliev, Oles Honchar Dnipro National University, postgraduate student, Dnipro, Ukraine.

Abstract:

Purpose. Development of a method for automated processing of satellite images, which would provide higher reliability and efficiency of determining the area of mining-related deforestation by the data of multispectral space imaging.

Methodology. For identification and quantitative evaluation of the deforested area a method for processing images from Landsat 7 and Landsat 8 satellites was developed, which includes the following stages: threshold binarization of the indexed image; morphological filtering of the binary image; vectorization of the binary image, determination of boundaries and area of vegetation; formation of the differential image, binarization, vectorization and determination of the boundaries and area of clearings; visualization of changes on the digital map, recording of attributes of the objects (areas of deforestation) into a dbf file and export of the vector layer into a kml-file.

Findings. The proposed method provides a possibility to perform detection of territories and determination of the areas of illegal logging as a result of mining in an automated mode. With the use of the developed method, processing of multispectral images of Landsat 7 of the year 2002 and Landsat 8 of the year 2013 was carried out. According to the Landsat satellite data, the area of deforestation on the observed territory amounted to around 27 thousand hectares.

Originality. In contrast to known methods for determination of the area of deforestation by satellite images of visible spectrum obtained at different time, the proposed method enables automatic determination of areas of deforestation owing to complex utilization of spectral channels of visible and near infrared bands. A complex mask of clouds and shadows was proposed, which, in comparison with the standard mask obtained with the algorithm of automatic cloud assessment (ACCA), helps to reduce errors in determination of the deforestation area. The use of the spectral index instead of radiometric and color indicators enables automatic exclusion of accidental non-vegetation objects from the results of processing. This also provides an opportunity to analyze temporal changes of the NDVI on the damaged forest territories over long periods of observations using the created vector layers with attributed information.

Practical value. The developed method can be implemented in software as a web-service for monitoring of anthropogenic changes in the environment due to the availability of satellite imagery and advanced processing technologies. Potential users of such a service are: state supervising authorities; police; emergency services; forestry service; municipal services; environmental services (taking measures to remediate damaged areas).

References:

1. Ravi, Jain, 2015. Environmental Impact of Mining and Mineral Processing: Management, Monitoring, and Auditing Strategies. Elsevier Science.

2. U. S. Geological Survey, Department of the Interior, 2016. Landsat 8 Data Users Handbook Version 2.0 [Accessed 29 August 2016].

3. Hamunyela, E., Verbesselt, J. and Herold, M., 2016. Using spatial context to improve early detection of deforestation from Landsat time series. Remote Sensing Environ, 172, pp.126–138.

4. Lewis, S. L., Edwards, D. P. and Galbraith, D., 2015. Increasing human dominance of tropical forests. Science, 349, pp. 827–832.

5. Vershigora, V. G., Husak, O. M., 2013. Analysis of the efficiency of detection of sources of forest fires by an operator using satellite images. Eastern-European Journal of Enterprise Technologies, 1(2(61)), рр. 17‒19.

6. Hansen, M. C., Krylov, A., Tyukavina, A., Potapov, P. V., Turubanova, S., Zutta, B., Suspense, I., Margono, B., Stolle, F. and Moore, R., 2016. Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11, 034008 [online]. Available at: <http://dx.doi.org/10.1088/1748-9326/11/3/034008>[Accessed 14 August 2016].

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

8. Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W. and Campbell, L. B., 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth [e-journal]. http://dx.doi.org/10.1080/17538947.2016. 1187673.

9. Zhe, Zhu, Curtis, E. Woodcock and Pontus Olofsson, 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment, 122, pр. 75–91.

10. DeVries, B., Verbesselt, J., Kooistra, L. and Herold, M., 2015. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sensing of Environment, 161, pр. 107–121.

11. Shedlovska, Y. I. and Hnatushenko, V. V., 2016. Shadow detection and removal using a shadow formation model. In: Proceedings of the 2016 IEEE 1st International Conference on Data Stream Mining and Processing, DSMP [e-journal]. pp. 187‒190. http://dx.doi.org/10.1109/DSMP. 2016.7583537.

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ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

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