Algorithm and Measurement of Forest Cover Area Change in the Khachmaz Region of Azerbaijan by Satellite Monitoring

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UDС

528.856:630*5

DOI:

https://doi.org/10.37482/0536-1036-2021-2-106-115

Abstract

The article presents the results of long-term observations of the forest cover state in the Khachmaz region of Azerbaijan. Both the results of Earth’s remote sensing and the data of earlier observations carried out by field monitoring were used in the study. The earlier data was stored in the form of thematic maps on paper. The results of remote sensing contained multispectral satellite images obtained over several years. Image processing was performed using the QGIS program and the Semi-Automatic Classification Plugin. The processing included radiometric and atmospheric correction, as well as classification of the study area by the type of land surface coverage. Classification was performed on the basis of the spectral curves analysis in various sections of the region. The areas occupied by forests were determined based on the classification. The set of observations included data from the available archival materials – thematic maps of the Khachmaz region. A software in MATLAB was developed for processing the maps and calculating the area of forests. The program operation algorithm includes histogram analysis of the image as a whole and separately of the legend. The histogram of the legend determines the number of thematic layers of the map. They do not include layers containing unused colors, such as the colors of neighboring areas, sections of the sea, etc. Then color correction of image pixels is performed, namely, quantization of colors according to the number of specific thematic layers. Later on, morphological processing of each layer is carried out. Areas containing less than the specified number of pixels are assigned the color of the surrounding layer. The number of pixels of each layer and all layers are calculated. Finally, the obtained ratios are used to calculate the area of each layer. The data of processing of archival materials together with the results of remote sensing are summarized in a joint table. According to the table, a diagram of changes in the forest area is constructed, as well as a model in the form of a polynomial showing this dynamics. The diagram analysis revealed a tendency to a decrease in the area of forests. The forest area has decreased by 21 % for 7 years. The developed algorithm splits thematic maps into separate layers in accordance with the colors of the legend.

Authors

Valida M. Mamedaliyeva, Candidate of Geography; ResearcherID: AAC-5454-2021,
ORCID: https://orcid.org/0000-0002-8775-8564

Affiliation

Institute of Ecology, Azerbaijan National Aerospace Agency, ul. S.S. Akhundova, 1, Baku, Republic of Azerbaijan; e-mail: valide.mamedaliyeva@mail.ru

Keywords

forest cover, area, dynamics of changes, Khachmaz region, electronic maps, thematic layer, image processing

For citation

Mamedaliyeva V.M. Algorithm and Measurement of Forest Cover Area Change in the Khachmaz Region of Azerbaijan by Satellite Monitoring. Lesnoy Zhurnal [Russian Forestry Journal], 2021, no. 2, pp. 106–115. DOI: 10.37482/0536-1036-2021-2-106-115

References

1. Alizade E.K., Guliyeva F.E. The Method to Evaluate the Anthropogenic Factor Impact on Forest Cover in Mountainous Areas. Vestnik Altayskogo gosudarstvennogo agrarnogo universiteta [Bulletin of Altai State Agrarian University], 2016, no. 3(137), pp. 84–90.
2. Begon M., Harper J.L., Townsend C.R. Ecology: From Individuals to Ecosystems. Vol. 2. Moscow, Mir Publ., 1989. 477 p.
3. QGIS Toolkit for Semi-Automatic Classification of Agricultural Land according to Sentinel Spacecraft Data.
4. Luganskiy N.A., Zalesov S.V., Luganskiy V.N. Forest Science. Yekaterinburg, USFEU Publ., 2010. 432 p.
5. Lukina N.V., Isaev A.S., Kryshen’ A.M., Onuchin A.A., Sirin A.A., Gagarin Yu.N., Bartalev S.A. Research Priorities in Forest Science – the Basis of Sustainable Forest Management. Lesovedenie [Russian Journal of Forest Science], 2015, no. 4, pp. 243–254.
6. Museibov M.A. Landscapes of the Republic of Azerbaijan. Baku, BSU Publ., 2003. 151 p.
7. Prilipko L.I. Vegetation Cover of Azerbaijan. Baku, Elm Publ., 1970. 168 p.
8. Proskuryakov M.A. Problem of Chronobiological Cyclic of Movement of Forest Ecosystems Properties. First Communication. Sibirskij Lesnoj Zurnal [Siberian Journal of Forest Science], 2015, no. 2, pp. 71–84. DOI: 10.15372/SJFS20150206
9. Suleymanov T.I., Mustafazade N.Kh., Guluzade R.K. The Data Processing of Air Pollution Monitoring in Baku City. Pribory i sistemy. Upravleniye, kontrol’, diagnostika [Instruments and Systems: Monitoring, Control, and Diagnostics], 2016, no. 10, pp. 41–45.
10. Chistik O.V., Golovatyy S.E., Poznyak S.S. General and Radiation Ecology. Minsk, ISEI BSU Publ., 2012. 313 p.
11. Campbell J., Wynne R.H. Introduction to Remote Sensing. New York, Guilford Press, 2011. 667 p.
12. Congedo L. Semi-Automatic Classification Plugin Documentation. Release 6.4.0.2. 2020. 239 p. Available at: https://edisciplinas.usp.br/pluginfile.php/5302121/mod_resource/ content/1/SCP_manual.pdf (accessed 14.02.20).
13. Gomarasca M.A. Basics of Geomatics. Dordrecht, Springer, 2009. 656 p. DOI: 10.1007/978-1-4020-9014-1
14. Group Method of Data Handling. Available at: https://www.gmdh.net (accessed 09.06.16).
15. Image Processing Toolbox Documentation – MathWorks. Available at: https://www.mathworks.com/help/images (accessed 08.12.19).
16. Jensen J.R. Introductory Digital Image Processing: A Remote Sensing Perspective. New Jersey, Prentice Hall, 2004. 526 p.
17. Knoke T. Shades of Forest Change. Book of Abstracts of an International Conference of IUFRO Unit 4.02.00 on Forest Cover Change, Freising, Germany, April 2–4, 2014. Freising, IUFRO, 2014, p. 3.
18. Manfreda S., McCabe M., Miller P., Lucas R., Madrigal V.P., Mallinis G. et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sensing, 2018, vol. 10, iss. 4, art. 641. DOI: 10.3390/rs10040641
19. Pahari K., Murai Sh. Modeling for Prediction of Global Deforestation Based on the Growth of Human Population. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, vol. 54, iss. 5-6, pp. 317–324.
20. Radiometric Calibration. L3 Harris Geospatial Documentation Center. Available at: https://www.l3harrisgeospatial.com/docs/RadiometricCalibration.html (accessed 17.12.19).
21. The Forests of the Republic. Ministry of Ecology and Natural Resources of Azerbaijan Republic. Available at: http://eco.gov.az/az/fealiyyet-istiqametleri/mesheler (accessed 25.10.19).
22. United States Geological Survey. Available at: https://www.usgs.gov (accessed 30.08.19).