Purpose. The aim of this research is monitoring of coniferous forests of Tukhlya forestry in Precarpathian region using medium and high resolution satellite images and images obtained from an unmanned aerial vehicle (UAV). Methodology. To monitor the condition of forests of Tukhlya Forestry, a technique based on using satellite images with different spectral characteristics and resolutions, images obtained from UAVs and, accordingly, their processing by different methods, was used. To substantiate the methods of further image processing and to develop effective approaches to the identification of areas with coniferous trees drying, spectrophotometric measurements of healthy and damaged coniferous vegetation were carried out. The analysis of the obtained spectral curves made it possible to select the appropriate ranges of the electromagnetic spectrum for the identification of damaged and dry vegetation. The research is based on using high and medium resolution satellite images, obtained from GeoEye-1 and Sentinel-2. Unmanned aerial vehicle surveying was used to obtain validation information and to analyse the obtained results. Results. Researches were conducted in the territory of Tukhlya forestry, Skole district, Lviv region. Three expeditions were carried out for field research. During the last expedition, surveying from the unmanned aerial vehicle were conducted for two test sites. For efficient using of spectral reflectance ranges, samples of different coniferous vegetation types were selected for spectrophotometric measurements. The analysis of the obtained spectral curves was used to select the vegetation indices that allow identification of damaged and healthy vegetation. To improve the interpretation capabilities of index images, a synthesized image from three vegetation indices was created. Controlled classification by maximum likelihood method was performed to determine the areas of sites with damaged coniferous vegetation. The obtained results were then analysed. Scientific novelty and practical significance. The scientific novelty is processing of the methods for detection of damaged and healthy coniferous vegetation in the territory of the Carpathian region. Spectrometric measurements of healthy and damaged vegetation are the theoretical basis, which makes it possible to substantiate the choice of spectral ranges for the most efficient separation of different types of coniferous vegetation and the choice of vegetation indices for their identification. The developed methodology of using remote sensing data for identification of damaged and healthy vegetation allows to detect not only dry and healthy vegetation, but also damaged vegetation. This will contribute to the timely cutting of such trees, which will not only save the healthy forest from further spread of pests, but also obtain wood that can still be used in the wood industry.
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2. Bochenek, Z., Ziolkowski, D., Bartold, M., Orlowska K., & Ochtyra A. (2017). Monitoring forest biodiversity and the impact of climate on forest environment using high-resolution satellite images. European Journal of Remote Sensing, 51(1), 166-181
https://doi.org/10.1080/22797254.2017.1414573
3. Burshtynska, Kh., Denys, Yu., Madiar, Yu., & Polishchuk, B. (2016). Methods of two-stage classification of forests by high resolution satellite images. Modern achievements in geodetic science and industry, 1, 148-155 (in Ukrainian)
4. Burshtynska, K., Polishchuk, B., & Madyar, J. (2014). The definition of the area of felling forests by high resolution satellite images. GLL, 3, 43-54
https://doi.org/10.15576/GLL/2014.3.43
5. Ceccato, P., Flasse, S., Tarantola, S., Jacquemond, S., & Gregoire, J. (2001). Detecting vegetation water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22-33
https://doi.org/10.1016/S0034-4257(01)00191-2
6. Cherepanov, A. S., & Druzhinina, E. G. (2009). Spectral properties of vegetation and vegetation indices. Geomatika, 3, 28-32 (in Russian)
7. Cherepanov, A. S. (2011). Vegetation indices. Geomatika, 2, 98-102 (in Russian)
8. Donets, V. V., Brovarets, A. A., & Brovchenko, V. V. (2017). Analysis of the features of field spectral equipment. Kosmichna nauka i tekhnolohiia, 3, 49-63 (in Russian)
9. Franklin, S. E., Wulder, M. A., Skakun, R. S., & Carroll A. L. (2003). Mountain pine beetle red attack forest damage classification using stratified Landsat TM data in British Columbia, Canada. Photogrammetric Engineering and Remote Sensing, 69(3), 283-288.
https://doi.org/10.14358/PERS.69.3.283
10. Katz, Ch. (2017). Small Pests, Big Problems: The Global Spread of Bark Beetles. Retrieved from: https://e360.yale.edu/features/small-pests-big-problems-the-global-sprea...
11. Kokhan, S. S., & Vostokov, A. B. (2009). Remote sensing of the Earth: theoretical basis: textbook. Kyiv: Vyshcha shkola, 511 s. (in Ukranian)
12. Krylov, A. M., Sobolev, A. A., & Vladimirova, N. A. (2011). Identification of bark beetle in the Moscow region using Landsat images. Lesnoy vestnik, 4, 54-60 (in Russian)
13. Kumar, D. (2011). Monitoring Forest Cover Changes Using Remote Sensing and GIS: A Global Prospective. Research Journal of Environmental Sciences, 5, 105-123
https://doi.org/10.3923/rjes.2011.105.123
14. Sherbinin, A., Carr, D., Cassels, S., & Jiang L. (2007). Population and environment. Annual Review of Environment and Resources, 32, 345-373
https://doi.org/10.1146/annurev.energy.32.041306.100243
15. Shpak, A. (2012). Comparative analysis of mountain forest classification methods using RapidEye satellite imagery. Visnyk Astronomichnoi shkoly, 8 (2), 212-216 (in Ukranian)
https://doi.org/10.18372/2411-6602.08.2212
16. Sidelnik, N. YA., Pushkin, A. A., & Kovalevskiy, S. V. (2018). Mapping of damaged forest stands and forestry facilities using satellite imagery and GIS technology. Trudy BGTU, 1, 5-12 (in Russian)
17. Stankevych, S. A., Tytarenko, O. V., & Shkliar, S. V. (2010). Effective processing of field spectrometry data in natural resource tasks. Reports of the National Academy of Sciences of Ukraine, 12, 110-115 (in Ukranian)
18. State Agency of Forest Resources of Ukraine. Retrieved from: http://dklg.kmu.gov.ua
19. State Enterprise "Lvivlisozahist". Retrieved from: https://lvivlisozahyst.co.ua/
20. Trigg, S. N., Curran, L. M., & McDonald, A. K. (2006). Utility of Landsat 7 satellite data for continued monitoring of forest cover change in protected areas in southeast Asia. Singapore Journal of Tropical Geography, 27(1), 49-66
https://doi.org/10.1111/j.1467-9493.2006.00239.x
21. Zatserkovnyi, V., Oberemok N., & Yahorlytska K. (2017). Application of GIS and remote sensing technologies in forest coenosis monitoring tasks. Naukoiemni tekhnolohii, 4 (36), 350-357 (in Ukranian) 1. Bardysh, B., & Burshtynska, Kh. (2014). Using of vegetation indices to identify the objects of the earth's surface. Modern achievements in geodetic science and industry, II (28), 82-88 (in Ukrainian)
2. Bochenek, Z., Ziolkowski, D., Bartold, M., Orlowska K., & Ochtyra A. (2017). Monitoring forest biodiversity and the impact of climate on forest environment using high-resolution satellite images. European Journal of Remote Sensing, 51(1), 166-181
https://doi.org/10.1080/22797254.2017.1414573
3. Burshtynska, Kh., Denys, Yu., Madiar, Yu., & Polishchuk, B. (2016). Methods of two-stage classification of forests by high resolution satellite images. Modern achievements in geodetic science and industry, 1, 148-155 (in Ukrainian)
4. Burshtynska, K., Polishchuk, B., & Madyar, J. (2014). The definition of the area of felling forests by high resolution satellite images. GLL, 3, 43-54
https://doi.org/10.15576/GLL/2014.3.43
5. Ceccato, P., Flasse, S., Tarantola, S., Jacquemond, S., & Gregoire, J. (2001). Detecting vegetation water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22-33
https://doi.org/10.1016/S0034-4257(01)00191-2
6. Cherepanov, A. S., & Druzhinina, E. G. (2009). Spectral properties of vegetation and vegetation indices. Geomatika, 3, 28-32 (in Russian)
7. Cherepanov, A. S. (2011). Vegetation indices. Geomatika, 2, 98-102 (in Russian)
8. Donets, V. V., Brovarets, A. A., & Brovchenko, V. V. (2017). Analysis of the features of field spectral equipment. Kosmichna nauka i tekhnolohiia, 3, 49-63 (in Russian)
9. Franklin, S. E., Wulder, M. A., Skakun, R. S., & Carroll A. L. (2003). Mountain pine beetle red attack forest damage classification using stratified Landsat TM data in British Columbia, Canada. Photogrammetric Engineering and Remote Sensing, 69(3), 283-288.
https://doi.org/10.14358/PERS.69.3.283
10. Katz, Ch. (2017). Small Pests, Big Problems: The Global Spread of Bark Beetles. Retrieved from: https://e360.yale.edu/features/small-pests-big-problems-the-global-sprea...
11. Kokhan, S. S., & Vostokov, A. B. (2009). Remote sensing of the Earth: theoretical basis: textbook. Kyiv: Vyshcha shkola, 511 s. (in Ukranian)
12. Krylov, A. M., Sobolev, A. A., & Vladimirova, N. A. (2011). Identification of bark beetle in the Moscow region using Landsat images. Lesnoy vestnik, 4, 54-60 (in Russian)
13. Kumar, D. (2011). Monitoring Forest Cover Changes Using Remote Sensing and GIS: A Global Prospective. Research Journal of Environmental Sciences, 5, 105-123
https://doi.org/10.3923/rjes.2011.105.123
14. Sherbinin, A., Carr, D., Cassels, S., & Jiang L. (2007). Population and environment. Annual Review of Environment and Resources, 32, 345-373
https://doi.org/10.1146/annurev.energy.32.041306.100243
15. Shpak, A. (2012). Comparative analysis of mountain forest classification methods using RapidEye satellite imagery. Visnyk Astronomichnoi shkoly, 8 (2), 212-216 (in Ukranian)
https://doi.org/10.18372/2411-6602.08.2212
16. Sidelnik, N. YA., Pushkin, A. A., & Kovalevskiy, S. V. (2018). Mapping of damaged forest stands and forestry facilities using satellite imagery and GIS technology. Trudy BGTU, 1, 5-12 (in Russian)
17. Stankevych, S. A., Tytarenko, O. V., & Shkliar, S. V. (2010). Effective processing of field spectrometry data in natural resource tasks. Reports of the National Academy of Sciences of Ukraine, 12, 110-115 (in Ukranian)
18. State Agency of Forest Resources of Ukraine. Retrieved from: http://dklg.kmu.gov.ua
19. State Enterprise "Lvivlisozahist". Retrieved from: https://lvivlisozahyst.co.ua/
20. Trigg, S. N., Curran, L. M., & McDonald, A. K. (2006). Utility of Landsat 7 satellite data for continued monitoring of forest cover change in protected areas in southeast Asia. Singapore Journal of Tropical Geography, 27(1), 49-66
https://doi.org/10.1111/j.1467-9493.2006.00239.x
21. Zatserkovnyi, V., Oberemok N., & Yahorlytska K. (2017). Application of GIS and remote sensing technologies in forest coenosis monitoring tasks. Naukoiemni tekhnolohii, 4 (36), 350-357 (in Ukranian)