Monitoring tree mortality in Ukrainian Pinus sylvestris L. forests using remote sensing data from earth observing satellites
DOI:
https://doi.org/10.15287/afr.2022.2328Keywords:
forest fires, pine, deforestation, maximum likelihood methodAbstract
This article considers the application of remote sensing data to solve the problems of forestry in the Polissia zone (Ukraine). The satellite remote sensing was shown to be applicable to monitoring the damage caused by diseases and pests to forest resources and to assessing the effects of fires. During the research, a detailed analysis and optimization of the information content of Sentinel-2 long-term data sets was performed to detect changes in the forest cover of Polissia, affected by pests and damaged by fires. The following classification algorithms were used for automated decryption: the maximum likelihood method; cluster classification without training; Principal Component Analysis (PCA); Random Forest classification. The results of this study indicate the high potential of Sentinel-2 data for application in applied problems of forestry and vegetation analysis, despite the decametric spatial resolution. Our proposed workflow has achieved an overall classification accuracy of 90 % for the Polissia region, indicating its reliability and potential for scaling to a higher level, and the proposed forecast model is stationary and does not depend on time parameters. To improve the classification results, testing of different combinations of bands emphasized the importance of Band 8 in combination with red edge bands, as well as other bands with a resolution of 10 m for summer scenes. The red margin shows clearly visible differences in the spectral profiles, but bands with a higher resolution of 10 m were crucial for good results.
References
Boyd D.S., & Danson F.M. 2005. Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography: Earth and Environment, 29(1), 1–26. https://doi.org/10.1191/0309133305pp432ra
Ding H., Shi J., Wang Y., & Wei L. 2015. An improved dark-object subtraction technique for atmospheric correction of Landsat 8. In MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 9815, 128-135. https://doi.org/10.1117/12.2205567
Fedonyuk T.P., Galushchenko O.M., Melnichuk T.V., Zhukov O.V., Vishnevskiy D.O., Zymaroieva A.A., & Hurelia V.V. 2020. Prospects and main aspects of the GIS-technologies application for monitoring of biodiversity (on the example of the Chornobyl Radiation-Ecological Biosphere Reserve). Space Science and Technology, 26(6). https://doi.org/10.15407/knit2020.06.075
Fedoniuk T., Borsuk O., Melnychuk T., Zymaroieva A., & Pazych V. 2021. Assessment of the consequences of forest fires in 2020 on the territory of the chornobyl radiation and ecological biosphere reserve. Scientific Horizons, 24(8), 26-36. https://doi.org/10.48077/scihor.24(8).2021.26-36
Isuhuaylas L.A.V., Hirata Y., Ventura Santos L.C., & Serrudo Torobeo N. 2018. Natural forest mapping in the Andes (Peru): A comparison of the performance of machine-learning algorithms. Remote Sensing, 10(5), 782. https://doi.org/10.3390/rs10050782
Knorr W., Arneth A., & Jiang L. 2016. Demographic controls of future global fire risk. Nature Climate Change, 6(8), 781-785. https://doi.org/10.1038/nclimate2999
Kravets P.V., & Pavlishchuk O.P. 2016. Lisova haluz Ukrainy v konteksti yevropeiskykh vymoh do zabezpechennia zakonnosti pokhodzhennia derevyny. Naukovyi visnyk NLTU Ukrainy, 26(8).
Kunah O.M., Papka O.S. 2016. Ecogeographical determinants of the ecological niche of the common milkweed (Asclepias syriaca) on the basis of indices of remote sensing of land images. Visnyk of Dnipropetrovsk University. Biology, Ecology, 24(1), 78-86. https://doi.org/10.15421/011609
Lee S.H., Han K.J., Lee K., Lee K.J., Oh K.Y., & Lee M.J. 2020. Classification of landscape affected by deforestation using high-resolution remote sensing data and deep-learning techniques. Remote Sensing, 12(20), 3372. https://doi.org/10.3390/rs12203372
Lozano O.M., Salis M., Ager A.A., Arca B., Alcasena F.J., Monteiro A.T., ... & Spano D. 2017. Assessing climate change impacts on wildfire exposure in Mediterranean areas. Risk Analysis, 37(10), 1898-1916. https://doi.org/10.1111/risa.12739
Scholze M., Knorr W., Arnell N.W., & Prentice I.C. 2006. A climate-change risk analysis for world ecosystems. Proceedings of the National Academy of Sciences, 103(35), 13116-13120. https://doi.org/10.1073/pnas.0601816103
Senf C., Seidl R., & Hostert P. 2017. Remote sensing of forest insect disturbances: Current state and future directions. International Journal of Applied Earth Observation and Geoinformation, 60, 49-60. https://doi.org/10.1016/j.jag.2017.04.004
Skydan O.V., Fedoniuk T.P., Pyvovar P.V., Dankevych V.Y., & Dankevych Y.M. 2021. Landscape fire safety management: the experience of Ukraine and the EU. News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences, 6(450), 125-132. https://doi.org/10.32014/2021.2518-170X.128
Tomppo E., Olsson H., Ståhl G., Nilsson M., Hagner O., & Katila M. 2008. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sensing of Environment, 112 (5), 1982-1999. https://doi.org/10.1016/j.rse.2007.03.032 .
UN, 2019. Global Assessment Report on Disaster Risk Reduction (GAR), 2019 https://gar.undrr.org/report-2019
USGS 2020. Geological Portal of the US Geological Survey. Electronic resource – access mode as of March 26, 2020: http://earthexplorer.usgs.gov
Valbuena R., Maltamo M., & Packalen P. 2016. Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods. Revista de Teledetección, (45), 15-25. https://doi.org/10.4995/raet.2016.4029
Wessel M., Brandmeier M., & Tiede D. 2018. Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sensing, 10(9), 1419. https://doi.org/10.3390/rs10091419
Zhukov O.V., Kunah O.M., Taran V.O., Lebedinska M.M. 2016. Spatial variability of soils electrical conductivity within arena of the river Dnepr valley (territory of the natural reserve «Dniprovsko-orilsky»). Biological Bulletin of Bogdan Chmelnitskiy Melitopol State Pedagogical University. 6(2), 129–157.
Zymaroieva A., Zhukov O., Fedoniuk T., Pinkina T., & Hurelia V. 2021. The relationship between landscape diversity and crops productivity: Landscape scale study. Journal of Landscape Ecology (Czech Republic), 14(1), 39-58. https://doi.org/10.2478/jlecol-2021-0003
https://firms.modaps.eosdis.nasa.gov
https://sinergise.com/en/solutions/sentinel-hub
The United States Geological Survey https://www.usgs.govDownloads
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