Remote sensing monitoring of changes in forest cover in the Volyn region: a cross section for the first two decades of the 21st century
Abstract
The aim of the article. This article highlights the significance of forest cover as an important indicator of the state of the environment. It discusses the findings of the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA) 2020 report, which states that the world's forest area has decreased by 178 million hectares since 1990. The case study of Volyn region shows how cloud processing and vegetation classification can help quantify forest dynamics from 2000 to 2020, allowing local authorities and decision makers to monitor and analyze trends in near real time. Overall, this work provides insights into the importance of monitoring forest dynamics and the potential for remote sensing technology to facilitate this process.
Data & Methods. Remote sensing is an effective tool for monitoring forest ecology and management, and Google Earth Engine (GEE) is an online platform that combines data from various agencies to analyze environmental data. The article presents a case study of the Volyn region and how cloud processing and vegetation classification were used to assess forest dynamics from 2000 to 2020. The study used data from Landsat 7 Collection 1 Tier 1 composites and the CART algorithm for binary decision tree building. The study was based on information provided by the Main Department of Statistics in the Volyn region on the area of forests and areas where logging was carried out during the specified period.
Research results. It is interesting to note that despite the decrease in logging activities, there is an increase in forest cover loss within forest ranges. This could be due to various reasons, such as illegal logging or natural disturbances like fires or disease outbreaks. The use of machine learning methods like CART classification can help to identify and monitor these changes, which can then be used to inform policy decisions and management practices to reduce forest cover loss. In general, in the Volyn region, there is a gradual decrease in the areas where various kinds of logging are carried out from 524 km2 in 2003 to 239 km2 in 2020. In contrast, forest cover loss within forest ranges increased rapidly from 37.85 km2 in 2015 to 84.01 km2 in 2017 and beyond from 5.53 km2 to 10.80 km2 in 2015 and 2017 respectively. In this study, the accuracy assessment was performed using 30% of the control points obtained initially, based on data on the reliability of the land cover. The manufacturer's accuracy and user accuracy were calculated to evaluate error omissions and possibilities of a pixel being categorized in a certain category. The spatial resolution of Landsat 7 data used in this study was 30 m, with a minimum calculation area of 0.337 hectares. The overall accuracy and the coefficient κ are the most representative measures of accuracy, with an average accuracy of classification of OAav=98.82% and κav=0.9764.
Downloads
References
FAO. (2020). Global Forest Assessment Resources 2020 Main report. Food and Agriculture Organization of the United Nations, 1–36.
Melnyk, A., & Manko, P. (2018). Classification of volyn forests according to data of multispectral satellite images. ScienceRise, 9, 25–30.
Uhl, A. V., Melnyk, O. V., Melnyk, Yu. A., & Melniichuk, M. M. (2022). Dystantsiinyi monitorynh urbanizovanykh terytorii. Suchasni tekhnolohii ta metody rozrakhunkiv u budivnytstvi, 18, 162–173. https://doi.org/10.36910/6775-2410-6208-2022-8(18)-17 [in Ukrainian]
Blackburn, G. A. (2002). Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery. Remote Sensing of Environment, 82(2–3), 311–321.
Pérez-Hoyos, A., García-Haro, F. J., & San-Miguel-Ayanz, J. (2012). Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe. ISPRS Journal of Photogrammetry and Remote Sensing, 74, 185–201. https://doi.org/10.1016/j.isprsjprs.2012.09.006
Loozen, Y., Rebel, K. T., de Jong, S. M., Lu, M., Ollinger, S. V, Wassen, M. J., & Karssenberg, D. (2020). Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method. Remote Sensing of Environment, 247, 111933. https://doi.org/10.1016/j.rse.2020.111933
Barrett, F., McRoberts, R. E., Tomppo, E., Cienciala, E., & Waser, L. T. (2016). A questionnaire-based review of the operational use of remotely sensed data by national forest inventories. Remote Sensing of Environment, 174, 279–289. https://doi.org/10.1016/j.rse.2015.08.029
Tang, H., Armston, J., Hancock, S., Marselis, S., Goetz, S., & Dubayah, R. (2019). Characterizing global forest canopy cover distribution using spaceborne lidar. Remote Sensing of Environment, 231, 111262. https://doi.org/10.1016/j.rse.2019.111262
Potapov, P. V, Turubanova, S. A., Tyukavina, A., Krylov, A. M., McCarty, J. L., Radeloff, V. C., & Hansen, M. C. (2015). Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment, 159, 28–43. https://doi.org/10.1016/j.rse.2014.11.027
d’Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M., & van der Velde, M. (2021). From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations. Remote Sensing of Environment, 266, 112708. https://doi.org/10.1016/j.rse.2021.112708
Waser, L. T., & Schwarz, M. (2006). Comparison of large-area land cover products with national forest inventories and CORINE land cover in the European Alps. International Journal of Applied Earth Observation and Geoinformation, 8(3), 196–207. https://doi.org/10.1016/j.jag.2005.10.001
Cvitanović, M., Lučev, I., Fürst-Bjeliš, B., Borčić, L. S., Horvat, S., & Valožić, L. (2017). Analyzing post-socialist grassland conversion in a traditional agricultural landscape – Case study Croatia. Journal of Rural Studies, 51, 53–63. https://doi.org/10.1016/j.jrurstud.2017.01.008
Mongus, D., & Žalik, B. (2015). An efficient approach to 3D single tree-crown delineation in LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 219–233. https://doi.org/10.1016/j.isprsjprs.2015.08.004
Jurjević, L., Liang, X., Gašparović, M., & Balenović, I. (2020). Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 227–241. https://doi.org/10.1016/j.isprsjprs.2020.09.014
Zakšek, K., & Schroedter-Homscheidt, M. (2009). Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 414–421. https://doi.org/10.1016/j.isprsjprs.2009.02.006
Garbarino, M., Borgogno Mondino, E., Lingua, E., Nagel, T. A., Dukić, V., Govedar, Z., & Motta, R. (2012). Gap disturbances and regeneration patterns in a Bosnian old-growth forest: a multispectral remote sensing and ground-based approach. Annals of Forest Science, 69(5), 617–625. https://doi.org/10.1007/s13595-011-0177-9
Isaienkov, K., Yushchuk, M., Khramtsov, V., & Seliverstov, O. (2021). Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 364–376. https://doi.org/10.1109/JSTARS.2020.3034186
Bilous, A., Myroniuk, V., Holiaka, D., Bilous, S., See, L., & Schepaschenko, D. (2017). Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine. Environmental Research Letters, 12(10), 105001. https://doi.org/10.1088/1748-9326/aa8352
Kuemmerle, T., Chaskovskyy, O., Knorn, J., Radeloff, V. C., Kruhlov, I., Keeton, W. S., & Hostert, P. (2009). Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sensing of Environment, 113(6), 1194–1207. https://doi.org/10.1016/j.rse.2009.02.006
Chaskovskyi, O. H., & Hrynyk, H. H. (2020). Otsiniuvannia vtrat lisovoho pokryvu Ukrainskykh Karpat dystantsiinymy metodamy za materialamy vidkrytykh dzherel suputnykovoi informatsii. Naukovyi visnyk NLTU Ukrainy, 30(1), 66–73. https://doi.org/10.36930/40300111 [in Ukrainian]
Melnyk, O., Manko, P., & Brunn, A. (2023). Remote sensing methods for estimating tree species of forests in the Volyn region, Ukraine. Frontiers in Forests and Global Change, 6. https://doi.org/10.3389/ffgc.2023.1041882
Melnyk O.V., & Manko P.V. (2019). Klasyfikatsiia lisovkrytykh terytorii za multyspektralnymy danymy. V Suchasni tekhnolohii ta metody rozrakhunkiv u budivnytstvi: zb.nauk.prats (Number 12, pp 112–122). Lutskyi NTU. [in Ukrainian]
Hansen, M. C., Potapov, P. V, Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V, Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science (New York, N.Y.), 342(6160), 850–853. https://doi.org/10.1126/science.1244693
Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S. N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P. S., Vermote, E. F., Vogelmann, J., Wulder, M. A., & Wynne, R. (2008). Free access to Landsat imagery. В Science (New York, N.Y.) (Vol 320, Number 5879, p 1011). https://doi.org/10.1126/science.320.5879.1011a
Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world’s largest megacity during the last 40 years. Remote Sensing of Environment, 127. https://doi.org/10.1016/j.rse.2012.09.011
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., & Thau, D. (2017). Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 225–244. https://doi.org/10.1016/j.isprsjprs.2017.01.019
Fadli, A. H., Kosugo, A., Ichii, K., & Ramli, R. (2019). Satellite-based monitoring of forest cover change in indonesia using google earth engine from 2000 to 2016. Journal of Physics: Conference Series, 1317(1), 12046. https://doi.org/10.1088/1742-6596/1317/1/012046
Michel, A., Prescher, A.-K., & Schwärzel, K. (2020). Forest Condition in Europe: The 2020 Assessment. ICP Forests Technical Report under the UNECE Convention on Long-range Transboundary Air Pollution (Air Convention). https://doi.org/10.3220/ICPTR1606916913000
Understanding Deforestation - Coalition for Rainforest Nations. (n.d.). Retrieved 14, March 2023, https://www.rainforestcoalition.org/understanding-deforestation/
The Montréal Process Criteria and Indicators. (n.d.). Retrieved 14, March 2023, https://montreal-process.org/The_Montreal_Process/Criteria_and_Indicators/index.shtml
Forests | UNEP - UN Environment Programme. (n.d.). Retrieved 14, March 2023, https://www.unep.org/explore-topics/forests?gclid=Cj0KCQjwtsCgBhDEARIsAE7RYh3CkYj_DAG2wQEDbxRC8NEgqUV6QZuDnZcr_BqLUJja9sJDpfHHkncaAvxFEALw_wcB
Kabinet ministriv Ukrainy. (2021). Pro zatverdzhennia Poriadku provedennia natsionalnoi inventaryzatsii lisiv ta vnesennia zminy u dodatok do Polozhennia pro nabory danykh, yaki pidliahaiut opryliudnenniu u formi vidkrytykh danykh. 18. https://zakon.rada.gov.ua/laws/show/392-2021-п#Text [in Ukrainian]
Sakal O.V. (2012). Efektyvne upravlinnia zemliamy lisohospodarskohopryznachennia. Derzhavna ustanova «Instytut ekonomiky pryrodokorystuvannia ta staloho rozvytkuNatsionalnoi akademii nauk Ukrainy».
Oliinyk Ye.M. (2019). Lisohospodarska diialnist v Ukraini. Analitychne doslidzhennia. Hromadska spilka «Bioenerhetychna asotsiatsiia Ukrainy». [in Ukrainian]
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Biometrics, 40(3), 874. https://doi.org/10.2307/2530946
McLachlan, & J., G. (1992). Discriminant analysis and statistical pattern recognition. https://doi.org/10.1002/0471725293
This work is licensed under a Creative Commons Attribution 4.0 International License.
