Methodology for determining deforestation areas in Lviv region using remote sensing data

• Autorzy: Chetverikov Borys , Trevoho Ihor , Babiy Lubov , Malanchuk Mariia

Identyfikatory

DOI

10.24425/gac.2022.141174

• Języki publikacji: EN

Abstrakty

EN

The object of the study is the processing of space images on the territory of the Carpathian territory in the Lviv region, obtained from the Landsat-8 satellite. The work aims to determine the area of deforestation in the Carpathian territory of the Lviv region from different time-space images obtained from the Landsat-8 satellite. Methods of cartography, photogrammetry, aerospace remote sensing of the Earth and GIS technology were used in the experimental research. The work was performed in Erdas Imagine software using the unsupervised image classification module and the DeltaCue difference detection module. The results of the work are classified as three images of Landsat-8 on the territory of the Carpathian territory in the Lviv region. The areas of forest cover for each of them for the period of 2016-2018 have been determined. During the three years, the area of forests has decreased by 14 hectares. Our proposed workflow includes six stages: analysis of input data, band composition of space images on the research territory, implementation of unsupervised classification in Erdas Imagine software and selection of forest class and determination of implementing this workflow, the vector layers of the forest cover of the Carpathians in the Lviv region for 2016, 2017, 2018 were obtained, and on their basis, the corresponding areas were calculated and compared.

Słowa kluczowe

EN

Landsat 8; remote sensing; space image; satellite monitoring system

PL

teledetekcja; monitoring satelitarny; obraz satelitarny

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Advances in Geodesy and Geoinformation
• Rocznik: 2022
• Tom: Vol. 71, no. 1
• Strony: art. no. e21, 2022
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Chetverikov Borys

• Lviv Polytechnic National University, Lviv, Ukraine

• https://orcid.org/0000-0001-8677-1735

autor

Trevoho Ihor

• Lviv Polytechnic National University, Lviv, Ukraine

• https://orcid.org/0000-0002-2368-9088

autor

Babiy Lubov

• Lviv Polytechnic National University, Lviv, Ukraine
• Kryvyi Rih National University, Kryvyi Rih, Ukraine

• https://orcid.org/0000-0002-5772-4865

autor

Malanchuk Mariia

• Lviv Polytechnic National University, Lviv, Ukraine

• https://orcid.org/0000-0003-0819-6573

Bibliografia

• [1] Abadi, M. and Grandchamp, E. (2008). Colour space influence for vegetation image classification application to Caribbean forest and agriculture. Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 710909. DOI: 10.1117/12.799886.
• [2] Beaumonta, L., Hughes, L., and Poulsen, M. (2005). Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Modell., 186, 250–269.
• [3] Burshtynska, K., Polishchuk, B., and Madyar, J. (2014). The definition of the area of felling forests by high resolution satellite images. Geomat., Landmanage. Landscape, 43-54. DOI: 10.15576/GLL/2014.3.43.
• [4] Burshtynska, K., Madyar, J., and Polishchuk, B. (2015). Deforestation monitoring at different periods by satellite imagery. In ISPRS WG IV/2 Workshop. Novosibirsk, 114–127.
• [5] Clerici, N., Weissteiner, C., and Gerard, F. (2012). Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories. Remote Sens., 4, 1781–1803. DOI: 10.3390/rs4061781.
• [6] DeVries, B., Verbesselt, J., Kooistra, L. et al. (2015). Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sens. Environ., 161, 107–121. DOI: 10.1016/j.rse.2015.02.012.
• [7] Dorozhynskyy, O., Chetverikov, B., and Babiy, L. (2013). Determining the influence of earthquake on the changes of objects using remote sensing data. Geomat. Landmanage. Landscape, 3, 7–15. DOI: 10.15576/GLL/2013.3.7.
• [8] Forest monitoring for Europe (2009). Ńonclusions, Uppsala, Sweden. Received 11-12 November 2009 from http://www-conference.slu.se/futforestmon/forestmon_conclusions.pdf.
• [9] Ganz, S., Adler, P., and Kändler, G. (2020). Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data. Forests, 11, 1322. DOI: 10.3390/f11121322.
• [10] Hamunyela, E., Verbesselt, J., and Herold, M. (2016). Using spatial context to improve early detection of deforestation from Landsat time series. Remote Sens. Environ., 172, 126–138. DOI: 10.1016/j.rse.2015.11.006.
• [11] Hatwell, J., Gaber, M.M., and Azad, R.M.A. (2020). CHIRPS: Explaining random forest classification. Artif. Intell. Rev., 53, 5747–5788. DOI: 10.1007/s10462-020-09833-6.
• [12] Hermosilla, T., Wulder, M.A., White, J. C. et al. (2016). Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. Int. J. Digital Earth. DOI: 10.1080/17538947.2016.1187673.
• [13] Hnatushenko, V.V., Hnatushenko, V.V., Mozhovyi, D.K. et al. (2016). Satellite technology of the forest fires effects monitoring. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 1(151), 70–76. Retrieved from https://landsat.gsfc.nasa.gov/satellites/landsat-8/landsat-8-mission-details/.
• [14] Kuemmerle, T., Chaskovskyy, O., Knorn, J. et al. (2009). Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sens. Environ., 113(6), 1194–1207. DOI: 10.1016/j.rse.2009.02.006.
• [15] Landsat. (2021). Retrieved from https://landsat.gsfc.nasa.gov.
• [16] Lewis, S.L., Edwards, D.P., and Galbraith, D. (2015). Increasing human dominance of tropical forests. Science, 349, 827–832. DOI: 10.1126/science.aaa9932.
• [17] Liu, Y., Gong, W., Hu, X. et al. (2018). Forest Type Identification with Random Forest Using Sentinel1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data. Remote Sens., 10(6), 946. DOI: 10.3390/rs10060946.
• [18] Lyalko, V. I., Sakhatsky, A. I., Hodorovsky, A.Y. et al. (2004). Features of the space control of forests of Ukraine and Siberia for an estimation of their state, fire risk and carbon cycle. Abstract Book. In Proc. of 24th EARSeL Symposium “New Strategies For European Remote SensinG”, IUC, Dubrovnik, Croatia, 25-27 May 2004.
• [19] Richards, J.A., and Xiuping, J. (2005). Remote sensing digital image analysis: an introduction. Switzerland: Birkhäuser.
• [20] Sakhatsky, A.I., McCallun, J., Khodorovsky, A.J. et al. (2002). Classification of space image for forest state identification within the Siberia region. In Pt. 1 IIASA, Laxenburg, Austria, IR-02-09, April 2002.
• [21] Vershigora, V.G., and Husak, O.M. (2013). Analysis of the efficiency of detection of sources of forest fires by an operator using satellite images. East.-Eur. J. Enterp. Technol., 1(2(61)), 17–19. DOI: 10.15587/1729-4061.2013.7008.
• [22] Zhe, Z., Curtis, W.E., and Olofsson, P. (2012). Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ., 122, 75–91. DOI: 10.1016/j.rse.2011.10.030.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)