Study of changes in land cover categories in Ukraine based on remote sensing data

Authors

  • L. Ren Kyiv National University of Construction and Architecture image/svg+xml

DOI:

https://doi.org/10.31548/zemleustriy2023.01.12

Keywords:

Land cover change, Google Earth Engine, land transfer matrix.

Abstract

Land cover change has been a hot area of research on global ecological change and sustainable development due to its importance in global ecological change. Understanding land cover change trends is the basis for rational planning and management of land resources and is of important value for achieving land protection and sustainable development. Land transfer matrix has great value in the research of land cover change, its results are not disturbed by the land cover category and quantity, and the data can be analyzed in different time periods according to the demand. However, the land transfer matrix produced by traditional methods has the problems of long production period and certain requirements for hardware performance. In this paper uses Google Earth Engine to obtain the public land cover dataset of Ukraine and uses raster calculation to quickly construct the land transfer matrix. The matrix data show that the land cover change in Ukraine from 2000-2015 is modest, with a total change of 2.244%. The proportion of cropland decreased and the proportion of Urban and Built-up Lands increased. The results show that the methods can quickly and effectively obtain data on land cover change in the study area and provide assistance in analyzing trends and patterns of land cover change.

Keywords: Land cover change, Google Earth Engine, land transfer matrix

Author Biography

  • L. Ren, Kyiv National University of Construction and Architecture

    postgraduate

References

Fischer, G., & Sun, L. (2001). Model based analysis of future land-use development in China. Agriculture, Ecosystems &Amp; Environment, 85(1–3), 163–176. DOI: https://doi.org/10.1016/s0167-8809(01)00182-7

De Freitas, M. W. D., Muñoz, P., Dos Santos, J. R., & Alves, D. S. (2018). Land use and cover change modelling and scenarios in the Upper Uruguay Basin (Brazil). Ecological Modelling, 384, 128–144. DOI: https://doi.org/10.1016/j.ecolmodel.2018.06.009

Zhou, Y., Li, X., & Liu, Y. (2020). Land use change and driving factors in rural China during the period 1995-2015. Land Use Policy, 99, 105048. DOI: https://doi.org/10.1016/j.landusepol.2020.105048

Cui, X., Liu, C., Shan, L., Lin, J., Zhang, J., Jiang, Y., & Zhang, G. (2021). Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China. International Journal of Environmental Research and Public Health, 19(1), 178. DOI: https://doi.org/10.3390/ijerph19010178

Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7), 1303–1330. DOI: https://doi.org/10.1080/014311600210191

Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182. DOI: https://doi.org/10.1016/j.rse.2009.08.016

Bartholomé, E., & Belward, A. S. (2005). GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9), 1959–1977. DOI: https://doi.org/10.1080/01431160412331291297

Manandhar, R., Odeh, I. O., & Pontius, R. G. (2010). Analysis of twenty years of categorical land transitions in the Lower Hunter of New South Wales, Australia. Agriculture, Ecosystems &Amp; Environment, 135(4), 336–346. DOI: https://doi.org/10.1016/j.agee.2009.10.016

Mallinis, G., Koutsias, N., & Arianoutsou, M. (2014). Monitoring land use/land cover transformations from 1945 to 2007 in two peri-urban mountainous areas of Athens metropolitan area, Greece. Science of the Total Environment, 490, 262–278. DOI: https://doi.org/10.1016/j.scitotenv.2014.04.129

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. DOI: https://doi.org/10.1016/j.rse.2017.06.031

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170. DOI: https://doi.org/10.1016/j.isprsjprs.2020.04.001

Phalke, A. R., Özdoğan, M., Thenkabail, P. S., Erickson, T., Gorelick, N., Yadav, K., & Congalton, R. G. (2020). Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 104–122. DOI: https://doi.org/10.1016/j.isprsjprs.2020.06.022

FAO GAUL 500m: Global Administrative Unit Layers 2015, First-Level Administrative Units. Available at : https://developers.google.com/earth-engine/

datasets/catalog/FAO_GAUL_SIMPLIFIED_500m_2015_level1

Xu X C，Li B J，Liu X P，Li X and Shi Q. (2021). Mapping annual global land cover changes at a 30 m resolution from 2000 to 2015. National Remote Sensing Bulletin， 25（9）：1896-1916 . DOI： 10.11834/jrs.20211261. [In Chinese]

Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., ... & Arino, O. (2021). ESA WorldCover 10 m 2020 v100. Available at : https://developers.google.com/earth-engine/datasets/catalog/

ESA_WorldCover_v100

Buchhorn, M., Lesiv, M., Tsendbazar, N. E., Herold, M., Bertels, L., & Smets, B. (2020). Copernicus Global Land Cover Layers—Collection 2. Remote Sensing, 12(6), 1044. DOI: https://doi.org/10.3390/rs12061044

Friedl, M., Sulla-Menashe, D. (2022). MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061. NASA EOSDIS Land Processes DAAC. Available at : https://developers.google.com/earth-engine/datasets/catalog/

MODIS_061_MCD12Q1

Pontius, R. G., Shusas, E., & McEachern, M. (2004). Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems &Amp; Environment, 101(2–3), 251–268. DOI: https://doi.org/10.1016/j.agee.2003.09.008

LIANG, J., CHEN, J., TONG, D., & LI, X. (2022). Planning control over rural land transformation in Hong Kong: A remote sensing analysis of spatio-temporal land use change patterns. Land Use Policy, 119, 106159. DOI: https://doi.org/10.1016/j.landusepol.2022.106159

Gutiérrez Angonese, J., & Grau, H. R. (2014). Assessment of swaps and persistence in land cover changes in a subtropical periurban region, NW Argentina. Landscape and Urban Planning, 127, 83–93. DOI: https://doi.org/10.1016/j.landurbplan.2014.01.021

Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20–22), 3761–3772. DOI: https://doi.org/10.1016/j.ecolmodel.2011.09.009

Ning, J., Liu, J., Kuang, W., Xu, X., Zhang, S., Yan, C., . . . Ning, J. (2018). Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015. Journal of Geographical Sciences, 28(5), 547–562. DOI: https://doi.org/10.1007/s11442-018-1490-0

Braimoh, A. K. (2006). Random and systematic land-cover transitions in northern Ghana. Agriculture, Ecosystems &Amp; Environment, 113(1–4), 254–263. DOI: https://doi.org/10.1016/j.agee.2005.10.019

Kin, D., & Karpinskyi, Y. (2020). Peculiarities of the method of calculation feature’s geodetic area on the reference ellipsoid in GIS. GeoTerrace-2020 . Vol. 2020, No. 1, 1-5. EAGE Publications BV. DOI: https://doi.org/10.3997/2214-4609.20205757.

Solomianchuk, L. Yu. (2017). Analiz vikoristannya danih distancijnogo zonduvannya zemli v silskomu gospodarstvi. [Analysis of the use of remote sensing data in agriculture]. Engineering geodesy, 99.

Kohan, S. S. (2011). Doslidzhennya dinamiki vegetacijnih indeksiv dlya ocinyuvannya stanu silskogospodarskih kultur na osnovi danih IRS-1D LISS-III [Research on the dynamics of vegetation indices for assessing the condition of agricultural crops based on IRS-1D LISS-ІІІ data]. Journal of Geodesy and Cartography, 4, 20-24.

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Published

2023-05-15

Issue

No. 1 (2023): Land management, cadastre and land monitoring

Section

Earth Remote Sensing

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