LAND COVER ANALYSIS OF TERRITORIAL COMMUNITIES OF ZHYTOMYR REGION USING GIS TECHNOLOGIES

Authors

DOI:

https://doi.org/10.35433/naturaljournal.2.2023.95-117

Keywords:

land-use, land cover change, rural area, urban area, GIS technologies.

Abstract

Today, the deep and wide implementation of geoinformation technologies in the many fields of human activity is due to the powerful development of three scientific and technical components: statistical, software, technical and space technologies. In this article, based on GIS technologies, an analysis of the state of land use and its changes in the territory of Zhytomyr oblast was carried out, also how russian aggression against Ukraine affected these processes. The structure and the dynamics of the main classes of the land cover of the oblast for past 7 years were analyzed, the main causes and consequences of such trends were determined, and the analysis of changes in the land cover was carried out. According to the results of this study, in 2022, 52% of the territory of the Zhytomyr oblast was under forested areas, which consist of two categories: forests and other forested areas. The first category remained unchanged during the studied period since the government system of protection and reproduction of forest resources functions effectively. While the second category significantly decreased due to the fact that firewood is the most available of the fuel resources for heating building, so the population began to harvest wood in the form of felling and clearing old gardens, forested bushes and rivers (irrigation canals), forest strip. Agriculture of the Zhytomyr oblast is developing due to extensification. According to Google Dynamic World data, in 2022, 34% of the territory of the Zhytomyr oblast is systematically used for growing agricultural crops. Over the past seven years, there has been a significant increase cultivated land by 27%. In the structure of the land cover of the Zhytomyr oblast, the grass cover is 4.9%, but it is gradually decreasing. A decrease was observed for all types of territorial communities until 2021 (10% annually on average), while in 2022 the decline slowed down significantly in rural and village territorial communities and stopped in urban ones. This dynamic is connected with two factors: 1) part of the gardens of rural households were sown with grass due to the fact that men were mobilized to the Armed Forces of Ukraine as a result of russian aggression, and growing grass requires the least human costs; 2) russian aggression caused a shortage of certain food products and their significant increase in price, while keeping cattle provides food for the rural household, so in 2022 most of the offspring from cattle were not sold and left for further maintenance, in turn, the increase in cattle requires more feed, an important component of which is grass.

References

Chen J., Liao A., Cao X., Chen L., Chen X., He C., et al. Global Land Cover Mapping at 30 m Resolution: A POK-Based Operational Approach. ISPRS J. Photogramm. Remote Sens, 2015. Vol. 103, Р. 7–27.

Constitution of Ukraine. (1996, June). URL: https://zakon.rada.gov.ua/laws/show/ ~ 93 ~ 254%D0%BA/96-%D0%B2%D1%80#n4603/. Abdelouhed F., Algouti A., Algouti A. Contribution of GIS and remote sensing in geological mapping, lineament extractions and hydrothermal alteration minerals mapping using aster satellite images: case study of central jebilets-morocco. Disaster Adv., 2021. Vol. 14. P. 15-25. Buchhorn M., Lesiv M., Tsendbazar N.-E. Copernicus Global Land Cover Layers - Collection 2. Remote Sens, 2020. Vol. 12. Article 1044.

Brown C. F., Brumby S. P., Guzder-Williams B. Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data. 2022. Vol. 9(1). P. 1-17.

Economic, Social, and Policy Analyses – Orbital Debris and Space Sustainability (NASA). Federal Grants & Contracts. 2022. Vol. 46, no. 10. P. 6. URL: https://doi.org/10.1002/fgc.32334 (date of access: 22.12.2022).

Zanaga D., Van De Kerchove R., De Keersmaecker W. ESA WorldCover 10 m 2020 V100 OpenAIRE: веб-сайт. 2021. URL: https://worldcover2020.esa.int/downloader. da

Cunha E. R., Santos C. A. G., da Silva R. M. Future scenarios based on a CAMarkov land use and land cover simulation model for a tropical humid basin in the Cerrado. Atlantic forest ecotone of Brazil. Land Use Policy. 2021. Vol. 101. Article 105141.

Gorelick N., Hancher M., Dixon M. Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sens. Environ. 2017. Vol. 202. P. 18–27.

Halder A., Ghosh A., Ghosh S. Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. Applied Soft Computing. 2011. Vol. 11(8). P. 5770–5781.

Hashem N., Balakrishnan P. Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar. Annals of GIS. 2015. Vol. 21(3). P. 233–247.

Herasymchuk R., Valerko L., Marteniuk G. Climate change tendencies on the territory of the city of Novohrad-Volynskyi in Zhytomyr region. Scientific Horizons. 2018. Vol. 65, № 2. P. 42–50. URL: https://doi.org/10.33249/2663-2144-2018-65-2-42-50 (дата звернення: 15.11.2022).

Sulla-Menashe D., Gray J. M., Abercrombie S. P., Friedl M. A. Hierarchical Mapping of Annual Global Land Cover 2001 to Present: The MODIS Collection 6 Land Cover Product. Remote Sens. Environ. 2019. Vol. 222. P. 183–194.

Javed A., Khan I. Land use/land cover change due to mining activities in Singrauli industrial belt, Madhya Pradesh using remote sensing and GIS. Journal of Environmental Research And Development. 2012. Vol. 6(3A).

Cegielska K., Noszczyk T., Kukulska A. Land use and land cover changes in postsocialist countries: Some observations from Hungary and Poland. Land use policy. 2018. Vol. 78. P. 1–18.

Mark M., Kudakwashe M. Rate of land-use/land-cover changes in Shurugwi district, Zimbabwe: drivers for change. Journal of Sustainable Development in Africa. 2010. Vol. 12(3). P. 107-121.

Mohanta, N. How many satellites are orbiting the Earth in 2021? Geospatial World. 2021, no. 05/28.

Plugar E., Plugar D., Stakhno N. Space technologies in achieving the aims of sustainable development. IOP Conference Series: Earth and Environmental Science. 2021.Vol. 853 (1), 012039.

Prakasam C. Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal taluk, Tamil nadu. International journal of Geomatics and Geosciences. 2010. Vol. 1, № 2. P. 150.

Praveen B., Gupta D. Multispectral-TIR Data Analysis by Split Window Algorithm for Coal Fire Detection and Monitoring. International Journal of Humanities and Social Science Invention.2019. Vol. 6. P. 33-37.

Sahani N., Ghosh T. GIS-based spatial prediction of recreational trail susceptibility in protected area of Sikkim Himalaya using logistic regression, decision tree and random forest model. Ecological Informatics. 2021. Vol. 64. Article 101352.

Sala, O.E. Chapin, F.S. Armesto, J.J. Berlow, E. Bloomfield, J. Dirzo, R., ... and Leemans, R. Global biodiversity scenarios for the year 2100. Science. 2000. vol. 287(5459), pp. 1770–1774.

Schirpke U., Tasser E. Trends in Ecosystem Services across Europe Due to Land- Use/Cover Changes. Sustainability. 2021. Vol. 13, Iss. 13. Article 7095.

Phiri D., Simwanda M., Salekin S. Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sens. 2020. Vol. 12. Article 2291.

Siebritz L.A., Desai A., Cooper, A.K., Coetzee S. The South African Spatial Data Infrastructure–Where are the Municipalities? International Journal of Spatial Data Infrastructures Research. 2022. Vol. 15. P. 143-170.

Hoque M. Z., Islam I., Ahmed M. Spatio-temporal changes of land use land cover and ecosystem service values in coastal Bangladesh. The Egyptian Journal of Remote Sensing and Space Science. 2022. Vol. 25, Iss. 1. P. 173–180.

Talukdar S., Singha P., Mahato S., Praveen B., Rahman A. Dynamics of ecosystem services (ESs) in response to land use land cover (LU/LC) changes in the lower Gangetic plain of India. Ecological Indicators. 2020. Vol. 106 – 121.

Schramm M., Pebesma E., Milenković M. The OpenEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities. Remote Sens. 2021. Vol. 13. Article 1125.

Adulaimi A. A., Pradhan B., Chakraborty S., Alamri A. Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS. Energies. 2021. Vol. 14, Iss. 16. Article 5095.

Trimble S.W., Crosson P. US soil erosion rates--myth and reality. Science, 2000. vol. 289(5477), pp. 248–250.

Stehman S. V., Pengra B. W., Horton J. A., Wellington D. F. Validation of the US geological survey's land change monitoring, assessment and projection (LCMAP) collection 1.0 annual land cover products 1985-2017. Remote Sensing of Environment. 2021. Vol. 265. Article 112646.

Viana C. M., Girão I., Rocha J. Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sensing. 2019. Vol. 11, Iss. 9. Article 1104.

Vitousek, P.M. Mooney, H.A. Lubchenco, J. and Melillo, J.M. Human domination of Earth's ecosystems. Science, 1997. vol. 277(5325), pp. 494–499.

Горобець О. В., Євпак І. І. Тенденції зміни клімату у Житомирській області. Кліматичні зміни та їх наслідки на території Житомирської області. Наука. Молодь. Екологія – 2017 : зб. матеріалів ХІІІ Всеукр. наук.-практ. конф. студ., асп. та молодих вчених, 25 трав. 2017 р. Житомир, 2017. С. 153–157.

Екологічний паспорт Житомирської області. Житомир: Житомир. облдержадмін., 2022. 187 с. URL: https://cutt.ly/RVnNFOV

Про місцеве самоврядування в Україні: Закон України вiд 21.05.1997 № 280/97- ВР. Відомості Верховної Ради України. 1997. № 24, ст. 170.

Регіони України : стат. збірник за 2020 рік : [у 2-х ч.]. Київ : Держ. служба статистики України, 2020. Ч. 1. URL: http://www.ukrstat.gov.ua/.

Published

2023-04-04