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Natural areas where settlements are located are a subject to constant anthropogenic impact, which has a negative impact on the environmental situation. In this regard, it becomes relevant to monitor the surface cover of such territories, using remote sensing data. The object of the study is the territory of the urban district “City of Chita”. The purpose is to conduct a spatiotemporal analysis of changes in the values of the NDVI vegetation index from satellite images from 2016 to 2023, to identify natural and artificially created forms of surface cover, describe their quantitative and qualitative composition within the boundaries of the urban district, as well as establish the nature of the dynamics of changes in the obtained values. The research objectives are as follows: preparation and collection of materials for remote sensing of the Earth; description of the territory and structural forms of the surface cover of Chita based on the NDVI index. The following research methodology and methods are used: methods of visual and automated decryption of multispectral images; geoinformation methods for analyzing raster and vector data. Archival multispectral Sentinel-2 images for the specified period have been used in the research process. A classification of surface cover forms of the studied territory has been carried out, and an assessment of changes in their areas over an 8-year period has been made for the administrative districts of the city (Tsentralny, Ingodinsky, Chernovsky and Zheleznodorozhny). Monitoring the surface cover of urbanized areas, using vegetation indices obtained from remote sensing data (e. g. Sentinel-2) allows to obtain up-to-date information on the quantitative and qualitative state of vegetation. The above mentioned has helped to make reasoned decisions in the field of territorial planning and urban development, including planning of protected areas, recreation parks, and public gardens in Chita.
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References |
1. Bosov M. A., Kokorina Yu. A. The use of remote sensing methods in assessing environmental safety on the example of the Ingodinsky district of Chita. Safety-2023: materials of the All-Russian Scientific and Practical Conference. Chita: ZabGU, 2023. P. 48–53. (In Rus.)
2. Kapitonova T. A., Krupnova T. G., Tikhonova S. A., Struchkova G. P., Rakova O. V. Assessment of the provision of green spaces in the urban industrial zone of Chelyabinsk using Landsat images. Bulletin of the Voronezh State University. The series “Geography. Geoecology”, no. 1, pp. 93–102, 2023. (In Rus.)
3. Kashirina E. V., Novikov A. A., Golubeva E. I., Novikova A. M. Assessment of the greening level of Sevastopol according to remote sensing data. Environmental Control Systems, no. 2, pp. 108–116, 2020. (In Rus.)
4. Kokorina Yu. A. Assessment of the NDVI index values on the territory of the city of Chita according to Sentinel-2. Youth scientific spring: materials of the L Scientific and Practical Conference of young researchers of ZabGU. Chita: ZabGU, 2023. P. 172–174. (In Rus.)
5. Lipina S. A., Startseva O. P. New financing opportunities for sustainable urban development projects in Russia. Issues of Innovative Economics, vol. 11, no. 4, pp. 1755–1772, 2021. (In Rus.)
6. Runova E. M., Stepanova O. A. Analysis of the dynamics of the vegetation index of green spaces in cities of the Irkutsk region during the period of vegetation activity. Journal of Agriculture and Environment, no. 1, 2024. Web. 20.06.2024. https://jae.cifra.science/archive/1-41-2024-january/10.23649/JAE.2024.41.11. (In Rus.)
7. Skachkova M. E., Guryeva O. S. Monitoring of the state of green spaces in St. Petersburg based on remote sensing materials. Ecology and Industry of Russia, vol. 27, no. 5, pp. 51–57, 2023. (In Rus.)
8. Fetisov D. M., Zhuchkov D. V., Goryukhin M. V. Assessment of the greening level of the city of Birobidzhan using multispectral data. Biosphere, vol. 13, no. 4, pp. 170–179, 2021. (In Rus.)
9. Khuzhakhmetova A.Sh., Voronina V. P., Lazarev S. E. Assessment of the spatial structure of tree and shrub plantations of the city of Volgograd according to multispectral satellite images. Izvestiya Nizhnevolzhsky Agrouniversity Complex: Science and Higher Professional Education, no. 3, pp. 218–232, 2022. (In Rus.)
10. Shabaikina V. A., Larina A. V., Saulin V. A. Assessment of the state of the landscaping system in Ruzaevka using multi-zone satellite images. Vector Geosciences, vol. 3, no. 3, pp. 96–105, 2020. (In Rus.)
11. Boori M. S., Choudhary K., Kupriyanov A. V. Crop growth monitoring through Sentinel and Landsat data based NDVI time-series. Computer Optics, vol. 44, no. 3, pp. 409–419, 2020. (In Eng.)
12. Congedo L. Semi-Automatic Classification Plugin Documentation. Journal of Open Source Software, no. 8.1.3.1, pp. 188–194, 2024. (In Eng.)
13. Huang Sh., Tang L., Hupy J. P., Wang Y., Shao G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, no. 32, 2020. (In Eng.)
14. Martin G. K., O’Dell K., Kinney P., Pescador-Jimenez M., Rojas-Rueda D., Canales R. A., Anenberg S. Tracking progress toward urban nature targets using landcover and vegetation indices: A global study for the 96 c40 cities. GeoHealth, 2024. (In Eng.)
15. Misra G., Cawkwell F., Wingler A. Status of Phenological Research Using Sentinel-2 Data: A Review. Remote Sensing, no. 12, 2020. (In Eng.)
16. Moreno R., Ojeda N., Azocar J., Venegas C., Inostroza L. Application of NDVI for identify potentiality of the urban forest for the design of a green corridors system in intermediary cities of Latin America: case study, Temuco, Chile. Urban Forestry & Urban Greening, vol. 55, 2020. (In Eng.)
17. Nowak B. Creating NDVI maps from Sentinel 2 multispectral aerial images. ResearchGate, 2020. (In Eng.)
18. Zhao Q., Qu Y. The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations. Remote Sensing, no. 16, 2024. (In Eng.)
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