An Investigation of The Forests of Pernik Province (Western Bulgaria) by The Use of The Perpendicular Vegetation Index (PVI)

• Autorzy: Grigorov Borislav

Identyfikatory

DOI

10.2478/ceer-2022-0046

• Języki publikacji: EN

Abstrakty

EN

The current research represents a pilot study for application of the Perpendicular Vegetation Index (PVI) for an area with forests in Bulgaria. It is the first of its kind when it comes to forest studying in the country to the best knowledge of the author. When it comes to soil background Landsat images and other spectral data may be used for monitoring forest territories as well. The study area is Pernik Province which is located in the western parts of Bulgaria. The main aim is to investigate the PVI for the forests of Pernik Province. The index has been calculated by the application of Landsat 8 bands. The PVI has been processed for several months of different years. The main focus is both on the beginning and the end of the growing season when there are significant changes in leaf biomass. The results are promising and show typical vegetation features in the beginning of the growing season (April), a well-developed vegetation (July) and a steadily decreasing biomass in November.

Słowa kluczowe

EN

remote sensing; biomass; vegetation index; Landsat

PL

teledetekcja; biomasa; wskaźnik roślinności; Bułgaria zachodnia

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: Civil and Environmental Engineering Reports
• Rocznik: 2022
• Tom: Vol. 32, no. 4
• Strony: 96--104
• Opis fizyczny: Bibliogr. 16 poz., mapy

Twórcy

autor

Grigorov Borislav

• Sofia University “St. Kliment Ohridski”

• https://orcid.org/0000-0002-5936-3573

Bibliografia

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• 2. Cusack, D, Silver, W and McDowell, W 2009. Biological Nitrogen Fixation in Two Tropical Forests: Ecosystem-Level Patterns and Effects of Nitrogen Fertilization. Ecosystems 12, 1299-1315.
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• 4. Edwards, D, Tobias, J, Sheil, D, Meijaard, E and Laurance, W 2014. Maintaining ecosystem function and services in logged tropical forests. Trends in Ecology & Evolution, 29 (9), 511-520. https://doi.org/10.1016/j.tree.2014.07.003.
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• 7. Kamenova, I and Dimitrov, P 2021. Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria. European Journal of Remote Sensing, 54 sup1, 89-108. DOI: 10.1080/22797254.2020.1839359.
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• 10. McDonald, A, Gemmell, F and Lewis, P 1998. Investigation of the Utility of Spectral Vegetation Indices for Determining Information on Coniferous Forests. Remote Sensing of Environment, 66 (3), 250-272.
• 11. Pal, S, Chakrabortty, R, Malik, S et al., 2018. Application of forest canopy density model for forest cover mapping using LISS-IV satellite data: a case study of Sali watershed, West Bengal. Model. Earth Syst. Environ. 4, 853-865, doi.org/10.1007/s40808-018-0445-x
• 12. Richardson, A and Wiegand, C 1977. Distinguishing Vegetation from Soil Background Information. Photogrammetric Engineering and Remote Sensing, 43 (12), 1541-1552.
• 13. Trong, H, Nguyen, T and Kappas, M 2020. Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam. International Journal of Forestry Research, 8896310, 18 pages. doi.org/10.1155/2020/8896310
• 14. USGS EarthExplorer - https://earthexplorer.usgs.gov/
• 15. https://land.copernicus.eu/pan-european/corine-land-cover/clc2018
• 16. https://pro.arcgis.com/en/pro-app/latest/arcpy/image-analyst/pvi.htm

Uwagi

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)