Gemi – a Possible Tool for Identification of Disturbances in Coniferous Forests in Pernik Povince (Western Bulgaria)

• Autorzy: Grigorov Borislav

Identyfikatory

DOI

10.2478/ceer-2022-0048

• Języki publikacji: EN

Abstrakty

EN

The Global Environmental Monitoring Index (GEMI) represents a vegetation index that allows for making analysis. The index is not that sensitive to atmospheric effects. GEMI has been applied for the investigation of disruptions in the coniferous forests in Pernik Province, which is situated in the western parts of Bulgaria. The basic data comes from Landsat 8 and Corine Land Cover. The results of the study show that the index performs well in the distinguishment of broad-leaved vegetation from the coniferous one. At the same time the index doesn’t always provide satisfying results when it comes to deforestation. In conclusion GEMI provides good results, yet it’s use should be controlled and supported by other vegetation indices.

Słowa kluczowe

EN

remote sensing; forestry; deforestation; coniferous forests

PL

teledetekcja; leśnictwo; wylesianie; las iglasty

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: Civil and Environmental Engineering Reports
• Rocznik: 2022
• Tom: Vol. 32, no. 4
• Strony: 116--122
• Opis fizyczny: Bibliogr. 9 poz.., ii.

Twórcy

autor

Grigorov Borislav

• Sofia University “St. Kliment Ohridski”, Bulgaria

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

Bibliografia

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• 2. McDonald, A, Gemmell, F, 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. https://doi.org/10.1016/S0034-4257(98)00057-1.
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• 4. Peddle, D, Brunke, S, Hall, F 2001. A Comparison of Spectral Mixture Analysis and Ten Vegetation Indices for Estimating Boreal Forest Biophysical Information from Airborne Data. Canadian Journal of Remote Sensing, 27 (6), 627-635. DOI: 10.1080/07038992.2001.10854903.
• 5. Pereira, J 1999. A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing, 37 (1), 217-226. doi: 10.1109/36.739156.
• 6. Soltanikazemi, MS, Minaei, H, Shafizadeh-Moghadam, A, Mahdavian, 2022. Field-scale estimation of sugarcane leaf nitrogen content using vegetation indices and spectral bands of Sentinel-2: Application of random forest and support vector regression. Computers and Electronics in Agriculture, 200, 107130. https://doi.org/10.1016/j.compag.2022.107130.
• 7. CLC 2018 - https://land.copernicus.eu/pan-european/corine-land-cover/clc2018.
• 8. USGS EarthExplorer - https://earthexplorer.usgs.gov/.
• 9. https://pro.arcgis.com/en/pro-app/latest/arcpy/image-analyst/gemi.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)