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Ibrahim Molla

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Proceedings Article | 9 October 2018 Paper

Application of remote sensing data for forest fires severity assessment

Roumen Nedkov, Emiliya Velizarova, Ibrahim Molla, Kameliya Radeva

Proceedings Volume 10790, 107901U (2018) https://doi.org/10.1117/12.2325742

KEYWORDS: Vegetation, Earth observing sensors, Landsat, Satellites, Sensors, Remote sensing, Multispectral imaging, Near infrared, Spatial resolution, Data modeling

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Forest fires continue to burn large territories, both within and outside Europe. It is suitably to assess fire-induced changes in the vegetation, which in turn affects infiltration, runoff, and erosion potential. Therefore it is important to identify potential areas of concern and prioritize field reconnaissance. The development of a burn severity map will facilitate quantifying of the post-fire assessment phase. In this study the potential of Normalized Burn Index (NBR), Normalized Difference Vegetation Index (NDVI) and normalized difference greenness indices (NDGI) derived from remote sensing methods (satellite data from different sensors Sentinel and Landsat) and Geographical Information System (GIS) have been analyzed for forest fire severity assessment. For more accurate assessment of the fire severity, a hybrid model was developed, using satellite data from different sensors - Sentinel and Landsat. For this purpose, the area, affected by fires occurred in august 2017 on the northwest slopes of the Ajtovska Mountain (East part of the Stara planina mountain) in the Eastern part of Bulgaria was studied. The forest fire events were spread on the area of (508.5 ha) and the affected vegetation was composed by deciduous forests (309.4ha), coniferous (62.4ha), mixed forests (61.4 ha) and grass and shrubs (75.3ha). Through the model developed, results applicable to the actual forest ecosystem conditions for different time intervals have been obtained. These results provide quantitative information about fire severity for distinct forest types, thus allowing for designing relevant fire severity maps.

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