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Gewählte Publikation:

Maxwald, M; Immitzer, M; Rauch, HP; Preti, F.
(2022): Analyzing Fire Severity and Post-Fire Vegetation Recovery in the Temperate Andes Using Earth Observation Data
FIRE-BASEL. 2022; 5(6), 211 FullText FullText_BOKU

In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation's development in different fire severity classes after a wildfire in September 2019. Random forest classification models were calculated using the fire severity classes (from the Relativized Burn Ratio-RBR) as a dependent variable and 23 multitemporal vegetation indices from 10 Sentinel-2 scenes as descriptive variables. The best vegetation indices to monitor post-fire vegetation recovery in the temperate Andes were found to be the Leaf Chlorophyll Content Index (LCCI) and the Normalized Difference Red-Edge and SWIR2 (NDRESWIR). In the first post-fire year, the vegetation had already recovered to a great extent due to vegetation types with a short life cycle (seasonal grass-species). Increasing index values correlated strongly with increasing fire severity class (fire severity class vs. median LCCI: 0.9997; fire severity class vs. median NDRESWIR: 0.9874). After one year, the vegetations' vitality in low severity and moderate high severity appeared to be at pre-fire level.
Autor*innen der BOKU Wien:
Immitzer Markus
Maxwald Melanie
Rauch Johann Peter

Find related publications in this database (Keywords)
remote sensing
fire severity
vegetation indices
random forest
vegetation recovery
northern South America

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