Pia Labenski
- Forschung
- Gruppe: Vegetation
- pia labenski ∂does-not-exist.kit edu
Karlsruher Institut für Technologie (KIT)
Institut für Geographie und Geoökologie
Kaiserstr. 12
76131 Karlsruhe
Germany
Topics
- Multispectral and LiDAR remote sensing
- Machine and deep learning algorithms
- Fuel and fire modeling for Central European forests
Ewald, M.; Labenski, P.; Westphal, E.; Metzsch-Zilligen, E.; Großhauser, M.; Fassnacht, F. E. (2025). Leaf litter combustion properties of Central European tree species. Forestry: An International Journal of Forest Research, 98 (1), 29–39. doi:10.1093/forestry/cpad026
Labenski, P.; Millin-Chalabi, G.; Pacheco-Pascagaza, A. M.; Senn, J. A.; Fassnacht, F. E.; Clay, G. D. (2024). An optical satellite-based analysis of phenology and post-fire vegetation recovery in UK upland moorlands. Environmental and Sustainability Indicators, 24, 100492. doi:10.1016/j.indic.2024.100492
Labenski, P. (2024, Juni 19). Assessing fuels in European temperate forests and heathlands using remote sensing. Dissertation. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000171668
Labenski, P.; Ewald, M.; Schmidtlein, S.; Heinsch, F. A.; Fassnacht, F. E. (2023). Quantifying surface fuels for fire modelling in temperate forests using airborne lidar and Sentinel-2: potential and limitations. Remote Sensing of Environment, 295, 113711. doi:10.1016/j.rse.2023.113711
Labenski, P.; Ewald, M.; Schmidtlein, S.; Fassnacht, F. E. (2022). Classifying surface fuel types based on forest stand photographs and satellite time series using deep learning. International journal of applied earth observation and geoinformation, 109, Article no: 102799. doi:10.1016/j.jag.2022.102799