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Humboldt-Universität zu Berlin - IRI THESys

Dr. Cornelius Senf

Postdoctoral Researcher
Understanding the dynamics of forest ecosystem using remote sensing


  • Remote sensing
  • Climate change
  • Forest ecosystems

"Climate change is impacting ecosystems worldwide. My aim is to improve our understanding of those changes using remote sensing." Cornelius Senf

Dr. Cornelius Senf was a postdoctoral researcher at IRI THESys.

He holds a Master of Science in physical geography and a PhD in remote sensing, both awarded from Humboldt-Universität zu Berlin. After his PhD, Cornelius spent one year at the University of Natural Resources and Life Sciences (BOKU) in Vienna, working on the integration of remote sensing into forest ecology.

His work at the IRI THESys focussed on the modeling of forest mortality and vegetation phenology from satellite time series using statistical models.

THESys Project
Selected Publications

Senf, C. and Seidl R. (2018) Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in Europe. Global Change Biology, 24(3), 1201-1211. https://doi.org/10.1111/gcb.13897

Senf, C., Seidl, R. and Hostert, P. (2017) Remote sensing of forest insect disturbances: current state and future directions. International Journal of Applied Earth Observation and Geo-information, 60, 49-60. https://doi.org/10.1016/j.jag.2017.04.004

Senf, C., Pflugmacher, D., Heurich, M. and Krueger T. (2017) A Bayesian hierarchical model for esti-mating spatial and temporal variation in vegetation phenology from Landsat time series. Remote Sensing of Environment, 194, 155-160. https://doi.org/10.1016/j.rse.2017.03.020

Senf, C., Campbell, E., Wulder, M. A., Pflugmacher, D. and Hostert P. (2017) A multi-scale analysis of western spruce budworm spatiotemporal outbreak patterns. Landscape Ecology, 32(3), 501-514. https://doi.org/10.1007/s10980-016-0460-0

Senf, C., Pflugmacher, D., Wulder, M. A. and Hostert, P. (2015) Characterizing spectral-temporal pat-terns of defoliator and bark beetle disturbances using Landsat time series. Remote Sensing of Envi-ronment, 170, 166-177. https://doi.org/10.1016/j.rse.2015.09.019