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Faculty of Mathematics and Natural Sciences - Geomatics

Dr. Cornelius Senf

Dr. Cornelius Senf


Postdoctoral researcher

E-Mail cornelius.senf@geo.hu-berlin.de
Office location Rudower Chaussee 16, Room 2'223
Phone +49 (0)30 2093-9433
Fax +49 (0)30 2093-6848
Postal address Unter den Linden 6, 10099 Berlin, Germany
Web corneliussenf.com
Twitter @corneliussenf
GitHub github.com/corneliussenf

About me

My research focusses on better understanding forest ecosystem dynamics across spatial and temporal scales. I employ a rich set of methods across the field of remote sensing and beyond, including the analysis of dense satellite time series, the integration of satellite data into statistical models, the combination of environmental/social-economic data and satellite data, and the analysis of uncertainty using Bayesian statistics. Regionally, I focus on the temperate forest biome, with special emphasis on the complexity of European forest ecosystems.

My free-time is mainly occupied by exploring the mountains (climbing/hiking/skiing, see here), cooking, as well as I am a cineaste and lover of jazz music.


Curriculum Vitae


Since 2018 Since Mai 2018 I am affiliated with the IRI THESys.

2016 - 2018

Postdoctoral researcher (DAAD P.R.I.M.E. Fellow), Humboldt-University of Berlin and University of Natural Resources and Life Sciences (BOKU) Vienna

  • Reconstructing European forest disturbance dynamics using Landsat

2013 - 2016

Doctoral researcher (Elsa Neumann Fellow), Humboldt-University of Berlin with guest stays at the Canadian Forest Service

  • Spatiotemporal analysis of budworm defoliation in British Columbia
  • Teaching

2012 - 2013

Research assistant, Humboldt-University of Berlin

  • Data fusion for mapping complex Mediterranean land cover
  • Teaching
2010 - 2012

Master of sciences in physical geography, Humboldt-University of Berlin and Bern University

  • Mapping rupper plantations from MODIS time series
2007 - 2010 Bachelor of sciences in geography and physics, Humboldt-University of Berlin and University Innsbruck

Please see my full CV for further information.


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. Link

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 Geoinformation, 60, 49-60. Link

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. Link

Senf, C., Leitão, J.P., Pflugmacher, D., van der Linden, S. and Hostert, P. (2015) Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sensing of Environment, 156, 527-536. Link

Senf, C., Pflugmacher, D., van der Linden, S. and Hostert, P. (2013) Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series. Remote Sensing, 5(6), 2795-2812. Link

For a full publication list and citation metrics see my Google Scholar profile. If you need access to one of the articles, please see my Research Gate profile or contact me directly.


Code and data

I am an advocate of sharing data and code for advancing research. Following are some programs we wrote as part of our daily research and which we would like to share with the research community. If you have any questions or feedback, need data we produced, or similar, don't hesitate to contact me.

sgdm: an R package for performing sparse generalized dissimilarity modelling

The sgdm package bundles a set of functions to run sparse generalized dissimilarity models using high-dimensional data sets as predictor, such as hyper-spectral or hyper-temporal remote sensing data. The model has been used successfully to map beta-biodiversity from air-borne hyperspectral imagery (Leitão 2015). You can install the package from GitHub.

phenoBayes: Bayesian hierarchical models for estimating spatial and temporal patterns in vegetation phenology from Landsat time series

The phenoBayes package (well, it's not a package yet) includes a set of models for estimating the spatial and temporal patterns of vegetation phenology from Landsat time series. It makes use of a Bayesian hierachical modeling apprach implemented in the free software Stan. The package includes simple models as well as processed-oriented models for testing hypothesis on environmental controls on vegetation phenology. You can download the code and data for building phenological models yourself from GitHub.