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Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Dr. Akpona Okujeni

Dr. Akpona Okujeni

Postdoctoral researcher

E-Mail akpona.okujeni@geo.hu-berlin.de
Office location Rudower Chaussee 16, Room 2'222
Phone +49 (0)30 2093-6894
Fax +49 (0)30 2093-6848
Postal address Unter den Linden 6, 10099 Berlin, Germany

About me

My scientific research focuses on bridging environmental applications and remote sensing techniques to better understand land systems under global change. To do so, I utilize high-spectral and dense-temporal satellite imagery, machine learning algorithms, and sophisticated model training strategies. My studies extend across a wide range of terrestrial ecosystems (e.g., urban areas, grasslands, shrublands, and forests) and aim at the quantification of land cover and vegetation properties (e.g., broad- to fine-scale fractional composition, phenology, and biomass) and environmental change processes (e.g., urban expansion, vegetation encroachment, and ecosystem disturbances).

Curriculum Vitae

since 2014

Post-doctoral Researcher. Geomatics Lab, HU-Berlin, Germany (Projects: EnMAP Core Science Team, UrbanEARS).

11-12 2012

Visiting scholar (DAAD fellowship), Columbia University, Lamont-Doherty Earth Observatory, New York.


Doctoral researcher, Geomatics Lab, Humboldt-Universität zu Berlin (Project: Berlin II)


Diplom-Student in Geography, Geoinformation Science and Geology, Humboldt-Universität zu Berlin.

A detailed CV can be found here.

Selected Publications

Okujeni, A., van der Linden, S., Suess, S., & Hostert, P. (2017). Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 1640-1650. 10.1109/JSTARS.2016.2634859.

Okujeni, A., van der Linden, S., Hostert, P. (2015). Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning. Remote Sensing of Environment, 158, 69-80. 10.1016/j.rse.2014.11.009.

Okujeni, A., van der Linden, S., Tits, L., Somers, B., & Hostert, P. (2013). Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sensing of Environment, 137, 184-197. 10.1016/j.rse.2013.06.007.

A full publication list can be found here.