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Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Dr. Akpona Okujeni

Name
Dr. Akpona Okujeni
Status

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

I am a geographer by education with more than 10 years of expertise in remote sensing. My research is motivated by the variety of exciting possibilities Earth observation offers to map and monitor our planet’s land surface. I am utilizing imaging spectroscopy (hyperspectral) data, multispectral time-series and machine learning to quantify land cover and vegetation patterns as well as land surface change processes. My studies extend across a wide range of terrestrial ecosystems and focus on innovative methodological developments and environmental research questions related ecosystem structure, ecosystem function and global change.

 

Visit my website for more information.

 

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.

2009-2014

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

2004-2009

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

 

Selected Publications

Okujeni, A., Jänicke, C., Cooper, S., Frantz, D., Hostert, P., Clark, M., Segl, K., & van der Linden, S. (2021). Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data. Remote Sensing of Environment. 10.1016/j.rse.2021.112558

Okujeni, A., Canters, F., Cooper, S.D., Degerickx, J., Heiden, U., Hostert, P., Priem, F., Roberts, D.A., Somers, B., & van der Linden, S. (2018). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment, 216, 482-496. 10.1016/j.rse.2018.07.011

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

 

My full publication list can be found here.