Akpona Okujeni

Akpona Okujeni

Postdoctoral Researcher / Senior Scientist

Earth Observation Lab, Humboldt-Universität zu Berlin

About me

My research is driven by the immense potential Earth observation offers to explore and understand the ecosystems of our planet. I mainly utilize imaging spectroscopy data and multispectral time series to quantify land and vegetation cover, retrieve vegetation parameters, and monitor ecosystem change and vegetation dynamics through space and time. My studies extend across diverse terrestrial ecosystems and address environmental challenges related to global change, such as disturbance and recovery processes, or drought and wildfire impacts. Implementing innovative and effective image processing workflows for solving real-world application challenges forms the foundation of my studies.

Interests
  • Remote sensing of vegetation
  • Urban remote sensing
  • Imaging spectroscopy
  • Multispectral time series
  • Image processing workflows
Education

  • PhD in Remote Sensing, 2014

    Humboldt-Universität zu Berlin, Germany

  • Diplom (MSc) in Geography, 2009

    Humboldt-Universität zu Berlin, Germany

Research

Quantifying vegetation properties

Quantifying vegetation properties

Monitoring ecosystem change and dynamics

Monitoring ecosystem change and dynamics

Mapping and monitoring urban areas

Mapping and monitoring urban areas

Implementing image processing workflows

Implementing image processing workflows

Publications

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A. Okujeni, K. Kowalski, K. E. Lewińska, S. Schneidereit, P. Hostert (2024). Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives. Remote Sensing of Environment.

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K. Kowalski, A. Okujeni, P. Hostert (2023). A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series. Remote Sensing of Environment.

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K. A. Pfoch, D. Pflugmacher, A. Okujeni, P. Hostert (2023). Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data - Towards a quantitative understanding of fire impacts. Science of Remote Sensing.

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B. Jakimow, A. Janz, F. Thiel, A. Okujeni, P. Hostert, S. van der Linden (2023). EnMAP-Box: Imaging spectroscopy in QGIS. SoftwareX.

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F. Schug, D. Frantz, A. Okujeni, P. Hostert (2022). Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data. Remote Sensing Letters.

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Data

Berlin-Urban-Gradient

A ready-to-use imaging spectrometry dataset for multi-scale unmixing and classification analyses in urban environments.

Simulated EnMAP (BA)

This dataset comprises spring, summer and fall 2013 simulated hyperspectral EnMAP mosaics for the San Francisco Bay Area, USA.

Simulated EnMAP (LT)

This dataset comprises spring, summer and fall 2013 simulated hyperspectral EnMAP mosaics for the Lake Tahoe region, USA.

Simulated EnMAP (SB)

This dataset comprises spring, summer and fall 2013 simulated hyperspectral EnMAP mosaics for the Santa Barbara region, USA.

Building height map (GER)

This dataset features a map of building height predictions for entire Germany based on Sentinel-1A/B and Sentinel-2A/B time series.

Land cover map (GER)

This dataset features a map of built-up and infrastructure, woody and non-woody vegetation fraction predictions for Germany.

Land cover map (AUT)

This dataset features a map of built-up and infrastructure, woody and non-woody vegetation fraction predictions for Austria.

Software & Tutorials

Software - EnMAP-Box 3

A free and open source python plug-in for QGIS, designed to process and visualize remote sensing data.

Software - FORCE

Processing engine for medium-resolution EO image archives, enabeling Analysis Ready Data generation and large area/time series analyses.

Tutorial - Forest biomass

Learn how to estimate forest aboveground biomass using the regression workflow of the EnMAP-Box.

Tutorial - Urban mapping

Learn how to map urban class fractions using the regression-based unmixing workflow of the EnMAP-Box.

Contact

  • akpona.okujeni(at)geo.hu-berlin.de
  • +49 (0)30 2093-6829
  • Postal address: Humboldt-Universität zu Berlin, Geography Department, Unter den Linden 6, 10099 Berlin, Germany,
  • Office location: Humboldt-Universität zu Berlin, Geography Department, Rudower Chaussee 16, 12489 Berlin, Germany