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Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Sam Cooper

Name
Sam Cooper
Status Doctoral researcher
E-Mail sam.cooper@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 research interests lie with linking remote sensing to vegetative processes allowing for large scale ecosystem assessment under natural and anthropogenic stressors.  I am interested in expanding localized models to suit more generalized applications in asking broad ecological questions.

Currently, my research focuses on quantitative land cover mapping and vegetation assessment using simulated hyperspectral EnMAP imagery and machine learning, while investigating potential synergies between data sources with varying spectral and temporal dimensionality (e.g. EnMAP & Landsat). 

 

Curriculum Vitae

since 06/2017

Humboldt-Universität zu Berlin, Geomatics Lab

Doctoral Researcher

EnMAP Core Science Team - Phase III "Monitoring Vegetation under Global Change"

2015 - 2017

South Dakota State University, Geospatial Sciences Center of Excellence

Graduate Research Assistant

2014 - 2015

South Dakota State University

Undergraduate Research Technician

2013

South Dakota Game, Fish & Parks, Wildlife Division

Internship 

Education

since 06/2017

Doctoral Candidate in Geography

Humboldt-Universität zu Berlin, Berlin, Germany

2015 - 2017

Master of Science in Geography 

South Dakota State University, Brookings, SD, USA

2009 - 2014

Bachelor of Science in Ecology and Environmental Science                                  

South Dakota State University, Brookings, SD, USA

Minor in Chemistry

2009 - 2014

Bachelor of Arts in Global Studies

South Dakota State University, Brookings, SD, USA

Minor in French Language & Culture

 

Publications

Cooper, S., Okujeni, A., Pflugmacher, D., van der Linden, S., & Hostert, P. (2021). Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping. International Journal of Applied Earth Observation and Geoinformation, 98, 102307. https://doi.org/10.1016/j.jag.2021.102307

 

Cooper, S.; Okujeni, A.; Jänicke, C.; Clark, M.; van der Linden, S.; Hostert, P (2020): Disentangling fractional vegetation cover: Regression-based unmixing of simulated spaceborne imaging spectroscopy data. Remote Sensing of Environment, 246, 111856. 10.1016/j.rse.2020.111856

 

Jänicke, C.; Okujeni, A.; Cooper, S.; Clark, M.; Hostert, P; van der Linden, S. (2020): Brightness gradient-corrected hyperspectral image mosaics for fractional vegetation cover mapping in northern California. Remote Sensing Letters, 11.1, 1-10. 10.1080/2150704X.2019.1670518

 

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

 

Cooper, S.D.; Roy, D.P.; Schaaf, C.B.; & Paynter, I. (2017): Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass. Remote Sensing, 9(6), 531. 10.3390/rs9060531