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Faculty of Mathematics and Natural Sciences - Geomatics

Sam Cooper

Sam Cooper
Status Doctoral researcher SCooper.jpg
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 ecologic and 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


South Dakota Game, Fish & Parks, Wildlife Division



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



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. 


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.