
Laboratory of Geo-information Science and Remote Sensing
The mission of the Laboratory of Geo-information Science and Remote Sensing is to improve spatial competences for a sustainable world through research and education.
Inaugural lectures
Education
In the field of education, the Laboratory is strongly participating in the Master Geo-Information Science.
More Education
Research
Geo-information has become a societal commodity and geo-information science is the driver of its innovation. This trend is evident in the activities of the Laboratory of Geo-Information Science and Remote Sensing (GRS).
Our research aims to Realize the Digital Earth of Locations.
Research topics
- Sensing & measuring
- Modelling & visualization
- Integrated land monitoring
- Human - space interaction
- Empowering & engaging communities
Latest PhD dissertations
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Machine learning for large-scale crop yield forecasting
Wageningen University. Promotor(en): I.N. Athanasiadis, B. Tekinerdogan, co-promotor(en): S.A. Osinga, S.J.C. Janssen - Wageningen: Wageningen University - ISBN: 9789464475999 -
Assessment, uncertainties and applications of global above-ground biomass maps from earth observation
Wageningen University. Promotor(en): Martin Herold, Lars Hein, co-promotor(en): Sytze de Bruin - Wageningen: Wageningen University - ISBN: 9789464476958 -
Earth observation data for assessing global urbanization-sustainability nexuses
Wageningen University. Promotor(en): M. Herold, E. van Leeuwen, co-promotor(en): N.E. Tsendbazar - Wageningen: Wageningen University - ISBN: 9789464476811
Latest publications
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Comparing forest and grassland drought responses inferred from eddy covariance and Earth observation
Agricultural and Forest Meteorology (2023), Volume: 341 - ISSN 0168-1923 -
Nitrogen management with reinforcement learning and crop growth models
Environmental Data Science (2023), Volume: 2 -
Boundary crossing als de modus operandi van een universiteit : Verbinden en verbreden
TH&MA : tijdschrift voor hoger onderwijs & management (2023), Volume: 2023, Issue: 3 - ISSN 1380-7110 - p. 3-23. -
How textural features can improve SAR-based tropical forest disturbance mapping
International Journal of applied Earth Observation and Geoinformation (2023), Volume: 124 - ISSN 0303-2434 -
A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters
Science of the Total Environment (2023), Volume: 904 - ISSN 0048-9697