Dr. Martin Schulz
| Alumni |
Contact
Curriculum vitae
2016 - present: Research associate at the LCRS Marburg
2012 - 2016: PhD student in the DFG project "Delineating the mountain cloud forest of Taiwan by means of topographic cloud immersion with moderate resolution satellite data and ground based observations" at the LCRS Marburg
2006 - 2011: Studies of geography, computer science & chemistry at the University of Marburg
Scientific interest
- fog remote sensing
- vegetation remote sensing
- downscaling of satellite imagery
- ground modeling
Regional interests:
- Central Europe
- Taiwan
Publications
Articles
2021 - Núñez, P.Á.; Silva, B.; Schulz, M.; Rollenbeck, R. & Bendix, J. (2021): Evapotranspiration estimates for two tropical mountain forest using high spatial resolution satellite data. International Journal of Remote Sensing 42(8), 2940--2962.
2019 - Kolbe, C.; Thies, B.; Egli, S.; Lehnert, L.; Schulz, M. & Bendix, J. (2019): Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit — Part 1 : Precipitation Area Delineation with Elektro-L2 and Insat-3D. Remote Sensing 11(19), 2302.
2017 - Schulz, M.; Li, C.; Thies, B.; Chang, S. & Bendix, J. (2017): Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data. PLOS ONE 12(2), 1-17.
2016 - Schulz, M.; Thies, B.; Chang, S. & Bendix, J. (2016): Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach. Atmospheric Measurement Techniques 9, 1135 - 1152.
2015 - Thies, B.; Groos, A.; Schulz, M.; Li, C.; Chang, S. & Bendix, J. (2015): Frequency of low clouds in Taiwan retrieved from MODIS data and its relation to cloud forest occurrence. Remote Sensing 7, 12986-13004.
2014 - Schulz, M.; Thies, B.; Chang, S. & Bendix, J. (2014): Automatic cloud top height determination in mountainous areas using a cost-effective time-lapse camera system . Atmospheric Measurement Techniques 7, 4185 - 4201.
2012 - Schulz, M.; Thies, B.; Cermak, J. & Bendix, J. (2012): 1km fog and low stratus detection using pan-sharpened MSG SEVIRI data. Atmospheric Measurement Techniques 5, 2469– 2480.
