Böttcher, K. (2019): Remote Sensing of boreal forest phenology: methods and applications. Dissertation, Martin Luther University of Halle-Wittenberg, Urn:nbn:de:gbv:3:4-1981185920-328937
Riedel, F.; M. Denk; Müller, I., Barth, N. & Gläßer, C. (2018): Prediction of soil parameters using the spectral range between 350 and 15000 nm: A case study based on the Permanent Soil Monitoring Program in Saxony, Germany. Geoderma 315: 188–198. DOI: 10.1016/j.geoderma.2017.11.027
Götze, C.; Denk, M.; Riedel, F. & Gläßer, C. (2017): Interlaboratory Comparison of Spectrometric Laboratory Measurements of a Chlorite Rock Sample. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 85: 307-316. DOI: 10.1007/s41064-017-0031-2
Götze, C; Gerstmann, H.; Gläßer, C.; Jung, A.: An approach for the classification of pioneer vegetation based on species-specific phenological patterns using laboratory spectrometric measurements. Physical Geography, 27: 1-17.
Götze, C.; Beyer, F.; Gläßer, C. (2016): Pioneer vegetation as an indicator of the geochemical parameters in abandoned mine sites using hyperspectral airborne data. Environmental Earth Science 75: 613. DOI 10.1007/s12665-016-5367-1
Gerstmann, H.; Möller, M.; Gläßer C. (2016): Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery. International Journal of Applied Earth Observation and Geoinformation 52: 115-125. DOI: 10.1016/j.jag.2016.06.001
Götze, C.; Gläßer, C.; Jung, A. (2016): Detecting heavy metal pollution of floodplain vegetation in a pot experiment using reflectance spectroscopy. International Journal of River Basin Management 14 (4): 1-24. DOI: 10.1080/15715124.2016.1205077
Elste, S.; Gläßer, C.; Walther, I.; Götze, C. (2015): Multi-temporal analysis of RapidEye data to detect natural vegetation phenology during the growing seasons in the northern Negev, Israel. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 2:117-127.
Thürkow, D.; Gläßer, C.; Scheuer, S. & Schiele, S. (2009): Visualization of Hydrological Processes with GEOVLEX: Introduction of an integrated methodical-technical Online Learning Approach. In: König, G. & Lehmann,H. (Eds.), E-Learning Tools, Techniques and Applications. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-6/W7. Proc. ISPRS working group VI/1 & VI/2 in Potsdam, 17-19 June 2009, 23-27.
Lausch, A. et al (2019): Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversit (Part I: Soil Characteristics), Remote Sensing 2019 (11): 2356. DOI: 10.3390/rs11202356
Gerstmann, H., Gläßer, C., Thürkow, D. & M. Möller: Detection of Phenology-Defined Data Acquisition Time Frames For Crop Type Mapping. FG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 86 (4): 1-13. DOI: 10.1007/s41064-018-0043-6
Gläßer, C; Gerstmann, H.; Conrad C.; & P. Knöfel (2017): Optimization of multi-temporal Land Use Classification by Integration of Phenological time frames. 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), page 1-3. Belgium-Brugge, (27.-29. Juni 2017)
Gerstmann, H. (2018): Phenological and spectral optimisation of multi-temporal land use classification. Dissertation. Martin Luther University of Halle-Wittenberg. URN urn:nbn:de:gbv:3:4- 2397
Denk, M. (2018): Qualitative and quantitative spectral characterization of iron- and steelworks by- products – Combining information from the visible light to the longwave infrared. Dissertation, Martin Luther University of Halle-Wittenberg, URN urn:nbn:de:gbv:3:4-22723
Gerstmann, H., Doktor, D., Gläßer, C. & M. Möller (2016): PHASE: A geostatistical model for the Kriging-based spatial prediction of crop phenology using public phenological and climatological observations. Computers and Electronics in Agriculture 127: 726–738. DOI: 10.1016/j.compag.2016.07.032
Denk, M.; Gläßer, C., Kurz, T. H.; Buckley, S. J.; Drissen, P. (2015): Mapping of iron and steelwork by- products using close range hyperspectral imaging: A case study in Thuringia, Germany. European Journal of Remote Sensing, Vertical Geology Conference VGC-14 Special issue, 48: 489-509. DOI: 10.5721/EuJRS20154828