Dissemination Materials

Università degli Studi della Tuscia (UNITUS)
Center for Ecological-Noosphere Studies of NAS RA (CENS)
CENS has grown into a comprehensive institution implementing a wide spectrum of fundamental and applied investigations in ecology, environmental protection and food safety. Interdisciplinary studies CENS has been dealing with include the assessment of ecological status of territories, development of scientific and methodological fundamentals of risk analysis, optimization of natural resource management processes, solution of problems in the area of human ecology.

With following link, you can find the scientific outcomes of recent investigations and publications.

Laboratório de Protecção e Segurança Radiológica, Instituto Superior Técnico/Campus Tecnológico Nuclear Universidade de Lisboa (UL)
Metals and low dose IR: Molecular effects of combined exposures using HepG2 cells as a biological model
Can be accessed at the link below:

Department of Remote Sensing (RSC), Institute of Geosciences and Geography, Faculty of Natural Sciences III, Martin Luther University Halle-Wittenberg

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

Ilia State University
Instituto Superior Técnico - Campus Tecnológico e Nuclear (CTN) - Universidade de Lisboa