Current Research:

Ph.D. project 'Using the historical range of variability of landscape elements to identify and monitor dry meadows and pastures in Switzerland - an RS & GIS based approach'

This study has the overall objective to develop, apply, and test a tool based on modern GIS and remote sensing techniques and data suitable for identification, planning and monitoring in nature conservation (specifically in the framework of the TWW project). Tests will be performed to evaluate the applicability of the technique for selected TWW monitoring questions. .

We will analyze the historical range of variability and thresholds (of VI’s, LAI, NPP) from multi-temporal remotely sensed imagery for mapped TWW polygons composited for individual months throughout the year. This historical range of variability and its relation to climate pattern will then be used to derive thresholds of change/no-change for monitoring the efficiency of the protective actions for maintaining these habitats. Such threshold will allow us to identify TWW polygons that show unusual deviations from the expected historical signal, thus suggesting that the vegetation inside of this polygon has changed unexpectedly. The method is applicable to other ecosystems as well, but we will specifically use TWW data to develop and test the method.

In a second application, we suggest using the available mapped TWW polygons to train a multi-temporal regression model to identify "missing polygons" using GLM (Generalized Linear Models). This approach will allow us to find pixels that show essentially the same historical signal as pixels that have been mapped and identified. This can be valuable to assist the TWW teams in areas where the a priori knowledge of the distribution of dry meadows and pastures is suboptimal. Additionally, testing such an approach will help evaluating this method for later possible additions to the acknowledged TWW polygons once the mapping project has been finished.

The mentors for this Ph.D. thesis are Dr. Niklaus Zimmermann (Section Landscape Dynamics and Spatial Development,WSL) and Prof. Dr. Klaus I. Itten, Dr. Mathias Kneubuehler, Dr. Tobias Kellenberger (Department of Geography, Remote Sensing Laboratories).

Studies

2001-2003:  MSc in Geo-Information Sciences, Department of Geo-Information Science and Remote Sensing, Wageningen University, The Netherlands.

1996–2001: Degree from the Department of Forestry and Environmental Sciences (5-year study), Aristotle University Of Thessaloniki, Greece.  

Scientific experience

2003 September-2004 February: Scientific researcher at the Department of Landscape Dynamics and Spatial development of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürich,Switzerland.

2003 February- 2003 August: Geographical Information Systems and Remote Sensing specialist for the European Projects  “Greenveins” and “SAFE” at the Swiss Federal Research Station for Agroecology and Agriculture (FAL), Zurich,Switzerland.

Memberships in research groups:

Participation at the 'Treeline Group' at WSL
Member of the Geotechnical Chamber of Greece (G.C.G)

Publications

Psomas A, Kneubühler M Huber S, Itten K & Zimmermann NE . (in revision.) Exploring seasonal and annual spectral characteristics of grassland habitats. Remote Sensing of Environment

Psomas A, Kneubühler, Itten KI and Zimmermann NE, 2007. Hyperspectral remote sensing for seasonal estimation of aboveground biomass in grassland habitats. Proceedings of the 10th International Symposium on Physical Measurements and Signatures in Remote Sensing in Davos , Switzerland 2007.

Psomas A, Zimmermann NE, Kneubühler M, Kellenberger T & Itten KI, 2005. Seasonal variability in spectral reflectance for discriminating grasslands along a dry-mesic gradient in Switzerland. Proceedings of the 4th EARSEL Conference on Imaging Spectroscopy in Warsaw, Poland 2005.

Huber S, Kneubühler M, Psomas A, Itten K & Zimmermann NE, (in revision). Estimating Biochemistry in Mixed Forests from HyMap Data using Band-Depth Analyses and Subset Regression Algorithms. Remote Sensing of Environment.

Huber S, Zimmermann NE, Kneubühler M, Psomas A & Itten KI, 2005. Estimating Nitrogen in Mixed Forests from HyMap Data using Band-Depth Analysis and Branch-and-Bound Algorithm. Proceedings of the 9th International Symposium on Physical Measurements and Signatures in Remote Sensing, 17-19 October, Beijing, China 2005, pp. 533.

Psomas, A. (2003). Vegetation mapping of floodplain areas by means of Artificial Neural networks. Centre for Geo-Information. Wageningen, Wageningen University: 85 pp.

Aristotle University of Thessaloniki,Greece