From 5 – 8 October 2015, the 3rd international conference on Phenology was held at Kusadasi/Turkey. It was jointly hosted by the Humboldt-University of Berlin and the Adnan Menderes University Aydin. 86 contributions from scientists of 23 countries were presented on the topics “Phenological observations, networks, data collection”, “Climate variablilty, change and trends”, “Phenological modelling”, and “Challenges, new approaches and progress”.
Within the session “Remote Sensing and Phenology”, Carina Kübert presented ongoing work of her PhD thesis on “Deriving phenological layers for Germany from remote sensing data: spatio-temporal analysis and validity”. One of our MSc students, Jeroen Staab, co-authered a presentation given by our former colleague Sarah Asam (now EURAC, Italy), showing first results of phenological monitoring for the entire Alps. Jeroen helped to derive phenological metrics during his internship at EURAC.
More details in the programme and abstract book (published by the German Meteorological Service (DWD)) which can be downloaded from the conference homepage
More details can also be found on twitter using the hashtags: #phenology #phenology2015
PhD: Monitoring the structural parameters of forest habitats by multi-source/multi-temporal remote sensing data
The Department of Remote Sensing at the institute of Geography and Geology invites applications for a PhD position starting from May, 20th 2015 for a period of 3 years. The successful candidate will conduct her/his PhD with a multidisciplinary focus on remote sensing and spatial statistics.
The structure of a natural forest landscape is characterized through elements such as the amount of foliage, canopy cover of woody plant species, structural properties of vegetation (i.e. diameter, basal area, vegetation height, aboveground biomass and amount of woody debris). Many of these (and other) factors related to the dynamics of a natural forest ecosystem (e.g. landscape structure, growing stock of forest stands, amount of coarse dead woody material and the vertical distribution of landscape elements) can presumably interact with natural disturbance agents such as biological infestations. Landscape structure can be assessed using remote sensing in a spatially and temporally continuous way. This project is part of a bigger framework on early detec-tion strategies for forest natural disturbance agents. In this part of the project, the derivations from various possible remote sensing sources (airborne and terrestrial LiDAR, UAV and possibly RADAR interferometry data) will be used to form a multi-temporal set of 3D information, which will further be applied to model the actual as well as changes in structural properties of selected natural forest habitats. The results will be validated by airborne- and field-based measurements on selected local test sites.
The methodology embraces a wide range from airborne-and spaceborne analysis of remote sensing products to statistical simulation of spatiotemporal processes. Therefore, the work additionally entails the use of ad-vanced spatial statistics. For gathering the required reference and validation data, a number of field trips are foreseen to the study site. A possible expansion of the results to further test sites in central Europe is also possible. The results of the work should be summarized in in scientific manuscripts intended for peer review presentation and publication.
― M.Sc. or Diploma degree or equivalent in geoinformatics, ecology (or forestry), physical geography or related fields,
― Sufficient knowledge and strong interest in remote sensing and spatial statistics,
― Advanced modelling/programming skills (preferably with R),
― Fluency in English. Knowledge of German language will be a great advantage.
― Interest in team integration and good teamwork skills.
The successful candidate will receive a PhD position for 24 + 12 months (TV-L E13/50%). Please submit your application (in English) containing a letter of interest, the CV, and – as one document – a detailed resume, a relevant recent research product (e.g. published article), and names and contact information of two academic references by E-Mail to:
Dr. Hooman Latifi, University of Würzburg
Application is opened until April 19th 2015. The University of Wuerzburg is an equal opportunity employer that tries to increase the number of women in research and teaching. Applicants with disabilities but otherwise equal qualifications will be preferred.
from January onwards we are looking at the DLR (German Aerospace Center, Munich, Germany) for a PostDoc for our large, cross-disciplinary project dealing with the large Mekong Basin in Southeast Asia. Focus will be the analyses of land surface dynamics in the trans-boundary Mekong Basin; an area 2.5 times the size of Germany, and covering all climate zones from high alpine to sub-tropical / tropical.
Method-wise, time series analyses based on MODIS, Sentinel-3, Landsat, and Sentinel-1/2 will be undertaken.
Several of our colleagues have worked in the region for the past 6-7 years, data and algorithms do partially exist, and a PhD student will also work with the post doctoral researcher.
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Skill set we are looking for:
Remote Sensing background and finished PhD degree with a remote sensing focus
- Solid experience with time series processing, especially with MODIS or other daily sensors
- Good programming skills in at least one of the following languages: IDL, or Python, or R
- Experience with publication (in English) in SCI Journals or higher ranking conference papers
- Intercultural competence and willingness to travel to the Mekong region twice a year (about 2 weeks each)
- Team player
- English as a working language is okay, German language skills not mandatory but a slight advantage
- First experience with training (basics in GIS or remote sensing) not mandatory, but would be welcome
More information on work foci will be given in a personal talk. Interested candidates can email C. Kuenzer – preferably before December 1st.
you will be based at DLR-EOC, Munich, the salary will be according to standard PostDocs salaries.