At our Department, we are dealing with the topic of phenology in different projects and applications. Here is a short overview on our activities concerning phenology and animal movement, agriculture, conservation and rstats:
in cooperation with Max-Planck Institute for Ornithology ()
is concerned with mapping, modeling and predicting local habitat use of migratory birds during the migration phase. The project makes use of synergies between optical and SAR data to characterize stopover sites of white-fronted geese (Anser albifrons
) during their migration from central Europe to northern Russia. Together with external colleagues, Benjamin Leutner was installing GPS tracker on several individuals. A new era of animal trackinig will start later this year when @Astro_Alex
will install an antenna to receive animal movement data at ISS
aims at the development of generic pre-processing and analyzing methods in remote sensing of agricultural areas including grassland. The biophysical parameters LAI and FPAR obtained throughout the vegetation period are derived and validated. Field campaigns at the TERENO test site DEMMIN also include observing the growing stage of crops using the BBCH scale. The successful partnership led to the project
This project is a further implementation of the measurement network in DEMMIN in the national and international scientific community, including universities, research organizations and the industry. In cooperation with the German Research Centre for Geosciences (GFZ
) environmetal data from more than 40 meteo-stations are recorded on landscape level including: climate data, phenology, soils & soil moisture, biomass and crop yield. The site is part of the environmental observation network TERENO
and the international validation network JECAM
, which is part of GEOGLAM
, the GEO Global Agricultural Monitoring initiative. Further, the in-situ data is supporting the implementation of the cal/val activities in the Copernicus in-situ component
was addressing one major challenge in using remote sensing for vegetation monitoring: The investigated objects – here: NATURA 2000 grassland habitats – are highly variably both in space and time. The different habitat species are subject to a strong annual cycle with different phenological stages, e.g. leaf development, blossom, ripeness and senescence.
Feasibilty study: Monitoring of Infestation using Remote Sensing
The study was funded as part of the initial research Fund of the Faculty of Philosophy I of the University of Würzburg, which aims at the promotion for young graduate students. Our idea was to combine the methodological output of msave with topics related to our research on bark-beetle infestations in the Bavarian Forest National Park: The alteration in the phenology of forest areas
as caused by infestation was modeled using a combined approach of spatial statistics and multi-temporal optical remote sensing data.
includes tools to visualize movement data by creating path animations from geo-location point data. Developed by Jakob Schwalb-Willmann.
: supports the combined use of animal movement and remote sensing data. Developed by Ruben Remelgado within the Opt4Environment project.
is a R package providing a wide range of tools for your every-day remote sensing processing needs. The available toolset covers many aspects from data import, pre-processing, data analysis, image classification and graphical display. RStoolbox builds upon the raster package, which makes it suitable for processing large data-sets even on smaller workstations. Moreover in most parts decent support for parallel processing is implemented. Developed by Benjamin Leutner.
B.Sc. and M.Sc. thesis
B.Sc. thesis “Explaining spatial behavior of storks using EO data” written by Leonard Hammer
M.Sc. thesis “Animal movement pattern analysis and remote sensing” written by Tejas Bhagwat
My selection highlights some of our projects with a direct link to plant and animal phenology. Also in other projects, phenology plays an important role – even if the term itself is not mentionned
: This is the case, as soon as we work with time series data (e.g. within the projects or CAWa
) or derive EO mosaics originating from different dates. Dealing with mono-temporal data such as from UAVs overflights (e.g. in order to derive a UAV-based cemetery map
), the user should think about vegetation development and whether the object of investigation might be covered by crown cover.
Further information can be found here:
database of animal tracking data
ICARUS project: report by DLR
list of our projects