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phenological data German Weather Service
phenological data habitat level, monthly
validation
validation
MODIS data EVI 250m, 16days
Rapid Eye data 6.5m, biweekly
corrected MODIS time series
phenological measures
corrected Rapid Eye time series
phenological measures
national scale: Germany & neighboring countries
2001 - 2012
habitat scale: German Alpine foothills NATURA 2000 habitats
2011 & 2012
phenological adjustment layers
“bridging the gap“
Based on a – so far linear – interpolation algorithm that accounts for the
actual day of acquisition, MODIS Enhanced Vegetation Index (EVI) time
series from 2010 were interpolated. Figure 2 shows a first visualization of
the phenological development over Southern Germany, Eastern France, the
Alps and Northern Italy using the day on which the EVI reaches its
maximum. The EVI is a vegetation index that is related to canopy structural
variations.
Within the NATURA 2000 Network each EU member is obliged to acquire
information about habitats and report their status to the European
Environmental Agency. The use of remote sensing is a common method in
vegetation science but not yet wide-spread within monitoring for NATURA
2000. That is due to the high spatial and temporal variability of
vegetation within the NATURA 2000 sites which make a monitoring with
medium spatial and temporal resolution data difficult.
A high spatial resolution is recommended for an effective monitoring of
heterogeneous and small scale habitat types, e.g. degraded raised bogs.
However, mono-temporal data does not account for temporal variability of
vegetation which makes it difficult to define the present habitat status and to
distinguish between natural seasonal variation and degradation. The
presented approach addresses this problem by using phenological
metrics derived by remote sensing data (and validated using
phenological observations) on two scales as a reference for ecological
assessment.
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Contact: [email protected]
1 Remote Sensing Unit of DLR – University of Wuerzburg, Department of Geography and Geology Am Hubland, 97074 Wuerzburg, Germany 2 German Aerospace Center (DLR) German Remote Sensing Data Center (DFD) 82234 Wessling, Germany
Vegetation
C. Kübert1, D. Klein2, M. Wegmann1, C. Conrad1, S. Dech1,2
Multi-sensor-concepts for the assessment of land
surface phenology using remote sensing data
Abstract
Data and Methods
Acknowledgements
This research is carried out within the German project msave (“multi-season remote sensing for monitoring vegetation”) and is funded by the
DLR Space Administration, with means provided by the German Federal Ministry of Economics and Technology, under project reference number 50 EE1032.
The coarse scale addresses the derivation of metrics based on temporal
high resolution MODIS data for Germany and neighboring countries from
the year 2002 to present. This data will be validated using phenological
data provided by the German Weather Service. Several statistical analyses
will be carried out on these data sets to better understand atmosphere-
biosphere interactions and to transfer this knowledge to so called
“phenological adjustment layers”.
For selected NATURA 2000 habitats in the German Alpine foothills,
phenological metrics derived from time series of spatial high resolution
Rapid Eye data will be
i) compared to own phenological observations tailored to dominant habitat
species and
ii) adapted to the “phenological adjustment layers”.
Figure 1: Preliminary flowchart of PhD thesis. Satellite data and
phenological data on two scales will be used to derive phenological metrics
and for validation purposes.
Preliminary results
Day of EVI maximum in 2010
as a proxy for phenological development
Figure 2: Day of EVI maximum in 2010 (preliminary result).
The resulting geographical pattern of phenology can be explained by the
behaviour of EVI values of different pixels in different climatic and geo-
ecological conditions throughout the year (Figure 3). A validation of the
underlying time series is to be carried out using phenological data from the
German Weather Service.
Figure 3: EVI time series of three different locations.
About 300 locations of different NATURA 2000 habitats were identified
within the German Alpine foothills as a first work package of the project
“msave”. For each of the eight different habitat types (e.g. Molinia meadows
on chalk and clay or Alkaline fens) at least five different dominant and
characteristic habitat species (e.g. Molinia caerulea) were chosen to
observe their phenological development using a modified BBCH-scale
during several field campaigns in 2011 and 2012. A combination of these
insitu-data with spatially high resolution Rapid Eye data will allow for the
derivation of phenological adjustment layers. They will account for the
variability of phenological states of one single habitat type
on a relative small scale.
Own phenological observations
early
late