phenological dataGerman Weather Service

phenological datahabitat level, monthly

validation

validation

MODIS dataEVI 250m, 16days

Rapid Eye data6.5m, biweekly

correctedMODIS time series

phenologicalmeasures

correctedRapid Eye time series

phenologicalmeasures

national scale:Germany & neighboring countries

2001 - 2012

habitat scale:German Alpine foothills NATURA 2000 habitats

2011 & 2012

phenologicaladjustment layers

“bridging the gap“

Based on a – so far linear – interpolation algorithm that accounts for theactual day of acquisition, MODIS Enhanced Vegetation Index (EVI) timeseries from 2010 were interpolated. Figure 2 shows a first visualization ofthe phenological development over Southern Germany, Eastern France, theAlps and Northern Italy using the day on which the EVI reaches itsmaximum. The EVI is a vegetation index that is related to canopy structuralvariations.

Within the NATURA 2000 Network each EU member is obliged to acquireinformation about habitats and report their status to the EuropeanEnvironmental Agency. The use of remote sensing is a common method invegetation science but not yet wide-spread within monitoring for NATURA2000. That is due to the high spatial and temporal variability ofvegetation within the NATURA 2000 sites which make a monitoring withmedium spatial and temporal resolution data difficult.A high spatial resolution is recommended for an effective monitoring ofheterogeneous and small scale habitat types, e.g. degraded raised bogs.However, mono-temporal data does not account for temporal variability ofvegetation which makes it difficult to define the present habitat status and todistinguish between natural seasonal variation and degradation. Thepresented approach addresses this problem by using phenologicalmetrics derived by remote sensing data (and validated usingphenological observations) on two scales as a reference for ecologicalassessment.

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Contact: carina.kuebert@uni-wuerzburg.de1 Remote Sensing Unit of DLR – University of Wuerzburg, Department of Geography and GeologyAm 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 temporalhigh resolution MODIS data for Germany and neighboring countries fromthe year 2002 to present. This data will be validated using phenologicaldata provided by the German Weather Service. Several statistical analyseswill 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 resolutionRapid Eye data will bei) compared to own phenological observations tailored to dominant habitat

species andii) adapted to the “phenological adjustment layers”.

Figure 1: Preliminary flowchart of PhD thesis. Satellite data andphenological data on two scales will be used to derive phenological metricsand 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 thebehaviour of EVI values of different pixels in different climatic and geo-ecological conditions throughout the year (Figure 3). A validation of theunderlying time series is to be carried out using phenological data from theGerman Weather Service.

Figure 3: EVI time series of three different locations.

About 300 locations of different NATURA 2000 habitats were identifiedwithin the German Alpine foothills as a first work package of the project“msave”. For each of the eight different habitat types (e.g. Molinia meadowson chalk and clay or Alkaline fens) at least five different dominant andcharacteristic habitat species (e.g. Molinia caerulea) were choosen toobserve their phenological development using a modified BBCH-scaleduring several field campaigns in 2011 and 2012. A combination of theseinsitu-data with spatially high resolution Rapid Eye data will allow for thederivation of phenological adjustment layers. They will account for thevariability of phenological states of one single habitat typeon a relative small scale.

Own phenological observations

early

late