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: carina.kuebert@uni-wuerzburg.de

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