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Description of the key activities related to the station network

Meteorological and pedological parameters are among the main factors of organismal diversity and the functionality of ecosystem cycles. A systematic and large-scale measurement and recording of these environmental parameters is performed in all exploratories with more than 300 measurement stations.

Climate stations

At each of the three exploratories, 100 climate stations have been installed on grassland research plots and inside the forests. Regarding the huge amount of stations, not all stations have been equipped in an equal manner but a gradual setup has been used. As of today, the following stations are operational:

  • 279 Core Environmental Monitoring Units (CEMUs))
  • 21 Enhanced CEMUs (EEMUs)

CEMU – This is the basic setup which is installed on all experimental plots on grassland and forest locations. Each CEMU measures air temperature and humidity (+200 cm), soil surface temperature (+10 cm), soil moisture (-10 cm, at VIP’s  additional -20 cm) and soil temperature (-5 cm, -10 cm, -20 cm, -50 cm) in intervals between 30 minutes and 1 hour.

EEMU – At 21 selected locations, the basic CEMU setup is extended by four component radiation balance, photosynthetic active radiation, wind speed and direction as well as precipitation sensor with a recording interval of 10 minutes. Some locations are also recording barometric pressure.
A visualisation of the air temperature recordings can be found: here

Observation towers

At the biosphere reserves Schorfheide-Chorin and Schwäbische Alb, two observation towers with a height of 37 m and 44 m respectively have been installed. Air temperature and humidity profiles, as well photosynthetic active radiation profiles are recorded in addition to precipitation and the above canopy radiation budget and wind dynamics. 2 more towers, installed by the Max-Planck-Institute for Biogeochemistry, can be used in the Hainich National Park.

Data handling and processing

The station network records about 45.000 values per day within each exploratory. The data stream is transmitted to Marburg for the pre-processing and quality checks and uploaded to the BExIS data base afterwards. Because of the increasing amount of data, a new climate database component has been developed which will be implemented into BExIS soon.

Figure 1. Tower at Swabian Alb
Figure 2. Climate station type AEMU at Hainich
Figure 3. Workshop for validation - at SEG43

Description of the key activities related to Remote Sensing

Background

In reaction to the increasing demand for remote sensing products and services from different research groups, the core project ‘Instrumentation’ was extended by a remote sensing component in the current project phase and is now named ‘Instrumentation and Remote Sensing’. With the extended core project, a better coordination of the remote sensing activities within the Biodiversity Exploratories is envisaged. The remote sensing subproject will provide area wide information on the land cover and land use of the three exploratories which will complete the meteorological data collected by the instrumentation project. By providing and updating different maps derived from remote sensing data, we want to enable also ‘non-remote sensing experts’ to integrate spatial and temporal components into their analysis to gain knowledge on functional biodiversity relationships at different scales.

Sensors

To cope with the manifold research questions and methods of the Biodiversity Exploratories, remote sensing data from a wide range of sensors will be acquired and pre-processed. This includes high resolution optical satellite data (e.g., Pléiades, RapidEye) as well as airborne hyperspectral and LiDAR sensors as shown in Table 1.

Table 1. Overview of the remote sensing products that will be used in the 4th phase of the Biodiversity Exploratories (2014-2017)

SystemPlattformRäuml. AuflösungAnzahl BänderSpektrale AuflösungTemporale AuflösungAbdeckung
RapidEyeSatellit5 m50.44 – 0.852009-2015 ~Min. drei phänologische Perioden pro Jahrvollflächig
Landsat*Satellit15 m – 120 m4 - 80.43 – 12.51972 - 2014, ~ jährlichvollflächig
MODIS**Satellit250 m – 1 km360.40 – 14.392001 - 2014, ~ täglichvollflächig
PléiadesSatellit50 cm – 2 m50.43 – 9.40Einmalig (2015)100 km² je Exploratorium
HyperspectralFlugzeug1 m> 2000.40 – 2.40Einmalig (2015)100 km² je Exploratorium
LiDARFlugzeug~14 Punkte/m2Full wave form--- Einmalig (2015)100 km² je Exploratorium
*The Landsat data set includes MSS, TM, ETM+ und OLI/TIRS sensors. **MODIS data from Terra and Aqua Satellites

Activities

Neighborhood analysis (land use and land cover mapping)
Ecosystem processes influencing the biodiversity are present at very different scales. It is hypothesised that several of the processes described for the intensively monitored EP’s and VIP’s are also influenced by the plot surroundings. In this work package, maps that describe the current land cover and land use of the Biodiversity Exploratories will be created. By that we wish to link the intensive observations of the EP’s and VIP’s to the larger landscape context.

The land cover classification key will be adapted to the ecosystem type. In forests, various tree species groups will be differentiated. For grasslands, agricultural land uses and shrubs will be the guiding factors for classification.

Figure 4. False color composite forest areas at the Hainich Biodiversity Exploratory acquired with AISA Eagle/Hawk Sensors at July 2012 (Source: personal communication Mr Henning Aberle, Chair of Forest Inventory and Remote Sensing, Göttingen, ForestHype project DLR).

Landscape structure analysis
The maps from work package I allow quantitative analyses of the horizontal landscape structure of the three exploratories. Linking it with the height information obtained from the LiDAR and Pléiades data analyses, we will extend the 2D information on horizontal landscape configuration by a vertical component to provide a more complete 3D description of landscape and vegetation structures.

Time series analysis
The relationships between ecosystem functions and land-use intensities are among the main research topics in the Biodiversity Exploratories programme. Often, the currently observed status is the result of processes that have been present over long time periods. Thus, the historical development of the landscape may contribute explaining the currently observed status. Remote sensing offers the possibility to make such descriptions using time series analysis techniques – when corresponding imagery is available.

By opening the Landsat archive to the public, NASA is providing a data store that can be used to build time series starting from the 1980s. The objective of this work package is to acquire, pre-process and analyse Landsat images to construct temporal trajectories which describe the history of land cover changes in the Biodiversity Exploratories. In a second step, we will analyse the potential of these change maps to be part of the explanatory features that serve as proxy for land-use intensity.

Figure 5. Left: spatial distribution of the forest areas in the Schorfheide Biodiversity Exploratory. Right: Proportion of the different patch sizes (ha) of the total forest area.
Figure 6. Workflow of a time series analysis of Landsat Satellite images for mapping changes and disturbances.

Scientific processing Uni Marburg

Prof. Dr. Thomas Nauss
Prof. Dr. Thomas Nauss
Philipps-Universität Marburg
Falk Hänsel
Falk Hänsel
Philipps-Universität Marburg
Stephan Wöllauer
Stephan Wöllauer
Philipps-Universität Marburg
Spaska Forteva
Spaska Forteva
Philipps-Universität Marburg

Scientific processing Uni Göttingen

Prof. Dr. Christoph Kleinn
Prof. Dr. Christoph Kleinn
Georg-August-University of Goettingen, Burckhardt Institute, Forest Inventory and Remote Sensing
Dr. Paul Magdon
Dr. Paul Magdon
Georg-August-Universität Göttingen
Dr. Stefan Erasmi
Dr. Stefan Erasmi
Johann Heinrich von Thünen Institut
Wanda Graf
Wanda Graf

Technical processing in the exploratories

Martin Fellendorf
Martin Fellendorf
Universität Ulm
Measurement engineer
Frank Suschke
Frank Suschke
Senckenberg Gesellschaft für Naturforschung,
Biodiversitäts-Exploratorium Schorfheide-Chorin
Measurement engineer
Matthias Groß
Matthias Groß
Technische Universität München (TUM)
Measurement engineer
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