The systematic and continuous recording of environmental data is an important element of the research infrastructure in the Biodiversity Exploratories.
Core Project 3 “Instrumentation and Remote Sensing” is responsible for (i) the systematic and large-area measurement and recording of meteorological and soil environmental variables and (ii) the provision of remote sensing and geospatial data on land cover and land use.
For this purpose, Core Project 3 develops and operates an important research infrastructure consisting of a very large network of climate stations, climate towers and remote sensing platforms, sensors and imagery. It regularly collects climatic and remote sensing data according to standardized procedures to produce coherent time series. These are made available through special database applications being developed in the project.
Instrumentation has therefore been a key component of the research infrastructure since the beginning of the Biodiversity Exploratories. Due to strong demand for remote sensing / geospatial data and services, the remote sensing component was added to the project in 2014. This will allow the establishment of an archive of geospatial data and remote sensing products accessible to all research groups, and coordinate the acquisition of new data, e.g., with drones and aircraft. The services offered by Core Project 3 do also contribute to making the geodata in the Biodiversity Exploratories standardized and harmonized.

The goal of the core project is to establish and support the measurement and sensor technology and to provide environmental data as processed basic products (“ready to be used be the Exploratories ´ researchers”) to address a wide range of research questions in the Biodiversity Exploratories. These data sets include:

  • Climate variables: Climate monitoring stations on grassland and forest stations, TreeTalker on forest experiments (FOX), climate towers for vertical profiles.
  • Vegetation structure variables: Indices of vegetation spatial structure from laser scanning and photogrammetric point clouds.
  • Audio environment: (ultrasound) recordings
  • Specific vegetation parameters: Tree sap flow measurement, LAI
  • Provision and processing of land use and land cover maps
  • Landscape structure: recording and quantification of landscape structure
  • Coordination and execution of drone surveys
  • Processing of Copernicus and Landsat satellite data to produce continuous time series and derived products (productivity, land use intensity, change analysis)

In addition to these products, the core project would like to foster the use of climate and geodata in biodiversity research. To this end, the core project offers workshops on the evaluation and use of climate and geodata.

Environmental measurement equipment

Meteorological and soil variables are among the main factors influencing the organismic diversity and functionality of ecosystem cycles. In order to carry out systematic and large-areameasurement and recording of these environmental variables, more than 400 permanent measuring devices are installed and maintained in the three Exploratories Schorfheide-Chorin, Hainich-Dün and on the Swabian Alb.

Climate stations

In each Exploratory, >140 measuring stations were set up, of which 50 each were installed in grassland and 91-111 each in the respective forest areas. Due to the very large number of stations, not all of them were equipped identically, but with graded sensor groups. Currently, the following station types are in use:

  • 279 Core Environmental Monitoring Units (CEMU)
  • 21 Enhanced Environmental Monitoring Stations (EEMU)
  • 148 Fox Environmental Monitoring Units (FEMU) a la Treetalker

Type CEMU is used on all experimental sample plots (EPs) of the Exploratories in the forest areas and grassland. In addition to general meteorological variables such as air temperature, surface temperature, and relative humidity, measurements are also taken in the soil. Five probes are deployed in the topsoil to continuously record soil temperature and moisture.

Type EEMU is deployed on selected intensive sample plots (VIPs) and topographically important sample areas, and also has complementary sensor technology to measure precipitation, wind speed and direction, incoming and outgoing radiation components of shortwave and long-wave radiation, photosynthetically active radiation (PAR), and atmospheric pressure.

Picture: The photo shows a fenced climate measuring station on an unmown meadow. The rectangular fence consists of thin wooden posts at the corners, connected on each of the four sides with three horizontal thin wooden slats nailed on and covered with wire mesh. The station consists of a metal pole with a control box at the bottom and two cross-shaped cross poles at the top with sensors at the ends, for example to measure the wind speed. For stabilisation, the ends of the struts are braced with thin wires to the corner posts of the fence. The station is powered by a solar module attached to the inside of the fence. In the background of the picture you can see a slightly hilly landscape in which meadows, fields and forests alternate. On the left of the landscape, a settlement can be seen in the distance.
Figure 1: Measuring station EEMU in Hainich

Type FEMU/ Treetalker is used on the forest experiment plots (FOX). In addition to general meteorological data such as air temperature and humidity, soil moisture and temperature measurements are also taken in the soil and solar radiation is recorded under the canopy in 12 spectral bands (450 – 860nm).

Picture: The photo shows a climate measuring station in an autumn forest with reddish-brown coloured leaves. The station consists of a metal rod stuck in the ground, on which a Treetalker sensor is mounted at the top, and a solar module of approximately DIN A 4 sheet size, which is attached below the sensor.
Figure 2a: Treetalker with solar module in Hainich
Picture: The photo shows from bottom to top in a summer forest under a blue sky a measurement technician doing maintenance work on the Treetalker sensor of a climate measurement station.
Figure 2b: Measurement technician during maintenance on the Swabian Alb

A visualization of the measured temperature time series can be seen here:


Climate towers

In the biosphere reserve Schorfheide-Chorin and on the Swabian Alb, a measurement tower with a height of 44 m and 37 m, respectively, was installed. Both towers include the sensor technology of the EEMUs and additionally measure the air temperature, air humidity and the PAR in the profile. Two further towers, which were built by the Max Planck Institute for Biogeochemistry, can also be used in the Hainich-Dün National Park.


Picture: The photo shows an autumnal deciduous forest under a blue sky photographed diagonally upwards. Further back in the picture, a climate measurement tower can be seen between the trees.
Figure 3: Climate storm in Schorfheide-Chorin
Picture: The photo shows a climate measurement tower photographed from bottom to top, standing in an autumnal deciduous forest under a blue sky.
Climate storm in Schorfheide-Chorin

Audio recordings

On selected plots, audio recorders capture soundscape in both audible and ultrasonic frequencies. Estimates of the occurrence and intensity of activity of birds, bats, and auditory ecosystem disturbances are recorded semi regularly. This provides the opportunity to systematically and simultaneously record and analyze differences in occurrence and activity between seasons and plots.

Remote sensing & geo data

Picture: The aerial view of a drone shows the canopy of the deciduous forest of an experimental plot in the Hainich. A path or stream meanders through the lower half of the photo. At the top right of the image, one can see a circular gap that was artificially created in the course of the FOX forest experiment.
Figure 4: High-resolution UAV image of a forest plot (EP) and a forest experiment (FOX) in Hainich, where circular gaps (upper right corner) were cut to investigate the dynamics of the gaps over time

The objective of the remote sensing component is to complement the large-area collection of meterological variables with area-wide remote sensing coverage in the three Exploratories. By providing and updating geospatial data and other products – derived from remote sensing data – as ready-processed Analysis Ready Data (ARD), it should be possible for non-remote sensing scientists to integrate spatial and temporal analyses based on remote sensing observations into their research and thus gain new insights into the functional relationships at different scale levels of biodiversity. For this purpose, a remote sensing infrastructure has been established in the core project and will be presented in the following.

Picture: The photo shows an uncut meadow in the sunshine. In the grass is a folding table on which a laptop and a fixed-wing drone are lying. On the left behind the table is a transport box. On the left at the edge of the picture is a large backpack. Further back in the meadow are groups of bushes and trees and behind them a deciduous forest.
Figure 5: Star wing drone in use on grassland.

Unmaned Aerial Systems (UAS) offer the possibility of temporally flexible high-resolution images of smaller areas (plots + environment). This is especially useful when image acquisition is to be synchronized with a particular field acquisition, e.g. to develop image classification models. Thus, both (1) the plot state can be documented during the field recordings and (2) training data can be collected for the development of classification models. The very high spatial resolution of the drone imagery allows the detection of very small features. Since 2017, the core project has been conducting drone aerial surveys of the Exploratories. For this purpose, the project has several drones (copters, fixed-wing aircraft) and different sensors (thermal, RGB, multi-spectral, hyperspectral) at its disposal. At the same time, Core Project 3 has established the necessary computer hardware and software and developed specialized data processing chains .

The use of drones, however, is subject to strict legal requirements which often require special permits, especially in nature conservation areas. In cooperation with the nature conservation authorities, the Core Project 3 was able to develop good solutions in many cases that allow the use of drones even in sensitive areas.

Satellite and aircraft data

Picture: The diagram shows in cross-section the profile of an A L S point cloud of an experimental plot in the Hainich. Shown from the ground to a height of about twentythree meters are point clusters classified as unclassified, as soil and as vegetation.
Figure 6 Cross-sectional profile of the ALS point cloud of an EP in Hainich

To address the diversity of research questions and methods within the biodiversity exploratories, a wide range of remote sensing platforms are used in addition to drones. These range from optical satellite imagery (e.g. Pléiades, RapidEye, Planet, Sentinel-2) to airborne hyperspectral and LiDAR sensors as shown in Table 1.

SystemPlattformSpatial Resolution (m)Number of bandsSpectral resolution(μm)Spektrale Auflösung (μm)Temporal resolutionCoverage
RapidEyeSatellit550.44 – 0.852009-2015 ~ min. three phenological periods per yeararea-wide
PlanetSatellit3.540.44-0.882020-2021, saeveral recordings per yeararea-wide
Landsat*Satellit15 – 120 4 - 80.43 – 12.51972 - 2014, ~ annuallyarea-wide
MODIS**Satellit250 – 1000360.40 – 14.392001 - 2014, ~ dailyarea-wide
Sentinel-2Satellit10-20130.49-2.22016-,~ every five daysarea-wide
PléiadesSatellit0.5– 250.43 – 9.40unique (2015)100 km² per Exploratory
HyperspektralPlane1> 2000.40 – 2.40unique (2015)100 km² per Exploratory
LiDARPlane~14 points/m²Full wave form---unique (2015)100 km² per Exploratory
Table 1. Overview of remote sensing products provided by Core Project 3. *The Landsat data set includes MSS, TM, ETM+ und OLI/TIRS sensors. **MODIS data from Terra and Aqua satellites.

Databases and processing

Climate data

The climate measurement facilities described above record >70,000 readings daily in each research area. These are automatically transmitted to Marburg via the mobile network and collected in the climate data time series database TubeDB (https://environmentalinformatics-marburg.github.io/tubedb/), which was specially developed for the Exploratories. The researchers can use these processed climate data directly via BExIS and visualize and export them according to individual requirements, such as quality correction, temporal aggregation and interpolation.

Picture: The screenshot shows the visualization of a temperature time series in a diagram in the user interface of the climate data time series database Tube D B. The background is white, the X and Y axes are shown as a grey grid and the temperature data as a continuous red zigzag line.
Figure 7 TubeDB: Interactive visualization of a temperature time series

Wildlife Cameras

We undertake the processing of wildlife camera images, primarily capturing the occurrence of mammals. The processing categorizes the images into images of animals, humans, and misrecorded images. The position of animals in the images is marked so that subsequent manual or machine species identification is simplified. Our image management software enables the review of the categorized images and the manual identification.

Picture: The photo shows a wildlife camera shot. In a summer forest, a deer crosses the area in front of the lens. Around the deer, a red frame marks the animal's position in the image.
Figure 8 Animal detection based on machine learning in wildlife camera images


Our audio data management software is the central collection place for the recorded audio data. In the web interface recordings can be listened to, visualized as spectrum The machine learning (ML) based automatic labeling runs in cycles of initial manual labeling, ML training, manual evaluation of the automatically generated labels and renewed ML training.

Picture: The screenshot shows the spectogram of a bat call in the user interface of the audio app. The audio signals are displayed against a black background in the form of red-purple narrow columns with equal distances to each other.
Figure 9 AudioDB: Spectrogram of a bat call recorded in the field.

Remote sensing data

The remote sensing database RSDB, (https://environmentalinformatics-marburg.github.io/rsdb/) manages the processed raster data, point clouds and vector data. The web interface provides functions for searching and exploring the stored geodata interactive visualizations, processing and data export. In addition, all data of the RSDB can be processed directly in R via the R package rsdb (https://github.com/environmentalinformatics-marburg/rsdb/tree/master/r-package).

Picture: The screenshot shows the aerial view of a drone in the user interface of the internet-based remote sensing database R S D B.
Figure 10 Screenshot of the RSDB web interface showing an RGB UAV image of a surface in layer view.

Current phases (2020-2023)

UAV flights in the forest and grassland

For many research projects in the Exploratories, it is important to take high-resolution images of the plots at the same time as or at a very close distance from one’s own field recordings. The use of drones has proven to be particularly efficient here. We have organized, conducted, and evaluated UAV surveys in both grassland and forest from 2017 until 2021 and will continue so.

Picture: The multi-spectral infrafrot aerial image of a drone shows in red, purple and turquoise the vegetation patterns of a grassland experiment plot and its surroundings.
Figure 11 High-resolution color infrared (CIR) orthomosaic of a grassland EP taken with a UAV and multispectral camera, showing the different patterns of vegetation in the plot and its surroundings

Landscape structure analyses

Ecosystem processes affecting biodiversity operate at very different scales. Ecologists frequently hypothesize that many of the processes observed on the intensively sampled EPs be significantly influenced by the neighborhood surrounding the plot. Current and historical land use and land cover maps can be used to examine the adjacent landscapes. This will allow the intensive observations on the EPs to be placed in the landscape context. Thematic classification keys adapted to the different questions will be developed. In the forest, for example, different tree species groups and other features are differentiated, in the grassland mainly agricultural use and woody structures (e.g. shrubs) serve as classification criteria. These thematic maps allow a quantitative description of the horizontal landscape structure of the three Exploratories. Elevation information e.g. from the Airborne Laserscanner (ALS) data extends this description by a vertical component, so that a 3D description of the vegetation and landscape structure is possible.

Picture: The land cover map shows an area in the Swabian Alb, in the middle of which the experimental plot is located. A five-hundred-meter buffer zone in the form of a red circle is drawn around the plot.
Figure 12: Section of the land cover map with a 500m buffer around the EP (red) in the Swabian Alb

Satellite image time series

The interactions between ecosystem functions and land use intensity is a central research topic in the  Biodiversity Exploratories ´ projects. The currently observed state is the result of processes acting over a long period of time. Therefore, describing the historical evolution can help to provide information about the current state. Remote sensing methods offer the possibility to describe the development of a landscape with the help of time series analyses. We provide such time series based on different satellite platforms. Through the publication of the Landsat-Archive a data source is available which allows a description of the development back to the 1980s. In addition, the data of the European Copernicus program are used to create current satellite image time series with high temporal resolution, which are used, for example, to record phenological processes.

Picture: The satellite image shows the Exploratory Hainich in its landscape state on the first of April in the year two thousand and eighteen. Clicking on the image shows in quick succession with one image per month the time series of the monthly development of the landscape until November two thousand and twenty.
Figure 13: Animated Sentinel-2 time series at the Hainich Exploratorium 2018-2020

Climate data

The network of climate stations distributed across the Exploratories provides time series of point measurements of the local microclimate. Using machine learning methods and with the help of Sentinel-2 time series and digital terrain models, we generate areal microclimate maps. These microclimate maps are complemented by reliability estimates which are also generated as maps.

Smaller gaps in the climate time series caused by station failures are optionally interpolated automatically from our climate data time series database with high precision. For larger gaps or for periods before the start of the climate stations, we offer an algorithm for filling the climate data utilizing external DWD weather data.

Precipitation is measured at some of our climate stations. Based on the DWD RADOLAN product, we can create precipitation time series for all plots.

Past phases

  • Recordings the phenological changes of the forest structure
  • Evaluation of historical satellite time series to capture disturbance intensity
  • Combined hyperspectral and laser scanner survey in the three Exploratories in 2015
  • Mapping forest structure metrics using Airborne Laser Scanner (ALS) data
  • Creation of RapidEye satellite time series

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

Cooperation partner

Dr. Stefan Erasmi
Dr. Stefan Erasmi
Johann Heinrich von Thünen Institut

Technical processing in the exploratories

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