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The course programme is basically divided into three areas:

  1. Theoretical foundations of selected topics of technology use in the national park management. This is mediated through university lectures as well as through two guest lectures from cooperating research institutes about the following issues: quality of environmental data, programming environmental applications, earth observation and data mining.
  2. Practical exercises to deepen the theoretical part of the lecture
  3. Events for the providing of information for the study location Germany as well as for the culture and the regional studies (see Opens internal link in current windowaccompanying programme)

National Park Management:

Lecturers: Dr. Harald Egidi und Claus-Andreas Lessander, National Park Office  Hunsrück-Hochwald

In this block, the objectives of a national park as well as national and international standards will be introduced at the beginning. In addition to the description of the process of the national park´s establishment and the importance of participation as well as network building, the necessary inventories can be discussed briefly (geo-ecological terrain factors, vegetation, fauna, socio-economic parameters, etc.). The importance of a national park management with regard to the development of the area starting from the current state towards the desired wilderness is presented through short-, medium- and long-term management measures of the National Park Hunsrück-Hochwald. Moreover, the mediation of different levels of monitoring processes and their connection to the national park management will be discussed.

In a subsequent excursion to the neighbouring national park Hunsrück-Hochwald, the natural environment basics, the forest composition, special locations, monitoring systems etc. will be presented to the participants.


Lecturers: Prof. Dr. Peter Fischer-Stabel, University of Applied Sciences Trier

Geographical Information Systems (GIS), satellite navigation or location-based services are just a few keywords, that point the increasing importance of spatial data and services in the management of protected areas. Therefore, the module "Geoinformation" handles the basics of management of spatial data and international standards to build an infrastructure for geospatial data. In addition to an introduction of geospatial data collection and modeling, different download- and display-services for raster and vector data will be presented as well as the creation of thematic maps.

In the accompanying practical exercises the participants will deepen their theoretical knowledge through the implementation of a spatial analysis. A special focus within the practical exercises will be on the analysis of airborne laser scan data (ALS). During this practical part, the students use data from the spatial data infrastructure of the National Park Hunsrück-Hochwald like terrain models, zoning, forest species distribution, road network, etc.


Programming Environmental Monitoring Applications with Arduino:

Lecturers: Prof. Dr. Anna Förster, Director of the Sustainable Communication Networks group at the University of Bremen; Prof. Dr. Klaus-Uwe Gollmer, University of Applied Sciences Trier, Environmental Campus Birkenfeld

Many functions of environmental monitoring, like temperature, humidity, light and rain, can be most efficiently implemented as automated applications, where a device is deployed in the wild, gathers the relevant information and sends it autonomously to a nearby server. This teaching module will introduce programming of simple environmental monitoring applications with Arduino. Arduino is a very user-friendly and cost-efficient hardware platform with simple programming environment, which allows complete programming novices to quickly implement a simple program like temperature or light monitoring. We will learn how to program simple applications, how to connect different sensors and how to deploy the device later in the real-world. Also typical problems in the wild will be discussed, lie protection of the devices from humidity or from animals and tourists. The main goal of this module is to enable the participants to use Arduino in their learning or research projects to more efficiently gather high-quality data.

Weather Station

Quality of Environmental Data:

Lecturer: Prof. Dr. Rolf Krieger, University of Applied Sciences Trier, Environmental Campus Birkenfeld

Today, environmental research, like many other research areas, has to struggle with a growing flood of structured data as well as unstructured data. Environmental monitoring networks continuously deliver data about the air, climate, water or radiation etc.  Usually, the data are stored, analysed and interpreted by environmental information systems. These systems provide a detailed picture about the environmental status and its evolution. In addition it is also important that experts and the interested general public can get information about completed, ongoing and planned research projects and even the generated results to support knowledge exchange and communication. Necessary metadata has to be gathered and made accessible through search engines. In this context it is important and necessary that the data meet basic quality requirements, such as timeliness, completeness, accuracy, accessibility, consistency etc. Data quality is an essential characteristic that determines the reliability of data for making decisions. This module will provide a useful introduction to this topic.  

Topics of the module include data quality models, organization of data quality projects and methods and tools used in these projects in general. In the practical exercises focus is also on environmental data, e.g. sensor data and metadata in the environmental sector relating to the national park.


Environmentally controlled experiments:

Lecturer: Dr. Natalie Beenaerts and Jan Clerinx, University Hasselt

A continuous stream of data delivered by environmental monitoring stations or models is needed to perform accurate environmental manipulation experiments. These data are quality-controlled to steer the environmental systems of the manipulation experiment e.g. climate predictions, pollution,.. in real time.

This module will focus on the entire process starting from reliably measuring and transferring a parameter of interest e.g. temperature, humidity… with the highest accuracy possible to a controlled experiment. The associated challenges will be addressed during the practical exercise where you will, at least for one parameter, transform a measurement into a manipulation.

You will use a set of the (semi)-continuous data streams from an existing observation tower in the heathlands, that measures biotic and abiotic parameters according to the European ICOS standards, to prepare at least for one parameter a possible manipulation scenario in the Ecotron Hasselt University.

Guest Lectures:

Wildlife Detection: Dr. Ulf Hohmann, Dr. Cornelia Ebert, Dipl. Biol. Carolin Tröger - (Research Institute for Forest Ecology and Forestry (FAWF))

Forest-dependent hoofed animals can significantly influence their habitat as herbivores. A deeper understanding of this habitat management potential is extremely important for the ecological and economic aspects. One of the key problem areas of wildlife ecology is the necessary determination of distribution and density from hoofed animals, because of their hidden and often nocturnal behaviour.

An Introduction to Big Data and Scientific Data Management: Prof. dr. Jan Van den Bussche, University Hasselt

We explain how Big Data has emerged from new developments in databases, large websites, social networks, and the management of scientific data.  For example, large data streams, as generated by sensors, are creating new challenges.  But also the wide diversity of data types in Big Data is driving new database technologies.  Starting from examples such as environmental monitoring, or scientific annotation and collaboration, we introduce the main different types of data.  We distinguish between structured data, unstructured data, semistructured data, graph data, linked data, spatial data, and temporal data.  We show how Big Data can lead to new scientific understanding through Artificial Intelligence.

Forest observation- Landesforsten Hoffman