Digitalisation of Natural Complexity to Solve Socially Relevant Ecological Problems
The State Excellence Program MV "Digitization in Research" supports the consortium "DIG-IT! Digitalisation of Natural Complexity to Solve Socially Relevant Ecological Problems" under the direction of Prof. Martin Wilmking. Inbetween July of 2019 and June of 2022, researchers will have 2 million euros at their disposal to develop a methodological toolbox that can independently capture and categorize ecological image and audio data using machine learning techniques (deep convolutional neural networks). Partners are the Fraunhofer Institut für Grafische Datenverarbeitung Rostock (Prof. Uwe von Lukas), Biomathematik (Prof. Mareike Fischer) and working groups of the Institut für Botany und Landscape Ecology (Profs. Joosten, Kreyling, Wilmking) and the Zoological Institute and Museum (Prof. Gerald Kerth).
By exploring the opportunities of digitalization for the ecological sciences, DIG-IT! will meet pressing ecological questions of high societal relevance with a future-oriented arsenal of methods and thereby qualify digitally competent ecologists and ecologically experienced biomathematicians and computer scientists. Dig-It! will address a broad array of questions including (but not limited to) service functions and stability of ecosystems under climate and land use change, species protection and innovative environmental monitoring. The overarching goal is to facilitate a "quantum leap" for the field of ecology through the development of universally applicable methods using self-learning algorithms ("Deep Convolutional Neural Networks"), because in the digital age the challenge no longer lies in the amount of available primary data, but in its evaluation. For this purpose, DIG-IT! will combine the developmental expertise for the automated analysis of image data (Fraunhofer Institute for Computer Graphics, Rostock and Biomathematics University Greifswald) with the application to urgent ecological questions (Botany / Landscape Ecology / Zoology University Greifswald).
Extract from the jury vote:
"The DIG-IT! project aims to address urgent ecological issues through the use of digital technologies, with the evaluation of primary data posing a particular challenge. (...) The jury is convinced of the scientific excellence of the project. The idea of developing an ecological toolbox is particularly impressive. It also appreciates the approach of docking onto already existing excellent research alliances such as WETSCAPES and RESPONSE [DFG Research Training Group "Biological RESPONSEs to Novel and Changing Environments"] and using their findings above all to promote young scientists."
The subproject of the Fraunhofer Institute is committed to the implementation of Deep Learning methods including applications for image data derived from ecological research. The strong structural form of artificial neural networks enables mechanical learning to identify target features and characteristics in images. Mechanical learning methods will significantly increase the efficiency of image feature identification through automated evaluation, enabling ecologists to process much greater digital image databases. Sample data for testing these methods will be supplied by biological research groups in the project that include a broad spectrum of data sources from pollen and roots, to bats, ensuring universal applicability in ecological sciences. The analyses (potentially supported by a PhD student) will focus on the automated detection of observationally defined objects in an image, incorporating automated segmentation to refine margins separating detected objects from background data. Next, isolated objects can be passed along to human experts or downstream software tools. Research tasks in the subproject are focused on “Deep Convolutional Neural Networks” given their proven suitability for evaluation image data. The method focus will shift gradually from “supervised” to “semi-supervised” until it reaches “unsupervised” classification. At this final stage, little to no data are used to classify prior to the start of the training. Additionally, the inclusion of semantical information (e.g. topology of root systems) will be assessed to enhance image analysis. This approach centers on improving the efficiency of image classification and processing, developing new fields of ecological research that relies on universally practicable methods.
Mareike Fischer manages the subproject “Biomathematics” with a PhD student and a PostDoc. Biomathematics mediates between mathematic theories and biological application, providing new tools to analyze biological data. M. Fischer will mediate between machine-learning-theoreticians and ecologists by incorporating biomathematical modeling and statistical evaluation. The subproject “Roots” will focus on modeling root growth temporally by using graph theory and explore if the level of root growth is statistically suitable to determine the nutrient distribution in the soil. Additionally, multidimensional data of bats, wood and pollen are further study targets for promoting deep learning methods in ecological research. Baseline data required to train machine learning approaches are numerous and include DNA (field of expertise for M. Fischer), sound recordings of bats, or the chemical properties of wood and pollen. DIG-IT!’s Deep Learning methods will identify data characteristics that are not visible to humans or can be derived from additional information.
Project coordination + wood anatomy
Martin Wilmking leadsthe coordination of DIG-IT! and manages the subproject “Wood Anatomy”. This subproject will be supported by a PhD student as well as collaboration with partners from Fraunhofer IGD and associated master theses. The PostDoc “catalyst and multiplier” will link all members within the consortium (catalyst) and provides DIT-IT!-internal expertise for external users (multiplier). Synergisms with ongoing research projects (e.g. “WETSCAPES” project) and in the dendro-ecological laboratory “DendroGreif”. Assigned to the coordinator are two technical employees: a half position for supporting coordination and a full-position for assisting the users with data acquisition, digital image processing, and data management.
Jürgen Kreyling manages the subproject “Roots” to coordinate “automated digitalization of the dynamic in root systems of plants”. The subproject will establish the automated acquisition of various root characteristics (e.g. length, diameter) from mini-rhizotron scans in various settings including simplified laboratory conditions to complex field conditions. The project will include a PhD student and collaborations with partners from Fraunhofer IGD and associated master theses. J. Kreyling’s working group is equipped with a greenhouse and established research sites in a local beech forest and the nature reserve “Karrendorfer Wiesen”, enabling highly-resolved rhizontronscans acquisition over time. Further research collaborations with the “WETCAPES” project are expected.
Automated identification of species/individuals of selected animal species
Gerald Kerth manages the subproject “automated identification of species/individuals of selected animal species”. This project will include a PhD student and collaborations with partners from Fraunhofer IGD and associated master theses. Field data will exclusively support this project, with a special focus on 10 different wintering grounds of bats in Mecklenburg-Western Pomerania. The established methods for the digital determination of species will be applied at reasearch sites of the two DBU (Deutschen Bundestiftung Umwelt) projects "FUN" and "Living underground".
Hans Joosten manages the subproject “pollen identification” and will include a PhD student and collaborations with partners from Fraunhofer IGD, biomathematics (UG) and associated master theses. Laboratory analysis will be conducted predominantly in the laboratories of the working group Peatland Studies and Palaeoecology where specialized equipment, from boring, sample collection and preparation to pollen analysis, is available. For high-resolution images, microscopes from Imaging-Centre of the Biology Department of University Greifswald will be accessed. For coring beeches during flowering stage, the established transect of the working group J. Kreyling will be utilized. The sampling of stratified sediments will be carried out in cooperation with the German Research Centre for Geosciences (GFZ) in Potsdam.