Pedometrics

In der UFZ-Arbeitsgruppe Pedometrics ist es unser Ziel, räumliche Bodenverbreitungsmuster und deren Wechselwirkungen mit der Biosphäre und der Hydrosphäre zu verstehen und zu modellieren.

Soil-Landscape Modelling

Leitung

Dr. Mareike Ließ

Wissenschaftler

Anika Gebauer
Ali Sakhaee (extern)
Dr. Javier Reyes
Dr. Matteo Poggio

Studentische Forschung

Lisa Krieg
Tobias Koch

Pedometrics ist eine interdisziplinäre Wissen-schaft, die Bodenkunde, Angewandte Mathematik/ Statistik und Geoinformationswissenschaft um-fasst. Untersuchungsgegenstand ist die räumlich-zeitliche Pedodiversität auf unterschiedlichen Skalen. Modellierungsansätze kommen dabei genauso zum Einsatz wie zahlreiche Aspekte der Sensorik und der Geodatananalyse. Gemein ist allen Forschungsansätzen, dass die Quantifizie-rung der Vorhersage-Unschärfe einen wichtigen Raum einnimmt. Aktuell kommt dem Einsatz von Algorithmen und Optimierungsverfahren aus dem Bereich des ‚Spatial Data Science‘ immer größere Bedeutung zu. Letztere entwickelte sich aus der Mustererkennung und der Theorie des rechnerge-stützten Lernens und untersucht die Anwendung und Konstruktion mathematischer Algorithmen, die Wissen aus komplexen Datenstrukturen ableiten können.


Aktuelle Projekte

SoilSpace3D-DE

The SoilSpace3D-DE research cooperation with the Thünen Institute of Climate-Smart Agriculture seeks to develop high resolution spatial soil-information in 2D and 3D at national scale (Germany) applying methods from data science/ pedometrics.

SOCmonit

The research project deals with the spatio-temporal monitoring of soil organic carbon with remote and proximal soil sensing methods on long-term field experiments. It is funded by the German Federal Ministry of Food and Agriculture (BMEL) according to the BMEL directive on the promotion of innovation in the field of soil as a contribution to climate protection under the Paris Agreement (COP21).

DSM-GuideEC

DSM-GuideEC is part of the "Platform for Biodiversity Monitoring and Research in South Ecuador" and investigates the soil landscapes of three mountain regions of different climate and vegetation with a focus on carbon sequestration and soil hydraulic properties. Besides research the project includes knowledge transfer of the applied methodology to local partner organisations of the public sector.

BonaRes

“BonaRes” is short for “Soil as a sustainable resource for the bioeconomy”. In this funding initiative of the German Federal Ministry for Education and Research (BMBF) the focus is on the sustainable use of soils as a limited resource. The ultimate goal of BonaRes is to extend the scientific understanding of soil ecosystems and to improve the productivity of soils and other soil functions while developing new strategies for a sustainable use and management of soils.

Hi-CAM, Project 7.1 - Agricultural Systems

The Helmholtz Climate Initative develops climate mitigation and adaptation strategies. Project 7.1 uses pedogenetic process knowledge together with national soil databases to create spatially continuous soil information with a resolution of approx. 200 m. The focus will be essentially on those soil properties that have been identified as suitable indicators for important soil functions such as water storage, carbon sequestration, and agricultural productivity. Pedotransfer functions shall be applied in case of limited data availability. Based on the generated soil information and the derived soil functional properties, this project contributes to the development of sustainable adaptation strategies for agriculture in response to climate change. This is done at national scale (Germany), taking into account regional features with regards to soil distribution.


Aktuelle Veröffentlichungen

Ellinger, M., Merbach, I., Werban, U., Ließ, M., 2019. Error propagation in spectrometric functions of soil organic carbon. Soil, 5, 275-288. https://doi.org/10.5194/soil-5-275-2019

Gebauer, A., Ellinger, M., Brito Gómez, V.M., Ließ, M., 2020. Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning. Soil, 6, 215–229. https://doi.org/10.5194/soil-6-215-2020

Gebauer, A., Brito Gómez, V.M., Ließ, M., 2019. Optimisation in machine learning: An application to topsoil organic carbon stocks prediction in a dry forest ecosystem. Geoderma 354, 113846. https://doi.org/10.1016/j.geoderma.2019.07.004

Ließ, M., 2020. At the interface between domain knowledge and statistical sampling theory: Conditional distribution based sampling for environmental survey (CODIBAS). Catena, 187,104423. https://doi.org/10.1016/j.catena.2019.104423

Stein,S., Eberhardt, E., Grosse, M., Helming, K., Hierold, W., Hoffmann, C., Kühnert, T., Ließ, M., Russel, D.J., Schulz, S., Specka, X., Svoboda, N., Zoarder, M.A.M., Heinrich, U., 2019. Report on available soil data for German agricultural areas, 2018. BonaRes Series 2019/01. BonaRes Centre for Soil Research. DOI: 10.20387/BonaRes-CD4Q-1PEM

Vogel H-J, Eberhardt E, Franko U, Lang B, Ließ M, Weller U, Wiesmeier M, Wollschläger U, 2019. Quantitative evaluation of soil functions: potential and state. Frontiers in Environmental Science, 7 (164), https://doi.org/10.3389/fenvs.2019.00164

Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spinotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franko, N., Wollschläger, U., Vogerl, H.-J., Kögel-Knabner, I., 2019. Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales. Geoderma 333, 149-162. https://doi.org/10.1016/j.geoderma.2018.07.026