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The surface area of rivers is a major determinant of the exchange of gases and energy between water and atmosphere. Water level fluctuations lead to permanent changes of the river surface area. This affects greenhouse gas emissions from streams not only by reduced surface gas emissions but also by high CO2 emissions from dry areas1. Previous work had shown that it is possible to quantify the surface area of the Elbe, Germanys third largest river, from Sentinel 2 satellite images.
Central objective is to develop a workflow which allows for an automatized quantification of the surface of both water and dry sediments of the Elbe river from Sentinel images. The workflow shall be used to establish a time series of the rivers water surface based on continuously available data on the water level. An analysis of the transfer to other rivers is intended.
Experiences with R and basic remote sensing and GIS skills are required as well as the willingness to handle large amount of data.
The remote sensing work will be supervised by Prof. Hanna Meyer (Remote Sensing and Spatial Modelling Research Group), the biogeochemical background by Dr. Matthias Koschorreck (Helmholtz Centre for Environmental Research – UFZ).
Assessing and improving the transferability of machine learning models is an important task for the large-scale mapping of environmental observations. In the context of spatial modelling and remote sensing, the transferability of a model relates to its ability to validly predict in geographic regions where training data is not available. These regions can be identified using the Area of Applicability method currently published and developed in the Uebersat Project (Meyer et al. 2021).
Understanding the influence of the spatial distribution of training data and predictor interactions on the models transferability is crucial for the interpretation of predictions. In Barbiero et al. (2020) the models transferability and generalization from different datasets was tested using a convex hull approach and related to structural characteristics of the dataset (e.g. correlations between predictors).
In this context, we offer a Master Thesis dealing with the following topics:
The Thesis will be part of the Ubersat Project and conducted in a collaborative team from ILOEK and IFGI.
Barbiero, Pietro, Giovanni Squillero, and Alberto Tonda. 2020. “Modeling Generalization in Machine Learning: A Methodological and Computational Study.” arXiv:2006.15680 [Cs, Stat], June. https://arxiv.org/abs/2006.15680.
Meyer, Hanna and Edzer Pebesma. 2021. “Predicting into Unknown Space? Estimating the Area of Applicability of Spatial Prediction Models.” Methods in Ecology and Evolution, June, 2041–210X.13650. https://doi.org/10.1111/2041-210X.13650.
Contact: Marvin Ludwig marvin.ludwig [at] wwu.de
In der Lausitz sind aufgrund des Braunkohletagebaus viele Flächen für die Wiedernutzbarmachung und Melioration sowie Renaturierung entstanden. Eine diese Flächen wurde durch die Genehmigung und finanzielle Unterstützung von VATTENFALL der Betriebs-Technischen Universität Cottbus (b-TU Cottbus) zur Verfügung gestellt. Diese habe in dem ehemaligen Einzugsgebiet des Baches Hühnerwasser ein nun künstliches Einzugsgebiet erschaffen, das den durch den Tagebau versiegten Bach erneut speisen soll. Das Renaturierungsprojekt wird seit 2005 umfassend wissenschaftlich begleitet und es werden viele Messungen im Bereich Klimatologie, Bodenkunde und Hydrologie durchgeführt. Es ist somit eine umfassende Datengrundlage vorhanden, sowohl thematisch, räumlich als auch zeitlich. Seitens der b-TU Cottbus wurde Interesse an einer Verschneidung der vorhandenen Daten mit noch zu erfassenden hochauflösenden multispektralen Drohnendaten geäußert. Von großem Interesse sind vor allem die Extrapolierung der Punktmessungen und die Erfassung von (Vegetations-)Strukturen um eine genauere Einschätzung des Renaturierungfortschrittes zu beurteilen.
Weiterführende Informationen zu dem Projekt
Kontaktpersonen an der WWU:
Jan Lehmann, jan.lehmann [at] wwu.de
Henning Schneidereit, henning.schneidereit [at] wwu.de
Kontaktperson an der b-TU Cottbus:
apl. Prof. Dr. Wolfgang Schaaf, schaaf [at] b-tu.de
Der Wald im Fichtelgebirge ist, wie viele andere Wälder auch, von Windwurf und Borkenkäferbefall betroffen. Die veränderte Landbedeckung und das somit veränderte Mikroklima beeinflussen auch die Eigenschaften des Bodens. Bodentemperatur und -Feuchtigkeit sind wichtige Faktoren für die biologische Aktivität und den Kohlenstoffumsatz im Boden. In diesem Projekt soll untersucht werden, wie sich die Bodentemperatur und -Feuchte durch das Fichtensterben verändert. Die Abschlussarbeit ist in das Forschungsprojekt Carbon4D eingebunden.
Mit 4 Profilsonden werden die Bodentemperatur und die Bodenfeuchte kontinuierlich an verschiedenen Standorten im Fichtelgebirge gemessen. Die Sensoren gehen bis in 1 m Tiefe und messen alle 10 cm die beiden Variablen. Ergänzend sind CO2 Haubenmessungen möglich. Zur Quantifizierung auf der Landschaftsskala wird das Fichtensterben mithilfe von Fernerkundungsdaten erfasst.
Welchen Einfluss hat das Fichtensterben auf Bodentemperatur und Bodenfeuchte in Raum und Zeit?
Ansprechpersonen:
Maiken Baumberger maiken.baumberger [at] wwu.de
Hanna Meyer hanna.meyer [at] wwu.de
Kleffmann Digital RS provides a wide range of services and data empowering farmers and advisors in the agribusiness to manage agricultural land. Part of this management is knowing the location of arable land which remains to be bare soil during winter time. Due to the nature of the season, winter observations over Europe exhibit a larger likelihood of being obstructed by clouds, disallowing the use of multispectral optical imagery, such as Sentinel-2.
Within the framework of this project, a method for segmenting bare soil agricultural fields from Sentinel-1 is to be developed. Depending on the scope of the work, the project could:
Contact: Hanna Meyer hanna.meyer [at] wwu.de
Kleffmann Digital RS provides a wide range of data to improve field management in agriculture and at the same time provides predictions for the harvest. Soil moisture is an important factor for the development in the field. So far, this is not available at a sufficiently high resolution. Within the framework of this project, a method for determining soil moisture in agricultural areas is to be developed. For this purpose, data on crops, precipitation, weather and soil type are to be considered together with satellite data and correlations are to be determined. The aim is to find a method for determining soil moisture as accurately as possible, in a first step for Germany. In a second step, models are to be created based on this method that can be transferred globally to a wide variety of areas. The influence of crops in their various stages on soil moisture should also be given special consideration.
This project is linked to the Carbon 4D project. Soil moisture is an important factor in determining soil organic carbon. The pilot farms of Kleffmann Digital can be approached to determine soil moisture. Contact can be made with other farmers throughout Germany. A sensor for determining soil moisture in 5 to 10 cm depth is available. Data on the current field crops is available or can be determined with the help of Kleffmann's own tools.
Contact: Hanna Meyer hanna.meyer [at] wwu.de