Hochschule für nachhaltige Entwicklung Eberswalde
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Open B.Sc. or M.Sc. Thesis Proposals and FIT Research Projects proposals

Sie sind herzlich eingeladen Ihre eigenen VorschlĂ€ge fĂŒr M.SC. und B.Sc. Arbeitsthemen mit mir zu diskutieren.


You are most welcome to suggest and discuss your particular topic of interest for your scientific thesis with me at any time.

Remote Sensing and UAV topics:

"Rapid UAV-based Ground Truthing Data Acquisition of Windthrow in Forests". Ground truthing for the purpose of regional storm damage detection is essential for the calibration of optical and SAR satellite imagery. This research entails the development of in-field flight planning techniques for the rapid assessment of windthrow areas using consumer UAVs. The following skills and tasks are required: R and/or Python programming, UAV flight planning, field work, photogrammetric and image (UAV-based and Satellite) processing, RTK-GNSS.
Master level. Working language English. Supervisor: S. Krause, Prof. J.-P. Mund.

“UAV Applications for Intensive Forest Monitoring and Inventory Plots”. This research involves the development of a methodology to incorporate UAVs into typical forest monitoring and inventory programs. The following skills and tasks are required: Review of current forest monitoring and inventory programs, review of current state-of-the-art in UAV for intensive monitoring and inventory, flight plan development and testing, plot geolocation accuracy under a dense canopy, feasibility assessment. Bachelor or Master level. Working language English or German. Supervisor: S. Krause, Prof. J.-P. Mund.

“UAV Applications in Operational Forestry”. This research involves the assessment and development of various UAV applications with regard to supporting forest management procedures such as harvest planning as well as pre- and post-harvest assessment. Extensive field work and collaboration with local foresters is expected. This research requires the following tasks and skills: digital image and computer skills, UAV flight planning techniques, UAV image data analysis using PIX4D or open drone map.
Bachelor or Master level. Working language English or German. Supervisor: S. Krause, Prof. J.-P. Mund.

“Scaling up UAV Forest Phenology Indices from TI Britz research station to Sentinel 1 and 2 data cube indices” This research involves the application of the Data Cube Method to develop, test establish a reliable geospatial and geostatistical method for scaling up and statistically combining regional high resolution tree phenology data from the TI research station Britz, close to Eberswalde to Sentinel 1& 2 times series and index data. Close collaboration with Mr. S. Krause and Dr. Sanders (TI)
and some field work is expected. Master level. Working language English or German.
Supervisor: Prof. Mund, Dr. T. Sanders, S. Krause.

“Classification and Segmentation Methods for UAV-Based High Resolution Imagery”. This research involves the exploration, assessment and development of methods in the classification and segmentation of high-resolution imagery (UAV). The following skills and tasks are required: algorithm development in R and/or Python, image processing and segmentation, potential ground truthing field work. Ideally this research would result in the development of an open access R package.
Master level. Working language English. Supervisor: S. Krause, Prof. J.-P. Mund.

“Testing methods for analyzing and comparing terrestrial and UAV dense point cloud data from forest observation plots”. This research includes 3D point cloud mapping and 3D mathematics and statistics such as voxel based approach as well as Shape Analysis in R with the R-Packages for Geometric Morphometrics, Shape Analysis. This project is offered in close cooperation with VINS3D.de and requires a practical working period at the premises of VINS3D in Berlin.
Master level. Working language English or German. Supervisor: T. Thiele, Prof. J.-P. Mund.

“Applying multispectral UAV and Sentinel 2 monitoring and forest crown analytics on a 2 ha forest plot close to Tornow, Eberswalde” (New HNEE forest study site). This research involves application of multispectral UAV image mosaics with regard to supporting forest management procedures and forest management decisions such as harvest planning as well as pre- and post-harvest assessment. Extensive field work and collaboration with local foresters and the manager of the new HNEE forest study site, Prof. T. Cremer and Prof. M. Mussong is expected. This research requires the following tasks and skills: digital image and computer skills, UAV flight planning techniques, UAV image data analysis using PIX4D or open drone map. Bachelor or Master level. Working language English or German.
Supervisor: Prof. J.-P. Mund, Prof. T. Cremer, Prof. M. Mussong.

“Monitoring grassland management intensity with Sentinel 1 and 2 data to support nature protection measures in National Park “Unteres Odertal”. This research aims at adapting and applying the methods and workflow published by Ms. Mareike Bekkema to the situation in the National Park “Unteres Odertal” close to Eberswalde. In the focus of this research is a combination of index based time series analysis to monitor grassland management and mowing management with Sentinel 1 and 2 images during the summer period of 2018 and 2019. The following resources are relevant for this research in close cooperation with the management of Nat. Parc “Unteres Odertal”:
Marijke Bekkema, VU University Amsterdam, Spatial Economics, Graduate Student, 2018; https://www.austriaca.at/0xc1aa5576_0x00390cda
Extensive field work for ground truthing in the lowlands of Nat. Park “Unteres Odertal” and collaboration with local rangers is expected. This research requires the following skills: knowledge of multispectral indices, patterns and trend analysis in Time Series Data,
Master level. Working language English or German. Supervisor: Prof. J.-P. Mund, S. Krause.

“Analyzing the potential of Sentinel-1 data for monitoring moor and swamp land in Eastern Germany” This research aims at testing and evaluating various SAR data analyzing algorithms from ESA SNAP and STEP on their capacity to detect and monitor smaller-sized moor and swamp land in Brandenburg regarding water level, vegetation cover and other landscape ecological parameters. This research requires the following skills: principal knowledge of satellite data analysis, preferably Sentinel 1 data and interest to increase your knowledge of interferometric applications. This research shall be implemented in close cooperation with Prof. Luthardt at HNEE FB-2.
Master level. Working language English or German. Supervisor: Prof. J.-P. Mund, Prof. Luthardt (FB-2).

GIS and other forestry topics:

“Regionalized Tree Volume and Biomass Allometric Equation Development using Terrestrial and Aerial Photogrammetry”. The use of non-destructive methods to develop regionalized allometric equations for forest monitoring programs is of interest for the large-scale assessment of for example carbon stocks. This research involves the following tasks and skills: Photogrammetric acquisition and processing of terrestrial and aerial datasets, statistical analysis in R (i.e. linear regression), and field work optional.
Master level. Working language English or German. Supervisor: S. Krause, Prof. J.-P. Mund.

“Testing and analyzing two digital forest management and decision support systems of Marteloscopes for Bioeconomy purposes” This research aims at testing a new digital decision support system developed by Prof. F. Bravo from UVA, Valladolid with forest inventory and marteloscope data gathered at various BioCoN Marteloscope sites in Spain, Germany, Finland and Vietnam. This research requires the following skills: forest mensuration and using allometric functions, coding in R or Python. This project is in close collaboration with the HNEE BioEcoN project (www.BioEcoN.eu).
Master level. Working language English. Supervisor: Dr. K. Beiler, Prof. F. Bravo, Prof. J.-P. Mund.

“Concept and design of a new HNEE digital learning platform and smartphone APP for location-based mobile learning with HNEE Campus trees” Within the HNEE ILL MoLLe project, an innovative learning app and database platform shall be conceptualized and developed in order to offer digital access to existing geodata and dendro-ecological attributes and parameters of trees on the HNEE campus and in the Forest Botanical Garden. The updated new tree cadaster of all campus trees and trees in the Forest botanical garden builds the fundament of all information to be distributed. The conceptual development of an innovative Learning and Teaching Platform and the technical design of an end-user friendly app shall support interdisciplinary, integrated self-organized, time-superior learning phases of undergrad and grad students at HNEE. This research requires the following skills APP design and development capacities, coding experiences, preferably JAVA and JAVAscript, Interest in experimental learning, database management and application.
Bachelor or Master level. Working language English or German. Supervisor: Prof. J.-P. Mund.

“Simulation of an optimum forest landscape for Brandenburg illustrating a best-case scenario for close-to-nature forests, including ecologically effective core area sizes and landscape connectivity.” The general idea is to create hypothetical distribution maps of Brandenburg forest cover by re-organizing current forest types towards optimal clustering and connectivity of close-to-nature core areas in suitable sites. In other words, the annealing of low-intensity managed stands into effective core areas and dispersal of intensely managed (single age/species stands) to edges, isolated patches of reduced conservation value or other particularly suited sites. Simulations are fed with categorical management scenarios e.g., current and hypothetical percentages of natural forest cover. The work integrates existing land use, climatic and geodata. Analytical tools include network analysis (i.e. graph theory) and probabilistic techniques (e.g., simulated annealing) to create optimization maps of contiguous core areas of close-to-nature forests according to effective core area size criteria and different forest management scenarios. The work requires fundamental understanding of spatial pattern analysis and geostatistics. Basic experience using GIS and coding in R is advantageous. Bachelor or Master level . Working language English or German. Supervisor: Dr. K. Beiler, Prof. J.-P. Mund.


Your individual research topics, based on your own suggestions are always welcome.

We encourage you to suggest us the topic of your special interest. In this case we expect a short abstract and rationale of your project idea in written form including methods and data considered for this research.

All listed project proposals are due to continuous academic development and thematic change.

Some research project proposals can be combined and covered by more than one student.

All research and thesis proposals remain open until they have been filled by an applicant.

2019-06-27         

HNEE FB-1 - Department for Remote Sensing and GIS

Prof. Mund; S. Krause & Dr. K. Beiler