MSc theses

  • Burkhalter, Felix. (ongoing). Fuel moisture mapping for assessing fire potential using EnMAP data
  • Kelch, Ronja. (ongoing). Woody encroachment in private and communal areas in Namibia: A fraction cover analysis along the rainfall gradient using Landsat data (1984-2023)
  • Sabanoglu, Tolga. (2024). Estimating fractional land cover forms using EnMAP and Sentinel-2 spectral-temporal metrics in Northeastern Namibia
  • Zheleznyy, Oleg. (2024). Satellite-based measures of postfire recovery in Siberian forests
  • Depperman, Lara. (2024). Quantifying Tree Mortality in Rewetted Peatlands: The Role of Water Levels
  • Alsleben, Jonas. (2024). Quantifying temperate forest mortality based on fractional cover time-series in Germany
  • Leo, Mira. (2024). A Sentinel-2 Satellite-Based Analysis of the Effects of the Russian Invasion in Ukraine in 2022 on Land Abandonment and Crop-Type Dynamics
  • Smith, Maeve. (2023). Drought effects in Germany - examining the potential difference between forest edge and interior with Sentinel-2 data
  • Lang, Clemens. (2023). Analysing Forest Change in Albania between 1990 and 2021
  • Bacher, Saskia. (2022). Understanding Drought Effects on Bark Beetle Disturbances in National Park Forests, Germany
  • Krüger, Kirsten. (2022). Analysing forest gap dynamics with high-resolution repeat lidar in the Berchtesgaden National Park
  • Schneidereit, Shawn. (2022). Diving into new dimensions: unmixing Sentinel-2 data to assess wetland vegetation dynamics in North-Eastern Germany
  • Bartsch, Julia. (2022). Long-term fire reconstruction in Chiquitano tropical dry forest
  • Gomez-Medina, Camila. (2022). Morphological Urban Areas and their material stocks in the United States
  • Isensee, Johannes. (2022). Upscaling UAV to Sentinel-2 for mapping grassland biomass (working title)
  • Edler, Raphaela. (2022). Comparing the German Forest Fire Danger Index and medium-resolution satellite Products for the Quantification of Fire Occurrence in central Europe
  • Happel, David. (2022). The effects of time series data augmentation in domain generalization for crop type mapping
  • Fahrenberg, Monique. (2022). Hydrometeorology thresholds for anomalies in GPP as a definition of extreme events
  • Derenthal, Marius. (2021). Baysian modeling of oil palm expansion in Santander, Colombia
  • Browning, Keri. (2021). Patterns of Grassland Management Intensity and Their Relation With Biodiversity in Southern Sweden
  • Nill, Leon. (2021). Revealing Spatio-temporal Dynamics of Arctic Shrub Expansion: Utilizing Vegetation Cover Fractions from Landsat Time Series
  • Pfoch, Kira. (2021). Mapping forest fire severity using multi-temporal unmixing of Sentinel-2 data - towards a quantitative understanding of fire impacts
  • Thiel, Fabian. (2020). Mapping forest canopy cover dynamics using spectral unmixing and Landsat time series
  • Jänicke, Clemens. (2020). Assessing immediate drought impacts on forest ecosystems by combining phenological parameters with drought indices
  • Ernst, Stefan. (2020). Implications of Sentinel-2 observation density on land cover classification
  • Blickensdörfer, Lucas. (2020). The Influence of Meteorological Conditions on National-Scale Crop Type Mapping and Model Transferability Based on Landsat and Sentinel-2 Time Series
  • Streif, Daniel. (2020). Regional adjustment of grassland start of season predictions in Germany through temperature sum calibration based on phenological ground observations and remote sensing time series
  • Wesemeyer, Max. (2020). Differentiation of grassland management in Brandenburg based on Sentinel-2 time series and image segmentation
  • Thomsen, Simon. (2019). Exploring differences in land management practises in the Ecuadorian Amazon using dense time series and GIS
  • Goddard, Adam. (2019). Simulating maize yields in Brandenburg with the Scalable Crop Yield Mapper
  • Kowalski, Katja. (2019). Characterizing Spring Phenology of Broadleaf Forests Across Germany Combining Landsat/Sentinel Time Series and Phenological Models
  • Themann, Britta. (2019). Upscaling of Field Measurements for the Validation of the Sentinel-2 Level-2A Product
  • Schmitt, Lara. (2019). Mapping Cropped Area and Unsown Area in the Rabi Season of Andhra Pradesh, India
  • James, Matthew. (2019). Urban form mapping: Utilising Sentinel-2 data to create urban form. maps for the Tokyo/Manchester region
  • Hemmerling, Jan. (2019). Tree species classification in temperate forests using dense multi- spectral time series
  • Szigeti, Martin. (2019). Mapping settlement types in Rio de Janeiro at different spatial resolutions with remote sensing data
  • Voß, Torben. (2018). Satellite based assessment of mangrove area and carbon stock of the Rufiji Delta (Tanzania)
  • Dudek, Robert. (2018). Mapping Greenhouse expansion in Almería, SE Spain
  • Schofield, Elen. (2018). Improved monitoring of insect disturbances through integrating Landsat forest change maps and Aerial Overview Surveys for British Columbia
  • Oeser, Julian. (2017). Using all available Landsat data for detecting and attributing forest disturbances
  • Koch, Jonas. (2017). Do spatial fire patterns change in times of conflict? A spatio-temporal analysis of MODIS fire data from 2000-2015 in the Democratic Republic of Congo
  • Hampel, Milenka. (2016). Using simulations from dense Landsat time series for crop yield estimations – a case study in Brandenburg, Germany
  • Gutermuth, Lisa. (2015). Satellite Imagery in Agriculture: Commercialisation and Sustainability in Germany
  • Funke, Dennis. (2014). Using Landsat data to monitor selective logging in the Brazilian Amazon – the role of dense time series
  • Rufin, Philippe. (2014). Remote Sensing of Pasture Degradation Processes in Southern Pará – Woody Encroachment Trajectories from Landsat TM and ETM* + Data (1984-2012)
  • Schwieder, Marcel. (2013). Estimating fractional shrub cover in southern Portugal using simulated EnMAP data: A comparison of machine learning regression techniques and data sampling
  • Held, Matthias. (2013). Feature selection for hyperspectral data: agglomerative clustering and support vector classification