Remote sensing based crop mapping is still challenging when just relying on optical information as the only data source. Due to the unavailability of adequate optical satellite images the integration of SAR is promising and can be explored within an innovation laboratory. The combination of SAR (TERRA-SAR-X and Sentinel-1) and optical images (RapidEye and Sentinel-2) for classification will improve the reliability and accuracy of crop maps. In addition, a sequential masking classification technique will be used to classify individual crop classes. These results will be compared with results of a one-step classification, in which all crop classes were classified at the same time. It has to be determined if the sequential masking approach will improve overall classification accuracies, compared to the one-step classification. As a study site the TERENO test site DEMMIN in Mecklenburg-Western Pomerania is suggested.
Mapping Fire from the Sky – Anna Bischof’s MSc Thesis on Savanna Fire Patterns
Wildfires are an essential ecological process in African savannas, shaping landscapes, influencing biodiversity, and playing a key role in nutrient cycling. Understanding their dynamics is crucial for both science and management – and this is where EAGLE MSc student...