@article{ForkuorUllmannGriesbeck2020,
  author    = {Forkuor, Gerald and Ullmann, Tobias and Griesbeck, Mario},
  title     = {Mapping and monitoring small-scale mining activities in Ghana using Sentinel-1 time series (2015-2019)},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {6},
  issn      = {2072-4292},
  doi       = {10.3390/rs12060911},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203204},
  year      = {2020},
  abstract  = {Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016-2016/2017, 2016/2017-2017/2018 and 2017/2018-2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25\% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions.},
  language  = {en}
}
@article{DhillonDahmsKuebertFlocketal.2020,
  author    = {Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Borg, Erik and Conrad, Christopher and Ullmann, Tobias},
  title     = {Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {11},
  issn      = {2072-4292},
  doi       = {10.3390/rs12111819},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207845},
  year      = {2020},
  abstract  = {This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m\(^2\)) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m\(^2\)). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).},
  language  = {en}
}
@article{UllmannNillSchiestletal.2020,
  author    = {Ullmann, Tobias and Nill, Leon and Schiestl, Robert and Trappe, Julian and Lange-Athinodorou, Eva and Baumhauer, Roland and Meister, Julia},
  title     = {Mapping buried paleogeographical features of the Nile Delta (Egypt) using the Landsat archive},
  series = {E\&G Quartnerny Science Journal},
  volume    = {69},
  journal   = {E\&G Quartnerny Science Journal},
  number    = {2},
  doi       = {10.5194/egqsj-69-225-2020},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-230349},
  pages     = {225-245},
  year      = {2020},
  abstract  = {The contribution highlights the use of Landsat spectral-temporal metrics (STMs) for the detection of surface anomalies that are potentially related to buried near-surface paleogeomorphological deposits in the Nile Delta (Egypt), in particular for a buried river branch close to Buto. The processing was completed in the Google Earth Engine (GEE) for the entire Nile Delta and for selected seasons of the year (summer/winter) using Landsat data from 1985 to 2019. We derived the STMs of the tasseled cap transformation (TC), the Normalized Difference Wetness Index (NDWI), and the Normalized Difference Vegetation Index (NDVI). These features were compared to historical topographic maps of the Survey of Egypt, CORONA imagery, the digital elevation model of the TanDEM-X mission, and modern high-resolution satellite imagery. The results suggest that the extent of channels is best revealed when differencing the median NDWI between summer (July/August) and winter (January/February) seasons (ΔNDWI). The observed difference is likely due to lower soil/plant moisture during summer, which is potentially caused by coarser-grained deposits and the morphology of the former levee. Similar anomalies were found in the immediate surroundings of several Pleistocene sand hills ("geziras") and settlement mounds ("tells") of the eastern delta, which allowed some mapping of the potential near-surface continuation. Such anomalies were not observed for the surroundings of tells of the western Nile Delta. Additional linear and meandering ΔNDWI anomalies were found in the eastern Nile Delta in the immediate surroundings of the ancient site of Bubastis (Tell Basta), as well as several kilometers north of Zagazig. These anomalies might indicate former courses of Nile river branches. However, the ΔNDWI does not provide an unambiguous delineation.},
  language  = {en}
}