@article{SchaeferFaethKneiseletal.2023,
  author    = {Sch{\"a}fer, Christian and F{\"a}th, Julian and Kneisel, Christof and Baumhauer, Roland and Ullmann, Tobias},
  title     = {Multidimensional hydrological modeling of a forested catchment in a German low mountain range using a modular runoff and water balance model},
  series = {Frontiers in Forests and Global Change},
  volume    = {6},
  journal   = {Frontiers in Forests and Global Change},
  doi       = {10.3389/ffgc.2023.1186304},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357358},
  year      = {2023},
  abstract  = {Sufficient plant-available water is one of the most important requirements for vital, stable, and well-growing forest stands. In the face of climate change, there are various approaches to derive recommendations considering tree species selection based on plant-available water provided by measurements or simulations. Owing to the small-parcel management of Central European forests as well as small-spatial variation of soil and stand properties, in situ data collection for individual forest stands of large areas is not feasible, considering time and cost effort. This problem can be addressed using physically based modeling, aiming to numerically simulate the water balance. In this study, we parameterized, calibrated, and verified the hydrological multidimensional WaSiM-ETH model to assess the water balance at a spatial resolution of 30 m in a German forested catchment area (136.4 km2) for the period 2000-2021 using selected in situ data, remote sensing products, and total runoff. Based on the model output, drought-sensitive parameters, such as the difference between potential and effective stand transpiration (Tdiff) and the water balance, were deduced from the model, analyzed, and evaluated. Results show that the modeled evapotranspiration (ET) correlated significantly (R2 = 0.80) with the estimated ET using MODIS data (MOD16A2GFv006). Compared with observed daily, monthly, and annual runoff data, the model shows a good performance (R2: 0.70|0.77|0.73; Kling-Gupta efficiency: 0.59|0.62|0.83; volumetric efficiency: 0.52|0.60|0.83). The comparison with in situ data from a forest monitoring plot, established at the end of 2020, indicated good agreement between observed and simulated interception and soil water content. According to our results, WaSiM-ETH is a potential supplement for forest management, owing to its multidimensionality and the ability to model soil water balance for large areas at comparable high spatial resolution. The outputs offer, compared to non-distributed models (like LWF-Brook90), spatial differentiability, which is important for small-scale parceled forests, regarding stand structure and soil properties. Due to the spatial component offered, additional verification possibilities are feasible allowing a reliable and profound verification of the model and its parameterization.},
  language  = {en}
}
@article{KanmegneTamgaLatifiUllmannetal.2023,
  author    = {Kanmegne Tamga, Dan and Latifi, Hooman and Ullmann, Tobias and Baumhauer, Roland and Thiel, Michael and Bayala, Jules},
  title     = {Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems},
  series = {Agroforestry Systems},
  volume    = {97},
  journal   = {Agroforestry Systems},
  number    = {1},
  issn      = {0167-4366},
  doi       = {10.1007/s10457-022-00791-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324139},
  pages     = {109-119},
  year      = {2023},
  abstract  = {Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in C{\^o}te d'Ivoire, close to the Ta{\"i} National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user's accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in C{\^o}te d'Ivoire.},
  language  = {en}
}