Forschungszentrum Landschaftsentwicklung und Bergbaulandschaften (FZLB)
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Severe disturbances of landscapes entail an ecosystem development with the formation of structures and functions which may reach either a new equilibrium state or a state similar to the original ecosystem. Natural disturbances can result from major events such as volcanoes, glaciers, or denudations from landslides. Major disturbance may also evolve from anthropogenic influences such as from mining operations. They all can be considered starting points for the development of ecosystems from ‘point zero’, which was one central research interest of Dieter Mueller-Dombois. In this paper results from research in the Lusatian post-mining landscapes (Eastern Germany) are presented. Different methodological approaches are discussed. The well-defined ‘point zero’ of the ecosystem development allows for research on chronosequence designs as well as real time series studies. Chronosequences have been investigated to gain insight into the medium to long-term direction of the development. Real time series are recorded to obtain a more detailed understanding. The paper is structured into three main parts: First, effects of ecosystem disturbances by mining in Lusatia and the initial conditions for restoration are presented. In the following part practical rehabilitation measures and land use options are discussed. Finally, the third part summarizes results of long-term monitoring in an artificial watershed. In conclusion, post-mining landscapes allow for relevant case studies of ecosystem development after severe disturbances. Particularly, the starting point of the initial phase is very well defined which distinguishes these anthropogenically disturbed landscapes from landscapes after natural disturbances.
The German government's decision to phase out lignite mining by 2038 or earlier, as recently 2030 has been agreed for the Rhineland, will trigger a number of transition processes in Germany's remaining lignite mining areas. The two largest lignite mining areas are located in geographically different regions: Rhineland in the west and Lusatia in the east. As the socio-economic and environmental conditions in these two mining areas are set to change dramatically, the German government has adopted extensive economic support measures. However, the environmental changes will also lead to changes in the ecosystem functions and services provided by the future post-mining landscapes.
Gerwin et al. (2023) compare the two main lignite producing regions of Germany in terms of their natural and cultural environments. The economic situation and its history are reflected and differences are outlined. Part of the differences in cultural development can be explained by the natural conditions, especially the edaphic factors and the climatic situation. Because of the specific geological settings, tailored mining technologies were developed and used in the two regions, with different effects on the resulting post-mining landscapes.
We conclude that the landscapes of Lusatia and the Rhineland have been radically restructured by the long and varied history of lignite mining. Both regions will change significantly as the mining industry continues to decline and is expected to cease altogether within the next decade. These changes in the post-mining landscapes and the ecosystem services will provide both challenges and opportunities. The preconditions for positive socio-economic development and for sustainable land use concepts that also consider ecological aspects are different for the two regions. The exchange of knowledge and experience between the two mining regions is crucial to the success of this major transformation process, despite, or perhaps because of, these differences.
Gerwin, W., Raab, T., Birkhofer, K., Hinz, C., Letmathe, P., Leuchner, M., Roß-Nickoll, M., Rüde, T., Trachte, K., Wätzold, F., Lehmkuhl, F. (2023): Perspectives of lignite post-mining landscapes under changing environmental conditions: what can we learn from a comparison between the Rhenish and Lusatian region in Germany? Environmental Sciences Europe 35:36. https://doi.org/10.1186/s12302-023-00738-z
Editorial for the Special Issue “Anthropogenic Geomorphology – Geomorphology in the Anthropocene”
(2023)
Background The decision of the German federal government to cease lignite mining until 2038 or—if possible— already earlier until 2030, will cause manifold transition processes in the remaining lignite mining districts of Germany. The two largest districts are located in geographically opposite regions: The Rhineland in the western part and Lusatia in the east of Germany. As particularly these two mining districts will experience severe changes in their socioeconomic as well environmental conditions, the federal government has adopted comprehensive economic support measures. However, the environmental changes will also cause altered ecosystem functions and services to be provided by the future post-mining landscapes.
Results In this paper, the two main lignite-producing regions of Germany are compared with regard to their natural and cultural settings. The economic situation and its history are reflected and differences are outlined. Part of the disparities in the cultural development can be explained by very different natural conditions, especially edaphic factors and climatic situation. Because of dissimilar geological settings, different mining technologies were developed and are in use in the two regions with distinct effects on the resulting post-mining landscapes.
Conclusion The long-standing and manifold lignite mining activities have radically restructured the landscapes in Lusatia and the Rhineland. With the ongoing decline of the mining industry and its complete cessation, presumably within the next decade, both regions will alter their faces significantly. These changes offer both challenges but also opportunities with respect to the post-mining landscapes and their ecosystem services they are going to provide. The prerequisites for a positive socioeconomic development and for sustainable land-use concepts that also consider ecological aspects are different for both regions. However, or especially because of these differences, the knowledge exchange and experience transfer between both mining regions are pivotal for the success of this extensive transformation process.
Tree and shrub species grown on post-mining sites as source of biomass for bio-based industry
(2021)
Reclamation sites in East-Germany are characterized by very sandy substrates being almost free of organic matter, with extremely low nutrient contents and an annual precipitation of only 570 mm and lower. To enhance the value of such marginal lands for a future regionally based bioeconomy the EU funded BeonNAT project plans the plantation of regional tree and shrub species. The obtained forest biomass will be used for the production of biodegradable bio-based products and bioactive compounds that will play an important role to replace fossil-based competing substitute products. Field trials in different European countries will be established to test the benefit of intercropping/mixed-forest in marginal cultural land versus the natural growing/monoculture. In Germany, one test site is located in the post-mining sites of Lusatia. In this field trial the biomass development, the impact on soil fertility and biodiversity will be investigated after planting Robinia pseudoacacia and Rubus fructicosus.
Open-cast coal-mining creates a severe disturbance of ecological systems, resulting in post-mining areas that have lost their soils entirely and are exposed to extreme conditions. Nonetheless, these areas allow us to investigate the process of primary succession. We studied the first 12 years of soil tardigrade succession at ‘Chicken Creek’, an experimental site within the open-cast coal-mining area in Welzow Süd, Germany. A total of ten tardigrade genera and 13 species or species groups were identified at the site. Over time, the organic carbon content, proportion of silt and nutrient availability in the soil increased, and the soil particle size distribution and pH value changed significantly. The vegetation cover and nematode abundance increased, whereas tardigrade densities peaked in 2008 and decreased thereafter, as did their mean body size. Species replacement was obvious within this 12 year study period, and tardigrade species diversity increased, indicating that ecological niche diversity also increased throughout this time. Vegetation cover, soil particle size distribution and time were the most important factors influencing the tardigrade community. We also discuss further potential factors and the ability of terrestrial tardigrades to persist despite many environmental constraints, such as fluctuations in pH and moisture, and to colonize new habitats faster than most other animal groups.
In recent years, probabilistic solution to the inversion of electromagnetic induction (EMI) data has been progressively developed for non-invasive subsurface characterization. However, Bayesian inversion of EMI data using forward solvers based on full solution ofMaxwell's equation is associated with computationally expensive modelings, particularly for large-scale surveys. Here, we incorporated artificial neural network (ANN) with Bayesian inference to obtain subsurface electromagnetic conductivity image (EMCI) fromEMI data down to 10mdepth. In this respect, a complex EMI forward model was replaced by a trained neural network (ANN proxy forward function) that can be evaluated comparably rapidly. The accuracy of the ANN-based forward solver was examined using different synthetic subsurface models. The proposed methodology was applied on EMI data measured with a DUALEM-421 s sensor from 10 ha study site in the Alken Enge area of Denmark. We compared the inversely estimated EMCI with the counterpart obtained from a quasi-three-dimensional (quasi-3D) spatiallyconstrained deterministic algorithm as a standard code. The network training procedure was performed within fewminutes, and once itwas trained, the ANN-based forward solver returned roughly 150,000 model responses per second. This value for the EMI forward solverwas around 400, demonstrating the computational efficiency of the ANN proxy forward function. The theoretical simulations demonstrated that the ANN-based forward solver accurately mimics the EMI response within the training range. Moreover, the proposed inversion strategy successfully delineated the subsurface EMCI from Alken Enge area. This approach thus facilitates rapid and accurate subsurface conductivity imaging using Bayesian inversion of multi-configuration EMI data, which is particularly pertinent for large-scale measurements.
Conventional geophysical inversion techniques suffer from several limitations including computational cost, nonlinearity, non-uniqueness and dimensionality of the inverse problem. Successful inversion of geophysical data has been a major challenge for decades. Here, a novel approach based on deep learning (DL) inversion via convolutional neural network (CNN) is proposed to instantaneously estimate subsurface electrical conductivity (σ) layering from electromagnetic induction (EMI) data. In this respect, a fully convolutional network was trained on a large synthetic data set generated based on 1-D EMI forward model. The accuracy of the proposed approach was examined using several synthetic scenarios. Moreover, the trained network was used to find subsurface electromagnetic conductivity images (EMCIs) from EMI data measured along two transects from Chicken Creek catchment (Brandenburg, Germany). Dipole–dipole electrical resistivity tomography data were measured as well to obtain reference subsurface σ distributions down to a 6 m depth. The inversely estimated models were juxtaposed and compared with their counterparts obtained from a spatially constrained deterministic algorithm as a standard code. Theoretical simulations demonstrated a well performance of the algorithm even in the presence of noise in data. Moreover, application of the DL inversion for subsurface imaging from Chicken Creek catchment manifested the accuracy and robustness of the proposed approach for EMI inversion. This approach returns subsurface σ distribution directly from EMI data in a single step without any iterations. The proposed strategy simplifies considerably EMI inversion and allows for rapid and accurate estimation of subsurface EMCI from multiconfiguration EMI data.