Brownfield soundscape restoration associated with the Miyawaki succession method
Bryce Lawrence, Research Group Landscape Ecology and Landscape Planning Department of Spatial Planning, TU Dortmund University and Vanessa Agnew, Faculty of Cultural Studies, TU Dortmund University.
The impact of green infrastructure on the acoustic environment is an emerging research area aimed at understanding how presence or absence of natural biotopes affect sound related ecosystem services of noise abatement, connection to nature, positive psychoacoustic perception, fidelity, or bird sound presence (Lawrence 2025). This longitudinal research project combines brownfield restoration with acoustic environment monitoring to understand how intensive biotope restoration following the Miyawaki plant succession method (Miyawaki and Golley 1993) affects these services over time. The project is carried out over a multi-year period to compare changes in the acoustic environment that can be associated with native biotope establishment. The case study on the north edge of the TU Dortmund University campus in Dortmund, Germany, is a brownfield area underlain with unconsolidated rubble, situated adjacent to the B1 motorway with an L-den value of 60-74 dB(A) (noise polluted). Acoustic data is analyzed using ecoacaoutic indices (Sueur 2018) to determine the proportion of biophonic and anthrophonic sources, the decibel value dB(A), and bird species diversity using the neural network model BirdNET (Kahl et al. 2021; Wood and Kahl 2024). The multi-year project develops an acoustic baseline at the initial planting stage in fall of 2025, with follow-up data summaries after the summer 2026 and 2027 seasons to assess changes. This project adds the temporal dimension of brownfield restoration to the green infrastructure and acoustic environment dialogue.
References
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Lawrence, B. T., Heying, D., & Gruehn, D. (2025). The Influence of Green Infrastructure on the Acoustic Environment: A Conceptual and Methodological Basis for Quiet Area Assessment in Urban Regions. Conservation, 5(2), 22.
Sueur, J. (2018). Sound analysis and synthesis with R (p. 637). Cham: Springer.
Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021). BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61, 101236.
Wood, C. M., & Kahl, S. (2024). Guidelines for appropriate use of BirdNET scores and other detector outputs. Journal of Ornithology, 165(3), 777-782.