Mr. Tobias Brehm Profile

Tobias Brehm

at German Federal Institute of Hydrology

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Publications (2)

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Proceedings Article | 20 November 2024 Paper

Detecting algal scum in an inland river using Planet and Sentinel-2 multispectral imagery

Marco Herrmann, Tobias Brehm, Julia Kleinteich, Franziska Klotz, Björn Baschek

Proceedings Volume 13191, 131910K (2024) https://doi.org/10.1117/12.3030011

KEYWORDS: Spatial resolution, Satellites

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Harmful algal blooms are an increasing problem for human health and ecosystems, especially in freshwater and marine coastal regions. Since 2017, cyanobacteria blooms mainly caused by Microcystis aeruginosa frequently occur in the small European inland river Moselle in late summer and autumn. Despite them being an important indicator for human safety, the temporal and spatial dynamics of these blooms are largely unknown. In order to gain a better understanding of this issue, we developed combined index-based models on Sentinel-2 data with 10 metre spatial resolution (R, G, B, NIR) and corresponding Planet SuperDove data with 3 metre spatial resolution to differentiate algal scum from water and riparian vegetation. Cloud-free almost simultaneous data of Planet SuperDove and Sentinel-2 was retrieved for portions of Moselle River in August 2022. Presence of algal scum in those areas was confirmed by field campaigns in that period. Retrieved satellite scenes were processed using ACOLITE software with the same settings to facilitate intercomparability. Then, areas visually detected in the imagery as “scum”, “ambiguous scum”, “water” or “vegetation” were digitalized and the spectral information for each class was retrieved. Based on this information, decision-tree based models were developed to differentiate algal scum and validated by analysing spatial overlap with manually digitalized areas from independent satellite scenes. Overall accuracy was substantially higher for the Planet SuperDove model (0.835 versus 0.523), yet accuracy of class “scum” was satisfying for both models (0.972 versus 0.895), thus showing the potential of 10-metre spatial resolution Sentinel-2 data in delineating algal scum.

Proceedings Article | 13 November 2024 Presentation + Paper

Applying deep learning methods for the bridge-based monitoring of floating macroplastics on rivers

Marcel Reinhardt, Björn Baschek, Tobias Brehm, Thomas Ternes

Proceedings Volume 13197, 1319706 (2024) https://doi.org/10.1117/12.3030927

KEYWORDS: Cameras, Environmental monitoring, RGB color model, Plastics, Object detection, Data modeling, Bridges, Spatial resolution, Remote sensing

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The increasing plastic pollution in water bodies poses a serious threat to the environment. Rivers are a major pathway in the transport of macroplastic litter from source areas (e.g. urban areas) into the environment (e.g. beaches, shores, lakes and oceans). For this reason, quantifying and monitoring macroplastic loads in rivers is a key step in assessing pollution levels and developing effective preventive measures. In-situ monitoring is time-consuming and tedious, and it only covers a limited timeframe. The potential of Deep Learning (DL) methods to automatically detect objects in water bodies has recently been demonstrated. In this study we propose a framework to automatically monitor macroplastic loads in rivers using DL. The approach was evaluated on the River Rhine using various items (e.g. plastic bottles, caps, bags, polystyrene, tree branches with and without leaves) collected at the test site. An RGB camera was installed on Niederwerth Bridge (Koblenz, Germany) which captured images of the river at 1-second intervals. One boat was used to introduce the objects upstream and another boat to collect them again downstream. Our dataset consists of about 800 images with objects manually labelled in three groups: plastic bottles, plastic litter and vegetation. We employed the well-tested YOLOv5 network, pre-trained on the MS COCO dataset. Despite the limited amount of training data, the validation showed promising results with a mean average precision (mAP@0.5) of about 94%. The model can be improved by including more diverse training data from different rivers, in different environmental conditions (e.g. illumination, water turbidity, waves) and using a wider variety of objects.

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