CN
C.F. Nobbe
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
The Wadden in Photos
A morphological analysis from action camera pictures
The Dutch Wadden Sea is a large intertidal area where complex morphodynamics govern the transport of water and sediment around the channels and mudflats. The behaviour of sediment becomes increasingly unpredictable due to anthropogenic influences and climate change. Research and monitoring on sediment and the dynamic mudflat are crucial in understanding and projecting the effects of these influences on the Wadden Sea.
This thesis focuses on a GoPro action camera installed on the mudflat near Holwerd. Over a three-month period, the camera took a picture every 15 minutes. The images show the changing mudflat, slowly transforming from a rolling landscape into a defined terrain. Analysis of the changing mudflat supports the research into morphology and sediment behaviour.
A Multilayer Perceptron (MLP) was constructed to perform semantic segmentation, where every pixel of the GoPro images is classified as either mud or water. A comprehensive model was set up, where manually labelled data and multiple image features were used to train the model and process the large image dataset. Model evaluation returned a macro-IoU of 0.71 and a macro-F1-score of 0.82. However, a detailed look at the classification results indicated critical model shortcomings and unnatural proportions of water and mud. Filtering of incorrect predictions resulted in a small dataset appropriate for further analysis.
The results indicate a strong correlation between predicted mud percentage and potential evaporation, revealing that this relationship can be observed from pictures. Spaghetti plots illustrated no patterns of change during single low-water periods. Finally, analysis of weekly average predictions reveals regions of growth and decrease on the mudflat. The channels and shallow pools are observed in particular, since these areas show divergent behaviour. It is theorised that the flow velocity influences the erosive and settling capacities of sediment. The shape of channels and shallow pools influences this velocity and, by extension, the morphological development of the mudflat.
Overall, the thesis demonstrates how an action camera on a mudflat can be used for observing both morphological changes and the forces that define this change. While the MLP does not deliver optimal results, it lays the foundation for future work on more advanced machine learning techniques. Finally, practical recommendations for future camera monitoring projects are given. ...
This thesis focuses on a GoPro action camera installed on the mudflat near Holwerd. Over a three-month period, the camera took a picture every 15 minutes. The images show the changing mudflat, slowly transforming from a rolling landscape into a defined terrain. Analysis of the changing mudflat supports the research into morphology and sediment behaviour.
A Multilayer Perceptron (MLP) was constructed to perform semantic segmentation, where every pixel of the GoPro images is classified as either mud or water. A comprehensive model was set up, where manually labelled data and multiple image features were used to train the model and process the large image dataset. Model evaluation returned a macro-IoU of 0.71 and a macro-F1-score of 0.82. However, a detailed look at the classification results indicated critical model shortcomings and unnatural proportions of water and mud. Filtering of incorrect predictions resulted in a small dataset appropriate for further analysis.
The results indicate a strong correlation between predicted mud percentage and potential evaporation, revealing that this relationship can be observed from pictures. Spaghetti plots illustrated no patterns of change during single low-water periods. Finally, analysis of weekly average predictions reveals regions of growth and decrease on the mudflat. The channels and shallow pools are observed in particular, since these areas show divergent behaviour. It is theorised that the flow velocity influences the erosive and settling capacities of sediment. The shape of channels and shallow pools influences this velocity and, by extension, the morphological development of the mudflat.
Overall, the thesis demonstrates how an action camera on a mudflat can be used for observing both morphological changes and the forces that define this change. While the MLP does not deliver optimal results, it lays the foundation for future work on more advanced machine learning techniques. Finally, practical recommendations for future camera monitoring projects are given. ...
The Dutch Wadden Sea is a large intertidal area where complex morphodynamics govern the transport of water and sediment around the channels and mudflats. The behaviour of sediment becomes increasingly unpredictable due to anthropogenic influences and climate change. Research and monitoring on sediment and the dynamic mudflat are crucial in understanding and projecting the effects of these influences on the Wadden Sea.
This thesis focuses on a GoPro action camera installed on the mudflat near Holwerd. Over a three-month period, the camera took a picture every 15 minutes. The images show the changing mudflat, slowly transforming from a rolling landscape into a defined terrain. Analysis of the changing mudflat supports the research into morphology and sediment behaviour.
A Multilayer Perceptron (MLP) was constructed to perform semantic segmentation, where every pixel of the GoPro images is classified as either mud or water. A comprehensive model was set up, where manually labelled data and multiple image features were used to train the model and process the large image dataset. Model evaluation returned a macro-IoU of 0.71 and a macro-F1-score of 0.82. However, a detailed look at the classification results indicated critical model shortcomings and unnatural proportions of water and mud. Filtering of incorrect predictions resulted in a small dataset appropriate for further analysis.
The results indicate a strong correlation between predicted mud percentage and potential evaporation, revealing that this relationship can be observed from pictures. Spaghetti plots illustrated no patterns of change during single low-water periods. Finally, analysis of weekly average predictions reveals regions of growth and decrease on the mudflat. The channels and shallow pools are observed in particular, since these areas show divergent behaviour. It is theorised that the flow velocity influences the erosive and settling capacities of sediment. The shape of channels and shallow pools influences this velocity and, by extension, the morphological development of the mudflat.
Overall, the thesis demonstrates how an action camera on a mudflat can be used for observing both morphological changes and the forces that define this change. While the MLP does not deliver optimal results, it lays the foundation for future work on more advanced machine learning techniques. Finally, practical recommendations for future camera monitoring projects are given.
This thesis focuses on a GoPro action camera installed on the mudflat near Holwerd. Over a three-month period, the camera took a picture every 15 minutes. The images show the changing mudflat, slowly transforming from a rolling landscape into a defined terrain. Analysis of the changing mudflat supports the research into morphology and sediment behaviour.
A Multilayer Perceptron (MLP) was constructed to perform semantic segmentation, where every pixel of the GoPro images is classified as either mud or water. A comprehensive model was set up, where manually labelled data and multiple image features were used to train the model and process the large image dataset. Model evaluation returned a macro-IoU of 0.71 and a macro-F1-score of 0.82. However, a detailed look at the classification results indicated critical model shortcomings and unnatural proportions of water and mud. Filtering of incorrect predictions resulted in a small dataset appropriate for further analysis.
The results indicate a strong correlation between predicted mud percentage and potential evaporation, revealing that this relationship can be observed from pictures. Spaghetti plots illustrated no patterns of change during single low-water periods. Finally, analysis of weekly average predictions reveals regions of growth and decrease on the mudflat. The channels and shallow pools are observed in particular, since these areas show divergent behaviour. It is theorised that the flow velocity influences the erosive and settling capacities of sediment. The shape of channels and shallow pools influences this velocity and, by extension, the morphological development of the mudflat.
Overall, the thesis demonstrates how an action camera on a mudflat can be used for observing both morphological changes and the forces that define this change. While the MLP does not deliver optimal results, it lays the foundation for future work on more advanced machine learning techniques. Finally, practical recommendations for future camera monitoring projects are given.