Human-Induced Hydrological Connectivity: Impacts of Footpaths on Beach Wrack Transport in a Frequently Visited Baltic Coastal Wetland

Karstens, Svenja; Kiesel, Joshua; Petersen, Lennart; Etter, Kilian; Deimling, Jens Schneider von; Vafeidis, Athanasios T.; Groß, Felix

doi:10.3389/fmars.2022.929274

Cited by 5 publications

(9 citation statements)

References 63 publications

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“…To the best of the authors' knowledge, only two studies [15,20] were carried out in the context of UAV monitoring of BW. Both of them performed object-based image analysis (OBIA) and achieved relatively high accuracy (producer accuracy > 80%) in classification.…”

Section: Assessment Of U-net Model Performance In Bw Segmentationmentioning

confidence: 99%

“…A subsequent study by the same authors employed a camera trap for the continuous monitoring of detached macrophytes deposited along shorelines, offering an efficient and pragmatic method for tracking ecological dynamics [19]. Concurrently, Karstens et al [20] utilized supervised machine learning methods to map and segment images acquired with UAVs to predict the locations of BW accumulation. Despite these advancements, the studies mentioned limitations, particularly in the number of images utilized for both segmentation and validation, and an imbalanced sample size of classes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices

Tiškus,

Bučas,

Gintauskas

et al. 2023

Drones

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This study delves into the application of the U-Net convolutional neural network (CNN) model for beach wrack (BW) segmentation and monitoring in coastal environments using multispectral imagery. Through the utilization of different input configurations, namely, “RGB”, “RGB and height”, “5 bands”, “5 bands and height”, and “Band ratio indices”, this research provides insights into the optimal dataset combination for the U-Net model. The results indicate promising performance with the “RGB” combination, achieving a moderate Intersection over Union (IoU) of 0.42 for BW and an overall accuracy of IoU = 0.59. However, challenges arise in the segmentation of potential BW, primarily attributed to the dynamics of light in aquatic environments. Factors such as sun glint, wave patterns, and turbidity also influenced model accuracy. Contrary to the hypothesis, integrating all spectral bands did not enhance the model’s efficacy, and adding height data acquired from UAVs decreased model precision in both RGB and multispectral scenarios. This study reaffirms the potential of U-Net CNNs for BW detection, emphasizing the suitability of the suggested method for deployment in diverse beach geomorphology, requiring no high-end computing resources, and thereby facilitating more accessible applications in coastal monitoring and management.

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Section: Assessment Of U-net Model Performance In Bw Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices

Tiškus,

Bučas,

Gintauskas

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…To test and implement a workflow for obtaining a virtual environment of a coastscape, we selected the case study site presented by Karstens et al (2022). The study site, Stein beach, which is situated in northern Germany in the outer part of the Kiel Fjord (Baltic Sea), accommodates a diverse range of vegetation (Figure 1).…”

Section: Case Study Sitementioning

confidence: 99%

“…DEMs were generated for our study area during UAS surveys with a RGB camera in 2021/2022 (see Karstens et al, 2022). RGB imagery on the sub-decimeter scale was conducted with a DJI ZenmuseX5S RGB camera mounted on a DJI Inspire II UAS at a flight height of 70 m, resulting in a lateral resolution of 2 cm.…”

Section: Prerequisites For Virtual Environment Creationmentioning

confidence: 99%

From coastal geomorphometry to virtual environments

Gross,

Petersen,

Wallmeier

et al. 2023

Front. Mar. Sci.

Self Cite

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Communicating environmental change and mitigation scenarios to stakeholders and decision-makers can be challenging. Immersive environments offer an innovative approach for knowledge transfer, allowing science-based scenarios to be discussed interactively. The use of such environments is particularly helpful for the analysis of large, multi-component geospatial datasets, as commonly employed in the classification of ecosystems. Virtual environments can play an important role in conveying and discussing the findings gathered from these geomorphometric datasets. However, textured meshes and point clouds are not always well suited for direct import to a virtual reality or the creation of a truly immersive environment, and often result in geometrical artifacts, which can be misinterpreted during the import to a game engine. Such technical hurdles may lead to viewers rejecting the experience altogether, failing to achieve a higher educational purpose. In this study, we apply an asset-based approach to create an immersive virtual representation of a coastal environment. The focus hereby is on the coastal vegetation and changes in species distribution, which could potentially be triggered by the impact of climate change. We present an easy-to-use blueprint for the game engine EPIC Unreal Engine 5. In contrast to traditional virtual reality environments, which use static textured mesh data derived from photogrammetry, this asset-based approach enables the use of dynamic and physical properties (e.g. vegetation moving due to wind or waves), which makes the virtual environment more immersive. This will help to stimulate understanding and discussion amongst different stakeholders, and will also help to foster inclusion in earth- and environmental science education.

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“…For best results, the size in cm² that is covered by a pixel and the total height covered by the DTM should be known. For the segmentation, a spatial range of 50 and radial range of 7 was chosen and support vector machine (SVM) as one option of machine learning methods (see Karstens et al 2022). Classes for segmentation included inter alia "Phragmites australis", "Other vegetation", "Sand", "Water" (Fig.…”

Section: Prerequisites For Virtual Environment Creationmentioning

confidence: 99%

Coastscapes for a Metaverse: From height maps to virtual vegetated environments

Groß

Petersen

Wallmeier

et al. 2022

Preprint

Self Cite

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Communicating environmental changes and scenarios to stakeholders and decision-makers can be challenging. Immersive environments offer a novel tool to transfer knowledge and allow the interactive discussion of scenarios. With the increase of space- and airborne remote sensing and coherent classification of ecosystems, many large-scale geospatial datasets are produced. Virtual environments can play an important role in conveying and discussing the findings gathered from these datasets. However, textured meshes and point clouds directly imported to a virtual reality are not always suited to create a truly immersive environment and often poste geometrical artifacts, which are miss-interpreted during the import to a game engine. In our study, we use an asset-based approach to create an immersive virtual representation of a coastscape. The focus hereby is on the coastal vegetation and changes in species distribution, potentially triggered by climate change impacts. We present an easy-to-use blueprint for the game engine EPIC Unreal Engine 5. In contrast to traditional virtual reality environments using static textured mesh data derived from photogrammetry, this asset-based approach enables the use of dynamics and physical properties (e.g. vegetation moving due to wind or waves) which makes the virtual environment more immersive.

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Human-Induced Hydrological Connectivity: Impacts of Footpaths on Beach Wrack Transport in a Frequently Visited Baltic Coastal Wetland

Cited by 5 publications

References 63 publications

U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices

U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices

From coastal geomorphometry to virtual environments

Coastscapes for a Metaverse: From height maps to virtual vegetated environments

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