Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

Terefenko, Paweł; Paprotny, Dominik; Giza, Andrzej; Morales-Nápoles, Oswaldo; Kubicki, Adam; Walczakiewicz, Szymon

doi:10.3390/rs11070843

Cited by 33 publications

(21 citation statements)

References 45 publications

(73 reference statements)

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“…Many Bayesian network based studies for coastal applications have been published but nearly all of them use discrete Bayesian networks [47][48][49][50][51][52][53][54] except the very few recent ones which use NPBNs [86][87][88][89]. In discrete BNs, variables have to be discretized into bins where the number and sizes of the bins need to be correctly fine-tuned to ensure that each bin represents homogeneous information and has nearly similar impact on the child node.…”

Section: Methodological Valuementioning

confidence: 99%

Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks

Niazi

Morales-Nápoles

Wesenbeeck

2021

Water

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The increasing risk of flooding requires obtaining generalized knowledge for the implementation of distinct and innovative intervention strategies, such as nature-based solutions. Inclusion of ecosystems in flood risk management has proven to be an adaptive strategy that achieves multiple benefits. However, obtaining generalizable quantitative information to increase the reliability of such interventions through experiments or numerical models can be expensive, laborious, or computationally demanding. This paper presents a probabilistic model that represents interconnected elements of vegetated hydrodynamic systems using a nonparametric Bayesian network (NPBN) for seagrasses, salt marshes, and mangroves. NPBNs allow for a system-level probabilistic description of vegetated hydrodynamic systems, generate physically realistic varied boundary conditions for physical or numerical modeling, provide missing information in data-scarce environments, and reduce the amount of numerical simulations required to obtain generalized results—all of which are critically useful to pave the way for successful implementation of nature-based solutions.

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Section: Methodological Valuementioning

confidence: 99%

Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks

Niazi

Morales-Nápoles

Wesenbeeck

2021

Water

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“…The implemented laser-based survey technique allowed for rapid and accurate collection of large amounts of topographic data. During the last decade, TLS has been successfully applied to topographic surveys and to the monitoring of coastal processes [3] , [4] , [5] . Highly accurate measurements of coastal changes were performed on three sections of south Baltic coast, each 500 m long.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Cliff erosion monitoring dataset on selected sites of Polish coast

Terefenko

2020

Data in Brief

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Coastal storms are highly unpredictable phenomena, frequently changing their characteristics and directly linked to global climate changes. They result in an intensive erosion processes and, are now a serious concern for the communities inhabiting the littoral zones. However, owing to the technical difficulties in registering morphological changes on cliff coasts, most short-term monitoring systems, analyses, and models have been implemented primarily along the dune coasts. Notwithstanding these difficulties, the changes on cliff coasts have been investigated quantitatively in order to properly identify the mechanisms controlling those phenomena. Here, we report on three soft-cliff systems in the southern Baltic Sea that were monitored with the use of terrestrial laser scanner (TLS) technology. A time series of thirteen topographic surveys were generated over a period of two years (12.2016–04.2018) and presented as coastal profiles with 50 meter spacing.

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“…The process of identifying the cliff base line was more challenging than that used to distinguish the shoreline. Numerous studies on cliffs assume manual delineation of the cliff baseline by relying mainly on aerial photographs, topographic maps, and in situ surveys [30][31][32][33]. Some examples of automatic delineation were reported by Palaseanu-Lovejoy and others [34], who used generalized coastal shoreline vectors, and by Terefenko and others [33], who considered a simplified methodology of rapid changes in altitude.…”

Section: Methodsmentioning

confidence: 99%

Multi-Temporal Cliff Erosion Analysis Using Airborne Laser Scanning Surveys

2019

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Rock cliffs are a significant component of world coastal zones. However, rocky coasts and factors contributing to their erosion have not received as much attention as soft cliffs. In this study, two rocky-cliff systems in the southern Baltic Sea were analyzed with Airborne Laser Scanners (ALS) to track changes in cliff morphology. The present contribution aimed to study the volumetric changes in cliff profiles, spatial distribution of erosion, and rate of cliff retreat corresponding to the cliff exposure and rock resistance of the Jasmund National Park chalk cliffs in Rugen, Germany. The study combined multi-temporal Light Detection and Ranging (LiDAR) data analyses, rock sampling, laboratory analyses of chemical and mechanical resistance, and along-shore wave power flux estimation. The spatial distribution of the active erosion areas appear to follow the cliff exposure variations; however, that trend is weaker for the sections of the coastline in which structural changes occurred. The rate of retreat for each cliff–beach profile, including the cliff crest, vertical cliff base, and cliff base with talus material, indicates that wave action is the dominant erosive force in areas in which the cliff was eroded quickly at equal rates along the cliff profile. However, the erosion proceeded with different rates in favor of cliff toe erosion. The effects of chemical and mechanical rock resistance are shown to be less prominent than the wave action owing to very small differences in the measured values, which proves the homogeneous structure of the cliff. The rock resistance did not follow the trends of cliff erosion revealed by volume changes during the period of analysis.

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Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

Cited by 33 publications

References 45 publications

Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks

Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks

Cliff erosion monitoring dataset on selected sites of Polish coast

Multi-Temporal Cliff Erosion Analysis Using Airborne Laser Scanning Surveys

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