Advanced Lake Shoreline Extraction Approach by Integration of SAR Image and LIDAR Data

Demir, Nusret; Bayram, Bülent; Şeker, Dursun Zafer; Oy, S.; Ince, Abdulkadir; Bozkurt, Serdar

doi:10.1080/01490419.2019.1581861

Cited by 15 publications

(13 citation statements)

References 36 publications

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“…Three separate experiments were carried out using 20 mL of each oil to investigate oil thickness. Then, experiments were carried out for each oil at increasing volumes (1,5,10,15,20,25,30,35,40, and 50 mL) in order to investigate oil diffusion. The experiment was conducted in an environment where normal exposure to sunlight and exposure to an electric lamp were set up to mimic natural conditions.…”

Section: Methodsmentioning

confidence: 99%

“…The oil was detected by changing the spilled volume (15,25,35,40,45, and 50 mL) and type (light crude oil, bunker A oil, and bunker C oil), and the thicknesses were subsequently measured ( Figures 10-12). We expected that as the spilled volume increased, the detection range would increase regu-larly.…”

Section: Spilled Oil Volume Detection Experimentmentioning

confidence: 99%

“…The LiDAR is able to operate normally in cloud cover, fog, haze, or rain, during the day and night [21][22][23][24][25][26], and has been miniaturized sufficiently to allow it to be installed in a small-size drone or boat [27,28]. The detected data is also intuitive upon viewing such that that expert interpretation is not required [29][30][31][32][33][34][35][36]. The LiDAR sensors have a much higher measurement distance and special resolution in comparison to microwave equipment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Geometrical Properties of Spilled Oil on Seawater Detected Using a LiDAR Sensor

Moon¹,

Jung²

2020

Journal of Sensors

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We report on a small-size light detection and ranging (LiDAR) sensor, which offers the possibility of being used in the field during oil spill incidents. In the present study, we develop an algorithm that can distinguish between seawater and oil through the use of a laser at 905 nm wavelength. We investigate the ability of the sensor to detect three different oil types (light crude, bunker A, and bunker C) through experiments and analyze the differences between the types and volumes of spilled oil (1, 5, 10, 15, 20, 25, 30, 35, 40, and 50 mL). The results showed that our algorithm for detecting oil spills over seawater was successful: the LiDAR sensor was able to detect different oil types and volumes. Spilled oil area coverage ranged by more than 50% of the detected area, and the viscosity of bunker C oil reached up to 73%. In addition, the experimental oil spills were mainly formed of oil films of 1 mm and 2 mm thicknesses, which confirmed geometrical properties. Follow-up research should further investigate the characteristics of oil slick thickness measured by the LiDAR system and undertake field tests to assess the feasibility of using the LiDAR system in pollution incidents.

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Section: Methodsmentioning

confidence: 99%

Section: Spilled Oil Volume Detection Experimentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geometrical Properties of Spilled Oil on Seawater Detected Using a LiDAR Sensor

Moon¹,

Jung²

2020

Journal of Sensors

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“…By reflecting the radar signal away from its transmitting source, water has a much lower return signal than land, which rougher texture scatters the incoming signal in all directions. A variety of image segmentation techniques have been proposed to leverage this contrast, including thresholding, fuzzy classification, and region growing (Demir et al, ; Nunziata et al, ; Vandebroek et al, ). However, radar backscattering on the Earth surface is also affected by a range of additional factors, including topography, soil moisture, and vegetation (Clement et al, , see), which confound the effect of surface roughness.…”

Section: Shoreline Detection: Sentinel 1 Split‐based Classificationmentioning

confidence: 99%

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

Valentín

Müller

2020

Geophysical Research Letters

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Beach erosion due to large storms critically affects coastal vulnerability, but is challenging to monitor and quantify. Attributing erosion to a specific storm requires a reliable counterfactual scenario: hypothetical beach conditions, absent the storm. Calibrating models to construct counterfactuals requires numerous observations that are rarely available. Storm paths are unpredictable, making long‐term instrumentation of specific beaches costly. Optical remote sensing is hampered by persistent cloud cover. We use Sentinel‐1 satellite radar imagery to monitor shoreline changes through clouds and propose regression discontinuity as a strategy to estimate the causal effect of large storms on beach erosion. Applied to 75 beaches across Puerto Rico, the approach detects shoreline changes with a root‐mean‐square error comparable to the resolution of the imagery. Hurricane Maria caused an erosion of 3 to 5 m along its path, up to 40 m at particular beaches. Results reveal strong local disparities that are consistent with simulated nearshore hydrodynamic conditions.

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“…Legal boundaries and Maritime zones are best described and mapped by reference to tide coordinated shorelines [24], [25]. Shoreline monitoring will help determine cost-effective, social, and environmentally friendly mechanisms for coastal flooding and erosion control [26]. Modern strategies for optimal land reclamation include spatial considerations for the shorelines [25].…”

Section: A Shoreline Extraction As An Analytical Framework For Sustamentioning

confidence: 99%

The Effectiveness of the Semi-Automatic Technique in Extracting Shoreline from Digital Elevation Models for Sustainable Development in Africa

Eboigbe¹,

Ugwuoti

Sam³

2020

EJGEO

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The accurate delineation of Shorelines is required to determine the respective tidal heights and monitor coastal erosion. In some coastlines, the difference between the high tide and the low tide could be as small as one meter but the accurate extraction of the respective vertical surfaces is required to understand the pattern of the sea wave energy and to chart tidal heights relative to a specific datum. The manual digitizing of shoreline is usually with high accuracies and flexibilities but could be rigorous and hectic especially for longer shorelines. Automatic extraction is more feasible where the wavelength intensity gradient within a sub-pixel level is at maximum for instantaneous shorelines with small tidal range. This is due to topological inconsistencies that could hinder such accuracies. In this study, an improved mean shift segmentation model extracts shorelines from digital elevation models from LiDAR and a UAV survey. The key element of an accurate DTM is the contouring network capable of delineating between the grid pixels each representing water and land whose elevation is below or above tidal datum. The study locations are the Freshwater and the Perranporth bay in the United Kingdom. The third is the Nigeria Maritime Academy Oron shoreline. The choice for the different study areas is to ascertain the reliability of this technique on different geographies. Comparison between manual digitizing and the mean shift segmentation were both by visual interpretation and by volumetric change analysis for two different years. The results obtained indicate that the mean shift segmentation can delineate tide-coordinated shorelines accurately. The limitation of this methodology is on digital images with poor spectral resolution. Recommendations include the use of this technique on open source GIS software and a practical solution to developing a monitoring infrastructure for coastlines in Africa.

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Advanced Lake Shoreline Extraction Approach by Integration of SAR Image and LIDAR Data

Cited by 15 publications

References 36 publications

Geometrical Properties of Spilled Oil on Seawater Detected Using a LiDAR Sensor

Geometrical Properties of Spilled Oil on Seawater Detected Using a LiDAR Sensor

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

The Effectiveness of the Semi-Automatic Technique in Extracting Shoreline from Digital Elevation Models for Sustainable Development in Africa

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