Wissenschaftliche Monitoringkonzepte für die Deutsche Bucht : WIMO : Abschlussbericht

Winter, Calvert J.; Bäcker, Vladimir; Adolph, Winny; Bartholomä, Alexander; Becker, Matthias J.; Behr, D.; Callies, Ulrich; Capperucci, R.; Ehlers, Manfred; Farke, Hubert; Geimecke, C.; Grayek, Sebastian; Hass, Christian; Heipke, Christian; Herrling, G.; Hillebrand, Helmut; Hodapp, Dorothee; Höller, Peter; Jung, Reiner; Krasemann, H.; Kröncke, Ingrid; Kwoll, E.; Marz, Josef; Meyerdierks, D.; Mielck, Finn; Millat, Gerald; Reimers, Helena; Reuter, Ryan R; Schmidt, Adriane; Staneva, Joanna; Stanev, Emil V.; van, J.E.E. Beusekom; Wirtz, Kai W.

doi:10.2314/gbv:860303926

Cited by 4 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In that context, it is paramount to access suitable methods and instrumentation that allow a more extensive spatial coverage and provide accuracies as temporal growth magnitudes across speciesrelated cycles are in the order of <10 cm. In the past, oyster reefs have been surveyed using measurement methods, such as terrestrial laser scanning (TLS) (Fodrie et al, 2014;Rodriguez et al, 2014;Ridge et al, 2017a), airborne (Grizzle et al, 2002;Herlyn, 2005) and satellite surveys (Dehouck et al, 2013;Gade et al, 2014;Winter et al, 2016). The time-consuming TLS can yield high-resolution point clouds of reef structures, but more efficient methods cover broader areas.…”

Section: Introductionmentioning

confidence: 99%

“…Optical airborne and satellite data are limited by daytime, tidal and weather conditions (Gade et al, 2014). To overcome these issues, electrooptical and high-resolution synthetic aperture radar (SAR) sensors of satellites were added to the optical data to measure the electromagnetic reflectance from the surface (Winter et al, 2016), independently from the tide, cloud coverage and poor light conditions (Nieuwhof et al, 2015). Choe et al (2012), Nieuwhof et al (2015) and Gade et al (2014) could confirm an improved classification of mussel beds and other intertidal habitats in optical imagery when combined with TerraSAR-X images due to high surface roughness of mussel beds and oyster reefs, which increase the reflecting backscatter of the signal from the rough surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…Choe et al (2012), Nieuwhof et al (2015) and Gade et al (2014) could confirm an improved classification of mussel beds and other intertidal habitats in optical imagery when combined with TerraSAR-X images due to high surface roughness of mussel beds and oyster reefs, which increase the reflecting backscatter of the signal from the rough surfaces. Winter et al (2016) tested a combination of remote sensing techniques for mapping and assessing intertidal flats in the German Wadden Sea, namely airborne LiDAR, electro-optical (RapidEye) and synthetic radar (SAR) sensors installed on satellites (Jung et al, 2015;Adolph et al, 2017a, Adolph et al, 2017b. Previous studies investigated mussel beds (including oysters), bedforms and other surface types via TerraSAR-X images but were limited in precisely determining the extension of bivalve beds (Müller et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-cost UAV monitoring: insights into seasonal volumetric changes of an oyster reef in the German Wadden Sea

Hoffmann,

Pfennings,

Hitzegrad

et al. 2023

Front. Mar. Sci.

View full text Add to dashboard Cite

This study aims to quantify the dimensions of an oyster reef over two years via low-cost unoccupied aerial vehicle (UAV) monitoring and to examine the seasonal volumetric changes. No current study investigated via UAV monitoring the seasonal changes of the reef-building Pacific oyster (Magallana gigas) in the German Wadden Sea, considering the uncertainty of measurements and processing. Previous studies have concentrated on classifying and mapping smaller oyster reefs using terrestrial laser scanning (TLS) or hyperspectral remote sensing data recorded by UAVs or satellites. This study employed a consumer-grade UAV with a low spectral resolution to semi-annually record the reef dimensions for generating digital elevation models (DEM) and orthomosaics via structure from motion (SfM), enabling identifying oysters. The machine learning algorithm Random Forest (RF) proved to be an accurate classifier to identify oysters in low-spectral UAV data. Based on the classified data, the reef was spatially analysed, and digital elevation models of difference (DoDs) were used to estimate the volumetric changes. The introduction of propagation errors supported determining the uncertainty of the vertical and volumetric changes with a confidence level of 68% and 95%, highlighting the significant change detection. The results indicate a volume increase of 22 m³ and a loss of 2 m³ in the study period, considering a confidence level of 95%. In particular, the reef lost an area between September 2020 and March 2021, when the reef was exposed to air for more than ten hours. The reef top elevation increased from -15.5 ± 3.6 cm NHN in March 2020 to -14.8 ± 3.9 cm NHN in March 2022, but the study could not determine a consistent annual growth rate. As long as the environmental and hydrodynamic conditions are given, the reef is expected to continue growing on higher elevations of tidal flats, only limited by air exposure. The growth rates suggest a further reef expansion, resulting in an increased roughness surface area that contributes to flow damping and altering sedimentation processes. Further studies are proposed to investigate the volumetric changes and limiting stressors, providing robust evidence regarding the influence of air exposure on reef loss.

show abstract