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Fakultät Agrarwissenschaften

Permanent URI for this communityhttps://hohpublica.uni-hohenheim.de/handle/123456789/9

Die Fakultät entwickelt in Lehre und Forschung nachhaltige Produktionstechniken der Agrar- und Ernährungswirtschaft. Sie erarbeitet Beiträge für den ländlichen Raum und zum Verbraucher-, Tier- und Umweltschutz.

Homepage: https://agrar.uni-hohenheim.de/

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Browsing Fakultät Agrarwissenschaften by Sustainable Development Goals "14"

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    Automatic classification of submerged macrophytes at Lake Constance using laser bathymetry point clouds
    (2024) Wagner, Nike; Franke, Gunnar; Schmieder, Klaus; Mandlburger, Gottfried; Wagner, Nike; Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria;; Franke, Gunnar; Institute of Landscape and Plant Ecology (320), University of Hohenheim, Ottilie-Zeller-Weg 2, 70599 Stuttgart, Germany; (G.F.); (K.S.); Schmieder, Klaus; Institute of Landscape and Plant Ecology (320), University of Hohenheim, Ottilie-Zeller-Weg 2, 70599 Stuttgart, Germany; (G.F.); (K.S.); Mandlburger, Gottfried; Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria;; Stateczny, Andrzej
    Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future.