Wagner, N.; Franke, G.; Schmieder, K.; Mandlburger, G. Automatic Classification of Submerged Macrophytes at Lake Constance Using Laser Bathymetry Point Clouds. Remote Sens.2024, 16, 2257.
Wagner, N.; Franke, G.; Schmieder, K.; Mandlburger, G. Automatic Classification of Submerged Macrophytes at Lake Constance Using Laser Bathymetry Point Clouds. Remote Sens. 2024, 16, 2257.
Wagner, N.; Franke, G.; Schmieder, K.; Mandlburger, G. Automatic Classification of Submerged Macrophytes at Lake Constance Using Laser Bathymetry Point Clouds. Remote Sens.2024, 16, 2257.
Wagner, N.; Franke, G.; Schmieder, K.; Mandlburger, G. Automatic Classification of Submerged Macrophytes at Lake Constance Using Laser Bathymetry Point Clouds. Remote Sens. 2024, 16, 2257.
Abstract
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 extensive monitoring of submerged vegetation, validated against established monitoring techniques. Employing full waveform airborne laser scanning, frequently used for topographic mapping and forestry applications on dry land, we extend its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of LiDAR (Light Detection And Ranging) point clouds, distinguishing the three vegetation classes (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy based on their height. The results reveal detailed three-dimensional representation of submerged vegetation, enabling the identification of vegetation structures and inference of vegetation types with reference to pre-existing knowledge. While the results within training areas demonstrate high precision and alignment with comparative data, the findings in test areas exhibit comprehensible deficiencies that are likely addressable through corrective measures in the future.
Keywords
airborne LiDAR; bathymetry; point cloud classification; submerged vegetation; lake monitoring
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
