USING BISPECTRAL FULL-WAVEFORM LIDAR TO MAP SEAMLESS COASTAL HABITATS IN 3D

In natural rivers, flow variability and sediment heterogeneity affect how sediment grains are transported. A unique law that predicts the total amount of sediment transportable by a river for a wide range of sediment mixtures and flow conditions exists, but unclear trends remain. Two improvements to this law – a standardized onset of sediment transport and a common reference transport height across all sizes – appear to be critical to having a functional multi-grain-size total sediment load.

This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.

The morphology and size of sediments influence erosion efficiency, sediment transport and the quality of aquatic ecosystem. In turn, the spatial evolution of sediment size provides information on the past dynamics of erosion and sediment transport. We have developed a new software which semi-automatically identifies and measures sediments based on 3D point clouds. This software is fast and efficient, offering a new avenue to measure the geometrical properties of large numbers of sediment grains.

Landscape evolution is highly dependent on climatic parameters, and the occurrence of intense precipitation events is considered to be an important driver of river incision. We compare the rate of erosion with the variability of river discharge in a mountainous landscape of SE France where high-magnitude floods regularly occur. Our study highlights the importance of the hypotheses made regarding the threshold that river discharge needs to exceed in order to effectively cut down into the bedrock.

Both landslide mapping and volume estimation accuracies are crucial to quantify landscape evolution and manage such a natural hazard. We developed a method to robustly detect landslides and measure their volume from repeat 3D point cloud lidar data. This method detects more landslides than classical 2D inventories and resolves known issues of indirect volume measurement. Our results also suggest that the number of small landslides classically detected from 2D imagery is underestimated.