In this paper, we propose a deep neural network architecture to estimate physical and optical properties of water bodies from bathymetric lidar waveforms. Although essential to the understanding of coastal and inland waters dynamics, this task remains challenging due to the complexity of waveform processing. Here, we use convolutional encoders to estimate seven parameters without the need for pre-processing, iterations, or existing measurements of the target properties. Using a data simulator based on radiative transfer models, the network is trained to be robust to a wide range of physical and acquisition settings. On simulated data, the results show the ability of the method to retrieve relevant Kd, depth, and bottom position estimates even for low signal-to-noise ratios in which the water bottom component is particularly weak and the water column difficult to identify.

Floods are among the most common and devastating natural hazards, imposing immense costs on our society and economy due to their disastrous consequences. Recent progress in weather prediction and spaceborne flood mapping demonstrated the feasibility of anticipating extreme events and reliably detecting their catastrophic effects afterwards. However, these efforts are rarely linked to one another and there is a critical lack of datasets and benchmarks to enable the direct forecasting of flood extent. To resolve this issue, we curate a novel dataset enabling a timely prediction of flood extent. Furthermore, we provide a representative evaluation of state-of-the-art methods, structured into two benchmark tracks for forecasting flood inundation maps i) in general and ii) focused on coastal regions. Altogether, our dataset and benchmark provide a comprehensive platform for evaluating flood forecasts, enabling future solutions for this critical challenge. Data, code & models are shared at https://github.com/Multihuntr/GFF under a CC0 license.

This paper is concerned with the decomposition of bathymetric lidar waveforms. Because of the presence of water, processing such data remains a challenge since water impacts their shape and signal-to-noise ratio, depending in particular on the associated turbidity. In this paper, we explore the use of attentive autoencoders to decompose bathymetric waveforms and recover their air/water interface, water column, and water bottom components simultaneously, without relying on assumptions about the impulse or target surface nature. On simulated waveforms, the method achieves lower decomposition error than existing approaches, handling overlapping echoes of very shallow waters and weak returns in deeper water. This opens to attractive strategies to process real bathymetric waveforms.

Mangroves provide several ecosystem services but suffer from multi-source degradation. Their global mapping at fine-scale needs efficient classification fusing very high resolution (VHR) satellite imageries. Visible (VIS) and near-infrared (NIR) imageries are great assets for mapping when supervised by machine learners. However those imageries commonly rely on three VIS and one NIR bands, impeding successful discrimination of diverse objects. The mangrove classification is besides largely supported by shallow learners. This research produced the first object-based mangrove classification using the VHR superspectral (16-band) WorldView-3 imagery supervised by convolutional neural networks. Four spectral datasets were segmented and classified using a U-net architecture: red-green-blue (RGB); RGB with yellow and coastal (VIS); VIS with red edge, NIR1 and NIR2 (VIS-NIR); and VIS-NIR with eight mid-infrareds (VIS-NIR-MIR). Even if very satisfactory results were computed for the four segmented datasets (averagely ranging from 0.92 to 0.96), the best model was derived from the 16-band full dataset.

Due to global changes, UAV flights are of great interest for the surveillance of coastal environments. Some UAVs are fitted with multispectral sensors to better discriminate coastal eco-geo-systems such as saltmarsh meadows and sandy dunes. This research evaluates photogrammetry-based horizontal (XY) and vertical (Z) accuracies derived from four separate channels (Green, G, Red, R, Red-Edge, RE, and Near-Infrared, NIR). The best X and Y accuracy is achieved by the NIR channel (0.07 m and 0.09 m, respectively). The best Z vertical accuracy (0.13 m) is reached with the full set of channels (R-G-RE-NIR). The individualized investigations of the saltmarsh vegetation and the sand dune show that the best vertical accuracies are attained with the NIR and RE with 0.10 m and 0.11 m, respectively. Our findings witness that the visible spectrum, represented by the G and R bands, provide lower performance than the infrared gamut.

Topo-bathymetric lidar is a powerful tool to survey coastal ecosystems while ensuring data continuity between land and water regardless of the nature of the terrain, and allowing the collection of information up to several dozens of metres deep. This study analyzes the potential of full waveform lidar data to monitor key ecosystems for climate change mitigation: seagrasses. It proposes an original way of processing topo-bathymetric lidar waveforms to map their spatial repartition and extent in Corsica (France). Waveform statistical and shape parameters are computed and used to produce a map of seagrass meadows that reaches over 86% of overall accuracy. Seagrass height is also extracted, offering perspectives for structural complexity assessment and ecosystem services quantification.