Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non-navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is diffi cult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near-infrared (NIR), Raman] of the SHOALS-1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The fi rst method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classifi cation and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (−4·2 cm verusu −4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (−3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method.

The geometry of river channels is a key descriptive element for hydromorphology, hydraulics and hydroecology. Gravel bed rivers usually have a mean water depth of ~0·5 m. For such shallow waters, the accuracy of bathymetric LiDAR data has to be precisely assessed. Alongside this accuracy investigation, methodological questions arise: How to assess the data quality of elevation LiDAR when comparing reference topographic points on river beds to laser beam footprints of several square metres at different locations? What are the consequences of uncertainties and scaling in accuracy estimations? In this study, we designed a methodology to assess the quality of LiDAR topographical data within rivers using a specifi c geostatistical method that conducts upscaling as well as interpolation of reference data that takes into account uncertainties. This method uses an anisotropic block kriging from DGPS points on LiDAR footprint areas within a channel-fi tted coordinate system. This assessment focused on a 1·5 km long reach of the Gardon gravel bed river, in the south of France. DGPS points pseudo-regularly located along the river were acquired at the same time as the LiDAR survey with the HawkEyeII system. LiDAR accuracy results for river bottom elevation show a negative bias for high depth. Added to that bias, a random error with 0·32 m standard deviation was found by considering upscaling and uncertainties in reference data, and a 0·20 m standard deviation was found if they were not considered. Consequently, if LiDAR bias can be corrected, measuring a water depth less than 32 cm, i.e. for 28% of the river area, is unrealistic.However, this experiment shows that LiDAR provides an accurate representation of the riverbed forms. It also provided a useful, continuous, topographic surface from the underwater river bed up to riparian areas.

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Small footprint full-waveform airborne lidar systems hold large opportunities for improved forest characterisation. To take advantage of full-waveform information, this paper presents a new processing method based on the decomposition of waveforms into a sum of parametric functions. The method consists of an enhanced peak detection algorithm combined with an advanced echo modelling including Gaussian and generalized Gaussian models. The study focussed on the qualification of the extracted geometric information. Resulting 3D point clouds were compared to the point cloud provided by the operator. 40 to 60 % additional points were detected mainly in the lower part of the canopy and in the low vegetation. Their contribution to Digital Terrain Models (DTMs), Canopy Height Models (CHMs) was then analysed. The quality of DTMs and CHM-based heights was assessed using field measurements on black pine plots under various topographic and stand characteristics. Results showed only slight improvements, up to 5 cm bias and standard deviation reduction. However both tree crowns and undergrowth were more densely sampled thanks to the detection of weak and overlapping echoes, opening up opportunities to study the detailed structure of forest stands.