Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems

Analysis of scanning systematic errors for airborne laser bathymetry

For the Palmer mechanical scanning pattern of an airborne laser bathymetry system, the potential errors of the scanning system are analyzed, and the associated error model is derived. The model composes the description of laser rays, water surface fluctuations, and refraction, and introduces certain simplifications concerning the water surface and column. Based on the scanning error model, the impact of each error source on the vertical and horizontal positioning accuracy is investigated and established through a numerical simulation. The quantitative impacts of each inaccuracy on the coordinates of the laser footprints on the sea surface and bottom were calculated, with a height of 100 m for the airborne platform and a water depth of 10 m. To verify the correctness of the simulation results and the error model based on a theoretical analysis, experiments are utilized with the system that we developed. Both the simulation analysis and experimental results show that this method can effectively obtain the systematic errors. The outcomes of the error model and analysis will give the theoretical foundations for lowering the effect brought on by each error source in the compensation scanning system and improving the point cloud accuracy in the ensuing data processing.

The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements

Bathymetric LiDAR technology is a technology used for simultaneous data acquisition regarding the morphology of the bottom of water reservoirs and the surrounding coastal zone, realized from the air, e.g., by plane or drone. Contrary to the air topographic LiDAR, which uses an infrared wavelength of 1064 nm, bathymetric LiDAR systems additionally use a green wavelength of 532 nm. The green laser can penetrate the water, which makes it possible to measure the depth of shallow water reservoirs, rivers, and coastal sea waters within three Secchi depths. This article presents the theoretical basis for the construction of a green laser. Against the background of other methods of measuring the bottom of water reservoirs, the technology using waves from the visible light range is presented in detail in the assessment of the bottom morphology of shallow water reservoirs. The possibilities of using green laser in lidar bathymetry implemented in particular in non-navigable regions are shown. The results of the researchers' work on river processes (erosion, sedimentation), design of stream restoration, determination of morphometric parameters of the riverbed, as well as assessment of the topography of the marine coastal bottom zones are summarized. The development direction of lidar bathymetry is discussed.

Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR

In addition to depth measurements, airborne LiDAR bathymetry (ALB) has shown usefulness in suspended sediment concentration (SSC) inversion. However, SSC retrieval using ALB based on waveform decomposition or near-water-surface penetration by green lasers requires access to full-waveform data or infrared laser data, which are not always available for users. Thus, in this study we propose a new SSC inversion method based on the depth bias of ALB. Artificial neural networks were used to build an empirical inversion model by connecting the depth bias and SSC. The proposed method was verified using an ALB dataset collected through Optech coastal zone mapping and imaging LiDAR systems. The results showed that the mean square error of the predicted SSC based on the empirical model of ALB depth bias was less than 2.564 mg/L in the experimental area. The proposed method was compared with the waveform decomposition and regression methods. The advantages and limits of the proposed method were analyzed and summarized. The proposed method can effectively retrieve SSC and only requires ALB-derived and sonar-derived water bottom points, eliminating the dependence on the use of green full-waveforms and infrared lasers. This study provides an alternative means of conducting SSC inversion using ALB.

Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior

On the Keweenaw Peninsula of Lake Superior, two stamp mills (Mohawk and Wolverine) discharged 22.7 million metric tonnes (MMT) of tailings (1901–1932) into the coastal zone off the town of Gay. Migrating along the shoreline, ca. 10 MMT of the tailings dammed stream and river outlets, encroached upon wetlands, and contaminated recreational beaches. A nearly equal amount of tailings moved across bay benthic environments into critical commercial fish spawning and rearing grounds. In the middle of the bay, Buffalo Reef is important for commercial and recreational lake trout and lake whitefish production (ca. 32% of the commercial catch in Keweenaw Bay, 22% along southern Lake Superior). Aerial photographs (1938–2016) and five LiDAR and multispectral over-flights (2008–2016) emphasize: (1) the enormous amounts of tailings moving along the beach; and (2) the bathymetric complexities of an equal amount migrating underwater across the shelf. However, remote sensing studies encounter numerous specific challenges in coastal environments. Here, we utilize a combination of elevation data (LiDAR digital elevation/bathymetry models) and in situ studies to generate a series of physical, chemical, and biological geospatial maps. The maps are designed to help assess the impacts of historical mining on Buffalo Reef. Underwater, sand mixtures have complicated multispectral bottom reflectance substrate classifications. An alternative approach, in situ simple particle classification, keying off distinct sand end members: (1) allows calculation of tailings (stamp sand) percentages; (2) aids indirect and direct assays of copper concentrations; and (3) permits determinations of density effects on benthic macro-invertebrates. The geospatial mapping shows how tailings are moving onto Buffalo Reef, the copper concentrations associated with the tailings, and how both strongly influence the density of benthic communities, providing an excellent example for the International Maritime Organization on how mining may influence coastal reefs. We demonstrate that when large amounts of mine tailings are discharged into coastal environments, temporal and spatial impacts are progressive, and strongly influence resident organisms. Next steps are to utilize a combination of hi-resolution LiDAR and sonar surveys, a fish-monitoring array, and neural network analysis to characterize the geometry of cobble fields where fish are successful or unsuccessful at producing young.

Development of Flood Risk and Hazard Maps for the Lower Course of the Siret River, Romania

The Siret River is one of the most important tributaries of the Danube River in the Romanian territory. With a total length of 596 km in this territory, the confluence section of the Siret River with the Danube represents an area where major floods have occurred over time. In the last 50 years, over 20 floods have been recorded in the lower area of the Siret River; and the most recent important flood, which happened in 2010, had a negative impact on the local population, the environment, and the economy. Although it is a buffer zone, the Danube River has a significant impact on the discharge rate of the Siret River. Since few studies have been conducted on the prediction of flooding in the lower area of the Siret River, the present study aims at presenting the most important steps to be taken for designing risk and hazard maps for floods, which could be further applied to other rivers. The confluence of the Siret River with the Danube, a distance of 35 km upstream, was chosen as a study area. Techniques of topographic and bathymetric measurements were combined in order to design the risk and hazard maps for floods in this area and to improve the digital terrain model of the minor riverbed for the studied river area. The 1D hydrodynamic model of the HEC-RAS software was used in this research for developing the flood risk and flood hazard maps. The hazard and risk maps were generated based on 10%, 5%, and 1% flood scenarios, which are in accordance with Floods Directive 2007/60/EC; no historical data were available for the 0.1% scenarios. Thus, in a flood scenario that can occur every 100 years, about 9500 inhabitants are vulnerable at a medium flood risk. In this scenario, over 19.5 km of road infrastructure, about 16.5 km of railways, eight cultural heritage indicators, and three environmental indicators may be affected.

Scan Line Intensity-Elevation Ratio (SLIER): An Airborne LiDAR Ratio Index for Automatic Water Surface Mapping

Owing to the characteristics of how a laser interacts with the water surface and water column, the measured Light Detection and Ranging (LiDAR) intensity values are different with respect to the laser wavelength, the scanning geometry and the reflection mechanism. Depending on the instantaneous water condition and the laser incidence angle, laser dropouts can appear, causing null returns or empty holes found in the collected LiDAR data. This variable intensity response offers a valuable opportunity for using airborne LiDAR sensors for automatic identification of water regions, and thus, we previously proposed an airborne LiDAR-based ratio index named the scan line intensity-elevation ratio (SLIER). Over the water surface, airborne LiDAR data are always found to have a high fluctuation of the intensity value and low variation of the elevation along each scan line, and thus, the water region has a higher SLIER value compared to the land. We examined the SLIER on a multispectral airborne LiDAR dataset collected by Optech Titan and a monochromatic airborne LiDAR dataset collected by Optech Galaxy on a natural rocky shore and a man-made shore. Our experiments showed that SLIER was able to provide a high separability between land and water regions and was able to outperform the traditional normalized difference water index (NDWI) for estimation of the water surface. With the use of SLIER as a mechanism for training data selection, our case studies demonstrated an overall accuracy of 98% in the use of either monochromatic or multispectral LiDAR data, regardless of the laser channel being used.