Lidar Bathymetry Research Articles

JALBTCX/NCMP emergency-response airborne Lidar coastal mapping & quick response data products for 2016/2017/2018 hurricane impact assessments

The Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) deployed to support regional-scale post-storm damage assessments following Hurricanes Irma (2017), Maria (2017), and Michael (2018) for the Federal Emergency Management Agency (FEMA) and the U.S. Army Corps of Engineers (USACE) following Hurricanes Matthew (2016) and Florence (2018). The JALBTCX has a long history of providing regional coastal surveys after storm impacts in the United States. These high-resolution, regional datasets extend beyond project boundaries, providing critical data to quantify large-scale impacts associated with storm events. These regional datasets directly support missions within the USACE, other federal agencies, academia, and the broader coastal community.

Propagated Uncertainty Models Arising From Device, Environment, and Target for a Small Laser Spot Airborne LiDAR Bathymetry and its Verification in the South China Sea

This contribution identifies the uncertainty sources influencing the component uncertainties for an airborne LiDAR bathymetry (ALB) measurement and presents the models for various component uncertainties (arising from the device, environment, and target for ALBs). Since various instrumental and environmental factors introduce vertical and horizontal uncertainties in ALB data, these uncertainties should be first analyzed and then precisely modeled to ensure the accuracy of the measurements. For this purpose, ten different effects that influence the accuracy of ALB data are systematically analyzed and modeled for four aspects in this article: the device aspect (laser pointing deflection, trajectory uncertainty, and boresight/lever arm offset), environmental aspect (atmospheric limitation, refraction on the sea surface, refraction in water, scattering in water, and water level fluctuation), target aspect (irregular bottom), and other aspect (accuracy of coordinate transformation model). In addition, the effect of the laser spot size is also discussed. To verify the presented uncertainty models, an ALB survey was operated around Yuanzhi Island in the South China Sea. For a water depth of 10 m, the theoretical overall root-sum-squares (RSS) for ten different effects of an ALB system (approximately 34 cm calculated using the total vertical uncertainty (TVU) models) is generally in accordance with the actual performance of the ALB data (approximately 39 cm) performance. The difference is mainly attributed to the limited accuracy of the ground truth data, and the difference between the water depth and laser ranging is reasonable. In this process, the topography data in the same region captured by a shipborne multibeam echo sounder (MBES) were used as the ground truth. The results indicated that for the typical ALB system, the laser pointing uncertainty and refraction uncertainty on the sea surface are primary uncertainty sources and should be corrected in a higher priority to meet the seafloor topographic accuracy demand of the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys (S-44). The proposed uncertainty models can be used not only to guide the actual measurement of an ALB system but also to provide the uncertainty correction reference for ALB data postprocessing.

A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry.

Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as “shallow water (SW)” and “deep water (DW)”. An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model’s parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m).

Analysis of the green light penetration from Airborne LiDAR Bathymetry in Shallow Water Area

An Airborne LiDAR can be used to map the depth of the water. This system is called an Airborne LiDAR Bathymetry. In order to measure the water depth, an Airborne LiDAR is utilized with the green light sensor beside the near-infrared light sensor. However, water condition affects the capability of the green light penetration. The clarity of the water and the vegetation are some of the restriction for green light to penetrate the water especially in inland water areas. The research examined the penetration capability of the green light of the Airborne LiDAR Bathymetry in shallow water areas. A comparison of depth values derived from the LiDAR system and the direct measurements was performed to asses the penetration accuracy of the green light. The depth comparison is derived from eleven channels in the research area. The direct measurement was conducted by using a tape to measure the depth, and the RTK GNSS to measure channel’s dimension. A Secchi Disk also used to measure the turbidity of the water. The vertical reference for the depth is Earth Gravitational Model 2008 (EGM 2008). Based on the research, the depth difference between LiDAR Bathymetry and the direct measurement ranges from 0.02 m to 0.22 m. The vertical accuracy of the LiDAR Bathymetry is 0.239 m within 95% of confidence level.

Airborne Hyperspectral Imaging for Submerged Archaeological Mapping in Shallow Water Environments

Nearshore areas around the world contain a wide variety of archeological structures, including prehistoric remains submerged by sea level rise during the Holocene glacial retreat. While natural processes, such as erosion, rising sea level, and exceptional climatic events have always threatened the integrity of this submerged cultural heritage, the importance of protecting them is becoming increasingly critical with the expanding effects of global climate change and human activities. Aerial archaeology, as a non-invasive technique, contributes greatly to documentation of archaeological remains. In an underwater context, the difficulty of crossing the water column to reach the bottom and its potential archaeological information usually requires active remote-sensing technologies such as airborne LiDAR bathymetry or ship-borne acoustic soundings. More recently, airborne hyperspectral passive sensors have shown potential for accessing water-bottom information in shallow water environments. While hyperspectral imagery has been assessed in terrestrial continental archaeological contexts, this study brings new perspectives for documenting submerged archaeological structures using airborne hyperspectral remote sensing. Airborne hyperspectral data were recorded in the Visible Near Infra-Red (VNIR) spectral range (400–1000 nm) over the submerged megalithic site of Er Lannic (Morbihan, France). The method used to process these data included (i) visualization of submerged anomalous features using a minimum noise fraction transform, (ii) automatic detection of these features using Isolation Forest and the Reed–Xiaoli detector and (iii) morphological and spectral analysis of archaeological structures from water-depth and water-bottom reflectance derived from the inversion of a radiative transfer model of the water column. The results, compared to archaeological reference data collected from in-situ archaeological surveys, showed for the first time the potential of airborne hyperspectral imagery for archaeological mapping in complex shallow water environments.

Bathymetry Retrieval Method of LiDAR Waveform Based on Multi-Gaussian Functions

Ma, Y.; Zhang, J.; Zhang, Z., and Zhang, J.Y., 2019. Bathymetry retrieval method of LiDAR waveform based on multi-Gaussian functions. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 324-331. Coconut Creek (Florida), ISSN 0749-0208.This paper provides a water depth inversion approach to laser radar waveform data based on multi-Gauss function in order to address the following two problems: the impact of noise on the traditional bathymetry and the poor accuracy of the water depth inversion in deep water. This approach employs L-M optimization algorithm to make multiple Gauss functions iterate and fit the LiDAR waveform data, and then uses peak detection method to extract the echo signal from the surface and the bottom of the water. In this paper, the water depth inversion is carried out using the simulation data of LiDAR water echo and the Aquarius LiDAR waveform data around the water area of the Ganquan Island respectively. Taking the mean relative error (MRE) and the mean absolute error (MAE) as the evaluation index of accuracy, the result shows that the detection model of LiDAR bathymetry has a higher ability of water depth inversion in the range of detectable water depth. For simulation data, the MAE is 20 cm and the MRE is below 7 % in the water depth range of 2 m to 10 m. While for real LiDAR waveform data, the MAE of water depth inversion is between 30 cm and 75 cm, and the MRE is below 12 %.

Water–land classification using three-dimensional point cloud data of airborne LiDAR bathymetry based on elevation threshold intervals

Water–land classification is a basis for water depth calculation or suspended sediment concentration inversion through airborne LiDAR bathymetry (ALB). Traditional classification methods using ALB waveform data offer high accuracy but exhibit low efficiency and convenience in engineering applications. The three-dimensional (3-D) point cloud data of ALB are easier to analyze and utilize than waveform data. Therefore, we propose a water–land classification method that uses the 3-D point cloud data of ALB based on the threshold intervals of water surface points. First, a random sample consensus algorithm is applied to rough water–land classification using the 3-D point cloud data derived by an infrared laser of ALB. Second, the water surface points derived from rough classification are used to determine the means, standard deviations, and threshold intervals. Finally, accurate water–land classification is achieved on the basis of the threshold intervals of the water surface points. The proposed method is applied to a practical ALB measurement using Optech coastal zone mapping and imaging LiDAR and achieves 98.26% accuracy in water–land classification.

Classifying airborne bathymetry data using the Random Forest algorithm

ABSTRACTThe information on the topography of the seabed included in data from airborne lidar bathymetry can be used for the detection of changes occurring at the bottom of the basin and for the detection of objects deposited on it. Processing of full waveform data enables obtaining data on the water surface and the identification of the underwater situation. The classification process based on the Random Forest (RF) algorithm is presented using data from lidar bathymetry. The classification was performed in two independent approaches using input vector consisting of 16 features. In the first approach, the entire point cloud was classified, in the second the point cloud did not contain points of the water surface. In the classification, traits based on the full waveform and resulting from point cloud geometry were used. The quantitative efficiency of classification was verified through error matrices. The obtained efficiency (100% water surface, 99.9% seabed and 60% objects) of the point classification of the objects enables the possibility for using the RF algorithm for detection of objects on the seabed. In comparison to Support Vector Machines, the RF algorithm has better results in the detection of points on the objects in point cloud with water surface.

DETECTION AND EXTRACTION OF WATER BOTTOM TOPOGRAPHY FROM LASERBATHYMETRY DATA BY USING FULL-WAVEFORM-STACKING TECHNIQUES

Abstract. Airborne LiDAR bathymetry allows an efficient and area-wide acquisition of water bottom points in shallow water areas. However, the measurement method is severely limited by water turbidity, impending a reliable detection of water bottom points at higher turbidity or in deeper water bodies. This leads to an incomplete acquisition of the water bottom topography. In this contribution, advanced processing methods are presented, which increase the penetration depth compared to the original processed data and enable a reliable extraction and detection of bottom points in deeper water bodies. The methodology is based on the analysis of correlated neighborhood information assuming a steady water bottom. The results confirm a significantly higher penetration depth with a high reliability of the additionally extracted water bottom points along with a larger coverage of the water bottom topography.

A Simplified End-User Approach to Lidar Very Shallow Water Bathymetric Correction

Airborne lidar bathymetry has been evolving in recent years, and the introduction of multispectral (MS) lidar, such as the Titan sensor from Teledyne Optech, Toronto, ON, Canada, allows for comprehensive mapping of aquatic and riparian areas. However, to derive useful riverbed geomorphology data, bathymetric correction should be applied to raw data based on the proximity of individual points within the laser point cloud to the overlying water surface. Current algorithms within off-the-shelf (OTS) software were developed for flat coastal waters and lakes and, therefore, may have difficulty with shallow river channels possessing discernible surface water gradients and/or complex riparian environments. A proposed simplified correction algorithm is based on a shift (scaling with k = 0.76) in depth values. Riverbed returns are normalized toward the overlying water surface, disregarding laser beam angle of incidence for each point. The resultant simplified bathymetric correction is, therefore, available to most end users and does not rely on black box OTS solutions. Validation of the method with an MS lidar data set is presented, and the expected neglectable introduced bias (sd = 1.4 cm) for very shallow (<2 m) water is confirmed.

IMPROVEMENTS IN LIDAR BATHYMETRY DATA ANALYSIS

Abstract. Airborne Lidar Bathymetry is a laser scanning technique to measure waterbody bottom topography in shallow waterbodies with limited turbidity. The topic has recently gained relevance due to the advent of new sensor technologies allowing for much higher spatial resolution in bathymetry data capture and due to guidelines demanding regular monitoring of waterbodies. In our contribution, we focus on three important aspects of lidar bathymetry: In the first part, systematic effects of wave patterns will be analysed in order to derive waterbody coordinate correction terms. In the second part, we will apply waveform-stacking techniques to enhance the detectability of water bottom points in lidar bathymetry full waveform signals. In the third part, a dedicated full wave-form analysis procedure is shown, which allows for deriving turbidity information from the decay of the signal intensity in the waterbody.

Between the tides: Modelling the elevation of Australia's exposed intertidal zone at continental scale

The intertidal zone represents a critical transition between marine and terrestrial ecosystems, supporting a complex mosaic of highly productive and biologically diverse habitats. However, our understanding of these important coastal environments is limited by a lack of spatially consistent topographic data, which can be extremely challenging and costly to obtain at continental-scale. Satellite remote sensing represents an important resource for monitoring extensive coastal zones. Previous approaches to modelling the elevation of the intertidal zone using earth observation (EO) data have been restricted to small study regions or have relied on manual image interpretation, thus limiting their ability to be applied consistently over large geographic extents. In this study, we present an automated open-source approach to generate satellite-derived elevation data for over 15,387 km2 of intertidal terrain across the entire Australian coastline. Our approach combines global tidal modelling with a 30-year time series archive of spatially and spectrally calibrated Landsat satellite data managed within the Digital Earth Australia (DEA) platform. The resulting National Intertidal Digital Elevation Model (NIDEM) dataset provides an unprecedented three-dimensional representation of Australia's vast exposed intertidal zone at 25 m spatial resolution. We validate our model against LiDAR, RTK GPS and multibeam bathymetry datasets, finding that modelled elevations are highly accurate across sandy beach (±0.41 m RMSE) and tidal flat environments (±0.39 m RMSE). Model performance was least accurate (±2.98 m RMSE) within rocky shores and reefs and other complex coastal environments with extreme and variable tidal regimes. We discuss key challenges associated with modelling intertidal elevation including tidal model performance and biased observations from sun-synchronous satellites, and suggest future directions to improve the accuracy and utility of continental-scale intertidal elevation modelling. Our model can be applied to tidally-influenced coastal environments globally, addressing a key gap between the availability of sub-tidal bathymetry and terrestrial elevation data.

Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters

Airborne Laser Bathymetry (ALB) is an attractive technology for the measurement of shallow water bodies because of the high acquisition rate and high point densities that can be achieved. Of special interest is the application of ALB in non-navigable areas where the only alternatives are conventional terrestrial surveying by wading with a pole, multi-media photogrammetry, or spectrally based depth retrieval. The challenge for laser based approaches in such very shallow waters (<2 m) is the difficulty of discriminating between echoes from the surface and the bottom. This work presents an algorithm for the detection of surface, volume, and bottom (SVB) designed to meet this challenge while requiring only a single wavelength (532nm) sensor. The accuracy of the algorithm is cross validated against reference measurements obtained from terrestrial survey with a total station and shows negligible bias and virtually no depth dependence for the experimental dataset.

Classification of Coral Reefs in the South China Sea by Combining Airborne LiDAR Bathymetry Bottom Waveforms and Bathymetric Features

Geographic information describing coral reefs plays an important role in constructing electronic chart systems and protecting the ecological environment of the ocean. To derive geographic information of coral reefs more effectively, this paper proposes a methodology to detect coral reefs by combining airborne LiDAR bathymetry (ALB) bottom waveform and bathymetric feature data. A feature vector was established by deriving bottom waveform variables (the peak amplitude, pulsewidth, area, skewness, kurtosis, and backscatter cross section) and bathymetric variables (the depth standard deviation, slope, bathymetric position index, Gaussian curvature, mean curvature, and roughness). Using a support vector machine classifier, coral reefs were detected by distinguishing two classes (coral reefs and others) on the seafloor. To evaluate the classification performance of coral reefs, the developed method was applied to Yuanzhi Island, South China Sea surveys, and verified by field data (aerial digital camera images and underwater video images). The results showed that the classification overall accuracy of coral reefs can be greatly improved from 80.59%/90.31% when ALB bottom waveform or bathymetric variables features were used separately to 93.57% when using a combination of ALB bottom waveform and bathymetric features. In addition, the kappa coefficient can also be greatly improved from approximately 0.61/0.80 to 0.87. And the new proposed method performs better compared to the current classification method using ALB data to detect coral reefs with an overall accuracy of 90.92% and Kappa of 0.81. This highlights the potential of ALB data, combining waveform data and bathymetric data, for precisely detecting coral reefs in shallow water areas.

机载激光雷达测深系统定位模型与视准轴误差影响分析

机载激光雷达测深系统定位模型与视准轴误差影响分析

POST-HURRICANE COASTAL MAPPING AND CHANGE ANALYSIS: NATIONAL COASTAL MAPPING PROGRAM

Hurricane and other extreme storm impacts pose one of the greatest threats to coastal environments, populations, and infrastructure. The U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP), executed by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), collects and processes high resolution aerial imagery and lidar data that provide regional datasets to support assessments of coastal change. The NCMP surveying methods and data products are invaluable tools for coastal management and have the potential to produce rapid-response data following extreme storm impacts. Emergency post-storm deployment of this system was used following Hurricane Matthew’s impact in October 2016 where the NCMP surveyed from Key Biscayne, FL to the VA/MD border between October 27th and December 2nd, collecting topographic and bathymetric lidar along the coast in a 1.5 km swath. Approximately one year later, Hurricane Irma impacted the Florida east and west coast in September 2017. The NCMP surveyed the entire Florida east coast, including the Keys, in addition to Collier County on the West Coast of Florida from September 22 to October 25.

Quantifying Airborne Lidar Bathymetry Quality-Control Measures: A Case Study in Frio River, Texas

Airborne Lidar Bathymetry (ALB) is an advanced and effective technology for mapping water bodies and measuring water depth in relatively shallow inland and coastal zones. The concept of using light beams to detect and traverse water bodies has been around since the 1960s; however, its popularity has increased significantly in recent years with the advent of relatively affordable hardware, supplemented with potent software applications to process and analyze resulting data. To achieve the most accurate final product, which is usually a digital elevation model (DEM) of the bottom of a water body, various quality-control (QC) measures are applied during and after an airborne mission. River surveys, in particular, present various challenges, and quantifying the quality of the end product requires supplemental surveys and careful analysis of all data sets. In this article, we discuss a recent ALB survey of the Frio River in Texas and summarize the findings of all QC measures conducted. We conclude the article with suggestions for successful ALB deployments at similar survey locations.

Depth from Satellite Images: Depth Retrieval Using a Stereo and Radiative Transfer-Based Hybrid Method

Satellite imagery is increasingly being used to provide estimates of bathymetry in near-coastal (shallow) areas of the planet, as a more cost-effective alternative to traditional methods. In this paper, the relative accuracy of radiative-transfer and photogrammetric stereo methods applied to World View 2 imagery are examined, using LiDAR bathymetry and towed video as ground truth, and it is demonstrated, with a case study, that these methods are complementary; where one method might have limited accuracy, the other method often has improved accuracy. The depths of uniform, highly-reflective (sand) sea bed are better estimated with a radiative transfer-based method, while areas where there is high visual contrast in the scene, as identified by using a local standard deviation measure, are better estimated using the photogrammetric technique. In this paper, it is shown that a hybrid method can give a potential improvement in accuracy of more than 50% (from 2.84 m to 1.38 m RMSE in the ideal case) compared to either of the two methods alone. Metrics are developed that can be used to characterize regions of the scene where each technique is superior, realizing an improved overall depth accuracy over either method alone of between 16.9% and 19.7% (demonstrating a realised RMSE of 2.36 m).

The non-fluvial nature of Western Norwegian rivers and the implications for channel patterns and sediment composition

Fluvial processes are considered decisive for the formation of river channel patterns. The concepts of fluvial morphology contain a functional relationship between catchment area, local bed slope and grain size that leads to characteristic channel patterns. In the postglacial landscapes of Western Norway, however, the channel patterns are highly diverse, do not follow the traditional concepts of fluvial models and are distributed seemingly randomly over the river environments. The objective of this study was to investigate whether Western Norwegian rivers have a characteristic morphology determined by non-fluvial features and whether existing fluvial morphology concepts must be supplemented to represent these characteristics. River channel patterns in Western Norway were classified for 53 rivers on the basis of (i) aerial photos, (ii) LiDAR bathymetry data, (iii) sediment sampling, (iv) validation of pre-classified patterns in the field and (v) maps of geologic deposits. Sediment composition was sampled using a novel, modified Wolman–Count method, focusing on the distribution of the largest grains (Dmax). The results show that Western Norwegian rivers are dominated by glacial and colluvial deposits and partly bed rock. These non-fluvial features determine the longitudinal profile shape, bed slope and channel patterns. The collected data allow the definition of two novel types of channel forms: (i) the ‘diamictic plane bed’, with a large variation in sediment size, and (ii) the ‘mixed riffle-pool type’, with pool riffle morphology but boulder elements related to post-glacial or colluvial processes. It is presumed that the characteristic morphology evolved during the Holocene due to riverbed incision and low fluvial sediment yields, which are typical for the region and caused by the lack of dynamic tectonics and by rock resistance to weathering. The results are in contrast to the fluvial channel formation processes observed in many rivers of the world. The study thus contributes to an improved understanding of river morphology and channel pattern formation in a post-glacial landscape. It was concluded that the non-fluvial nature of the rivers studied has significant implications for flood protection measures, hydropower utilization, river ecology and ecological restoration since it determines sediment characteristics and channel stability.

NUMERICAL SIMULATION AND EXPERIMENTAL VALIDATION OF WAVE PATTERN INDUCED COORDINATE ERRORS IN AIRBORNE LIDAR BATHYMETRY

Abstract. Airborne LiDAR bathymetry (ALB) requires a refraction correction on the basis of Snell’s law at the air-water interface and a speedof- light correction to be applied on the raw laser data in order to achieve a geometric accurate representation of the water bottom. Strictly speaking, this requires exact knowledge about the local water surface inclination. If this information is not available, certain simplifications have to be introduced in correction methods. Common correction methods assume either a horizontal or a locally tilted planar water surface as well as an infinitesimally small thin laser ray, thus neglecting effects caused by the finite laser pulse diameter penetrating a curved surface. In our simulation approach, the refraction of finite diameter laser pulses passing the air/water interface is modeled differentially in a strict manner. The simulation tool is able to predict wave induced coordinate errors which have to be expected due to the neglections made in common refraction correction methods. Moreover, wave pattern dependent correction terms were be derived from systematic portions of the errors revealed by the simulations. The goal of this paper is to experimentally validate the coordinate errors predicted by the simulation tool. For that purpose, airborne laser bathymetry data of a 12 by 50 meter open air wave pool were processed, and the results were compared to reference data of the empty pool acquired by terrestrial laser scanning. The comparison showed that the effects predicted in the numerical simulation are confirmed by the experimental validation.