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.

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Potential of a Non-linear Full-Waveform Stacking Technique in Airborne LiDAR Bathymetry

PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science ◽

10.1007/s41064-021-00147-y ◽

2021 ◽

Author(s):

David Mader ◽

Katja Richter ◽

Patrick Westfeld ◽

Hans-Gerd Maas

Keyword(s):

Water Depth ◽

High Reliability ◽

Bottom Topography ◽

Airborne Lidar ◽

Majority Voting ◽

Waveform Data ◽

Full Waveform ◽

Airborne Lidar Bathymetry ◽

Water Bottom

AbstractAirborne LiDAR bathymetry is an efficient measurement method for area-wide acquisition of water bottom topography in shallow water areas. However, the method has a limited penetration depth into water bodies due to water turbidity. This affects the accuracy and reliability of the determination of water bottom points in waters with high turbidity or larger water depths. Furthermore, the coverage of the water bottom topography is also limited. In this contribution, advanced processing methods are presented with the goal of increasing the evaluable water depth, resulting in an improved coverage of the water bottom by measurement points. The methodology moves away from isolated evaluation of individual signals to a determination of water bottom echoes, taking into account information from closely adjacent measurements, assuming that these have similar or correlated characteristics. The basic idea of the new processing approach is the combination of closely adjacent full-waveform data using full-waveform stacking techniques. In contrast to established waveform stacking techniques, we do not apply averaging, which entails low-pass filtering effects, but a modified majority voting technique. This has the effect of amplification of repeating weak characteristics and an improvement of the signal-noise-ratio. As a consequence, it is possible to detect water bottom points that cannot be detected by standard methods. The results confirm an increased penetration water depth by about 27% with a high reliability of the additionally extracted water bottom points along with a larger coverage of the water bottom topography.

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WATER TURBIDITY ESTIMATION FROM LIDAR BATHYMETRY DATA BY FULL-WAVEFORM ANALYSIS – COMPARISON OF TWO APPROACHES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2021-681-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 681-688

Author(s):

K. Richter ◽

D. Mader ◽

P. Westfeld ◽

H.-G. Maas

Keyword(s):

Measurement Data ◽

Airborne Lidar ◽

Waveform Analysis ◽

Estimation Methods ◽

Data Set ◽

Water Turbidity ◽

Waveform Data ◽

Full Waveform ◽

Novel Approach ◽

Airborne Lidar Bathymetry

Abstract. Airborne LiDAR bathymetry is an efficient technique for surveying the bottom of shallow waters. In addition, the measurement data contain valuable information about the local turbidity conditions in the water body. The extraction of this information requires appropriate evaluation methods examining the decay of the recorded waveform signal. Existing approaches are based on several assumptions concerning the influence of the ALB system on the waveform signal, the extraction of the volume backscatter, and the directional independence of turbidity. The paper presents a novel approach that overcomes the existing limitations using two alternative turbidity estimation methods as well as different variants of further processed full-waveform data. For validation purposes, the approach was applied to a data set of a shallow inland water. The results of the quantitative evaluation show, which method and which data basis is best suited for the derivation of area wide water turbidity information.

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IMPROVEMENTS IN LIDAR BATHYMETRY DATA ANALYSIS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w10-113-2019 ◽

2019 ◽

Vol XLII-2/W10 ◽

pp. 113-117 ◽

Cited By ~ 1

Author(s):

H.-G. Maas ◽

D. Mader ◽

K. Richter ◽

P. Westfeld

Keyword(s):

Laser Scanning ◽

Bottom Topography ◽

Airborne Lidar ◽

Wave Form ◽

Form Analysis ◽

Full Waveform ◽

Full Wave ◽

Systematic Effects ◽

Airborne Lidar Bathymetry ◽

Coordinate Correction

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.

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Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2017.02.012 ◽

2017 ◽

Vol 58 ◽

pp. 191-200 ◽

Cited By ~ 17

Author(s):

Kutalmis Saylam ◽

Rebecca A. Brown ◽

John R. Hupp

Keyword(s):

Shallow Water ◽

Satellite Imagery ◽

Airborne Lidar ◽

Water Bodies ◽

North Slope ◽

Airborne Lidar Bathymetry ◽

Alaskan North Slope

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Refined Geometric Modeling of Laser Pulse Propagation in Airborne LiDAR Bathymetry

PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science ◽

10.1007/s41064-021-00146-z ◽

2021 ◽

Author(s):

Katja Richter ◽

David Mader ◽

Patrick Westfeld ◽

Hans-Gerd Maas

Keyword(s):

Geometric Modeling ◽

Water Surface ◽

Pulse Propagation ◽

Geometric Model ◽

Airborne Lidar ◽

Surface Geometry ◽

Accurate Representation ◽

Airborne Lidar Bathymetry ◽

The Impact ◽

Water Bottom

AbstractTo achieve a geometrically accurate representation of the water bottom, airborne LiDAR bathymetry (ALB) requires the correction of the raw 3D point coordinates due to refraction at the air–water interface, different signal velocity in air and water, and further propagation induced effects. The processing of bathymetric LiDAR data is based on a geometric model of the laser bathymetry pulse propagation describing the complex interactions of laser radiation with the water medium and the water bottom. The model comprises the geometric description of laser ray, water surface, refraction, scattering in the water column, and diffuse bottom reflection. Conventional geometric modeling approaches introduce certain simplifications concerning the water surface, the laser ray, and the bottom reflection. Usually, the local curvature of the water surface and the beam divergence are neglected and the travel path of the outgoing and the returned pulse is assumed to be identical. The deviations between the applied geometric model and the actual laser beam path cause a coordinate offset at the water bottom, which affects the accuracy potential of the measuring method. The paper presents enhanced approaches to geometric modeling which are based on a more accurate representation of water surface geometry and laser ray geometry and take into account the diffuse reflection at the water bottom. The refined geometric modeling results in an improved coordinate accuracy at the water bottom. The impact of the geometric modeling methods on the accuracy of the water bottom points is analyzed in a controlled manner using a laser bathymetry simulator. The findings will contribute to increase the accuracy potential of modern ALB systems.

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

Sensors ◽

10.3390/s18124153 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4153 ◽

Cited By ~ 3

Author(s):

Kutalmis Saylam ◽

John Hupp ◽

John Andrews ◽

Aaron Averett ◽

Anders Knudby

Keyword(s):

Quality Control ◽

Careful Analysis ◽

Airborne Lidar ◽

Control Measures ◽

Water Bodies ◽

Data Sets ◽

Coastal Zones ◽

Airborne Lidar Bathymetry ◽

The 1960S ◽

Elevation Model

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.

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A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry

Sensors ◽

10.3390/s19235065 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5065 ◽

Cited By ~ 2

Author(s):

Xing ◽

Wang ◽

Xu ◽

Lin ◽

Li ◽

...

Keyword(s):

Shallow Water ◽

Deep Water ◽

Water Surface ◽

Decomposition Method ◽

Trust Region ◽

Airborne Lidar ◽

High Signal ◽

Bathymetric Range ◽

Airborne Lidar Bathymetry ◽

Water Bottom

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).

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CLASSIFICATION OF DATA FROM AIRBORNE LIDAR BATHYMETRY WITH RANDOM FOREST ALGORITHM BASED ON DIFFERENT FEATURE VECTORS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w16-143-2019 ◽

2019 ◽

Vol XLII-2/W16 ◽

pp. 143-148

Author(s):

T. Kogut ◽

M. Weistock ◽

K. Bakuła

Keyword(s):

Random Forest ◽

Confusion Matrix ◽

Airborne Lidar ◽

Random Forest Algorithm ◽

Data Set ◽

Waveform Data ◽

Feature Vectors ◽

Full Waveform ◽

Airborne Lidar Bathymetry

Abstract. Modern full-waveform laser bathymetric scanners offer the possibility of a practical application of airborne laser bathymetry (ALB) data algorithms as a valuable source of information in the study of the aquatic environment. The reliability of the obtained results and the efficiency of the classification depend on the applied features. The input data for the classifier should consist of variables that have the ability to discriminate within the data set, for the detection and classification of objects on the seabed. The automatic detection of underwater objects is based on machine learning solutions. In this paper, the ALB data were used to present a classification process based on the random forest algorithm. The classification was carried out using two independent approaches with two feature vectors. The quality of classifications based on the full-waveform features vector and the geometric features vector was compared. The efficiency of each classification was verified using a confusion matrix. The obtained efficiency of the point classification in both cases was about 100% for the water surface, 99.9% for the seabed and about 60% for underwater objects. Better results for the classification of objects were obtained for the features vector based on features obtained directly from full-waveform data than for the vector obtained from geometric relationships in the point cloud.

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