scholarly journals FEASIBILITY INVESTIGATION ON SINGLE PHOTON LIDAR BASED WATER SURFACE MAPPING

2018 ◽  
Vol IV-1 ◽  
pp. 109-116
Author(s):  
G. Mandlburger ◽  
B. Jutzi
Keyword(s):  
Shallow Water ◽  
Water Level ◽  
Water Surface ◽  
Single Photon ◽  
Ground Truth ◽  
Green Laser ◽  
Airborne Lidar ◽  
Large Area ◽  
Surface Mapping

<p><strong>Abstract.</strong> The recent advent of single photon sensitive airborne LiDAR (Light Detection And Ranging) sensors has enabled higher areal coverage performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light capable of penetrating clear shallow water. Although primarily designed for large area topographic mapping, the technique can also be used for mapping the water surface and shallow water bathymetry. In this contribution we investigate the capability of Single Photon LiDAR for large area mapping of water surface heights. While interface returns from conventional green-only bathymetric sensors generally suffer from water level underestimation due to the water penetration capabilities of green laser radiation, the specific questions are, if Single Photon LiDAR (i) is less affected by this well known effect due to the high receiver sensitivity and (ii) consequently delivers a higher number of water surface echoes. The topic is addressed empirically in a case study by comparing the water surface responses of Single Photon LiDAR (Navarra, Spain) and Multi-Photon Topo-Bathymetric LiDAR (Neubacher Au, Austria) for selected water bodies with a horizontal water surface (reservoirs, ponds). Although flown at different altitudes, both datasets are well comparable as they exhibit the same strip point density of ca. 14<span class="thinspace"></span>points/m<sup>2</sup>. The expected superiority of Single Photon LiDAR over conventional green-only bathymetric LiDAR for mapping water surfaces could not be verified in this investigation. While both datasets show good agreement compared to a reference water level when aggregating points into cells of 10<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>10<span class="thinspace"></span>m<sup>2</sup> (mean deviations &amp;lt;<span class="thinspace"></span>5<span class="thinspace"></span>cm), higher resolution Single Photon LiDAR based water surface models (grid size 1&amp;ndash;5<span class="thinspace"></span>m) show a systematic water level underestimation of 5&amp;ndash;20<span class="thinspace"></span>cm. However, independently measured ground truth observations and simultaneous data acquisition of the same area with both techniques are necessary to verify the results.</p>

scholarly journals On the Feasibility of Water Surface Mapping with Single Photon LiDAR

2019 ◽  
Vol 8 (4) ◽  
pp. 188 ◽  
Author(s):  
Gottfried Mandlburger ◽  
Boris Jutzi
Keyword(s):  
High Resolution ◽  
Water Level ◽  
Water Surface ◽  
Laser Light ◽  
Single Photon ◽  
Green Laser ◽  
Sensor Technology ◽  
Large Area ◽  
Surface Mapping ◽  
Surface Models

Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable of penetrating clear shallow water. The technology is designed for large-area topographic mapping, which also includes the water surface. While the penetration capabilities of green lasers generally lead to underestimation of the water level heights, we specifically focus on the questions of whether Single Photon LiDAR (i) is less affected in this respect due to the high receiver sensitivity, and (ii) consequently delivers sufficient water surface echoes for precise high-resolution water surface reconstruction. After a review of the underlying sensor technology and the interaction of green laser light with water, we address the topic by comparing the surface responses of actual Single Photon LiDAR and Multi-Photon Topo-Bathymetric LiDAR datasets for selected horizontal water surfaces. The anticipated superiority of Single Photon LiDAR could not be verified in this study. While the mean deviations from a reference water level are less than 5 cm for surface models with a cell size of 10 m, systematic water level underestimation of 5–20 cm was observed for high-resolution Single Photon LiDAR based water surface models with cell sizes of 1–5 m. Theoretical photon counts obtained from simulations based on the laser-radar equation support the experimental data evaluation results and furthermore confirm the feasibility of Single Photon LiDAR based high-resolution water surface mapping when adopting specifically tailored flight mission parameters.

scholarly journals A CASE STUDY ON THROUGH-WATER DENSE IMAGE MATCHING

2018 ◽  
Vol XLII-2 ◽  
pp. 659-666 ◽  
Author(s):  
G. Mandlburger
Keyword(s):  
Image Matching ◽  
Water Surface ◽  
Point Clouds ◽  
Water Clarity ◽  
Aerial Images ◽  
Mean Deviation ◽  
Range Image ◽  
Surface Mapping ◽  
Dense Image

In the last years, the tremendous progress in image processing and camera technology has reactivated the interest in photogrammetrybased surface mapping. With the advent of Dense Image Matching (DIM), the derivation of height values on a per-pixel basis became feasible, allowing the derivation of Digital Elevation Models (DEM) with a spatial resolution in the range of the ground sampling distance of the aerial images, which is often below 10&amp;thinsp;cm today. While mapping topography and vegetation constitutes the primary field of application for image based surface reconstruction, multi-spectral images also allow to see through the water surface to the bottom underneath provided sufficient water clarity. In this contribution, the feasibility of through-water dense image matching for mapping shallow water bathymetry using off-the-shelf software is evaluated. In a case study, the SURE software is applied to three different coastal and inland water bodies. After refraction correction, the DIM point clouds and the DEMs derived thereof are compared to concurrently acquired laser bathymetry data. The results confirm the general suitability of through-water dense image matching, but sufficient bottom texture and favorable environmental conditions (clear water, calm water surface) are a preconditions for achieving accurate results. Water depths of up to 5&amp;thinsp;m could be mapped with a mean deviation between laser and trough-water DIM in the dm-range. Image based water depth estimates, however, become unreliable in case of turbid or wavy water and poor bottom texture.

scholarly journals Shallow Water Measurements Using a Single Green Laser Corrected by Building a Near Water Surface Penetration Model

2017 ◽  
Vol 9 (5) ◽  
pp. 426 ◽  
Author(s):  
Jianhu Zhao ◽  
Xinglei Zhao ◽  
Hongmei Zhang ◽  
Fengnian Zhou
Keyword(s):  
Shallow Water ◽  
Water Surface ◽  
Green Laser

Shallow water surface related multiple elimination: a case study from North Sea

2015 ◽  
Author(s):  
Raheel Malik* ◽  
Simon Baldock ◽  
Becky Miller ◽  
Satyakee Sen ◽  
Zhaojun Liu
Keyword(s):  
Shallow Water ◽  
North Sea ◽  
Water Surface ◽  
Multiple Elimination

scholarly journals SEMANTIC CHANGE DETECTION OF RIVER GROUND POINTS IN AIRBORNE LIDAR BATHYMETRY DATA USING VOXEL OCCUPANCIES

2019 ◽  
Vol IV-2/W7 ◽  
pp. 9-15
Author(s):  
R. Boerner ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Water Surface ◽  
Semantic Information ◽  
Reference Data ◽  
Ground Truth ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Semantic Change ◽  
Data Set ◽  
Airborne Lidar Bathymetry ◽  
Semantic Reference

<p><strong>Abstract.</strong> This paper proposes a method to get semantic information of changes in bathymetric point clouds. This method aims for assigning labels to river ground points which determine if either the point can be compared with a reference DEM, if there are no data in the reference or if there are no water points inside the new Data of wet areas of the reference data. This labels can be further used to specify areas where differences of DEMS can be calculated, the comparable areas. The Areas where no reference data is found specify areas where the reference DEM will have a higher variance due to interpolation which should be considered in the comparison. The areas where no water in the new data was found specify areas there no refraction correction in the new data can be done and which should be considered with a higher variance of the ground points or there the water surface should be tried to reconstruct. The proposed approach uses semantic reference data to specify water areas in the new scan. An occupancy analysis is used to specify if voxels of the new data exist in the reference or not. In case of occupancy, the labels of the reference are assigned to the new data and in case of no occupancy, the label of changed data is assigned. A histogram based method is used to separate ground and water points in wet areas and a second occupancy analysis is used to specify the semantic changes in wet areas. The proposed method is evaluated on a proposed data set of the Mangfall area where the ground truth is manually labelled.</p>

scholarly journals A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5065 ◽  
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).

scholarly journals MAPPING OF WATER SURFACE LEVELS AND SLOPES WITH SINGLE PHOTON LIDAR – A CASE STUDY AT THE RIVER RHINE

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 57-64
Author(s):  
G. Mandlburger ◽  
R. Weiß ◽  
T. Artz
Keyword(s):  
High Resolution ◽  
Water Surface ◽  
Point Cloud ◽  
Single Photon ◽  
Numerical Models ◽  
River Rhine ◽  
Surface Mapping ◽  
Federal Institute ◽  
Precise Knowledge ◽  
Pulse Density

Abstract. Precise knowledge of water surface level heights is crucial for safe ship navigation and as basis for calibration of hydrodynamic-numerical models. While Airborne Laser Scanning (ALS) is a well established technique for topographic mapping, ALS-based water surface mapping using conventional infrared lasers suffers from the high degree of specular reflection which leads to data voids for off-nadir angles beyond 5–7 degrees. The advent of single photon sensitive ALS systems using green laser sources presents the prospect of large-area, high-resolution water surface mapping due to the high receiver sensitivity and measurement rate of such systems. Building on previous studies on subject matters, we present the results of a pilot project initiated and conducted by the German Federal Institute of Hydrology (BfG, Koblenz) at the Rhine River. Three specific test sites with varying water surface and flow velocity properties were captured on October 30th and 31th, 2019 with the Leica SPL100 from flying altitudes of 3000 m, 2500 m, 1600 m, and 800 m, respectively. As anticipated, the water surface laser pulse density was high and exhibited 20–145 points/m2 depending on flying altitude. After quality control, strip adjustment, and point cloud analysis, three water surface classification methods were implemented based on: (i) height quantiles, (ii) point cloud segmentation, and (iii) inverse DTM filtering. All approaches featured relative and absolute water level height accuracies better than 10 cm. We conclude that Single Photon LiDAR based high resolution mapping of water surface levels and tilts is feasible when employing application specific data acquisition parameters, i.e., off-nadir angle &amp;leq;10° and flying altitude &amp;leq;3000 m.

scholarly journals Reconstruction of Bog Lake Water Level Fluctuations Using Method of Simple Modelling: A Case Study on Lake Rėkyva

2011 ◽  
Vol 1 (4) ◽  
pp. 15-20
Author(s):  
Julius Taminskas ◽  
Adomas Mažeikis ◽  
Rita Linkevičienė
Keyword(s):  
Water Level ◽  
Lake Water ◽  
Meteorological Data ◽  
Precipitation Amount ◽  
Water Level Fluctuations ◽  
Data Series ◽  
Large Area ◽  
Lake Water Level ◽  
Bog Lakes

Considering lack of observations and meteorological data, the article analyses the reconstruction of bog lake water level fluctuations. The main issue of using advanced methods for determining water level fluctuations and balance can be lack of verified data. The proposed method uses only a variable length period of precipitation amount data series. Bog lake Rėkyva can be distinguished from other bog lakes due to its large area, and therefore has been chosen for this case study. The main conclusion is that the proposed method is suitable for determining trends towards water level fluctuation over long time periods.

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