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Monitoring Aquatic Environments Using LiDAR

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 23535

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Special Issue Editors

Dr. Martin A. Montes

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1. Science Systems and Applications, Inc., (SSAI), Lanham, MD, USA
2. NASA Goddard Space Flight Center, Greenbelt, MD, USA
Interests: LiDAR in aquatic environments; statistical modeling of continental aerosols; toxic phytoplankton blooms
Special Issues, Collections and Topics in MDPI journals

Dr. Luca Fiorani

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Guest Editor

ENEA Research Center, 00044 Frascati, Italy
Interests: laser sensors for natural phenomena; climate change; ecology; sustainable development

Dr. Fraser Dalgleish

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Guest Editor

L3Harris Technologies, Space & Airborne Systems, NASA Boulevard, Melbourne, FL 32919, USA
Interests: underwater imaging applications; computer vision in underwater laser imaging applications; real-time environmental monitoring and events detection; application of electro-optic imaging numerical model and deconvolution technique in image enhancement and pulse resolution improvements
Special Issues, Collections and Topics in MDPI journals

Dr. Grady Tuell

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Guest Editor

3D Ideas, LLC, Madison, WI, USA
Interests: lidar; water

Special Issue Information

Dear Colleagues,

The study of freshwater and marine ecosystems based on Light Detection And Ranging (LiDAR) and other electro-optical systems technologies has received a major attention in recent years due to the development of more advanced sensors, embedded photonic and electronic subsystems, and the availability of suitable laser devices. The launch of new spaceborne LiDAR systems (e.g., ICESAT-2) and the increased capabilities of autonomous robotic airborne and maritime platforms have further enabled this expansion.

Traditionally, LiDAR applications have focused on water column and bottom return signals and have been supported by systems consisting of relatively simple configurations (e.g., single receivers with one or two wavelengths) and fixed geometries (e.g., one field-of-view). Likewise, the simulation of LiDAR waveforms has commonly been performed using models based on single scattering or small-angle approximations. Nowadays, more advanced LiDAR sensors are capable of multi-angle measurements (e.g., ICESAT-2), hyperspectral analysis of time-resolved pulses, and characterization of suspended particles by linear-depolarization changes. Furthermore, hybrid processing algorithms have been proposed using passive optical information. This Special Issue aims to present a collection of original research articles and review papers on LiDAR technologies and applications related to the characterization of water components, water interfaces (e.g., air–water, water–bottom), and bottom characteristics. Topics of interest include, but are not limited to:

-nonlinear scanners and robotic platforms;
-LiDAR inversions and machine learning;
-complex LiDAR simulators for 3D geometries;
-multispectral and multi-angular spaceborne LiDAR applications;
-detection and discrimination of floating algae and bottom types/shapes;
-thin layers and particle orientation;
-hyperspectral fluorescence LiDAR and oil detection; and
-innovative LiDAR validation approaches.

Dr. Martin A. Montes
Dr. Luca Fiorani
Dr. Fraser Dalgleish
Dr. Grady Tuell
Guest Editor

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Keywords

LiDAR
electro-optical sensors
waveform inversion
radiative transfer modeling
bathymetry
active remote sensing
aquatic ecosystems.

Published Papers (11 papers)

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24 pages, 11571 KiB

Open AccessArticle

Underwater Multispectral Laser Serial Imager for Spectral Differentiation of Macroalgal and Coral Substrates

by Matthieu Huot, Fraser Dalgleish, Eric Rehm, Michel Piché and Philippe Archambault

Remote Sens. 2022, 14(13), 3105; https://doi.org/10.3390/rs14133105 - 28 Jun 2022

Cited by 3 | Viewed by 2110

Abstract

The advancement of innovative underwater remote sensing detection and imaging methods, such as continuous wave laser line scan or pulsed laser (i.e., LiDAR—Light Detection and Ranging) imaging approaches can provide novel solutions for studying biological substrates and manmade objects/surfaces often encountered in underwater coastal environments. Such instruments can be used shipboard or coupled with proven and available deployment platforms as AUVs (Autonomous Underwater Vehicles). With the right planning, large areas can be surveyed, and more extreme and difficult-to-reach environments can be studied. A prime example, and representing a certain navigational challenge, is the under ice in the Arctic/Antarctic or winter/polar environments or deep underwater survey. Among many marine biological substrates, numerous species of macroalgae can be found worldwide in shallow down to 70+ m (clear water) coastal habitats and are essential ecosystem service providers through the habitat they provide for other species, the potential food resource value, and carbon sink they represent. Similarly, corals also provide important ecosystem services through their structure and diversity, are found to harbor increased local diversity, and are equally valid targets as “keystone” species. Hence, we expand current underwater remote sensing methods to combine macroalgal and coral surveys via the development of a multispectral laser serial imager designed for classification via spectral response. By using multiple continuous wave laser wavelength sources to scan and illuminate recreated benthic environments composed of macroalgae and coral, we show how elastic (i.e., reflectance) and inelastic (i.e., fluorescence) spectral responses can potentially be used to differentiate algal color groups and certain coral genus. Experimentally, three laser diodes (450 nm, 490 nm, 520 nm) are sequentially used in conjunction with up to 5 emission filters (450 nm, 490 nm, 520 nm, 580 nm, 685 nm) to acquire images generated by laser line scan pattern via high-speed galvanometric mirrors. Placed directly adjacent to a large saltwater imaging tank fitted with optical viewports, the optical system records target substrate spectral response using a photomultiplier preceded by a filter and is synchronously digitized to the scan rate by a high sample rate Analog-to-Digital Converter (ADC). Acquired images are normalized to correct for imager optical effects allowing for fluorescence intensity-based pixel segmentation via intensity thresholding. Overall, the multispectral laser serial imaging technique shows that the resulting high resolution data can be used for detection and classification of benthic substrates by their spectral response. These methods highlight a path towards eventual pixel-wise spectral response analysis for spectral differentiation. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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30 pages, 27344 KiB

Open AccessArticle

SOLS: An Open-Source Spaceborne Oceanic Lidar Simulator

by Zhenhua Zhang, Peng Chen and Zhihua Mao

Remote Sens. 2022, 14(8), 1849; https://doi.org/10.3390/rs14081849 - 12 Apr 2022

Cited by 4 | Viewed by 2229

Abstract

In recent years, oceanic lidar has seen a wide range of oceanic applications, such as optical profiling and detecting bathymetry. Furthermore, spaceborne lidars, CALIOP and ICESat-2, designed for atmospheric and ice science applications, have been used for ocean backscattering retrievals, but, until now, there has been no spaceborne lidar specifically designed for ocean detection. There is a demand for an effective lidar simulator to study the detection potential capability of spaceborne oceanic lidar. In this study, an open-source spaceborne oceanic lidar simulator named SOLS was developed, which is available freely. Moreover, the maximum detectable depth and corresponding optimal wavelength for spaceborne lidar were analyzed at a global scale by using SOLS. The factors controlling detection limits of a spaceborne ocean profiling lidar in different cases were discussed. Then, the maximum detectable depths with different relative measurement errors and the influence of solar background radiance were estimated. Subsequently, the effects of laser and detector parameters on maximum detectable depths were studied. The relationship between the lidar detectable depth and the ocean mixed layer depth was also discussed. Preliminary results show that the maximum detectable depth could reach deeper than 120 m in the oligotrophic sea at low latitudes. We found that 490 nm is the optimal wavelength for most of the open seawater. For coastal water, 532 nm is a more suitable choice considering both the technical maturity and geophysical parameters. If possible, a lidar equipped with 440 nm could achieve the greatest depth in oligotrophic seawater in subtropical gyres north and south of the equator. The upper mixed layer vertical structure in most of the global open ocean is within the lidar maximum detectable depth. These results show that SOLS can help the design of future spaceborne oceanic lidar systems a lot. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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25 pages, 9577 KiB

Open AccessArticle

Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data

by Signe Schilling Hansen, Verner Brandbyge Ernstsen, Mikkel Skovgaard Andersen, Zyad Al-Hamdani, Ramona Baran, Manfred Niederwieser, Frank Steinbacher and Aart Kroon

Remote Sens. 2021, 13(20), 4101; https://doi.org/10.3390/rs13204101 - 13 Oct 2021

Cited by 8 | Viewed by 2363

Abstract

Boulders on the seabed in coastal marine environments provide key geo- and ecosystem functions and services. They serve as natural coastal protection by dissipating wave energy, and they form an important hard substrate for macroalgae, and hence for coastal marine reefs that serve as important habitats for fish. The aim of this study was to investigate the possibility of developing an automated method to classify boulders from topo-bathymetric LiDAR data in coastal marine environments. The Rødsand lagoon in Denmark was used as study area. A 100 m × 100 m test site was divided into a training and a test set. The classification was performed using the random forest machine learning algorithm. Different tuning parameters were tested. The study resulted in the development of a nearly automated method to classify boulders from topo-bathymetric LiDAR data. Different measure scores were used to evaluate the performance. For the best parameter combination, the recall of the boulders was 57%, precision was 27%, and F-score 37%, while the accuracy of the points was 99%. The most important tuning parameters for boulder classification were the subsampling level, the choice of the neighborhood radius, and the features. Automatic boulder detection will enable transparent, reproducible, and fast detection and mapping of boulders. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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18 pages, 7408 KiB

Open AccessArticle

An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data

by Chunyi Zhong, Peng Chen and Delu Pan

Remote Sens. 2021, 13(19), 3875; https://doi.org/10.3390/rs13193875 - 28 Sep 2021

Cited by 7 | Viewed by 1822

Abstract

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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21 pages, 7795 KiB

Open AccessArticle

A Novel Fast Multiple-Scattering Approximate Model for Oceanographic Lidar

by Zhenhua Zhang, Peng Chen, Zhihua Mao and Dapeng Yuan

Remote Sens. 2021, 13(18), 3677; https://doi.org/10.3390/rs13183677 - 15 Sep 2021

Cited by 2 | Viewed by 2096

Abstract

An effective lidar simulator is vital for its system design and processing algorithms. However, laser transmission is a complex process due to the effects of sea surface and various interactions in seawater such as absorption, scattering, and so on. It is sophisticated and difficult for multiple scattering to accurately simulate. In this study, a multiple-scattering lidar model based on multiple-forward-scattering-single-backscattering approximation for oceanic lidar was proposed. Compared with previous analytic models, this model can work without assuming a homogeneous water and fixed scattering phase function. Besides, it takes consideration of lidar system and environmental parameters including receiver field of view, different scattering phase functions, particulate sizes, stratified water, and rough sea surface. One should note that because the scattering phase function is difficult to determine accurately, the simulation accuracy may be reduced in a complex oceanic environment. The Cox–Munk model used in our method simulates capillarity waves but ignores gravity waves, and the pulse stretching is not included. The wide-angle scattering occurs in the dense subsurface phytoplankton, which sometimes makes it hard to use this model. In this study, we firstly derived this method based on an analytical solution by convolving Gaussians of the forward-scattering contribution of layer dr and the energy density at R in the small-angle-scattering approximation. Then, the effects of multiple scattering and water optical properties were analyzed using the model. Meanwhile, the validation with Monte Carlo model was implemented. Their coefficient of determination is beyond 0.9, the RMSE is within 0.02, the MAD is within 0.02, and the MAPD is within 8%, which indicates that our model is efficient for oceanographic lidar simulation. Finally, we studied the effects of FOV, SPF, rough sea surface, stratified water, and particle size. These results can provide reference for the design of the oceanic lidar system and contribute to the processing of lidar echo signals. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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20 pages, 6927 KiB

Open AccessArticle

Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser

by Gang Liang, Xinglei Zhao, Jianhu Zhao and Fengnian Zhou

Remote Sens. 2021, 13(18), 3628; https://doi.org/10.3390/rs13183628 - 11 Sep 2021

Cited by 8 | Viewed by 1836

Abstract

The discrimination of water–land waveforms is a critical step in the processing of airborne topobathy LiDAR data. Waveform features, such as the amplitudes of the infrared (IR) laser waveforms of airborne LiDAR, have been used in identifying water–land interfaces in coastal waters through waveform clustering. However, water–land discrimination using other IR waveform features, such as full width at half maximum, area, width, and combinations of different features, has not been evaluated and compared with other methods. Furthermore, false alarms often occur when water–land discrimination in coastal areas is conducted using IR laser waveforms because of environmental factors. This study provides an optimal feature for water–land discrimination using an IR laser by comparing the performance of different waveform features and proposes a dual-clustering method integrating K-means and density-based spatial clustering applications with noise algorithms to improve the accuracy of water–land discrimination through the clustering of waveform features and positions of IR laser spot centers. The proposed method is used for practical measurement with Optech Coastal Zone Mapping and Imaging LiDAR. Results show that waveform amplitude is the optimal feature for water–land discrimination using IR laser waveforms among the researched features. The proposed dual-clustering method can correct mislabeled water or land waveforms and reduce the number of mislabeled waveforms by 48% with respect to the number obtained through traditional K-means clustering. Water–land discrimination using IR waveform amplitude and the proposed dual-clustering method can reach an overall accuracy of 99.730%. The amplitudes of IR laser waveform and the proposed dual-clustering method are recommended for water–land discrimination in coastal and inland waters because of the high accuracy, resolution, and automation of the methods. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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18 pages, 2636 KiB

Open AccessArticle

Interpretation of Spectral LiDAR Backscattering off the Florida Coast

by Martin A. Montes, Anni K. Vuorenkoski, Ben Metzger and Bryan Botson

Remote Sens. 2021, 13(13), 2475; https://doi.org/10.3390/rs13132475 - 25 Jun 2021

Viewed by 1583

Abstract

A multispectral backscattering LiDAR (Light detection and range) system (hereafter Oculus) was integrated into a wave glider and used to estimate the scattering order (i.e., single vs multiple collisions) of LIDAR backscattering, the water inherent optical properties (IOPs), the biogeo-chemical characteristics of particulate scatterers (i.e., relative size, composition) and their motion) on shelf waters of South East Florida. Oculus has a dual-wavelength configuration (473 and 532 nm) and two detection geometries (off- and on-axis). Characteristics of scatterers were investigated based on two complementary LiDAR-derived proxies (the Structural Dissimilarity Index and the spectral slope of LiDAR backscattering). In March 2017, field measurements showed a covariation between direct and diffuse backscattering contributions during morning hours and away from shore. LiDAR attenuation coefficients explained up to 57% of IOPs variability. The analysis of LiDAR-derived proxies suggested higher turbidity and larger particulates near the coast Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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19 pages, 6589 KiB

Open AccessArticle

Assessing Marginal Shallow-Water Bathymetric Information Content of Lidar Sounding Attribute Data and Derived Seafloor Geomorphometry

by Kim Lowell and Brian Calder

Remote Sens. 2021, 13(9), 1604; https://doi.org/10.3390/rs13091604 - 21 Apr 2021

Cited by 3 | Viewed by 1641

Abstract

Shallow-water depth estimates from airborne lidar data might be improved by using sounding attribute data (SAD) and ocean geomorphometry derived from lidar soundings. Moreover, an accurate derivation of geomorphometry would be beneficial to other applications. The SAD examined here included routinely collected variables such as sounding intensity and fore/aft scan direction. Ocean-floor geomorphometry was described by slope, orientation, and pulse orthogonality that were derived from the depth estimates of bathymetry soundings using spatial extrapolation and interpolation. Four data case studies (CSs) located near Key West, Florida (United States) were the testbed for this study. To identify bathymetry soundings in lidar point clouds, extreme gradient boosting (XGB) models were fitted for all seven possible combinations of three variable suites—SAD, derived geomorphometry, and sounding depth. R² values for the best models were between 0.6 and 0.99, and global accuracy values were between 85% and 95%. Lidar depth alone had the strongest relationship to bathymetry for all but the shallowest CS, but the SAD provided demonstrable model improvements for all CSs. The derived geomorphometry variables contained little bathymetric information. Whereas the SAD showed promise for improving the extraction of bathymetry from lidar point clouds, the derived geomorphometry variables do not appear to describe geomorphometry well. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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16 pages, 5046 KiB

Open AccessTechnical Note

Inverse Histogram-Based Clustering Approach to Seafloor Segmentation from Bathymetric Lidar Data

by Jaehoon Jung, Jaebin Lee and Christopher E. Parrish

Remote Sens. 2021, 13(18), 3665; https://doi.org/10.3390/rs13183665 - 14 Sep 2021

Cited by 5 | Viewed by 1937

Abstract

A current hindrance to the scientific use of available bathymetric lidar point clouds is the frequent lack of accurate and thorough segmentation of seafloor points. Furthermore, scientific end-users typically lack access to waveforms, trajectories, and other upstream data, and also do not have the time or expertise to perform extensive manual point cloud editing. To address these needs, this study seeks to develop and test a novel clustering approach to seafloor segmentation that solely uses georeferenced point clouds. The proposed approach does not make any assumptions regarding the statistical distribution of points in the input point cloud. Instead, the approach organizes the point cloud into an inverse histogram and finds a gap that best separates the seafloor using the proposed peak-detection method. The proposed approach is evaluated with datasets acquired in Florida with a Riegl VQ-880-G bathymetric LiDAR system. The parameters are optimized through a sensitivity analysis with a point-wise comparison between the extracted seafloor and ground truth. With optimized parameters, the proposed approach achieved F1-scores of 98.14–98.77%, which outperforms three popular existing methods. Further, we compared seafloor points with Reson 8125 MBES hydrographic survey data. The results indicate that seafloor points were detected successfully with vertical errors of −0.190 ± 0.132 m and −0.185 ± 0.119 m (μ ± σ) for two test datasets. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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13 pages, 7545 KiB

Open AccessTechnical Note

Global Ocean Studies from CALIOP/CALIPSO by Removing Polarization Crosstalk Effects

by Xiaomei Lu, Yongxiang Hu, Ali Omar, Rosemary Baize, Mark Vaughan, Sharon Rodier, Jayanta Kar, Brian Getzewich, Patricia Lucker, Charles Trepte, Chris Hostetler and David Winker

Remote Sens. 2021, 13(14), 2769; https://doi.org/10.3390/rs13142769 - 14 Jul 2021

Cited by 9 | Viewed by 2086

Abstract

Recent studies indicate that the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite provides valuable information about ocean phytoplankton distributions. CALIOP’s attenuated backscatter coefficients, measured at 532 nm in receiver channels oriented parallel and perpendicular to the laser’s linear polarization plane, are significantly improved in the Version 4 data product. However, due to non-ideal instrument effects, a small fraction of the backscattered optical power polarized parallel to the receiver polarization reference plane is misdirected into the perpendicular channel, and vice versa. This effect, known as polarization crosstalk, typically causes the measured perpendicular signal to be higher than its true value and the measured parallel signal to be lower than its true value. Therefore, the ocean optical properties derived directly from CALIOP’s measured signals will be biased if the polarization crosstalk effect is not taken into account. This paper presents methods that can be used to estimate the CALIOP crosstalk effects from on-orbit measurements. The global ocean depolarization ratios calculated both before and after removing the crosstalk effects are compared. Using CALIOP crosstalk-corrected signals is highly recommended for all ocean subsurface studies. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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13 pages, 3540 KiB

Open AccessTechnical Note

Airborne Lidar Observations of a Spring Phytoplankton Bloom in the Western Arctic Ocean

by James H. Churnside, Richard D. Marchbanks and Nathan Marshall

Remote Sens. 2021, 13(13), 2512; https://doi.org/10.3390/rs13132512 - 27 Jun 2021

Cited by 6 | Viewed by 2077

Abstract

One of the most notable effects of climate change is the decrease in sea ice in the Arctic Ocean. This is expected to affect the distribution of phytoplankton as the ice retreats earlier. We were interested in the vertical and horizontal distribution of phytoplankton in the Chukchi Sea in May. Measurements were made with an airborne profiling lidar that allowed us to cover large areas. The lidar profiles showed a uniform distribution of attenuation and scattering from the surface to the limit of lidar penetration at a depth of about 30 m. Both parameters were greater in open water than under the ice. Depolarization of the lidar decreased as attenuation and scattering increased. A cluster analysis of the 2019 data revealed four distinct clusters based on depolarization and lidar ratio. One cluster was associated with open water, one with pack ice, one with the waters along the land-fast ice, and one that appeared to be scattered throughout the region. The first three were likely the result of different assemblages of phytoplankton, while the last may have been an artifact of thin fog in the atmosphere. Full article

(This article belongs to the Special Issue Monitoring Aquatic Environments Using LiDAR)

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