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Journal articles on the topic "Topo-Bathymetric lidar":
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Letard, M., A. Collin, D. Lague, T. Corpetti, Y. Pastol, and A. Ekelund. "USING BISPECTRAL FULL-WAVEFORM LIDAR TO MAP SEAMLESS COASTAL HABITATS IN 3D." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 463–70. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-463-2022.
Abstract. Mapping coastal habitats is essential to their preservation, but the presence of water hinders seamless data collection over land-water interfaces. Thanks to its dual-wavelength and optical properties, topo-bathymetric lidar can address this task efficiently. Topo-bathymetric lidar waveforms contain relevant information to classify land and water covers automatically but are rarely analysed for both infrared and green wavelengths. The present study introduces a point-based approach for the classification of coastal habitats using bispectral waveforms of topo-bathymetric lidar surveys and machine learning. Spectral features and differential elevations are fed to a random forest algorithm to produce three-dimensional classified point clouds of 17 land and sea covers. The resulting map reaches an overall accuracy of 86%, and 65% of the prediction probabilities are above 0.60. Using this prediction confidence, it is possible to map coastal habitats and eliminate the classification errors due to noise in the data, that generate a clear tendency of the algorithm to over-estimate some classes at the expense of some others. By filtering out points with a low prediction confidence (under 0.7), the classification can be highly improved and reach an overall accuracy of 97%.
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Arav, Reuma, Camillo Ressl, Robert Weiss, Thomas Artz, and Gottfried Mandlburger. "Evaluation of Active and Passive UAV-Based Surveying Systems for Eulittoral Zone Mapping." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 9–16. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-9-2024.
Abstract. The eulittoral zone, which alternates between being exposed and submerged, presents a challenge for high-resolution characterization. Normally, its mapping is divided between low and high water levels, where each calls for a different type of surveying instrument. This leads to inconsistent mapping products, both in accuracy and resolution. Recently, uncrewed airborne vehicle (UAV) based photogrammetry was suggested as an available and low-cost solution. However, relying on a passive sensor, this approach requires adequate environmental conditions, while its ability to map inundated regions is limited. Alternatively, UAV-based topo-bathymetric laser scanners enable the acquisition of both submerged and exposed regions independent of lighting conditions while maintaining the acquisition flexibility. In this paper, we evaluate the applicability of such systems in the eulittoral zone. To do so, both topographic and topo-bathymetric LiDAR sensors were loaded on UAVs to map a coastal region along the river Rhein. The resulting point clouds were compared to UAV-based photogrammetric ones. Aspects such as point spacing, absolute accuracy, and vertical offsets were analysed. To provide operative recommendations, each LiDAR scan was acquired at different flying altitudes, while the photogrammetric point clouds were georeferenced based on different exterior information configurations. To assess the riverbed modelling, we compared the surface model acquired by the topo-bathymetric LiDAR sensor to multibeam echosounder measurements. Our analysis shows that the accuracies of the LiDAR point clouds are hardly affected by flying altitude. The derived riverbed elevation, on the other hand, shows a bias which is linearly related to water depth.
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Webster, Tim, Candace MacDonald, Kevin McGuigan, Nathan Crowell, Jean-Sebastien Lauzon-Guay, and Kate Collins. "Calculating macroalgal height and biomass using bathymetric LiDAR and a comparison with surface area derived from satellite data in Nova Scotia, Canada." Botanica Marina 63, no. 1 (February 25, 2020): 43–59. http://dx.doi.org/10.1515/bot-2018-0080.
AbstractThe ability to map and monitor the macroalgal coastal resource is important to both the industry and the regulator. This study evaluates topo-bathymetric lidar (light detection and ranging) as a tool for estimating the surface area, height and biomass of Ascophyllum nodosum, an anchored and vertically suspended (floating) macroalga, and compares the surface area derived from lidar and WorldView-2 satellite imagery. Pixel-based Maximum Likelihood classification of low tide satellite data produced 2-dimensional maps of intertidal macroalgae with overall accuracy greater than 80%. Low tide and high tide topo-bathymetric lidar surveys were completed in southwestern Nova Scotia, Canada. Comparison of lidar-derived seabed elevations with ground-truth data collected using a survey grade global navigation satellite system (GNSS) indicated the low tide survey data have a positive bias of 15 cm, likely resulting from the seaweed being draped over the surface. The high tide survey data did not exhibit this bias, although the suspended canopy floating on the water surface reduced the seabed lidar point density. Validation of lidar-derived seaweed heights indicated a mean difference of 30 cm with a root mean square error of 62 cm. The modelled surface area of seaweed was 28% greater in the lidar model than the satellite model. The average lidar-derived biomass estimate was within one standard deviation of the mean biomass measured in the field. The lidar method tends to overestimate the biomass compared to field measurements that were spatially biased to the mid-intertidal level. This study demonstrates an innovative and cost-effective approach that uses a single high tide bathymetric lidar survey to map the height and biomass of dense macroalgae.
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Eren, Firat, Jaehoon Jung, Christopher E. Parrish, Nicholas Sarkozi-Forfinski, and Brian R. Calder. "Total Vertical Uncertainty (TVU) Modeling for Topo-Bathymetric LIDAR Systems." Photogrammetric Engineering & Remote Sensing 85, no. 8 (August 1, 2019): 585–96. http://dx.doi.org/10.14358/pers.85.8.585.
Wieser, M., M. Hollaus, G. Mandlburger, P. Glira, and N. Pfeifer. "ULS LiDAR SUPPORTED ANALYSES OF LASER BEAM PENETRATION FROM DIFFERENT ALS SYSTEMS INTO VEGETATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 233–39. http://dx.doi.org/10.5194/isprsannals-iii-3-233-2016.
This study analyses the underestimation of tree and shrub heights for different airborne laser scanner systems and point cloud distribution within the vegetation column. Reference data was produced by a novel UAV-borne laser scanning (ULS) with a high point density in the complete vegetation column. With its physical parameters (e.g. footprint) and its relative accuracy within the block as stated in Section 2.2 the reference data is supposed to be highly suitable to detect the highest point of the vegetation. An airborne topographic (ALS) and topo-bathymetric (ALB) system were investigated. All data was collected in a period of one month in leaf-off condition, while the dominant tree species in the study area are deciduous trees. By robustly estimating the highest 3d vegetation point of each laser system the underestimation of the vegetation height was examined in respect to the ULS reference data. This resulted in a higher under-estimation of the airborne topographic system with 0.60 m (trees) and 0.55 m (shrubs) than for the topo-bathymetric system 0.30 m (trees) and 0.40 m (shrubs). The degree of the underestimation depends on structural characteristics of the vegetation itself and physical specification of the laser system.
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Wieser, M., M. Hollaus, G. Mandlburger, P. Glira, and N. Pfeifer. "ULS LiDAR SUPPORTED ANALYSES OF LASER BEAM PENETRATION FROM DIFFERENT ALS SYSTEMS INTO VEGETATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 233–39. http://dx.doi.org/10.5194/isprs-annals-iii-3-233-2016.
This study analyses the underestimation of tree and shrub heights for different airborne laser scanner systems and point cloud distribution within the vegetation column. Reference data was produced by a novel UAV-borne laser scanning (ULS) with a high point density in the complete vegetation column. With its physical parameters (e.g. footprint) and its relative accuracy within the block as stated in Section 2.2 the reference data is supposed to be highly suitable to detect the highest point of the vegetation. An airborne topographic (ALS) and topo-bathymetric (ALB) system were investigated. All data was collected in a period of one month in leaf-off condition, while the dominant tree species in the study area are deciduous trees. By robustly estimating the highest 3d vegetation point of each laser system the underestimation of the vegetation height was examined in respect to the ULS reference data. This resulted in a higher under-estimation of the airborne topographic system with 0.60 m (trees) and 0.55 m (shrubs) than for the topo-bathymetric system 0.30 m (trees) and 0.40 m (shrubs). The degree of the underestimation depends on structural characteristics of the vegetation itself and physical specification of the laser system.
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Hansen, Signe Schilling, Verner Brandbyge Ernstsen, Mikkel Skovgaard Andersen, Zyad Al-Hamdani, Ramona Baran, Manfred Niederwieser, Frank Steinbacher, and Aart Kroon. "Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data." Remote Sensing 13, no. 20 (October 13, 2021): 4101. http://dx.doi.org/10.3390/rs13204101.
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.
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Hansen, Signe Schilling, Verner Brandbyge Ernstsen, Mikkel Skovgaard Andersen, Zyad Al-Hamdani, Ramona Baran, Manfred Niederwieser, Frank Steinbacher, and Aart Kroon. "Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data." Remote Sensing 13, no. 20 (October 13, 2021): 4101. http://dx.doi.org/10.3390/rs13204101.
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.
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Mandlburger, Gottfried, Martin Pfennigbauer, Roland Schwarz, Sebastian Flöry, and Lukas Nussbaumer. "Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor." Remote Sensing 12, no. 6 (March 19, 2020): 986. http://dx.doi.org/10.3390/rs12060986.
We present the sensor concept and first performance and accuracy assessment results of a novel lightweight topo-bathymetric laser scanner designed for integration on Unmanned Aerial Vehicles (UAVs), light aircraft, and helicopters. The instrument is particularly well suited for capturing river bathymetry in high spatial resolution as a consequence of (i) the low nominal flying altitude of 50–150 m above ground level resulting in a laser footprint diameter on the ground of typically 10–30 cm and (ii) the high pulse repetition rate of up to 200 kHz yielding a point density on the ground of approximately 20–50 points/m2. The instrument features online waveform processing and additionally stores the full waveform within the entire range gate for waveform analysis in post-processing. The sensor was tested in a real-world environment by acquiring data from two freshwater ponds and a 500 m section of the pre-Alpine Pielach River (Lower Austria). The captured underwater points featured a maximum penetration of two times the Secchi depth. On dry land, the 3D point clouds exhibited (i) a measurement noise in the range of 1–3 mm; (ii) a fitting precision of redundantly captured flight strips of 1 cm; and (iii) an absolute accuracy of 2–3 cm compared to terrestrially surveyed checkerboard targets. A comparison of the refraction corrected LiDAR point cloud with independent underwater checkpoints exhibited a maximum deviation of 7.8 cm and revealed a systematic depth-dependent error when using a refraction coefficient of n = 1.36 for time-of-flight correction. The bias is attributed to multi-path effects in the turbid water column (Secchi depth: 1.1 m) caused by forward scattering of the laser signal at suspended particles. Due to the high spatial resolution, good depth performance, and accuracy, the sensor shows a high potential for applications in hydrology, fluvial morphology, and hydraulic engineering, including flood simulation, sediment transport modeling, and habitat mapping.
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Mandlburger, G., M. Pfennigbauer, R. Schwarz, and F. Pöppl. "A DECADE OF PROGRESS IN TOPO-BATHYMETRIC LASER SCANNING EXEMPLIFIED BY THE PIELACH RIVER DATASET." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 1123–30. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-1123-2023.
Abstract. Topo-bathymetric laser scanning featuring high spatial resolution and a seamless transition within the littoral zone from land to water evolved considerably in the last decade due to progress in both sensor technology and processing methods. Unlike early systems that focused solely on maximizing depth of penetration, topo-bathymetric scanners enable detailed description of coastal and inland waters at a level of detail that opens up applications in hydromorphology, hydraulic engineering, ecohydraulics, and hydrobiology. Since 2013, a near-natural river section of the pre-alpine Pielach River has been repeatedly surveyed with bathymetric LiDAR sensors from manned and unmanned aerial platforms. The captured time series not only constitutes a valuable data basis for analyzing morphometric change in response to recurring flood peaks, but also allows to trace the progress in sensor, platform and data processing technology. In this contribution we demonstrate that over the last ten years, the depth performance could be increased by approximately 60 %, starting from 1 Secchi depths to more than 2 Secchi depths with sub-m spatial resolution. We furthermore focus on current approaches for improving the geometric sensor calibration, which allow integrated processing of GNSS-, IMU- and LiDAR observations for concurrent calculation of both flight trajectories and 3D point clouds with an absolute accuracy better than 5 cm. This is specifically important for repeat surveys and monitoring of fluvial processes. While this contribution confirms substantial progress in the field, further topics like precise modeling of dynamic water surfaces, full waveform processing in complex target situations including littoral vegetation and submerged deadwood, and detection and modeling of underwater vegetation are identified as future research areas.
Dissertations / Theses on the topic "Topo-Bathymetric lidar":
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Letard, Mathilde. "Environnemental knowledge extraction from topo-bathymetric lidar : machine learning and deep neural networds for point clouds and waveforms." Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENB072.
Les interfaces terre-eau, fortement vulnérables au changement climatique et à la pression anthropique, requièrent une surveillance accrue. Toutefois, l’observation ininterrompue des zones submergées et émergées demeure un défi en raison de la présence d’eau. La télédétection lidar topobathymétrique constitue une solution adéquate en assurant une représentation continue des zones terre-eau, matérialisée par des nuages de points 3D et des formes d’ondes 1D. Cependant, une pleine exploitation de ces données requiert des outils encore en attente de développement. Cette thèse présente plusieurs méthodes d’extraction de connaissances des données lidar topo-bathymétriques, incluant des approches de classification basées sur des nuages de points bi-spectraux et des formes d’ondes bispectrales. En outre, des réseaux de neurones profonds sont conçus pour la segmentation sémantique, la détection et la classification d’objets, ainsi que l’estimation de paramètres physiques de l’eau à partir des formes d’ondes bathymétriques. L’utilisation de modèles de transfert radiatif guide des approches visant à réduire la nécessité de données labélisées, améliorant ainsi le traitement des formes d’ondes lidar dans les eaux très peu profondes ou turbides Land-water interfaces face escalating threats from climate change and human activities, necessitating systematic observation to comprehend and effectively address these challenges. Nevertheless, constraints associated with the presence of water hinder the uninterrupted observation of submerged and emerged areas. Topo-bathymetric lidar remote sensing emerges as a suitable solution, ensuring a continuous representation of landwater zones through 3D point clouds and 1D waveforms. However, fully harnessing the potential of this data requires tools specifically crafted to address its unique characteristics. This thesis introduces methodologies for extracting environmental knowledge from topobathymetric lidar surveys. Initially, we introduce methods for classifying land and seabed covers using bi-spectral point clouds or waveform features. Subsequently, we employ deep neural networks for semantic segmentation, component detection and classification, and the estimation of water physical parameters based on bathymetric waveforms. Leveraging radiative transfer models, these approaches alleviate the need for manual waveform labeling, thereby enhancing waveform processing in challenging settings like extremely shallow or turbid waters
Book chapters on the topic "Topo-Bathymetric lidar":
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Bulot, Angéline, Antoine Collin, Mathilde Letard, Eric Feunteun, Loic Le Goff, Yves Pastol, and Bruno Caline. "Spatial Modeling of the Benthic Biodiversity Using Topo-Bathymetric Lidar and Neural Networks." In European Spatial Data for Coastal and Marine Remote Sensing, 223–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16213-8_15.
Caline, Bruno, Antoine Collin, Yves Pastol, Mathilde Letard, and Eric Feunteun. "New Insights into the Shallow Morpho-Sedimentary Patterns Using High-Resolution Topo-Bathymetric Lidar: The Case Study of the Bay of Saint-Malo." In European Spatial Data for Coastal and Marine Remote Sensing, 219–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16213-8_14.
Conference papers on the topic "Topo-Bathymetric lidar":
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Letard, Mathilde, Antoine Collin, Dimitri Lague, Thomas Corpetti, Yves Pastol, Anders Ekelund, Gerard Pergent, and Stephane Costa. "Towards 3D Mapping of Seagrass Meadows with Topo-Bathymetric Lidar Full Waveform Processing." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554262.
Deunf, Julian Le, Rudresh Mishra, Yves Pastol, Romain Billot, and Steve Oudot. "Seabed prediction from airborne topo-bathymetric lidar point cloud using machine learning approaches." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9706113.
Letard, Mathilde, Antoine Collin, Thomas Corpetti, Dimitri Lague, Yves Pastol, Helene Gloria, Dorothee James, and Antoine Mury. "Classification of coastal and estuarine ecosystems using full-waveform topo-bathymetric lidar data and artificial intelligence." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9705797.
Webster, Tim. "Results from 3 seasons of surveys in maritime Canada using the Leica Chiroptera II shallow water topo-bathymetric lidar sensor." In OCEANS 2017 - Aberdeen. IEEE, 2017. http://dx.doi.org/10.1109/oceanse.2017.8084681.
Webster, T. L., K. McGuigan, N. Crowell, and N. Fee. Using topo-bathymetric LiDAR to map near shore benthic environments. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305941.
Brodie, Katherine, Brittany Bruder, Richard Slocum, and Nicholas Spore. Simultaneous mapping of coastal topography and bathymetry from a lightweight multicamera UAS. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41440.
A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and 0.34/–0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System–Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS–INS input and no GCPs.
