LiDAR Datasets Research Articles

A CFD-BASED AIR QUALITY DISPERSION MODELING FOR URBAN AREAS USING OPENFOAM

Abstract. Air pollution in urban areas posed a significant threat to public health and environmental sustainability. This research addressed the urgent need for accurate air quality monitoring systems in urban environments, focusing on the University of the Philippines-Diliman as the study area. The study used 3D modeling and Computational Fluid Dynamics (CFD) to simulate and assess air quality dispersion in the urban setting of the Campus. The research objectives included: (a) developing a 3D model of the Campus using LiDAR and geospatial techniques, (b) modeling the three-dimensional PM2.5 dispersion in the area using CFD, and (c) validating the effectiveness of the CFD-based PM2.5 dispersion model using on-ground air quality monitoring data. The LiDAR and GIS datasets, including Digital Terrain Models, Digital Surface Models, orthophotos, building footprints, and road networks, were utilized to create the 3D campus model. Meteorological data (wind speed, direction, cloudiness, solar irradiation), and emission parameters (pollutant sources and concentrations) were integrated into the model for CFD simulations. SimFlow, an OpenFOAM-based software, facilitated the dispersion modeling process. The study's results revealed the impact of urban factors on PM2.5 dispersion, highlighting challenges in areas with restricted air movement due to buildings and narrow streets. Discrepancies between model predictions and on-ground measurements suggested the influence of unaccounted local factors. Nevertheless, the model demonstrated its utility in capturing general PM2.5 trends. In conclusion, the combination of 3D modeling and CFD simulation proved to be a robust approach for urban air quality monitoring. While improvements were needed to address local influences, this research provided a foundation for better air quality comprehension and urban management, essential for achieving sustainable cities and climate action goals.

Measurements of dune erosion processes during the RealDune/REFLEX experiments

Nearshore hydro- and morphodynamic data were collected during a field experiment under calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime. The experiment was conducted on the Sand Engine near Kijkduin, the Netherlands, from October 18, 2021, to January 7, 2022. Two artificial unvegetated dunes were constructed just above the high water line to measure storm erosion and dune impacts from higher water levels and waves. During the experiment, three storms occurred that resulted in significant erosion of both dunes. The collected hydrodynamic data include pressure sensor and velocimeter data along two cross-shore transects. The collected morphodynamic data include bathymetry and topography surveys, optical backscatter sensor data in the inner surf zone, and a continuous cross-shore line-scanning lidar data set of the dune face. This comprehensive data set can be used to (1) study relevant nearshore hydrodynamic and morphodynamic processes that occur during calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime, and (2) validate existing dune erosion models.

Quantification of the spatial distribution of individual mangrove tree species derived from LiDAR point clouds

AbstractMangrove forests have unquestionably high environmental and ecological value. Mangrove trees are believed to have habitat zonation that is controlled mainly by the relative sea level. However, earlier discussions of mangrove habitats have remained limited in terms of their quality and quantity because of a lack of high-resolution spatial information of microtopography and trees. To clarify mangrove habitability over a wide forest area, we compounded mobile laser scanning (MLS) and aerial laser scanning (ALS) LiDAR dataset of the Miyara River mangrove on Ishigaki Island, Okinawa, Japan. The MLS provided sub-canopy data, while the unmanned aerial vehicle ALS data mainly provided a point cloud of the canopy. We corrected point clouds and combined these data. The results indicated that ALS is unable to reconstruct the microtopography of the dense mangrove area well. Moreover, tree species were not identifiable from the ALS data. However, by applying MLS to the mangrove forest, we obtained high-resolution microtopography and tree information inside the forest, although the measurement area was limited to comparison with ALS. By combining ALS and MLS point clouds, 3D point clouds of the forest were well reconstructed. From these point clouds, a high-resolution digital elevation model was created. Subsequently, we segmented trees individually from composite MLS point clouds and identified each tree species. Consequently, the spatial distribution of thousands of mangrove trees was reconstructed at the Miyara River mouth. The spatial distribution of mangrove tree species together with earlier aerial photographs suggests that mangrove species have been segregated in accordance with changes in their elevation and environment over 40 years. Our findings suggest that the distribution of the species changed sensitively along with dynamic variation of the microtopography.

Post-fire stabilization of thaw-affected permafrost terrain in northern Alaska

In 2007, the Anaktuvuk River fire burned more than 1000 km2 of arctic tundra in northern Alaska, ~ 50% of which occurred in an area with ice-rich syngenetic permafrost (Yedoma). By 2014, widespread degradation of ice wedges was apparent in the Yedoma region. In a 50 km2 area, thaw subsidence was detected across 15% of the land area in repeat airborne LiDAR data acquired in 2009 and 2014. Updating observations with a 2021 airborne LiDAR dataset show that additional thaw subsidence was detected in < 1% of the study area, indicating stabilization of the thaw-affected permafrost terrain. Ground temperature measurements between 2010 and 2015 indicated that the number of near-surface soil thawing-degree-days at the burn site were 3 × greater than at an unburned control site, but by 2022 the number was reduced to 1.3 × greater. Mean annual ground temperature of the near-surface permafrost increased by 0.33 °C/yr in the burn site up to 7-years post-fire, but then cooled by 0.15 °C/yr in the subsequent eight years, while temperatures at the control site remained relatively stable. Permafrost cores collected from ice-wedge troughs (n = 41) and polygon centers (n = 8) revealed the presence of a thaw unconformity, that in most cases was overlain by a recovered permafrost layer that averaged 14.2 cm and 18.3 cm, respectively. Taken together, our observations highlight that the initial degradation of ice-rich permafrost following the Anaktuvuk River tundra fire has been followed by a period of thaw cessation, permafrost aggradation, and terrain stabilization.

Bathymetric LiDAR and Semi‐Automated Feature Extraction Assist Underwater Archaeological Surveys

ABSTRACTCritical data concerning key developments in global human history now lie submerged on continental shelves where investigations confront significant challenges. Whereas underwater excavations and surveys are expensive and weather dependent and require specialized training and equipment, remote sensing methods can improve chances for success offshore. A refinement in one method, a semi‐automated analysis protocol that can help to identify Pleistocene and Holocene era archaeological deposits in bathymetric LiDAR datasets, is presented here. This method employs contour mapping to identify potential archaeological features in shallow water environments in Apalachee Bay, Florida. This method successfully re‐identified multiple previously recorded archaeological sites in the study region and detected at least four previously undocumented archaeological sites. These results suggest that this procedure can expand on methods to identify and record submerged archaeological deposits in sediment‐starved, shallow‐water environments.

Dynamic clustering transformer network for point cloud segmentation

Point cloud segmentation is one of the most important tasks in LiDAR remote sensing with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Existing methods typically utilize hierarchical architectures for feature representation. However, the commonly used sampling and grouping methods in hierarchical networks are not only time-consuming but also limited to point-wise 3D coordinates, ignoring the local semantic homogeneity of point clusters. To address these issues, we propose a novel 3D point cloud representation network, called Dynamic Clustering Transformer Network (DCTNet). It has an encoder–decoder architecture, allowing for both local and global feature learning. Specifically, the encoder consists of a series of dynamic clustering-based Local Feature Aggregating (LFA) blocks and Transformer-based Global Feature Learning (GFL) blocks. In the LFA block, we propose novel semantic feature-based dynamic sampling and clustering methods, which enable the model to be aware of local semantic homogeneity for local feature aggregation. Furthermore, instead of traditional interpolation approaches, we propose a new semantic feature-guided upsampling method in the decoder for dense prediction. To our knowledge, DCTNet is the first work to introduce semantic information-based dynamic clustering into 3D Transformers. Extensive experiments on an object-based dataset (ShapeNet), and an airborne multispectral LiDAR dataset demonstrate the State-of-the-Art (SOTA) segmentation performance of DCTNet in terms of both accuracy and efficiency. Our code will be made publicly available.

Estimation of canopy height based on multi-source remote sensing data using forest structure aided sample selection

ABSTRACT Forest canopy height data are crucial for estimating forest carbon storage and assessing forest ecology. By utilizing satellite imagery, canopy height data obtained from airborne or spaceborne LiDAR have been expanded from footprint and plot levels to spatially continuous elevation mapping of forests. However, current research suggests that estimating forest canopy height without forest type data presents a challenge in how to effectively integrate multi-source LiDAR data and ensure the samples adequately represent various forest types for higher estimation accuracy. Therefore, this study proposes a forest canopy height estimation method that considers forest structure and integrates multi-source LiDAR data to overcome the challenge. First, a stratified sampling method based on forest structure (SSMFS) was proposed to select training samples and enhance their representativeness. Second, we combined GEDI and ATL08 data to create a multi-source spaceborne LiDAR dataset, enhancing geographic coverage and increasing canopy height samples. Third, the spaceborne LiDAR-based canopy height estimation model incorporates previously unconsidered canopy openness features and uses SSMFS to select training samples. Finally, we improved spaceborne LiDAR canopy height accuracy by creating a residual correction model that adjusts for differences between airborne scanner (ALS) and spaceborne LiDAR estimates. This study, conducted in Zhangwu County, achieved an accuracy of R2 = 0.71, MAE = 1.20 m, and RMSE = 1.71 m. These results show a 51.06% increase in R2, a 26.38% decrease in MAE, and a 24.00% decrease in RMSE compared to recent research. In summary, this study profoundly amplifies predictive accuracy, providing a clear advantage in the delineation of regional forest canopy maps.

Assessing Conservation Conditions at La Fortaleza de Kuelap, Peru, Based on Integrated Close-Range Remote Sensing and Near-Surface Geophysics

We combined datasets from multiple research projects and remote sensing technologies to evaluate conservation conditions at La Fortaleza de Kuelap, a pre-Hispanic site in Peru that suffered significant damage under heavy seasonal rains in April 2022. To identify the causes of the collapse and where the monument is at further risk, we modeled surface hydrology using a DTM derived from drone LiDAR data, reconstructed a history of collapses, and calculated the volume of the most recent by fusing terrestrial LiDAR and photogrammetric datasets. In addition, we examined subsurface water accumulation with electrical resistivity, reconstructed the stratification of the monument with seismic refraction, and analyzed vegetation loss and ground moisture accumulation using satellite imagery. Our results point to rainwater infiltration as the most significant source of risk for La Fortaleza’s perimeter walls. Combined with other adverse natural conditions and contemporary conservation interventions, this led to the 2022 collapse. Specialists need to consider these factors when tasked with conserving monuments located in comparable high-altitude perhumid environments. This integration of analytical results demonstrates how multi-scalar and multi-instrumental approaches provide comprehensive and timely assessments of conservation needs.

Monitoring solutions for remote locations: A data gathering approach for remote nature-based solution sites

Nature-based solutions have gained popularity as an approach to tackling hydro-meteorological hazards (HMHs) in both urban and rural settings. Despite this popularity, challenges persist regarding the evidence base for their effectiveness and data scarcity at the feature or site scale. Flood modelling is a common approach to quantifying the effectiveness of NbS; however, the accuracy of these models heavily depends on the accuracy of the DEM, land cover, and hydraulic/hydrological data utilised. Remote and rural settings often face data scarcity due to the challenging nature of data collection, and insufficient funding for monitoring. Additionally, NbS features vary in size and scale, with many being small (<1 m in width), posing challenges for accurate representation in national LiDAR datasets. Technological advancements in remote sensing technologies, such as unmanned aerial vehicles, handheld LiDAR, and GPS-GNSS, offer opportunities to gather high-resolution, high-accuracy data in these challenging locations. This article proposes a methodological framework for collecting elevation data at remote NbS sites that can tackle areas affected by both sparse and dense vegetation cover. This approach proves valuable in both pre-NbS implementation, through facilitating NbS opportunity and environmental risk identification, and post-NbS implementation, through aiding in geo-spatial feature location, improving existing DEM data for flood modelling, and monitoring temporal changes.

Deriving high-accuracy lake water-level changes from multi-source satellite datasets

ABSTRACT Satellite data have exhibited a great potential to track lake water-level changes. By integrating multi-source satellite datasets (i.e., Ice, Cloud and land Elevation Satellite-2 (ICESat-2) lidar datasets, Landsat imagery, and Global Surface Water Dataset (GSWD)), we proposed a method to track annual water-level changes of Lake Mead, USA over 1984–2018. Specifically, we first extracted ICESat-2 profiles as elevation information. Second, we used Landsat imagery to extract annual lake boundaries and calculate lake areas. Third, we constructed the relationship between elevations and inundation frequencies provided by the GSWD for ICESat-2 points, and applied it to extract annual water levels by using inundation frequency of Landsat derived lake boundaries. Finally, we created the relationship between lake water level and lake areas via a cubic polynomial model to refine the annual lake water levels. Our water level results agreed well with the in-situ data (R 2 = 0.99, p < 0.01, RMSE = 0.64 m). Our results showed that the ICESat-2 data performed well in providing accurate elevation information around the shore of lake. Using satellite datasets, our proposed method has the potential to derive water levels for water bodies worldwide in the future.

Semantic segmentation of raw multispectral laser scanning data from urban environments with deep neural networks

Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile robotics. Current state-of-the-art point cloud semantic segmentation methods rely heavily on the availability of 3D laser scanning data. This is problematic in regards of low-latency, real-time applications that use data from high-precision mobile laser scanners, as those are typically 2D line scanning devices. In this study, we experiment with real-time semantic segmentation of high-density multispectral point clouds collected from 2D line scanners in urban environments using encoder - decoder convolutional neural network architectures. We introduce a rasterized multi-scan input format that can be constructed exclusively from the raw (non-georeferenced profiles) 2D laser scanner measurement stream without odometry information. In addition, we investigate the impact of multispectral data on the segmentation accuracy. The dataset used for training, validation and testing was collected with multispectral FGI AkhkaR4-DW backpack laser scanning system operating at the wavelengths of 905 nm and 1550 nm, and consists in total of 228 million points (39 583 scans). The data was divided into 13 classes that represent various targets in urban environments. The results show that the increased spatial context of the multi-scan format improves the segmentation performance on the single-wavelength lidar dataset from 45.4 mIoU (a single scan) to 62.1 mIoU (24 consecutive scans). In the multispectral point cloud experiments we achieved a 71 % and 28 % relative increase in the segmentation mIoU (43.5 mIoU) as compared to the purely single-wavelength reference experiments, in which we achieved 25.4 mIoU (905 nm) and 34.1 mIoU (1550 nm). Our findings show that it is possible to semantically segment 2D line scanner data with good results by combining consecutive scans without the need for odometry information. The results also serve as motivation for developing multispectral mobile laser scanning systems that can be used in challenging urban surveys.

Individual-Tree Segmentation from UAV–LiDAR Data Using a Region-Growing Segmentation and Supervoxel-Weighted Fuzzy Clustering Approach

Accurate individual-tree segmentation is essential for precision forestry. In previous studies, the canopy height model-based method was convenient to process, but its performance was limited owing to the loss of 3D information, and point-based methods usually had high computational costs. Although some hybrid methods have been proposed to solve the above problems, most canopy height model-based methods are used to detect subdominant trees in one coarse crown and disregard the over-segmentation and accurate segmentation of the crown boundaries. This study introduces a combined approach, tested for the first time, for treetop detection and tree crown segmentation using UAV–LiDAR data. First, a multiscale adaptive local maximum filter was proposed to detect treetops accurately, and a Dalponte region-growing method was introduced to achieve crown delineation. Then, based on the coarse-crown result, the mean-shift voxelization and supervoxel-weighted fuzzy c-means clustering method were used to identify the constrained region of each tree. Finally, accurate individual-tree point clouds were obtained. The experiment was conducted using a synthetic uncrewed aerial vehicle (UAV)–LiDAR dataset with 21 approximately 30 × 30 m plots and an actual UAV–LiDAR dataset. To evaluate the performance of the proposed method, the accuracy of the remotely sensed biophysical observations and retrieval frameworks was determined using the tree location, tree height, and crown area. The results show that the proposed method was efficient and outperformed other existing methods.

Transformer based 3D semantic segmentation of urban bicycle infrastructure

ABSTRACT This work investigates Point Cloud Semantic Segmentation (PCSS) to detect bicycle paths from 3D point cloud data of bicycle-mounted LiDARs. For the task of PCSS, an existing Convolutional Neural Network architecture (CNN) is first pre-trained on the Semantic KITTI data set, an important data set for PCSS in LiDAR scans of driving situations. Furthermore, a new, semantically-labelled 3D LiDAR data set, the Salzburg Bicycle LiDAR Data Set (SBLD), is presented, which consists of 16,008 point clouds recorded by a ROS2-enabled sensor bicycle with five 3D LiDARs covering every direction. After fine-tuning the CNN on the SBLD train set, the segmentation performance is evaluated on the SBLD test set. The CNN shows promising results in recognising bike paths, vegetation, terrain and buildings in the SBLD. To further push the segmentation performance, the existing CNN is enhanced with self-attention blocks. The evaluation of this enhanced architecture shows a performance gain of 2.79% points compared to the original architecture. The proposed segmentation approach shows practical potential for detecting bike paths. The SBLD is provided to the scientific community to further drive research in the field of sensor bicycles.

HPPLO-Net: Unsupervised LiDAR Odometry Using a Hierarchical Point-to-Plane Solver

High-precision LiDAR odometry (LO) plays an essential role in autonomous driving. Generally, due to inaccurate data associations and the existence of outliers, it is a challenging task to estimate ego-motions reliably and efficiently given sparse and complexly distributed point clouds in outdoor environments. In this paper, we propose an unsupervised method for LiDAR odometry, named HPPLO-Net, to predict the relative pose of a LiDAR sensor in a hierarchical way. Specifically, we achieve accurate 6-DoF (Degree of Freedom) pose estimation between the source and target point clouds using a differentiable Point-to-Plane solver with the assistance of scene flow. The novel Point-to-Plane solver consists of a multi-scale aggregation (MSA) normal estimation layer and a differentiable weighted Point-to-Plane SVD module. The MSA layer is introduced to find reliable normal vectors of the pseudo target point cloud by aggregating multi-scale contextual information. The differentiable weighted Point-to-Plane SVD is embedded in the network to solve the pose matrix and alleviate the problem of lacking accurate data association in two LiDAR scans, which exists in the point-to-point alternatives. To reduce the impact of noises and outliers, our method can learn and update the inlier mask and the (residual) flow uncertainty at each layer of the hierarchy. We demonstrate the effectiveness of our method on the KITTI Odometry Dataset, Ford Campus Vision and Lidar DataSet, and the Apollo-SouthBay Dataset. Our method has achieved superior performance than recent unsupervised learning-based methods and some traditional geometry-based methods and has also achieved promising performance close to A-LOAM with mapping optimization. The source code of our method is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IMRL/HPPLO-Net</uri> .

Arid erosion mapping: comparing LiDAR and structure from motion

Rapid development and uptake in uncrewed aerial vehicles (UAVs) for environmental monitoring, specifically using three-dimensional data from LiDAR and structure from motion (SfM), has enabled improved condition assessment, including fine-scale erosion monitoring. Comparing the precision of LiDAR and SfM for measuring erosion is essential in enabling appropriate method selection. Additionally, knowledge regarding optimal flight heights allows for consideration of the trade-off among survey areas, flight times and precision. We assessed UAV-based LiDAR and SfM for providing high-precision digital surface models (DSM) of substantial gully erosion on a conservation reserve in the southern Australian arid rangelands. The gullies exist in low-slope chenopod shrublands with calcareous soils, and are of significant management concern, with erosion occurring rapidly over short periods following irregular and intense rainfall events. Root mean squared error (RMSE) values for SfM-derived DSMs with resolutions of 2, 4 and 6 cm were lower than comparable LiDAR datasets (SfM = 0.72–1.39 cm; LiDAR = 2.08–3.15 cm). Additionally, 2 cm SfM-derived datasets exhibit notably lower RMSE values than 4 and 6 cm datasets (2 cm = 0.72 cm; 4–6 cm = 2.08–3.15 cm). Change detection over the 1-year study period highlighted erosion in locations of management concern. We propose that, although both methods are of value, SfM is preferred over LiDAR because of its simplicity, reduced cost, and the additional monitoring capabilities of visible-colour imagery, with no notable sacrifice in precision. Visible-colour survey areas and times can be optimised by increasing flight height without dramatic losses in precision. The use of either method will be of great benefit for the monitoring of arid gully erosion and assessing the effectiveness of management interventions, allowing adaptive management and leading to improved condition of arid rangelands into the future.

Cold-Season Precipitation Sensitivity to Microphysical Parameterizations: Hydrologic Evaluations Leveraging Snow Lidar Datasets

Abstract Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) Model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are conducted for two cold seasons (2018 and 2019) centered on the Colorado Rockies’ ∼750-km2 East River watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that microphysical parameterization choice alone has a modest impact on total precipitation on the order of ±3% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15% ± 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8 and 17 cm, and percent biases of 3%–15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization but conclude that snow lidar is a valuable nontraditional indicator of model performance.

Validating GEDI tree canopy cover product across forest types using co-registered aerial LiDAR data

Reliable tree canopy cover (TCC) products are vital for national forest inventory, land process modeling and forest dynamic monitoring. The new generation of space-based laser altimeter, GEDI, offers a three-dimensional (3D) insight on the forest structure, shaping the paradigm of structural variable estimation. However, the generality of newly released GEDI level-2 TCC product version 2 was less investigated across various forest types. Additionally, satellite-derived product validation usually suffers from the geolocation mismatch between satellite and reference data. In this study, we comprehensively validated the GEDI TCC product across seven forest types using the reference TCC derived from several public and private aerial LiDAR datasets after geographical registration, and crossly compared with a commonly-used passive satellite product (i.e., GFCC TCC). As the reference aerial TCC maps were derived using various aerial LiDAR instruments, we investigated the consistency of TCC estimation among them using simulation datasets and found that the distributions of TCC relative bias (biasR, %) were almost identical and the differences of relative RMSE (rRMSE, %) was less than 0.2%. Through the registration process, we found that the geolocation offsets of GEDI footprints tended to be independent of azimuth directions and their average was about 10 m, verifying the necessity of registration during the validation process. Importantly, the post-registration validation of GEDI TCC showed an average RMSE of 0.10 and an average R2 of 0.85 for all forest types, resulting in a decrease of RMSE of up to 0.15 and an increase of R2 of up to 0.33 compared to the pre-registration validation. The inter-comparison also exhibited improved consistency between GEDI and GFCC TCC products after registration. Further, we found a non-negligible dependence of GEDI TCC on the slope factor but almost independence on forest type, encouraging the spread of GEDI TCC product.

3DMASC: Accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric lidar data

Three-dimensional data have become increasingly present in earth observation over the last decades. However, many 3D surveys are still underexploited due to the lack of accessible and explainable automatic classification methods, for example, new topo-bathymetric lidar data. In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow. This workflow introduces multi-cloud classification through dual-cloud features, encrypting local spectral and geometrical ratios and differences. 3DMASC uses classical multi-scale descriptors adapted to all types of 3D point clouds and new ones based on their spatial variations. In this paper, we present the performances of 3DMASC for multi-class classification of topo-bathymetric lidar data in coastal and fluvial environments. We show how multivariate and embedded feature selection allows the building of optimized predictor sets of reduced complexity, and we identify features particularly relevant for coastal and riverine scene descriptions. Our results show the importance of dual-cloud features, lidar return-based attributes averaged over specific scales, and of statistics of dimensionality-based and spectral features. Additionally, they indicate that small to medium spherical neighbourhood diameters (<7 m) are sufficient to build effective classifiers, namely when combined with distance-to-ground or distance-to-water-surface features. Without using optional RGB information, and with a maximum of 37 descriptors, we obtain classification accuracies between 91 % for complex multi-class tasks and 98 % for lower-level processing using models trained on less than 2000 samples per class. Comparisons with classical point cloud classification methods show that 3DMASC features have a significantly improved descriptive power. Our contributions are made available through a plugin in the CloudCompare software, allowing non-specialist users to create classifiers for any type of 3D data characterized by 1 or 2 point clouds (airborne or terrestrial lidar, structure from motion), and two labelled topo-bathymetric lidar datasets, available on https://opentopography.org/.

Year-long buoy-based observations of the air–sea transition zone off the US west coast

Abstract. Two buoys equipped with Doppler lidars owned by the US Department of Energy (DOE) were deployed off the coast of California in autumn of 2020 by Pacific Northwest National Laboratory. The buoys collected data for an entire annual cycle at two offshore locations proposed for offshore wind development by the Bureau of Ocean Energy Management. One of the buoys was deployed approximately 50 km off the coast near Morro Bay in central California in 1100 m of water. The second buoy was deployed approximately 40 km off Humboldt County in northern California in 625 m of water. The buoys provided the first-ever continuous measurements of the air–sea transition zone off the coast of California. The atmospheric and oceanographic characteristics of the area and estimates of annual energy production at both the Morro Bay and Humboldt wind energy areas show that both locations have a high wind energy yield and are prime locations for future floating offshore wind turbines. This article provides a description and comprehensive analysis of the data collected by the buoys, and a final post-processed dataset is uploaded to a data archive maintained by the DOE. Additional analysis was conducted to show the value of the data collected by the DOE buoys. All post-processed data from this study are available on the Wind Data Hub website: https://a2e.energy.gov/data# (last access: 14 September 2023). Near-surface, wave, current, and cloud datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783807 (Krishnamurthy and Sheridan, 2023b) and https://doi.org/10.21947/1959715 (Krishnamurthy and Sheridan, 2023a), respectively. Lidar datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783809 (Krishnamurthy and Sheridan, 2023d) and https://doi.org/10.21947/1959721 (Krishnamurthy and Sheridan, 2023c), respectively.

HYBRID ADJUSTMENT OF UAS-BASED LiDAR AND IMAGE DATA

Abstract. Several advancements are going with Unmanned Aerial Systems (UAS) with the addition of multiple sensors and simultaneous data acquisition to obtain detailed geo-data for various applications. However, simultaneous data acquisition with multiple sensors, namely camera, and LiDAR, will also result in possible discrepancies associated with them, and they need to be solved to use a reliable and accurate final product. Several errors can be associated with both camera and LiDAR datasets due to the different characteristics of the sensors and terrain conditions. This research paper aimed to minimize the errors between LiDAR and the image datasets simultaneously acquired with an Unmanned Aerial System (UAS) by implementing a hybrid adjustment approach with a criterion for the roughness and threshold angle between surface normals. The initial trajectory of the UAS, raw LiDAR measurements, and image observations were the inputs used for the hybrid adjustment. The hybrid adjustment workflow minimizes the discrepancies with a least-squares-based simultaneous adjustment for both LiDAR and image datasets. For the hybrid adjustment process, three types of correspondences were established, namely: between image pairs, overlapping LiDAR strips, and between Image tie points and LiDAR strips. For quality control, mean Cloud-to-Cloud distances (C2C) were compared between both LiDAR and camera point clouds before and after hybrid adjustment. The surface-level analysis of the results was also carried out to analyze the errors before and after hybrid adjustment at a surface level for different types of surfaces. The results showed that the alignment between the point clouds has significantly improved from the range of meters to a centimeter-level after implementing the hybrid adjustment process. The proposed hybrid adjustment workflow can be used in mapping applications where a centimeter-level accuracy is requested.