Light Detection And Ranging Model Research Articles

Multi-approach integration of ALS and TLS point clouds for a 3-D building modeling at LoD3

Registering, documenting, updating, revitalizing, expanding, and renovating old urban buildings require proper documentation. The adoption of 3D survey techniques is essential to grant efficiency and agility to such purposes. This article discusses a multi-approach integration of Light Detection and Ranging (LiDAR) data collected by aerial and terrestrial platforms, meant for the 3D modeling of a building at Level of Detail 3. The selected building presents challenging elements for modeling, such as blocks with different heights and indented facades. It is located on the campus of the Federal University of Paraná (UFPR) in Curitiba, Brazil, on a site with irregular terrain and surrounded by trees, what made the terrestrial laser scanning process difficult. For its three-dimensional reconstruction, data from an Aerial Laser Scanning system were integrated with data from a Terrestrial Laser Scanner (TLS). Based on the 3D modeling, an as-is Building Information Modeling model of the building’s exterior was created. To validate the results, measurements of the building were obtained by means of an Electronic Distance Measurement (EDM) device and they were then compared with measurements extracted from the point cloud-based BIM model. The results demonstrate that there was a correspondence between the EDM and the LiDAR-derived measures, attaining a satisfactory statistical agreement. The article focuses on the accuracy of LiDAR models for the cadastral update of buildings, providing information for decision making in documentation projects and construction interventions. The main contribution of this work consists in a multi-approach workflow for delivering an effective and precise solution for accomplishing an as-is BIM documentation, highlighting advantages, drawbacks, and the potential of this set of methods for integrating multi-source LiDAR point clouds.

Performance Evaluation of MEMS-Based Automotive LiDAR Sensor and Its Simulation Model as per ASTM E3125-17 Standard

Measurement performance evaluation of real and virtual automotive light detection and ranging (LiDAR) sensors is an active area of research. However, no commonly accepted automotive standards, metrics, or criteria exist to evaluate their measurement performance. ASTM International released the ASTM E3125-17 standard for the operational performance evaluation of 3D imaging systems commonly referred to as terrestrial laser scanners (TLS). This standard defines the specifications and static test procedures to evaluate the 3D imaging and point-to-point distance measurement performance of TLS. In this work, we have assessed the 3D imaging and point-to-point distance estimation performance of a commercial micro-electro-mechanical system (MEMS)-based automotive LiDAR sensor and its simulation model according to the test procedures defined in this standard. The static tests were performed in a laboratory environment. In addition, a subset of static tests was also performed at the proving ground in natural environmental conditions to determine the 3D imaging and point-to-point distance measurement performance of the real LiDAR sensor. In addition, real scenarios and environmental conditions were replicated in the virtual environment of a commercial software to verify the LiDAR model's working performance. The evaluation results show that the LiDAR sensor and its simulation model under analysis pass all the tests specified in the ASTM E3125-17 standard. This standard helps to understand whether sensor measurement errors are due to internal or external influences. We have also shown that the 3D imaging and point-to-point distance estimation performance of LiDAR sensors significantly impacts the working performance of the object recognition algorithm. That is why this standard can be beneficial in validating automotive real and virtual LiDAR sensors, at least in the early stage of development. Furthermore, the simulation and real measurements show good agreement on the point cloud and object recognition levels.

Possibilities and limitations of using iPhone 13 Pro with built-in LiDAR sensor in cave research – on the example of paleoflow analysis in Mylna Cave (Western Tatra Mts, Poland)

The study tested the capabilities of the Apple iPhone 13 Pro device using two measurement techniques, LiDAR (Light Detection and Ranging) and SfM (Structure from Motion), in a cave environment by measuring scallops in Mylna Cave in Western Tatra Mountains. The tested device provides an interesting and inexpensive alternative for cave research using TLS (Terrestrial Land Scanner) type scanners or more expensive MLS (Mobile Laser Scanning) type scanners. The study used a dedicated 3D Scanner App™ application to create two terrain models: LiDAR and SfM. A comparative analysis of the models shows that the SfM model is characterised by greater detail. The results obtained for this model indicate that the scallops measured in Wielki Chodnik passage of Mylna Cave belong to at least two different generations of forms. In the LiDAR method case, the obtained models’ resolution was not precise enough to identify small (<3 cm) scallops. For three LiDAR models, the average length of scallops was 10.32 cm; for three SfM models, it was 5.16 cm. The length of scallops obtained from models allowed for calculating paleoflow velocity and, thus, the flow rate. The average velocity value for LiDAR models was 28.98 cm s−1, and for SfM models – 48.10 cm s−1 and the average flow rate obtained from SfM data was 1.93 m³ s−1. It corresponds well with the today-observed Kościeliski Potok flow rate of 1.7 m³ s−1 according to the 1966–2000 period (Baścik et al. 2014). On the contrary, the average paleoflow rate obtained from the LiDAR model, which is 0.94 m³ s−1, does not match the contemporary flow rate. Based on the asymmetry of scallops, the paleoflow direction was determined. It is consistent with the current direction of the Kościeliski Potok flow. The spatial imaging techniques used with iPhone 13 Pro differ regarding the DEM creation method and model details. It is influenced by parameters related to lighting, distance, scanned surface character and microclimatic conditions of the cave.

Synthetic LiDAR-Labeled Data Set Generation to Train Deep Neural Networks for Object Classification in IoT at the Edge

Light detection and ranging (LiDAR) sensors are increasing in popularity due to the advantages they provide over 2-D sensors in IoT object detection and classification applications, because of their ability to provide very precise distances to objects. Deep learning algorithms need a huge amount of data during training to obtain high accuracy results. When using 2-D images, a vast quantity of data sets are publicly available, but this is not the case for LiDAR point clouds. Each LiDAR model generates a point cloud with unique properties, which causes the data sets not to be compatible between different LiDAR models. As a result, when using deep learning with LiDARs, it is necessary to generate the data sets manually. For this purpose, the data must be captured and then labeled one by one, which is a very time and cost-consuming process. To overcome this issue and to reduce the development time when using LiDAR sensors with deep learning algorithms, a methodology is proposed in this article to automatically generate point cloud data sets using a 3-D simulator for autonomous cars. In this regard, a data set can be generated for any LiDAR model by adding the specific LiDAR parameters to the simulator. Besides, custom scenarios can be designed and generated, based on the final deployment location, to provide a simulated solution very close to the final implementation. With the proposed methodology, a simulation can be performed to select the LiDAR that best fits certain application requirements, in contrast to the traditional approach where the LiDAR must first be purchased.

Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces.

This work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error of simulated and measured time domain signal amplitude is . In addition, the of the number of points and mean intensity values received from the virtual and real targets are and , respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points and mean intensity values up until now. Moreover, the distance error is below the range accuracy of the actual LiDAR sensor, which is 2 cm for this use case. In addition, the proving ground measurement results are compared with the state-of-the-art LiDAR model provided by commercial software and the proposed LiDAR model to measure the presented model fidelity. The results show that the complete signal processing steps and imperfections of real LiDAR sensors need to be considered in the virtual LiDAR to obtain simulation results close to the actual sensor. Such considerable imperfections are optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by the sunlight.

UAS Hyperspatial LiDAR Data Performance in Delineation and Classification across a Gradient of Wetland Types

Wetlands play a critical role in maintaining stable and productive ecosystems, and they continue to be at heightened risk from anthropogenic and natural degradation, especially along the rapidly developing Atlantic Coastal Plain of North America. As such, strategies to develop up-to-date and high-resolution wetland inventories and classifications remain highly relevant in the context of accelerating sea-level rise and coastal changes. Historically, satellite and airborne remote sensing data along with traditional field-based methods have been used for wetland delineation, yet, more recently, the advent of Uncrewed Aerial Systems (UAS) platforms and sensors is opening new avenues of performing rapid and accurate wetland classifications. To test the relative advantages and limitations of UAS technologies for wetland mapping and classification, we developed wetland classification models using UAS-collected multispectral and UAS-collected light detection and ranging (LiDAR) data relative to airborne-derived LiDAR models of wetland types ranging from palustrine to estuarine. The models were parameterized through a pixel-based random forest algorithm to evaluate model performance systematically and establish variable importance for a suite of variables including topographic, hydrologic, and vegetation-based indices. Based on our experimental results, the average overall classification accuracy and kappa coefficients for the UAS LiDAR-derived models are 75.29% and 0.74, respectively, compared to 79.80% and 0.75 for the airborne LiDAR-derived models, with significant differences in the spatial representation of final wetland classes. The resulting classification maps for the UAS models capture more precise wetland delineations than those of airborne models when trained with ground reference data collected at the same time as the UAS flights. The similar accuracy between the airborne and UAS models suggest that the UAS LiDAR is comparable to the airborne LiDAR. However, given poor revisit time of the airborne surveys and the high spatial resolution and precision of the UAS data, UAS-collected LiDAR provides excellent complementary data to statewide airborne missions or for specific applications that require hyperspatial data. For more structurally complex wetland types (such as the palustrine scrub shrub), UAS hyperspatial LiDAR data performs better and is much more advantageous to use in delineation and classification models. The results of this study contribute towards enhancing wetland delineation and classification models using data collected from multiple UAS platforms.

Investigating the suitability of GIS and remotely-sensed datasets for photovoltaic modelling on building rooftops

Rising energy demands and net-zero targets have led to development and implementation of renewable technologies. Rooftop photovoltaic (PV) solar panels offer a viable solution while minimising complex construction or excessive infrastructure development. However, critical physical factors influence building suitability to maximise cost-benefit requirements. This research adopted a Geographic Information System (GIS) approach to identify building suitability by analysing and comparing Digital Surface Models (DSM) derived from Light Detection and Ranging (LiDAR) and orthophotography data using UK standardised PV formulas. Geospatial workflows processed rooftop features and modelled outputs for solar irradiation, panel type, kWh, CO2, payback and costs while 3D models and solar web applications were used to validate results. Both models suggested a range of between 14.2 and 15.2 GWh potential for an installed capacity of between 21.1 and 22.3 MW. Residential models met between 62.1% (LiDAR) and 66.6% (orthophotography) of average consumer demand with PV potential exceeding 94% of residential dwellings. LiDAR and orthophotography models had strong agreement with existing PV installations. The methodology can be scaled to a regional level and expanded for larger PV capacity. Moreover, the process can assist policymakers with informed decisions on renewable technologies alongside developments such as Peer to Peer (P2P) solar trading.

Comprehensive LiDAR simulation with efficient physically-based DART-Lux model (I): Theory, novelty, and consistency validation

Light Detection And Ranging (LiDAR) remote sensing is increasingly needed to assess the 3D architecture of Earth's surface. Physically-based LiDAR radiative transfer (RT) models are essential tools for interpreting LiDAR signals, designing LiDAR systems, and validating information retrieval methods. Discrete Anisotropic Radiative Transfer (DART) is one of the most accurate and comprehensive 3D RT models that simulate LiDAR signals of urban and natural landscapes. Its physical modeling relies on a forward Monte Carlo mode optimized by a ray-tracking technique, also called DART-RC (Ray Carlo) mode. However, DART-RC is not adapted to simulate massive LiDAR signals of large landscapes due to its constraints of high memory demand and long computational time. Therefore, we developed a novel computationally efficient LiDAR modeling method based on a new DART modeling mode called DART-Lux. It simulates LiDAR signal by adapting the bidirectional path tracing algorithm of DART-Lux to the time and power measurements and by implementing the LiDAR instrument and multiple product outputs in DART-Lux. We verified the accuracy of DART-Lux for LiDAR modeling using DART-RC as a reference for several case studies with different LiDAR configurations (i.e., single-pulse waveform, multi-pulse point cloud, multi-pulse photon counting, with and without solar signal) on realistic scenes from the RAMI experiment. Results stress that i) DART-Lux is consistent with DART-RC, for example, R2 = 1 and rRMSE = 0.21% for the waveform of a forest simulated with a huge number of rays; ii) DART-Lux converges faster than DART-RC: its processing time is usually about half that of DART-RC, and over ten times smaller if the solar signal is simulated; iii) DART-Lux memory usage can be a hundred times less than DART-RC. Also, several sensitivity studies with various sensor configurations and solar directions illustrate the usefulness of DART-Lux for impact studies. This new DART-Lux LiDAR model opens promising perspectives for large-scale LiDAR applications with 3D modeling. It is already part of the official DART version freely available to scientists (https://dart.omp.eu).

Comparing LiDAR and SfM digital surface models for three land cover types

Abstract Airborne light detection and ranging (LiDAR) and unmanned aerial vehicle structure from motion (UAV-SfM) are two major methods used to produce digital surface models (DSMs) for geomorphological studies. Previous studies have used both types of DSM datasets interchangeably and ignored their differences, whereas others have attempted to locally compare these differences. However, few studies have quantified these differences for different land cover types. Therefore, we simultaneously compared the two DSMs using airborne LiDAR and UAV-SfM for three land cover types (i.e. forest, wasteland, and bare land) in northeast China. Our results showed that the differences between the DSMs were the greatest for forest areas. Further, the average elevation of the UAV-SfM DSM was 0.4 m lower than that of the LiDAR DSM, with a 95th percentile difference of 3.62 m for the forest areas. Additionally, the average elevations of the SfM DSM for wasteland and bare land were 0.16 and 0.43 m lower, respectively, than those of the airborne LiDAR DSM; the 95th percentile differences were 0.67 and 0.64 m, respectively. The differences between the two DSMs were generally minor over areas with sparse vegetation and more significant for areas covered by tall dense trees. The findings of this research can guide the joint use of different types of DSMs in certain applications, such as land management and soil erosion studies. A comparison of the DSM types in complex terrains should be explored in the future.

Longitudinal-Scanline-Based Arterial Traffic Video Analytics with Coordinate Transformation Assisted by 3D Infrastructure Data

High-resolution vehicle trajectory data can be used to generate a wide range of performance measures and facilitate many smart mobility applications for traffic operations and management. In this paper, a Longitudinal Scanline LiDAR-Camera model is explored for trajectory extraction at urban arterial intersections. The proposed model can efficiently detect vehicle trajectories under the complex, noisy conditions (e.g., hanging cables, lane markings, crossing traffic) typical of an arterial intersection environment. Traces within video footage are then converted into trajectories in world coordinates by matching a video image with a 3D LiDAR (Light Detection and Ranging) model through key infrastructure points. Using 3D LiDAR data will significantly improve the camera calibration process for real-world trajectory extraction. The pan-tilt-zoom effects of the traffic camera can be handled automatically by a proposed motion estimation algorithm. The results demonstrate the potential of integrating longitudinal-scanline-based vehicle trajectory detection and the 3D LiDAR point cloud to provide lane-by-lane high-resolution trajectory data. The resulting system has the potential to become a low-cost but reliable measure for future smart mobility systems.

Storm Surge Hazard Assessment of the Levee of a Rapidly Developing City-Based on LiDAR and Numerical Models

Rapidly developing cities could require an urgent hazard assessment to ensure the protection of their economy and population against natural disasters. However, these cities that have rapidly developed should have historical records of observations that are too short to provide sufficient data information for such an assessment. This study used ocean numerical models (i.e., Finite-Volume Community Ocean Model (FVCOM) and Parabolic Mild-Slope Wave Module (MIKE 21 PMS) to reconstruct data for a storm surge hazard assessment of the levee at Weifang (China). LiDAR (Light Detection and Ranging) data were also used to obtain 3D point cloud data and the structure of the levee. The designed levee height was calculated based on the simulations and 3D point cloud data, and the results were compared with measured heights to evaluate whether the levee is sufficiently high to satisfy the safety requirement. The findings of this work will enhance the marine disaster prevention capacity of the region and could help reduce economic losses associated with marine-related disasters. The results could also provide support for future work on disaster prevention in the field of coastal marine engineering.

Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors

BackgroundThe increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.ResultsThrough a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha−1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha−1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha−1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models.ConclusionsThrough the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.

Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m2). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior.

Comparison of stand characteristic parameters and biomass estimations from light detection and ranging and structure-from-motion point clouds

The accurate estimation and inversion of stand characteristic parameters and biomass is the premise for the evaluation of forest productivity, forest ecosystem carbon storage, and ecological forest service capacity. The horizontal accuracies of structure from motion (SfM) and light detection and ranging (LiDAR) point clouds were identical, whereas the descriptions of the upper canopy indices were similar ( R = 0.91 to 0.93). However, the descriptions of the lower canopy indices and vertical structure variables were different based on comparisons of the two point cloud types in the same region. Variables that described the vertical structure of the canopy in the SfM point clouds were much less important in the factor estimation models, exhibiting high correlations with basal area and volume. The factor estimation models with high correlations with tree height and diameter at breast height (DBH) exhibited similar importance for LiDAR variables. LiDAR ( R 2 = 0.515 to 0.895) was superior to SfM ( R 2 = 0.63 to 0.835) in estimating the same parameters, particularly those with high correlations with vertical structures. The combination of unmanned aerial vehicle aerial photogrammetry and LiDAR technology might enable the estimation of stand characteristic parameters and the inversion of biomass, as well as the estimation of characteristic parameters such as Lorey’s H and DBH, which are highly sensitive for large datasets. The LiDAR and SfM model outcomes were comparable. We found that the model outcomes exhibited the greatest differences for volume (ΔrRMSE % = 13.94 % ) and the lowest for average DBH (ΔrRMSE = 0.8 % )

Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms

Accurate estimates of aboveground biomass (AGB) in tropical forests are critical for supporting strategies of ecosystem functioning conservation and climate change mitigation. However, such estimates at regional and local scales are still highly uncertain. Airborne Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) can characterize the structural and functional diversity of forests with high accuracy at a sub-meter resolution, and potentially improve the AGB estimations. In this study, we compared the ability of different data sources (airborne LiDAR and HSI, and their combination) and regression methods (linear model - LM, linear model with ridge regularization - LMR, Support Vector Regression - SVR, Random Forest - RF, Stochastic Gradient Boosting - SGB, and Cubist - CB) to improve AGB predictions in the Brazilian Amazon. We used georeferenced inventory data from 132 sample plots to obtain a reference field AGB and calculated 333 metrics (45 from LiDAR and 288 from HSI) that could be used as predictors for statistical AGB models. We submitted the metrics to a correlation filtering followed by a feature selection procedure (recursive feature elimination) to optimize the performance of the models and to reduce their complexity. Results showed that both LiDAR and HSI data used alone provided relatively high accurate models if adequate metrics and algorithms are chosen (RMSE = 67.6 Mg.ha−1, RMSE% = 36%, R2 = 0.58, for the best LiDAR model; RMSE = 68.1 Mg.ha−1, RMSE% = 36%, R2 = 0.58, for the best HSI model). However, HSI-only models required more metrics (5–12) than LiDAR-only models (2–5). Models combining metrics from both datasets resulted in more accurate AGB estimates, regardless of the regression method (RMSE = 57.7 Mg.ha−1, RMSE% = 31%, R2 = 0.70, for the best model). The most important LiDAR metrics for estimating AGB were related to the upper canopy cover and tree height percentiles, while the most important HSI metrics were associated with the near infrared and shortwave infrared spectral regions, particularly the leaf/canopy water and lignin-cellulose absorption bands. Finally, an analysis of variance (ANOVA) showed that the remote sensing data source (LiDAR, HSI, or their combination) had a greater effect size than the regression algorithms. Thus, no single algorithm outperformed the others, although the LM method was less suitable when applied to the HSI and hybrid datasets. Results show that the synergistic use of LiDAR and hyperspectral data has great potential for improving the accuracy of the biomass estimates in the Brazilian Amazon.

Estimating the Aboveground Carbon Density of Coniferous Forests by Combining Airborne LiDAR and Allometry Models at Plot Level.

Forest carbon density is an important indicator for evaluating forest carbon sink capacities. Accurate carbon density estimation is the basis for studying the response mechanisms of forest ecosystems to global climate change. Airborne light detection and ranging (LiDAR) technology can acquire the vertical structure parameters of forests with a higher precision and penetration ability than traditional optical remote sensing. Combining top of canopy height model (TCH) and allometry models, this paper constructed two prediction models of aboveground carbon density (ACD) with 94 square plots in northwestern China: one model is plot-averaged height-based power model and the other is plot-averaged daisy-chain model. The correlation coefficients (R2) were 0.6725 and 0.6761, which are significantly higher than the correlation coefficients of the traditional percentile model (R2 = 0.5910). In addition, the correlation between TCH and ACD was significantly better than that between plot-averaged height (AvgH) and ACD, and Lorey’s height (LorH) had no significant correlation with ACD. We also found that plot-level basal area (BA) was a dominant factor in ACD prediction, with a correlation coefficient reaching 0.9182, but this subject requires field investigation. The two models proposed in this study provide a simple and easy approach for estimating ACD in coniferous forests, which can replace the traditional LiDAR percentile method completely.

Airborne LiDAR Measurements and Model Simulations of Tides, Waves, and Surface Slope at the Mouth of the Columbia River

Airborne light detection and ranging (LiDAR) measurements and model simulations are used to investigate the temporal and spatial variability of the water surface elevation at the mouth of the Columbia River. A series of 15-km transects repeated in 2-3-h flights over a two week period resolve processes at a range of scales that are important to the dynamics of the river mouth region, including tides, surface slope, surface gravity waves, and wave setup. Water surface elevations agree well with a nearby tide gauge with an average difference of 0.01 m and an root mean square error of 0.39 m. Significant wave heights derived from the LiDAR measurements agree well with in situ wave drifter measurements and are observed to react to both bathymetry and spatial/temporal variations in currents, increasing up to 228 % on large ebb tides. Surface slopes varied from 2.7 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> to -2.6 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> over the course of a typical tide to as large as 6.3 × 10-5 to -4.8 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> during a larger tide. The magnitude of the setup and set down due to wave height amplification during ebb tide was estimated to be 4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> . These observations demonstrate that many of the key dynamical variables at river mouths can be determined from airborne remote sensing measurements of water surface elevation and suggest that forthcoming altimetry products, such as those from the Surface Water Ocean Topography altimeter, may be able to provide new insight on monitoring in these complex regions.

Estimation of hydromorphological attributes of a small forested catchment by applying the Structure from Motion (SfM) approach

Structure from Motion (SfM) represents a good low-cost alternative to generate high resolution topography where LiDAR (Light Detection and Ranging) data is scarce or unaffordable. In this work, we demonstrate the advantages of high resolution elevation models (DEM) obtained using the SfM technique to delineate catchment boundaries and the stream network. The SfM-based DEM was compared with LiDAR data, distributed by the Mexican Government, and a previous high resolution topographic map generated by a RTK-GPS system. Aerial images were collected on a forested ecohydrological monitoring site in northwest Mexico using a commercial grade digital camera attached to a tethered helium balloon. Here we applied the SfM method with the removal of the vegetation, similarly to the more advance LiDAR methods. This was achieved by adjusting the point cloud classification parameters (maximum angle, maximum distance and cell size), which to our knowledge, has not has not been reported in the available SfM literature. The SfM terrain model showed minimal differences in ground elevation in the center of the image domain (0-0.5 m) while errors increased on the edges of the domain. The SfM model generated the largest catchment area, main and total channel length (1.07 ha, 106.1 and 223 m, respectively) while LiDAR model obtained the smallest area and main channel length (0.77 ha and 92.9 m, respectively). On the other hand, the SfM model had a better and accurate representation of the river network among all models evaluated due to its closest proximity to the observed GPS-tracked main channel. We concluded that the integration of low cost unmanned aerial vehicles and the SfM method is a good alternative to estimate hydro-morphological attributes in small catchments. Furthermore, we found that high resolution SfM-based terrain models had a fairly good representation of small catchments which is useful in regions with limited data availability. The main findings of this research provide scientific value within the field of hydrological remote sensing in particular in the acquisition of high resolution topography in remote areas without access to more expensive LiDAR or survey techniques. High resolution DEMs allow for a better characterization of catchment area size and stream network delineation which influence hydrological processes (i.e. soil moisture redistribution, runoff, ET).

Identification of PV system shading using a LiDAR-based solar resource assessment model: An evaluation and cross-validation

Photovoltaic (PV) systems are subject to several different systematic de-rating factors, such as soiling, degradation, inverter mismatch and shading. With increasing penetration of PV in the local grid, Distribution Network Service Providers (DNSPs) are inclined to assess such losses, in order to accurately estimate the total regional power output of distributed PV. The most influential de-rating factor is shading, which can cause ramps on the generated power output, similar to clouds. In this study we evaluate and compare two fundamentally different methods for module orientation parametrisation and shading analysis of PV systems that have been developed in previous work. In the first method, LiDAR (Light Detection and Ranging) data are used to derive the PV module orientation and shading, referred to herein as the LiDAR model. The second method, referred to as the QCPV-Tuning model, is based on reported PV power generation, which is firstly quality controlled and parameterised in order to derive module orientation and a loss factor, LF, representing systematic de-rating factors. Secondly, variations in de-ratings throughout the day, mainly due to shading, are explored in a process referred to as Tuning. For both methods, binary time series are derived expressing the presence of shading, which are used to evaluate how the methods corroborate. We evaluate four cases; (case 1) evaluates the original versions of the LiDAR and QCPV-Tuning models, while in cases 2–4 improvements to the models are introduced. A new filter for extracting representative LiDAR data points for the shading analysis was introduced for the LiDAR model (case 2). For the QCPV-Tuning model significant inaccuracies in the parametrisation of the module orientation were identified due to strong shading in either morning or evening and were thus corrected to observed parameters (case 3). For (case 4) improvements on both models were introduced. The Pearson correlation coefficients of shading events for the methods were 0.28, 0.36, 0.42 and 0.50 for cases 1–4, respectively. A mismatch in the timing of shading events motivated the comparison of the mean hourly shading, with correlation coefficients of 0.34, 0.43, 0.49 and 0.57 for cases 1–4, respectively. The results of this study show that both methods can confidently be used for solar resource assessment, given the suggested improvements.

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.