Terrestrial LiDAR Research Articles

Small unmanned aerial model accuracy for photogrammetrical fluvial bathymetric survey

Fluvial systems offer a challenging and varied environment for topographic survey, displaying a rapidly varying morphology, vegetation assemblage and degree of submergence. Traditionally theodolite or GPS based systems have been used to capture cross-section and breakline based topographic data which has subsequently been interpolated. Advances in survey technology has resulted in an improved ability to capture larger volumes of information with infrared terrestrial and aerial LiDAR systems capturing high density (<0.02 m) point data across terrestrial surfaces. The rise of Structure from Motion (SfM) photogrammetry, coupled with small unmanned aerial vehicles (sUAV), has potential to record elevation data at reach scale sub decimetre density. The approach has the additional advantage over LiDAR of seeing through clear water to capture bed detail, whilst also generating ortho-rectified photographic mosaics of the survey reach. However, data accuracy has yet to be comprehensively assessed. Here we present a survey protocol for sUAV deployment and provide a reach scale comparison between a theodolite and SfM sUAV survey on the River Sprint, Kendal, the River Ehen at Egremont, England and the Afon Elwy, at Llanfair Talhaiarn, Wales. Comparative analysis between theodolite survey and SfM suggest similar accuracy and precision across terrestrial surfaces with error lowest over solid surfaces, increasing with vegetation complexity. Submerged SfM data, captured bed levels generally to within ±0.25 m with only a weak relationship recorded between error and flow depth. Significantly, associated error when linked to channel D50 highlights the ability of unmanned aerial vehicles to capture accurate fluvial data across a range of river biotopes and depths to 2.4 m.

Terrestrial lidar scanning reveals fine-scale linkages between microstructure and photosynthetic functioning of small-stature spruce trees at the forest-tundra ecotone

The forest-tundra ecotone (FTE) is exhibiting myriad responses to rapid environmental change. Microstructural variability (at cm to m length scales) of vegetation canopies and geomorphic features may modulate the response of FTE vegetation to regional climate changes. Understanding the influence of microstructure on tree function at the FTE is particularly relevant during vulnerable early growth stages. During these stages, individual trees are tightly coupled to conditions of the surface boundary layer, which can be more conducive to growth than the conditions above the boundary layer. Until recently, however, it has been difficult to characterize microstructure in a replicable, transferable manner. This study builds upon substantial research on ecological responses of trees at the FTE to growth environment conditions by integrating high-resolution terrestrial lidar scanning (TLS) to characterize microstructure. Our main goal was to use TLS technology to understand the effects of microstructure on photosynthetic functioning (i.e., chlorophyll fluorescence) of small-stature white spruce (Picea glauca (Moench) Voss) trees at the FTE. Our specific objectives were to: 1) determine how much variance in photosynthetic functioning is explained by microstructure; 2) identify microstructural metrics that most strongly control variance in photosynthetic functioning; and 3) determine the scales at which microstructural metrics most strongly drive variance in photosynthetic functioning. Random Forest modeling demonstrated that 28% of variance in photosynthetic functioning can be explained through variation in fine-scale environmental conditions that are modulated by microstructure alone. Insolation and canopy roughness were the most important predictors of photosynthetic functioning, and the sensitivity of photosynthetic functioning to canopy roughness was scale-dependent. This suggests that microstructure affects spatial heterogeneity in the boundary layer that may influence carbon assimilation of small-stature spruce trees. This research emphasizes the importance of quantifying microstructure in study systems where fine-scale heterogeneity of the growth environment may modulate plant responses to regional climate change.

Sensitivity of Above-Ground Biomass to Terrestrial LIDAR-Derived Tree Height in Berkelah Tropical Rainforest, Malaysia

Tree height can be derived from airborne and terrestrial LIDAR in a nondestructive way. This study aims to analyze and investigate whether above-ground biomass (AGB) is sensitive or not to tree height derived from terrestrial LIDAR point cloud data in Berkelah tropical rainforest, Malaysia. To select the unit of analysis, a non-probability sampling of which purposive sampling approach was adopted. Accordingly, 32 sample plots were measured and scanned during the field data collection. Upper and lower canopy trees height was derived from airborne and terrestrial LIDAR, respectively. Moreover, terrestrial LIDAR was used to derive DBH of all upper and lower canopy trees. DBH measured in the field was used to validate the DBH manually derived from terrestrial laser scanner (TLS) point cloud data. To calculate AGB of both lower and upper canopy trees, the DBH derived from TLS point cloud data was used. The coefficient of determination R2 and RMSE of the DBH manually derived from TLS point cloud data validated by field measured DBH were 0.99 and 1.37 cm, respectively. This result revealed the existence of almost one to one relationship and based on the statistical test undertaken; there is no statistically significant difference between the two DBH measurements. Moreover, for sensitivity of AGB, when TLS tree height was validated by corresponding trees height from airborne LIDAR, 0.72 and 2.42 m were found for R2 and RMSE, respectively. Based on the findings, AGB is not sensitive to tree height derived from terrestrial LIDAR point cloud data.

Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner

This paper details the collection, geo-referencing, and data processing algorithms for a fully-automated, permanently deployed terrestrial lidar system for coastal monitoring. The lidar is fixed on a 4-m structure located on a shore-backing dune in Duck, North Carolina. Each hour, the lidar collects a three-dimensional framescan of the nearshore region along with a 30-min two-dimensional linescan time series oriented directly offshore, with a linescan repetition rate of approximately 7 Hz. The data are geo-referenced each hour using a rigorous co-registration process that fits 11 fixed planes to a baseline scan to account for small platform movements, and the residual errors from the fit are used to assess the accuracy of the rectification. This process decreased the mean error (defined as the magnitude of the offset in three planes) over a two-year period by 24.41 cm relative to using a fixed rectification matrix. The automated data processing algorithm then filters and grids the data to generate a dry-beach digital elevation model (DEM) from the framescan along with hourly wave runup, hydrodynamic, and morphologic statistics from the linescan time series. The lidar has collected data semi-continuously since January 2015 (with gaps occurring while the lidar was malfunctioning or being serviced), resulting in an hourly data set spanning four years as of January 2019. Examples of data products and potential applications spanning a range of spatial and temporal scales relevant to coastal processes are discussed.

Evaluating maize phenotype dynamics under drought stress using terrestrial lidar

BackgroundMaize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain key growth stages. Although light detection and ranging (lidar) technology showed great potential in acquiring three-dimensional (3D) vegetation information, it has been rarely used in monitoring maize phenotype dynamics at an individual plant level.ResultsIn this study, we used a terrestrial laser scanner to collect lidar data at six growth stages for 20 maize varieties under drought stress. Three drought-related phenotypes, i.e., plant height, plant area index (PAI) and projected leaf area (PLA), were calculated from the lidar point clouds at the individual plant level. The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively. All three phenotypes showed a pattern of first increasing and then decreasing during the growth period. The high drought tolerance group tended to keep lower plant height and PAI without losing PLA during the tasseling stage. Moreover, the high drought tolerance group inclined to have lower plant area density in the upper canopy than the low drought tolerance group.ConclusionThe results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth stages influenced by drought stress.

Leaf and wood classification framework for terrestrial LiDAR point clouds

Abstract Leaf and wood separation is a key step to allow a new range of estimates from Terrestrial LiDAR data, such as quantifying above‐ground biomass, leaf and wood area and their 3D spatial distributions. We present a new method to separate leaf and wood from single tree point clouds automatically. Our approach combines unsupervised classification of geometric features and shortest path analysis. The automated separation algorithm and its intermediate steps are presented and validated. Validation consisted of using a testing framework with synthetic point clouds, simulated using ray‐tracing and 3D tree models and 10 field scanned tree point clouds. To evaluate results we calculated accuracy, kappa coefficient and F‐score. Validation using simulated data resulted in an overall accuracy of 0.83, ranging from 0.71 to 0.94. Per tree average accuracy from synthetic data ranged from 0.77 to 0.89. Field data results presented and overall average accuracy of 0.89. Analysis of each step showed accuracy ranging from 0.75 to 0.98. F‐scores from both simulated and field data were similar, with scores from leaf usually higher than for wood. Our separation method showed results similar to others in literature, albeit from a completely automated workflow. Analysis of each separation step suggests that the addition of path analysis improved the robustness of our algorithm. Accuracy can be improved with per tree parameter optimization. The library containing our separation script can be easily installed and applied to single tree point cloud. Average processing times are below 10 min for each tree.

Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest

Leaf inclination angle and leaf angle distribution (LAD) are important plant structural traits, influencing the flux of radiation, carbon and water. Although leaf angle distribution may vary spatially and temporally, its variation is often neglected in ecological models, due to difficulty in quantification.In this study, terrestrial LiDAR (TLS) was used to quantify the LAD variation in natural European beech (Fagus Sylvatica) forests. After extracting leaf points and reconstructing leaf surface, leaf inclination angle was calculated automatically. The mapping accuracy when discriminating between leaves and woody material was very high across all beech stands (overall accuracy = 87.59%). The calculation accuracy of leaf angles was evaluated using simulated point cloud and proved accurate generally (R2 = 0.88, p < 0.001; RMSE = 8.37°; nRMSE = 0.16). Then the mean (θmean), mode (θmode), and skewness of LAD were calculated to quantify LAD variation.Moderate variation of LAD was found in different successional status stands (θmean∈ [36.91°, 46.14°], θmode∈ [17°, 43°], skewness ∈ [0.07, 0.48]). Rather than the previously assumed spherical distribution or reported planophile distribution, here we find that LAD tended towards a uniform distribution in young and medium stands, and a planophile distribution in mature stands. A strong negative correlation was also found between plot θmean and plot median canopy height, making it possible to estimate plot specific LAD from canopy height data.Larger variation of LAD was found on different canopy layers (θmean∈ [33.64°, 52.97°], θmode∈ [14°, 64°], skewness ∈ [−0.30, 0.71]). Beech leaves grow more vertically in the top layer, while more obliquely or horizontally in the middle and bottom layer.LAD variation quantified by TLS can be used to improve leaf area index mapping and canopy photosynthesis modelling.

Predicting the Influence of Rain on LIDAR in ADAS

While it is well known that rain may influence the performance of automotive LIDAR sensors commonly used in ADAS applications, there is a lack of quantitative analysis of this effect. In particular, there is very little published work on physically-based simulation of the influence of rain on terrestrial LIDAR performance. Additionally, there have been few quantitative studies on how rain-rate influences ADAS performance. In this work, we develop a mathematical model for the performance degradation of LIDAR as a function of rain-rate and incorporate this model into a simulation of an obstacle-detection system to show how it can be used to quantitatively predict the influence of rain on ADAS that use LIDAR.

Application of Terrestrial LiDAR for Displacement Detecting on Risk Slope

Application of Terrestrial LiDAR for Displacement Detecting on Risk Slope

Combining Ground‐Penetrating Radar With Terrestrial LiDAR Scanning to Estimate the Spatial Distribution of Liquid Water Content in Seasonal Snowpacks

AbstractMany communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow.

Observed and modeled near-wake flow behind a solitary tree

This study reports simultaneous measurements of wind at single point positions up- and downstream of a tree and a numerical experiment with the aim of quantifying the interaction of a solitary tree and the wind field. Relative to the inflow velocity, the velocity deficit in the wake of the tree showed strong seasonal dependence, with wake velocities changing between 70% and 10% of the upstream value from no-leaf winter conditions to full-leaf summer conditions. Whereas for the winter tree the turbulence intensity in the wake is everywhere reduced relative to the upwind flow, for the summer tree the turbulent intensity is markedly reduced in the inner wake, but increased in the outer wake.For the numerical experiment, the combination of (i) a high-detail tree model, based on terrestrial lidar scanning, and (ii) observations of the total bending moment on the tree, taken from strain gauges mounted on the stem, provided the tree parameterization. By this approach, the drag coefficient is not calibrated to fit the observed wind speed in the wake, but the total observed bending moment. Mean wind speed observations in the wake of both the winter and summer tree were well reproduced by the model with mean absolute errors lower than or equal to 5% throughout the wake transect. Also the turbulence intensity in the wake were well reproduced for the summer tree, whereas it was overestimated for the winter tree. Effects of changing tree model and grid resolution are demonstrated and discussed. Based on the presented findings, we recommend to estimate the total bending moment (or drag force) on modelled trees to ensure transferability of results between different numerical setups.

Practical optimal registration of terrestrial LiDAR scan pairs

Point cloud registration is a fundamental problem in 3D scanning. In this paper, we address the frequent special case of registering terrestrial LiDAR scans (or, more generally, levelled point clouds). Many current solutions still rely on the Iterative Closest Point (ICP) method or other heuristic procedures, which require good initializations to succeed and/or provide no guarantees of success. On the other hand, exact or optimal registration algorithms can compute the best possible solution without requiring initializations; however, they are currently too slow to be practical in realistic applications.Existing optimal approaches ignore the fact that in routine use the relative rotations between scans are constrained to the azimuth, via the built-in level compensation in LiDAR scanners. We propose a novel, optimal and computationally efficient registration method for this 4DOF scenario. Our approach operates on candidate 3D keypoint correspondences, and contains two main steps: (1) a deterministic selection scheme that significantly reduces the candidate correspondence set in a way that is guaranteed to preserve the optimal solution; and (2) a fast branch-and-bound (BnB) algorithm with a novel polynomial-time subroutine for 1D rotation search, that quickly finds the optimal alignment for the reduced set. We demonstrate the practicality of our method on realistic point clouds from multiple LiDAR surveys.

New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species

Leaf angle distribution (LAD) is an important property which influences the spectral reflectance and radiation transmission properties of vegetation canopies, and hence interception, absorption and photosynthesis. It is a fundamental parameter of radiative transfer models of vegetation at all scales. Yet, the difficulty in measuring LAD causes it to be also one of the most poorly characterized parameters, and is typically either assumed to be random, or to follow one of a very small number of parametric ‘archetype’ functions. Terrestrial LiDAR scanning (TLS) is increasingly being used to measure canopy structure, but LAD estimation from TLS has been limited thus far. We introduce a fast and simple method for detection of LAD information from terrestrial LiDAR scanning (TLS) point clouds. Here, it is shown that LAD information can be obtained by simply accumulating all valid planes fitted to points in a leaf point cloud. As points alone do not have any normal vector, subsets of points around each point are used to calculate the normal vectors. Importantly, for the first time we demonstrate the effect of distance on the reliable LAD information retrieval with TLS data. We test, validate, and compare the TLS-based method with established leveled digital photography (LDP) approach. We do this using data from both real trees covering the full range of existing leaf angle distribution type, but also from 3D Monte Carlo ray tracing. Crucially, this latter approach allows us to simulate both images and TLS point clouds from the same trees, for which the LAD is known explicitly a priori. This avoids the difficulty of assessing LAD manually, which being a difficult and potentially error-prone process, is an additional source of error in assessing the accuracy of LAD extraction methods from TLS or photography. We show that compared to the LDP measurement technique, TLS is not limited by leaf curvature, and depending on the distance of the TLS from the target, is potentially capable of retrieving leaf angle information from more complex, non-flat leaf surfaces. We demonstrate the possible limitation of TLS measurement techniques for the retrieval of LAD information for more distant canopies, or for taller trees (h > 20 m).

Examining Forest Structure With Terrestrial Lidar: Suggestions and Novel Techniques Based on Comparisons Between Scanners and Forest Treatments

AbstractTerrestrial laser scanners (TLSs) provide a tool to assess and monitor forest structure across forest landscapes. We present TLS methods, suggestions, and mapped guidelines for planning TLS acquisitions at varying scales and forest densities. We examined rates of point‐density decline with distance from two TLS that acquire data at relatively high and low point density and found that the rates were nearly identical between scanners (pvalue &lt;0.01), suggesting that our findings are applicable to a range of TLS types. Using unique, TLS‐adapted processing methods, we determined the relative accuracy of TLS‐derived plot‐scale estimates of tree height, diameter‐at‐breast‐height, height‐to‐canopy, tree counts, as well as treatment‐scale tree density and patch metrics, using both high point density and low point density TLS among thinned and nonthinned forest treatments. The high‐density TLS consistently provides more accurate estimates of plot‐level metrics (R2 = 0.46 to 0.87) than the low‐density TLS (R2 = −0.14 to 0.53). At treatment scales, tree density estimates are similar among scanners (R2 = 0.95 vs. 0.71), as are canopy cover and patch metrics. We develop and present the normalized density‐distance index (NDDI), which can account for up to 59% of the variance in estimate error and can be used to guide TLS‐data acquisition plans. This index indicates whether a given location has generally higher point density (higher NDDI) relative to the distance from the scanner and can be used as a proxy for uncertainty. Using NDDI as a guide for fair comparison between scanners, both plot‐ and treatment‐scale estimates improved.

Automated Estimation of Standing Dead Tree Volume Using Voxelized Terrestrial Lidar Data

Standing dead trees (SDTs) are an important forest component and impact a variety of ecosystem processes, yet the carbon pool dynamics of SDTs are poorly constrained in terrestrial carbon cycling models. The ability to model wood decay and carbon cycling in relation to detectable changes in tree structure and volume over time would greatly improve such models. The specific objectives of this paper were to: 1) develop an automated SDT volume estimation algorithm providing accurate volume estimates for trees scanned with terrestrial lidar in dense forests; 2) assess the volume estimation algorithm’s accuracy with respect to large and small branches; and 3) characterize the impact of occlusion with regards to volume estimation accuracy and the ability of the algorithm to mitigate challenges posed by lower quality point clouds. A voxel-based volume estimation algorithm, “TreeVolX,” was developed and incorporates several methods designed to robustly process point clouds of varying quality levels. The algorithm operates on horizontal voxel slices by segmenting the slice into distinct branch or stem sections then applying an adaptive contour interpolation and interior filling process to create solid reconstructed tree models. The concept of vertical point cloud resampling is introduced to facilitate the modeling of lower quality point clouds with a small voxel size. TreeVolX estimated large and small branch volume with a root-mean-square error of 7.3% and 13.8%, respectively, and the adaptive contour interpolation was shown to significantly reduce volume estimation errors in the case of significantly occluded point clouds.

A Simulation Study Using Terrestrial LiDAR Point Cloud Data to Quantify Spectral Variability of a Broad-Leaved Forest Canopy.

In this analysis, a method for construction of forest canopy three-dimensional (3D) models from terrestrial LiDAR was used for assessing the influence of structural changes on reflectance for an even-aged forest in Belgium. The necessary data were extracted by the developed method, as well as it was registered the adjacent point-clouds, and the canopy elements were classified. Based on a voxelized approach, leaf area index (LAI) and the vertical distribution of leaf area density (LAD) of the forest canopy were derived. Canopy–radiation interactions were simulated in a ray tracing environment, giving suitable illumination properties and optical attributes of the different canopy elements. Canopy structure was modified in terms of LAI and LAD for hyperspectral measurements. It was found that the effect of a 10% increase in LAI on NIR reflectance can be equal to change caused by translating 50% of leaf area from top to lower layers. As presented, changes in structure did affect vegetation indices associated with LAI and chlorophyll content. Overall, the work demonstrated the ability of terrestrial LiDAR for detailed canopy assessments and revealed the high complexity of the relationship between vertical LAD and reflectance.

Improved Biomass Calibration and Validation With Terrestrial LiDAR: Implications for Future LiDAR and SAR Missions

Future NASA and ESA satellite missions plan to better quantify global carbon stocks through detailed observations of forest structure, but ultimately rely on uncertain ground measurement approaches for calibration and validation. A substantial amount of uncertainty in estimating plot-level biomass can be attributed to inadequate and unrepresentative allometric relationships used to convert plot-level tree measurements to estimates of aboveground biomass. These allometric equations are known to have high errors and biases, particularly in carbon-rich forests, because they were calibrated with small and often biased samples of destructively harvested trees. To overcome this issue, we present and test a framework for nondestructively estimating tree and plot-level biomass with terrestrial laser scanning (TLS). We modeled 243 trees from 12 species with TLS and created ten low-RMSE allometric equations. The full 3-D reconstructions, TLS allometry, and Jenkins et al. (2003) allometry were used to calibrate SAR- and LiDAR-based empirical biomass models to investigate the potential for improved accuracy and reduced uncertainty. TLS reduced plot-level RMSE from 18.5% to 9.8% and revealed a systematic negative bias in the national equations. At the calibration stage, allometric uncertainty accounted for 2.8–28.4% of the total RMSE, increasing in relative contribution as calibration improved with sensor fusion. Our findings suggest that TLS plot acquisitions and nondestructive allometry can play a vital role for reducing uncertainty in calibration and validation data for biomass mapping in the upcoming NASA and ESA missions.

Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties

Reliable measurements of the 3D distribution of Leaf Area Density (LAD) in forest canopy are crucial for describing and modelling microclimatic and eco-physiological processes involved in forest ecosystems functioning. To overcome the obvious limitations of direct measurements, several indirect methods have been developed, including methods based on Terrestrial LiDAR scanning (TLS). This work focused on various LAD estimators used in voxel-based approaches. LAD estimates were compared to reference measurements at branch scale in laboratory, which offered the opportunity to investigate in controlled conditions the sensitivity of estimations to various factors such as voxel size, distance to scanner, leaf morphology (species), type of scanner and type of estimator. We found that all approaches to retrieve LAD estimates were highly sensitive to voxel size whatever the species or scanner and to distance to the FARO scanner. We provided evidence that these biases were caused by vegetation heterogeneity and variations in the effective footprint of the scanner. We were able to identify calibration functions that could be readily applied when vegetation and scanner are similar to those of the present study. For different vegetation and scanner, we recommend replicating our method, which can be applied at reasonable cost. While acknowledging that the test conditions in the laboratory were very different from those of the measurements taken in the forest (especially in terms of occlusion), this study revealed existence of strong biases, including spatial biases. Because the distance between scanner and vegetation varies in field scanning, these biases should occur in a similar manner in the field and should be accounted for in voxel-based methods but also in gap-fraction methods.

MOBILE LASER SCANNING SYSTEMS FOR GPS/GNSS-DENIED ENVIRONMENT MAPPING

Abstract. Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy.

A SLIDING WINDOW METHOD FOR DETECTING CORNERS OF OPENINGS FROM TERRESTRIAL LIDAR DATA

Abstract. Architectural building models (LoD3) consist of detailed wall and roof structures including openings, such as doors and windows. Openings are usually identified through corner and edge detection, based on terrestrial LiDAR point clouds. However, singular boundary points are mostly detected by analysing their neighbourhoods within a small search area, which is highly sensitive to noise. In this paper, we present a global-wide sliding window method on a projected façade to reduce the influence of noise. We formulate the gradient of point density for the sliding window to inspect the change of façade elements. With derived symmetry information from statistical analysis, border lines of the changes are extracted and intersected generating corner points of openings. We demonstrate the performance of the proposed approach on the static and mobile terrestrial LiDAR data with inhomogeneous point density. The algorithm detects the corners of repetitive and neatly arranged openings and also recovers angular points within slightly missing data areas. In the future we will extend the algorithm to detect disordered openings and assist to façade modelling, semantic labelling and procedural modelling.