Terrestrial LiDAR Research Articles

MULTI-SOURCE POINT CLOUD SEMANTIC SEGMENTATION USING NEURAL NETWORK

Abstract. The purpose of this study is to enhance point cloud semantic segmentation by using point clouds from multiple distinct technologies on the same capture location and to determine whether employing various technologies throughout the acquisition process yields better performance during classification. The different point clouds were captured in the same geographical location and have previously been aligned and classified by professionals of the field. Three locations have been scanned with airborne lidar, terrestrial lidar and photogrammetry using UAV or helicopter. The use of various sources of capture on the same location opens the door to creating new features, such as the proportion of each source involved in the semantic segmentation of point clouds. This plurality of sources also enables us to spread various features, such as RGB colors, that have been propagated to other sources via the neighborhood. The initial results lean towards capture using different technologies as the overall accuracy increase by two to four points and the mean Matthews correlation coefficient increase by four to seven points. The main drawbacks are the cost of some technologies, as well as the processing time, which is greater than with a single technology.

A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds

Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m3. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected.

Remote Sensing and Nuclear Techniques for Soil Erosion Research in Forest Areas: Case Study of the Crveni Potok Catchment

Hilly terrains characterized by the looseness of the surface soil layer and the slope of the topographic surface are subjected to diverse erosion processes, which could greatly impact forest management. An erosive process was identified along the unstable slopes in most of the Crveni potok catchment in the southeastern part of Serbia. The results of this study indicate two erosion types of variable intensity, dominated by weak and moderate erosion, while severe erosion is identified at several locations. Water pressure in the pores of loose soil in the Crveni potok catchment generates gully erosion in parts of the catchment that are geologically predisposed by the composition of the parent substrate. The paper discusses the factors influencing gully erosion, such as soil density, the slope of the topographic surface, rainfall (precipitation amount, annual distribution), and vegetation characteristics. The set of used methodologies, including remote sensing, nuclear, and geospatial ones, provided an integrated assessment of factors affecting erosion in unstable forest areas. The soil erosion rates in the study area are estimated from inventories of fallout radionuclide 137Cs using conversion models. Airborne photogrammetry, terrestrial Structure-from-Motion 360-degree camera photogrammetry, and Terrestrial LiDAR Scanning (TLS) are used to model complex geomorphological features of forest terrain. These techniques are found to be convenient for the quantification of the existing state of erosion (length, width, volume, and density of the gully systems) in the forest gullies. The remote sensing techniques provided an insight into the physiographic characteristics of the studied area important for determining its parts affected by erosion. The models obtained using different techniques are compared, techniques are evaluated, and their advantages and disadvantages are pointed out. This approach enables the identification of priority targets for soil conservation and site-specific remediation measures to foster sustainable forest management.

Exploring the Branch Wood Supply Potential of an Agroforestry System with Strategically Designed Harvesting Interventions Based on Terrestrial LiDAR Data

Agroforestry systems hold potential for wood and tree biomass production without the need of felling trees. Branch wood harvesting provides access to considerable amounts of lignocellulosic biomass while leaving the tree standing. Aiming at alternatives for wood provision, we assessed the actual woody structure of a silvopastoral system in the African Savannah ecoregion, utilising terrestrial LiDAR technology and quantitative structure models to simulate branch removals and estimate harvesting yields. In addition, the stand structure and harvested wood were examined for the provision of four types of assortments meeting local needs, and operational metrics for each treatment were derived. The stand had large variability in woody structures. Branch harvesting interventions removed up to 18.2% of total stand volume, yielded 5.9 m3 ha−1 of branch wood, and delivered 2.54 m3 ha−1 of pole wood quality, retaining on average more than 75% of the original tree structures. Among the most intense simulations, a mean of 54.7 litres (L) of branch wood was provided per tree, or approximately 34.2 kg of fresh biomass. The choice of an ideal harvesting treatment is subject to practitioners’ interests, while the discussion on aspects of the operation, and stand and tree conditions after treatment, together with outputs, assist decision making. The partitioning of tree structures and branch removal simulations are tools to support the design of tending operations aiming for wood and tree biomass harvesting in agroforestry systems while retaining different functional roles of trees in situ.

Bank re‐erosion action to promote sediment supply and channel diversification: Feedback from a restoration test on the Rhine

AbstractRiver regulation alters hydrological and sediment regimes and consequently affects habitat complexity and dynamics, biodiversity and ecosystem services. Although channel bank erosion is a key geomorphological process supplying alluvial channels with coarse sediments and diversifying aquatic and riparian habitats, banks have often been stabilized to limit erosion risk to human activities and facilities. The objective of this paper is to assess the effects, and their sustainability, of bank protection removal on a 300‐m long reach of the Old Rhine (France/Germany) to promote sediment supply, channel diversification and a rehabilitation of fluvial morpho‐sedimentary processes. This action was combined with the construction of two island groynes to locally increase bank erosion processes. Yearly detailed monitoring was implemented over 6 years, including classical bathymetric surveys, airborne topo‐bathymetric and terrestrial LiDAR, and bed grain‐size and bedload tracking. Following a Q15 flood, the restoration induced a weak sediment supply. The restoration diversified habitats due to the implementation of the two island groynes, inducing bank scouring and the creation of new macroforms, as well as local bed grain‐size diversification and fining. The cross‐sectional diversity of the restored water channel was close to the regularization engineering phase. Channel bedform diversification persisted 6 years due to the persistence of the two island groynes. The action induced the rehabilitation of fluvial forms, in a static manner, rather than the rehabilitation of fluvial morpho‐sedimentary processes, which raises questions about the sustainability of the benefits of such management actions in terms of fluvial functionality and naturality.

Revealing microhabitat requirements of an endangered specialist lizard with LiDAR

A central principle of threatened species management is the requirement for detailed understanding of species habitat requirements. Difficult terrain or cryptic behaviour can, however, make the study of habitat or microhabitat requirements difficult, calling for innovative data collection techniques. We used high-resolution terrestrial LiDAR imaging to develop three-dimensional models of log piles, quantifying the structural characteristics linked with occupancy of an endangered cryptic reptile, the western spiny-tailed skink (Egernia stokesii badia). Inhabited log piles were generally taller with smaller entrance hollows and a wider main log, had more high-hanging branches, fewer low-hanging branches, more mid- and understorey cover, and lower maximum canopy height. Significant characteristics linked with occupancy were longer log piles, an average of three logs, less canopy cover, and the presence of overhanging vegetation, likely relating to colony segregation, thermoregulatory requirements, and foraging opportunities. In addition to optimising translocation site selection, understanding microhabitat specificity of E. s. badia will help inform a range of management objectives, such as targeted monitoring and invasive predator control. There are also diverse opportunities for the application of this technology to a wide variety of future ecological studies and wildlife management initiatives pertaining to a range of cryptic, understudied taxa.

Fast Registration of Terrestrial LiDAR Point Clouds Based on Gaussian-Weighting Projected Image Matching

Terrestrial point cloud registration plays an important role in 3D reconstruction, heritage restoration and topographic mapping, etc. Unfortunately, current research studies heavily rely on matching the 3D features of overlapped areas between point clouds, which is error-prone and time-consuming. To this end, we propose an automatic point cloud registration method based on Gaussian-weighting projected image matching, which can quickly and robustly register multi-station terrestrial point clouds. Firstly, the point cloud is regularized into a 2D grid, and the point density of each cell in the grid is normalized by our Gaussian-weighting function. A grayscale image is subsequently generated by shifting and scaling the x-y coordinates of the grid to the image coordinates. Secondly, the scale-invariant features (SIFT) algorithm is used to perform image matching, and a line segment endpoint verification method is proposed to filter out negative matches. Thirdly, the transformation matrix between point clouds from two adjacent stations is calculated based on reliable image matching. Finally, a global least-square optimization is conducted to align multi-station point clouds and then obtain a complete model. To test the performance of our framework, we carry out the experiment on six datasets. Compared to previous work, our method achieves the state-of-the-art performance on both efficiency and accuracy. In terms of efficiency, our method is comparable to an existing projection-based methods and 4 times faster on the indoor datasets and 10 times faster on the outdoor datasets than 4PCS-based methods. In terms of accuracy, our framework is ~2 times better than the existing projection-based method and 6 times better than 4PCS-based methods.

A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds

The recent popularization of airborne lidar scanners has provided a steady source of point cloud datasets containing the altitudes of bare earth surface and vegetation features as well as man-made structures. In contrast to terrestrial lidar, which produces dense point clouds of small areas, airborne laser sensors usually deliver sparse datasets that cover large municipalities. The latter are very useful in constructing digital representations of cities; however, reconstructing 3D building shapes from a sparse point cloud is a time-consuming process because automatic shape reconstruction methods work best with dense point clouds and usually cannot be applied for this purpose. Moreover, existing methods dedicated to reconstructing simplified 3D buildings from sparse point clouds are optimized for detecting simple building shapes, and they exhibit problems when dealing with more complex structures such as towers, spires, and large ornamental features, which are commonly found e.g., in buildings from the renaissance era. In the above context, this paper proposes a novel method of reconstructing 3D building shapes from sparse point clouds. The proposed algorithm has been optimized to work with incomplete point cloud data in order to provide a cost-effective way of generating representative 3D city models. The algorithm has been tested on lidar point clouds representing buildings in the city of Gdansk, Poland.

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

LiDAR Voxel-Size Optimization for Canopy Gap Estimation

Terrestrial laser scanning of forest structure is used increasingly in place of traditional technologies; however, deriving physical parameters from point clouds remains challenging because LiDAR returns do not have defined areas or volumes. While voxelization methods overcome this challenge, estimation of canopy gaps and other structural attributes are often performed by reducing the point cloud to two-dimensions, thus decreasing the fidelity of the data. Furthermore, relatively few studies have evaluated voxel-size effects on estimation accuracy. Here, we show that voxelized laser-scanning data can be used for canopy-gap estimation without performing dimensionality reduction to the point cloud. Both airborne and terrestrial LiDAR were used to estimate canopy gaps along six vertical transects and four height intervals. Voxel-based estimates were evaluated against hemispherical photography and a sensitivity analysis was performed to identify an optimal voxel size. While the results indicate that our approach can be used with both airborne and terrestrial LiDAR, voxel size has a considerable influence on canopy-gap estimation. Results from our sensitivity analysis indicate that TLS estimation performs best when using 10 cm voxels, yielding canopy gaps ranging from 32–78%. The optimal voxel size for ALS estimation was obtained with 25 cm voxels, yielding estimates ranging from 25–68%.

Soil moisture response to seasonal drought conditions and post‐thinning forest structure

AbstractProlonged drought conditions in semi‐arid forests can lead to widespread vegetation stress and mortality. However, the distribution of these effects is not spatially uniform. We measured soil water potential at high spatial and temporal resolution using 112 sensors distributed across a ponderosa pine forest in northern Arizona, USA, during two abnormally dry years with below‐average total precipitation. We used the data to assess the effects of fore‐summer drought period on the timing, magnitude, and extent of drying throughout the top 100 cm of the soil profile. Additionally, we use high spatial resolution terrestrial lidar measurements of forest structure to develop relationships between soil drying and fine‐scale forest structure. We find that increasing drought from 2019 to 2020 caused significantly earlier onset of soil dying at all depths (25, 50 and 100 cm) and more days below a critical drying threshold for ponderosa pine. Additionally, our results show that significantly drier soils are found in areas with higher stand‐level basal area, canopy cover and tree density, and shorter trees. Our results from the unprecedented spatial and temporal resolution data suggest that tailored restoration thinning with specific tree density and size parameters can be used to increase and prolong the availability of deep soil water to trees during drought.

Forest fragmentation impacts the seasonality of Amazonian evergreen canopies

Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.

Using tree stems in multi-temporal terrestrial lidar scanning data to monitor landslides on vegetated slopes

Using tree stems in multi-temporal terrestrial lidar scanning data to monitor landslides on vegetated slopes

Graph-Based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds

Terrestrial lidar is capable of resolving trees at the branch/leaf level with accurate and dense point clouds. The separation of leaf and wood components is a prerequisite for the estimation of branch/leaf-scale biophysical properties and realistic tree model reconstruction. Most existing methods have been tested on trees with similar structures; their robustness for trees of different species and sizes remains relatively unexplored. This study proposed a new graph-based leaf–wood separation (GBS) method for individual trees purely using the xyz-information of the point cloud. The GBS method fully utilized the shortest path-based features, as the shortest path can effectively reflect the structures for trees of different species and sizes. Ten types of tree data—covering tropical, temperate, and boreal species—with heights ranging from 5.4 to 43.7 m, were used to test the method performance. The mean accuracy and kappa coefficient at the point level were 94% and 0.78, respectively, and our method outperformed two other state-of-the-art methods. Through further analysis and testing, the GBS method exhibited a strong ability for detecting small and leaf-surrounded branches, and was also sufficiently robust in terms of data subsampling. Our research further demonstrated the potential of the shortest path-based features in leaf–wood separation. The entire framework was provided for use as an open-source Python package, along with our labeled validation data.

CNN-based Method for Segmenting Tree Bark Surface Singularites

The analysis of trunk shape and, in particular, the geometric structures on the bark surface are of main interest for different applications linked to the wood industry or biological studies. Bark singularities are often external records of the history of the development of internal elements. The actors of the forest sector grade the trees by considering these singularities through standards. In this paper, we propose a method using terrestrial LiDAR data to automatically segment singularities on tree surfaces. It is based on the construction of a relief map combined with a convolutional neural network. The algorithms and the source code are available with an online demonstration allowing to test the defect detection without any software installation.

A debris flow deposit in Mammoth Cave: field characterization

This work presents an analysis of a debris flow deposit below Earth’s surface in the Mammoth Cave System in Kentucky, USA, and is the first study to characterize an in-cave debris flow to this level of detail. The deposit, named Mt. Ararat by cavers, has a maximum thickness of 7 m, a head-to-tail length of 75 m, and a total volume of about 3400 m3, as determined by terrestrial LiDAR and electrical resistivity surveys. The deposit is chaotic, angular, matrix-supported, and roughly inversely graded, with grain sizes, quantified through various grain-size distribution measuring techniques, ranging from clay through boulders larger than 1 m. The clasts are predominantly Mississippian Big Clifty sandstone, which is allochthonous in this part of the cave. The angularity of the blocks in the deposit indicate that they had not experienced significant erosion; and therefore, are determined to have been transported only a relatively short distance over a short time. The deposit profile is compound in appearance with two heads. We thus interpret this as a debris flow deposit resulting from two distinct flow events, and present a chronology of events leading to the present-day Mt. Ararat in Mammoth Cave. The findings of this work will inform further studies of karst-related erosional events, sediment transport, and deposition at different scales in karst aquifers, as well as the ways in which surface and subsurface processes interact to contribute to karst landscape evolution.

Mapping tropical forest trees across large areas with lightweight cost-effective terrestrial laser scanning

Key messageWe used lightweight terrestrial laser scanning (TLS) to detect over 3000 stems per hectare across a 12-ha permanent forest plot in French Guiana, 81% of them < 10 cm in trunk diameter. This method retrieved 85% of the trees of a classic inventory. Finally, TLS revealed that stem positions of the classic inventory had geolocation errors of up to 6 m.ContextAccurate position mapping of tropical rainforest trees is crucial for baseline studies of tropical forest ecology but is labor-intensive. Terrestrial lidar scanning (TLS) is broadly used in temperate forest inventories, but its use in rainforests is restricted to the determination of individual tree volumes within small survey areas.AimsMapping tree stems across one large (12-ha) rainforest plot, including trees less than 10 cm DBH, and evaluating the precision of traditional mapping approaches.MethodsWe used lightweight TLS, co-registered the acquisitions, and developed a new efficient algorithm to process the TLS data.ResultsWe detected 36,422 stems of which 29,665 (81%) were < 10 cm in diameter at breast height (DBH). Of the trees ≥ 10 cm DBH previously censused in the plot, 85% were identified by TLS. Automatic DBH estimation from TLS data had an RMSE of 6 cm. RMSE was improved to 3 cm by a manual verification of the shape and quality of the stem points. The initial census map had substantial bias in tree geolocation with a maximum value around 6 m.ConclusionLightweight TLS technology is a promising tool for the estimation of stem tapering and volume. Here, we show that it also facilitates the establishment of large tropical forest inventories, by improving the positioning of trees, thus increasing the accuracy of forest inventories and their cost-effectiveness.

An Algorithm for Fitting Sphere Target of Terrestrial LiDAR.

The sphere target played a vital role in terrestrial LiDAR applications, and solving its geometrical center based on point cloud was a widely concerned problem. In this study, we proposed a newly finite random search algorithm for sphere target fitting. Based on the point cloud data and the geometric characteristics of the sphere target, the algorithm realized the target sphere fitting from the perspective of probability and statistics with the help of parameter estimation. Firstly, an initial constraint space was constructed, and the initial center and radius were determined by finite random search. Then, the optimal spherical center and radius were determined gradually through continuous iterative optimization. We tested the algorithm with the simulated and realistic point cloud. Experimental results showed that the proposed algorithm could be effectively applied to all kinds of point cloud fitting. When the coverage rate was bigger than 30%, the fitting accuracy could reach within 0.01 mm for all kinds of point clouds. When the coverage rate was less than 20%, the fitting accuracy can reach ±1 mm, although it was reduced to a certain extent.

Enhanced Surf Zone and Wave Runup Observations with Hovering Drone-Mounted Lidar

AbstractWe demonstrate that a hovering, drone-mounted laser scanner (lidar) paired with a survey-grade satellite and inertial positioning system measures the wave transformation across the surf zone and the resulting runup with accuracy almost equal to a stationary truck-mounted terrestrial lidar. The drone, a multirotor small uncrewed aircraft system (sUAS), provides unobstructed measurements by hovering above the surf zone at 20-m elevation while scanning surfaces along a 150-m-wide cross-shore transect. The drone enables rapid data collection in remote locations where terrestrial scanning may not be possible. Allowing for battery changes, about 17 min of scanning data can be acquired every 25 min for several hours. Observations were collected with a wide (Hs = 2.2 m) and narrow (Hs = 0.8 m) surf zone, and are validated with traditional land-based survey techniques and an array of buried pressure sensors. Thorough postprocessing yields a stable () back beach topography estimate comparable to the terrestrial lidar (). Statistical wave properties and runup values are calculated, as well as bathymetry inversions using a relatively simple nonlinear correction to wave crest phase speed in the surf zone, illustrating the utility of drone-based lidar observations for nearshore processes.

A Comparison of Volumetric Reconstruction Methods of Archaeological Deposits Using Point-Cloud Data from Ahuahu, Aotearoa New Zealand

Collection of 3D data in archaeology is a long-standing practice. Traditionally, the focus of these data has been visualization as opposed to analysis. Three-dimensional data are often recorded during archaeological excavations, with the provenience of deposits, features, and artefacts documented by a variety of methods. Simple analysis of 3D data includes calculating the volumes of bound entities, such as deposits and features, and determining the spatial relationships of artifacts within these. The construction of these volumes presents challenges that originate in computer-aided design (CAD) but have implications for how data are used in archaeological analysis. We evaluate 3D construction processes using data from Waitetoke, Ahuahu Great Mercury Island, Aotearoa, New Zealand. Point clouds created with data collected by total station, photogrammetry, and terrestrial LiDAR using simultaneous localization and mapping (SLAM) are compared, as well as different methods for generating surface area and volumes with triangulated meshes and convex hulls. The differences between methods are evaluated and assessed in relation to analyzing artifact densities within deposits. While each method of 3D data acquisition and modeling has advantages in terms of accuracy and precision, other factors such as data collection and processing times must be considered when deciding on the most suitable.