Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments

Three Dimensional (3D) modeling is crucial for studying, analyzing, reconstructing, and documenting our environment, in general, and man-made structures, in particular. 3D modeling of structures of interest can be directly established by a Terrestrial Laser Scanner (TLS). Nevertheless, several TLS scans from different positions/orientations are necessary to have a complete coverage of the surveyed structure. Transformation of the collected scans into a common coordinate system through a registration procedure is necessary in order to have a meaningful 3D model of the structure in question. The registration process requires large overlap area among the TLS scans for reliable estimation of the transformation parameters. In this paper, the large overlap area requirement between the TLS scans is reduced/eliminated using a photogrammetric data, which can be economically and quickly collected, as additional information for the registration process. Different registration primitives can be used to estimate the transformation parameters among the different scans. Manmade structures are rich with linear and planar features. Moreover, linear and planar features can be reliably extracted from both photogrammetric data and TLS scans. Therefore, in this research, planar and linear features are chosen as the registration primitives. Quality control of the aligned scans is necessary to evaluate the registration results and to compare the performance of linear and planar feature as possible primitives. In this research, quality control of the registration results is qualitatively and quantitatively evaluated. The qualitative quality control of the registration outcome is conducted by plotting and visual inspection of registered laser scans. Quantitative quality analysis, on the other hand, is conducted by calculating the point-to-plane normal distances between the photogrammetrically reconstructed models and TLS scans after the registration process, and also between some of the TLS scans if there are any available overlap areas. In addition, the results from a variant of the commonly used ICP registration are compared with those derived from the proposed registration. Experimental results from a real dataset will show the feasibility of the proposed technique where less than 10 cm point-to-plane normal distance between the registered surfaces has been observed.

A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments

Abstract. In the context of forest inventory, there is a growing need for 3D data to produce detailed geometric information. While terrestrial laser scanning (TLS) is traditionally used for this purpose , several factors have prompted the exploration of alternative solutions, such as handheld mobile laser scanners (MLS). One key limitation of TLS is its static data acquisition, which makes it less suited for the complex and heterogeneous nature of forest environments. A primary challenge with TLS in forestry is the occlusion effect, where parts of trees (such as stems, branches, or leaves) may not be captured due to obstacles between the scanner and the target. Additionally, TLS is known for long acquisition times, which, while yielding high-quality data, may exceed the requirements for standard forest inventory tasks. The cost associated with TLS is also significant; although feasible for small forest patches, scaling these methods to larger areas would demand substantial resources. Similarly, while handheld MLS devices offer more flexibility in data acquisition and the possibility to cover a wider area in the same acquisition time, professional versions are still relatively costly, adding to the need for more affordable alternatives. This underlines the demand for a low-cost, efficient method for 3D data acquisition in forest inventories. In this study, forest structural variables obtained with a low-cost MLS (LC-MLS; Mandeye) were compared with two professional MLS devices (GeoSlam Horizon and GreenValley LiGrip H120) and a professional TLS (Trimble X7). With the open-source software 3DFin, we processed the point cloud data from all the devices, enabling the extraction of diameters at breast height (DBH) and total tree heights (TH). The LC-MLS device shows a positive bias in DBH measurements (1.62 cm), indicating it tends to overestimate compared to the TLS reference. Despite this, it demonstrates competitive quality relative to the two other MLS systems. In terms of TH, the LC-MLS has a negative bias of −2.16 m, suggesting it underestimates tree height. When compared to other professional MLS devices, the LC-MLS exhibits a higher RMSE% in TH measurements (12.97%), indicating less accuracy in tree height estimation.

In situ leaf area index (LAI) measurements are essential to validate widely-used large-area or global LAI products derived, indirectly, from satellite observations. Here, we compare three common and emerging ground-based sensors for rapid LAI characterisation of large areas, namely digital hemispherical photography (DHP), two versions of a widely-used commercial LAI sensor (LiCOR LAI-2000 and 2200), and terrestrial laser scanning (TLS). The comparison is conducted during leaf-on and leaf-off conditions at an unprecedented sample size in a deciduous woodland canopy. The deviation between estimates of these three ground-based instruments yields differences greater than the 5% threshold goal set by the World Meteorological Organization. The variance at sample level is reduced when aggregated to plot scale (1 ha) or site scale (6 ha). TLS shows the lowest relative standard deviation in both leaf-on (11.78%) and leaf-off (13.02%) conditions. Whereas the relative standard deviation of effective plant area index (ePAI) derived from DHP relates closely to TLS in leaf-on conditions, it is as large as 28.14–29.74% for effective wood area index (eWAI) values in leaf-off conditions depending on the thresholding technique that was used. ePAI values of TLS and LAI-2x00 agree best in leaf-on conditions with a concordance correlation coefficient (CCC) of 0.796. In leaf-off conditions, eWAI values derived from DHP with Ridler and Calvard thresholding agrees best with TLS. Sample size analysis using Monte Carlo bootstrapping shows that TLS requires the fewest samples to achieve a precision better than 5% for the mean ± standard deviation. We therefore support earlier studies that suggest that TLS measurements are preferential to measurements from instruments that are dependent on specific illumination conditions. A key issue with validation of indirect estimates of LAI is that the true values are not known. Since we cannot know the true values of LAI, we cannot quantify the accuracy of the measurements. Our radiative transfer simulations show that ePAI estimates are, on average, 27% higher than eLAI estimates. Linear regression indicated a linear relationship between eLAI and ePAI–eWAI (R2 = 0.87), with an intercept of 0.552 and suggests that caution is required when using LAI estimates.

The possibility of analysing the characteristics and volume of inchannel large wood (LW) is of importance for river management but the traditional manual field activities are usually time-consuming and not easy to apply at a larger spatial scale. This paper presents an alternative and faster method to detect the characteristics and measurements of large wood in rivers by using the terrestrial laser scanner (TLS) technology. Field-measurements data and TLS scans were collected in August 2013 along 14 ha of the Piave River (Italy) analysing 230 and 208 woody elements for the manual method and the TLS one, respectively. TLS data were processed using the Cyclone 7 software and the LW measurements were extracted adopting two specific tools. The resulting low margin of error in the comparison between field data and those derived from TLS surveys confirmed the ability of TLS in the detection of large wood and wood jams characteristics. The greatest deviations were found for wood jams height which the TLS showed a tendency to overestimate (+24.37%) and LW length with a slight underestimation (–19.76%). Considering the wood volume, the relative difference between the TLS and manual method was within a negligible margin of error of ±7%. Characteristics and measurements of LW in rivers can be obtained from TLS surveys, but some progress in this technique is still needed to allow a better management of the 3D point cloud and a faster extraction of the wood measurements. The proposed method represents an alternative tool for faster and repeated surveys of wood characteristics in a complex river environment, ensuring a reliable quantification of spatial and temporal variation of wood volume.

As 3D geospatial information is demanded, terrestrial laser scanners which can obtain 3D model of objects have been applied in various fields such as Building Information Modeling (BIM), structural analysis, and disaster management. To acquire precise data, performance evaluation of a terrestrial laser scanner must be conducted. While existing 3D surveying equipment like a total station has a standard method for performance evaluation, a terrestrial laser scanner evaluation technique for users is not established. This paper categorizes and analyzes error sources which generally occur in terrestrial laser scanning. In addition to the prior researches about categorizing error sources of terrestrial Laser scanning, this paper evaluates the error sources by the actual field tests for the smooth in-situ applications. The error factors in terrestrial laser scanning are categorized into interior error caused by mechanical errors in a terrestrial laser scanner and exterior errors affected by scanning geometry and target property. Each error sources were evaluated by simulation and actual experiments. The 3D coordinates of observed target can be distortedby the biases in distance and rotation measurement in scanning system. In particular, the exterior factors caused significant geometric errors in observed point cloud. The noise points can be generated by steep incidence angle, mixed-pixel and crosstalk. In using terrestrial laser scanner, elaborate scanning plan and proper post processing are required to obtain valid and accurate 3D spatial information.

Remote sensing plays an important role within the field of forest inventory. Airborne laser scanning (ALS) has become an effective tool for acquiring forest inventory data. In most ALS-based forest inventories, accurately positioned field plots are used in the process of relating ALS data to field-observed biophysical properties. The geo-referencing of these field plots is typically carried out by means of differential global navigation satellite systems (dGNSS), and often relies on logging times of 15–20 min to ensure adequate accuracy under different forest conditions. Terrestrial laser scanning (TLS) has been proposed as a possible tool for collection of field data in forest inventories and can facilitate rapid acquisition of these data. In the present study, a novel method for co-registration of TLS and ALS data by posterior analysis of remote-sensing data – rather than using dGNSS – was proposed and then tested on 71 plots in a boreal forest. The method relies on an initial position obtained with a recreational-grade GPS receiver, in addition to analysis of the ALS and TLS data. First, individual tree positions were derived from the remote-sensing data. A search algorithm was then used to find the best match for the TLS-derived trees among the ALS-derived trees within a search area, defined relative to the initial position. The accuracy of co-registration was assessed by comparison with an accurately measured reference position. With a search radius of 25 m and using low-density ALS data (0.7 points m−2), 82% and 51% of the TLS scans were co-registered with positional errors within 1 m and 0.5 m, respectively. By using ALS data of medium density (7.5 points m−2), 87% and 78% of the scans were co-registered with errors within 1 m and 0.5 m of the reference position, respectively. These results are promising and the method can facilitate rapid acquisition and geo-referencing of field data. Robust methods to identify and handle erroneous matches are, however, required before it is suitable for operational use.

Summary Vegetation structure is an important determinant of species habitats and diversity. It is often represented by simple metrics, such as canopy cover, height and leaf area index, which do not fully capture three‐dimensional variations in density. Terrestrial laser scanning (TLS) is a technology that can better capture vegetation structure, but methods developed to process scans have been biased towards forestry applications. The aim of this study was to develop a methodology for processing TLS data to produce vegetation density profiles across a broader range of habitats. We performed low‐resolution and medium‐resolution TLS scans using a Leica C5 Scanstation at four locations within eight sites near Wollongong, NSW, Australia (34·38–34·41°S, 150·84–150·91°E). The raw point clouds were converted to density profiles using a method that corrected for uneven ground surfaces, varying point density due to beam divergence and occlusion, the non‐vertical nature of most beams and for beams that passed through gaps in the vegetation without generating a point. Density profiles were evaluated against visual estimates from three independent observers using coarse height classes (e.g. 5–10 m). TLS produced density profiles that captured the three‐dimensional vegetation structure. Although sites were selected to differ in structure, each was relatively homogeneous, yet we still found a high spatial variation in density profiles. There was also large variation between observers, with the RMS error of the three observers relative to the TLS varying from 16·2% to 32·1%. Part of this error appeared to be due to misjudging the height of vegetation, which caused an overestimation in one height class and an underestimation in another. Our method for generating density profiles using TLS can capture three‐dimensional vegetation structure in a manner that is more detailed and less subjective than traditional methods. The method can be applied to a broad range of habitats – not just forests with open understoreys. However, it cannot accurately estimate near‐surface vegetation density when there are uneven surfaces or dense vegetation prevents sufficient ground returns. Nonetheless, TLS density profiles will be an important input for research on species habitats, microclimates and nutrient cycles.

Tree crown structural parameters are key inputs to studies spanning forest fire propagation, invasive species dynamics, avian habitat provision, and so on, but these parameters consistently are difficult to measure. While airborne laser scanning (ALS) provides uniform data and a consistent nadir perspective necessary for crown segmentation, the data characteristics of terrestrial laser scanning (TLS) make such crown segmentation efforts much more challenging. We present a data fusion approach to extract crown structure from TLS, by exploiting the complementary perspective of ALS. Multiple TLS point clouds are automatically registered to a single ALS point cloud by maximizing the normalized cross correlation between the global ALS canopy height model (CHM) and each of the local TLS CHMs through parameter optimization of a planar Euclidean transform. Per-tree canopy segmentation boundaries, which are reliably obtained from ALS, can then be adapted onto the more irregular TLS data. This is repeated for each TLS scan; the combined segmentation results from each registered TLS scan and the ALS data are fused into a single per-tree point cloud, from which canopy-level structural parameters readily can be extracted.

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

Estimation of vertical plant area density from single return terrestrial laser scanning point clouds acquired in forest environments

Assessing the repeatability of terrestrial laser scanning for monitoring gully topography: A case study from Aratula, Queensland, Australia

Montreuil, A-L., Bullard, J.E., Chandler, J.H., 2013. Detecting seasonal variations in embryo dune morphology using a terrestrial laser scanner. In: Conley, D.C., Masselink, G., Russell, P.E. and O'Hare, T.J. (eds.)Coastal embryo dunes are rarely the focus of research efforts despite the fact that they are often precursors to the development of established coastal dune fields. The aim of this paper is to quantify morphological changes within an embryo dune field using a terrestrial laser scanner (TLS), with particular emphasis on determining how the number, height and orientation of dunes changes from season to season in relation to external forcing factors. The study site was located on the upper (> MHWS) section of a macrotidal beach on the north Lincolnshire coast (UK) where the wind regime includes both onshore and offshore components. Dune morphology was monitored approximately every three months over a period of 16 months (July 2009– October 2010) using TLS. The volume of sand within the embryo dune field ranged from a minimum of 12,622.54 m3 in January 2010 to a maximum of 13,263.17 m3 in June 2010. The majority of volume gain was a result of seaward accretion in response to onshore aeolian sediment supply as opposed to either a gain in height or an expansion of the dune field in an alongshore direction. Sediment volume was reduced in the embryo dune field as a result of two severe storm surge events that occurred during the winter months. The storm surges caused elongated areas of erosion between dunes, aligned with the dominant wind direction. Between October 2009 and January 2010 the volume of the dunes decreased by 315.49 m3 corresponding to a volumetric ratio of sand thickness of −0.026 m month−1. However, subsequent surveys show that the dunes then progressively recovered. This research demonstrates the potential of high resolution terrestrial laser scanning for identifying small-scale morphological changes in coastal dune fields, essential for relating detected change to evolutionary processes.

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively.

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R2=0.77, RMSE=7%), annual grasses (R2=0.70, RMSE=21%), perennial grasses (R2=0.36, RMSE=12%), forbs (R2=0.52, RMSE=6%), bare earth or litter (R2=0.49, RMSE=19%), and the biomass of shrubs (R2=0.71, RMSE=175g) and herbaceous vegetation (R2=0.61, RMSE=99g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.