PHOTOGRAMMETRIC INVENTORY OF MONUMENTS IN THE ASPECT OF LASER SCANNING

Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests

This study explores the feasibility of applying single-scan airborne, static terrestrial and mobile laser scanning for improving the accuracy of tree height growth measurement. Specifically, compared to the traditional works on forest growth inventory with airborne laser scanning, two issues are regarded: “Can the new technique characterize the height growth for each individual tree?” and “Can this technique refine the minimum growth-discernable temporal interval further?” To solve these two puzzles, the sampling principles of the three laser scanning modes were first examined, and their error sources against the task of tree-top capturing were also analyzed. Next, the three-year growths of 58 Nordic maple trees (Crimson King) for test were intermittently surveyed with one type of laser scanning each time and then analyzed by statistics. The evaluations show that the height growth of each individual tree still cannot be reliably characterized even by single-scan terrestrial laser scanning, and statistical analysis is necessary in this scenario. After Gaussian regression, it is found that the minimum temporal interval with distinguishable tree height growths can be refined into one month based on terrestrial laser scanning, far better than the two years deduced in the previous works based on airborne laser scanning. The associated mean growth was detected to be about 0.12 m. Moreover, the parameter of tree height generally under-estimated by airborne and even mobile laser scanning can be relatively revised by means of introducing static terrestrial laser scanning data. Overall, the effectiveness of the proposed technique is primarily validated.

SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning

Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB estimation.

There are several different methods in laser scanning technology including terrestrial laser scanner (TLS), airborne laser scanner (ALS), and mobile laser scanner (MLS). In addition to these scanners, there are personal laser scanners (PLS). PLS are examined under two main categories as handheld personal laser scanner (HPLS) and backpack personal laser scanner (BPLS) which are the latest additions to these laser scanning technologies. Today, the use of personal laser scanner technology is a popular research and application topics. The primary advantage of PLS lies in its high mobility in different topography conditions and rapid data acquisition. Unlike TLS and MLS, the operator carries the PLS device in the work area at standard walking speed, which is sufficient to collect data. Also, PLS technology eliminates the limitations of moving TLS equipment from one station point to another station point during the data collection process and installing instruments on a tripod again. In this paper, a case study was conducted using the LiBackpack DGC50 Mobile Scanner, which is the PLS technique, for the cadastral updating surveying in the Karaağaç District of Edirne province. It has been concluded that backpack laser scanners provide sufficient accuracy for cadastral studies in the study area.

Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

In the publication, the authors demonstrated the possibility of using terrestrial and airborne laser scanning technology for both inventory and modeling of architectural objects. First of all, they presented the use of laser scanning technology, for creation of 3D models of sacral objects, both the large ones, as well as the smaller ones constituting small architecture objects. For the airborne laser scanning, the subject of the study was the Sanctuary of the Divine Mercy in Krakow along with the surrounding complex of sacral buildings. Modelling and visualization of the Sanctuary were made in the TerraSolid and MicroStation programs on the basis of a point cloud with a density of 12 points/m2. Data from terrestrial laser scanning were obtained on the basis of measurement with the use of terrestrial laser scanner Leica ScanStation P40 and included the Chapel of Our Lady of Czestochowa located in the Lasek Mogilski in Krakow.

Accurate estimates of vegetation structure are important for a large number of applications including ecological modeling and carbon budgets. Light detection and ranging (LiDAR) measures the three-dimensional structure of vegetation using laser beams. Most LiDAR applications today rely on airborne platforms for data acquisitions, which typically record between 1 and 5 “discrete” returns for each outgoing laser pulse. Although airborne LiDAR allows sampling of canopy characteristics at stand and landscape level scales, this method is largely insensitive to below canopy biomass, such as understorey and trunk volumes, as these elements are often occluded by the upper parts of the crown, especially in denser canopies. As a supplement to airborne laser scanning (ALS), a number of recent studies used terrestrial laser scanning (TLS) for the biomass estimation in spatially confined areas. One such instrument is the Echidna® Validation Instrument (EVI), which is configured to fully digitize the returned energy of an emitted laser pulse to establish a complete profile of the observed vegetation elements. In this study we assess and compare a number of canopy metrics derived from airborne and TLS. Three different experiments were conducted using discrete return ALS data and discrete and full waveform observations derived from the EVI. Although considerable differences were found in the return distribution of both systems, ALS and TLS were both able to accurately determine canopy height (Δ height r2 > 0.90, p < 0.01). When using more spatially explicit approaches for modeling the biomass and volume throughout the stands, the differences between ALS and TLS observations were more distinct; however, predictable patterns exist based on sensor position and configuration.

BackgroundPlant growth is a good indicator of crop performance and can be measured by different methods and on different spatial and temporal scales. In this study, we measured the canopy height growth of maize (Zea mays), soybean (Glycine max) and wheat (Triticum aestivum) under field conditions by terrestrial laser scanning (TLS). We tested the hypotheses whether such measurements are capable to elucidate (1) differences in architecture that exist between genotypes; (2) genotypic differences between canopy height growth during the season and (3) short-term growth fluctuations (within 24 h), which could e.g. indicate responses to rapidly fluctuating environmental conditions. The canopies were scanned with a commercially available 3D laser scanner and canopy height growth over time was analyzed with a novel and simple approach using spherical targets with fixed positions during the whole season. This way, a high precision of the measurement was obtained allowing for comparison of canopy parameters (e.g. canopy height growth) at subsequent time points.ResultsThree filtering approaches for canopy height calculation from TLS were evaluated and the most suitable approach was used for the subsequent analyses. For wheat, high coefficients of determination (R2) of the linear regression between manually measured and TLS-derived canopy height were achieved. The temporal resolution that can be achieved with our approach depends on the scanned crop. For maize, a temporal resolution of several hours can be achieved, whereas soybean is ideally scanned only once per day, after leaves have reached their most horizontal orientation. Additionally, we could show for maize that plant architectural traits are potentially detectable with our method.ConclusionsThe TLS approach presented here allows for measuring canopy height growth of different crops under field conditions with a high temporal resolution, depending on crop species. This method will enable advances in automated phenotyping for breeding and precision agriculture applications. In future studies, the TLS method can be readily applied to detect the effects of plant stresses such as drought, limited nutrient availability or compacted soil on different genotypes or on spatial variance in fields.

Abstract. Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

The warming climate, biodiversity loss, and escalating natural disturbances emphasize the need for sustainable forest management, which relies on understanding tree growth and competition. Laser scanning has opened new possibilities for measuring these processes. This thesis aims to develop approaches to evaluate stem and crown growth and competition using laser scanning point clouds, exploring their utility in assessing and quantifying competition dynamics and growth patterns in forest stands. Study I developed approaches for assessing stem and crown competition using terrestrial laser scanning (TLS) point clouds and investigated the effect of different thinning treatments on competition in Scots pine (Pinus sylvestris L.)-dominated forests. The results indicated that TLS-derived competition decreased across different thinning methods compared to the control plots for both moderate and intensive thinning. Thinning from below showed the greatest reduction in competition, followed by thinning from above and systematic thinning. Study I demonstrates that TLS provides an advanced solution for assessing tree crown characteristics and growing space, highlighting a novel approach to understanding competition between trees. Study II investigated the use of bi-temporal TLS and low-altitude airborne laser scanning (ALS), individually and in combination, to assess the relationship between tree stem volume growth (ΔV) and crown structure, including its change (ΔC), over a 7-year monitoring period. The results showed a strong correlation between ΔV and crown metrics (top height, projection area, and perimeter) for Scots pine. For Norway spruce, ΔV weakly correlated with 3D crown area (CA3D), volume (CV), and its change (ΔCV). Birch ΔV showed weak to moderate correlations with CA2D, crown perimeter, and ΔCV. Random Forest (RF) analyses revealed that changes in crown structure were important for explaining ΔV variations for Norway spruce and birch, while top height (CHmax) was the key metric for Scots pine. In conclusion, Study II showed that multisensor laser scanning data can serve to evaluate the relationships between ΔV and tree crown structure. Study III examined the utility of TLS and low-altitude ALS data in describing the competitive stress of individual trees using two approaches. The object-based approach quantified competition by identifying and characterizing neighboring trees, while the point cloud-based approach evaluated competition through point cloud structures representing competitive vegetation around a target tree. The results showed that object-based competition indices (CIs) correlated more strongly with in situ-based CIs compared to point cloud-based CIs and were more consistent between TLS and ALS. Overall, Study III demonstrated that TLS is effective for small-scale competition assessments, while low-altitude ALS has similar potential for describing competition on a large scale. This thesis demonstrates the capability of the developed laser scanning-based approaches to assess stem and crown growth and competition. It shows how TLS and ALS enhance our understanding of tree growth and their responses to neighboring trees, helping identify processes driving changes in forest dynamics. These findings offer concrete steps toward more precise and efficient forest management, although further refinement of the methodologies is needed to optimize their use across varying forest ecosystems.

Terrestrial Laser Scanner (TLS) has become a familiar instrument to be used in wide range of engineering application. It can be used for the rapid capture of accurate and highly detailed 3D point cloud datasets. The advantage of laser scanner is that it can record huge number of points in a short period of time. The main idea in this contribution assesses the accuracy of TLS relative to other traditional surveying instruments. This is done throughout four different case studies. In all case studies the 3D coordinates, obtained using total station (TS) are assumed the reference coordinates. First, a control point network, that consists of nine points, is measured using TS, TLS, and real time kinematic global navigation satellite system (RTK-GPS). The precision of each instrument is investigated considering the standard deviation (SD) of measurements. In addition, the accuracy of TLS and RTK-GPS is investigated considering the measurements RMS. Secondly, a grid levelling for a 30,000m2 ground terrain was performed using TS and TLS. After words, the RMS of TLS measurements is computed and a grid of 5mx5m is generated from both surfaces; formed using TS and TLS measurements. Thirdly, the effect of incidence angle on TLS measurements is assessed by measuring fifty-six points fixed on a building facade using different incident angles. Those points were measured using both TS and TLS, and then the absolute height differences between TS and TLS measurements were calculated to figure out the effect of decreasing the incidence angle on measurements. In the fourth case study, the accuracy of TLS on steep-vertical cut measurements is investigated by surveying a downhill area of 500m2 by both TS and TLS, the RMS of TLS measurements was calculated. Finally, based on the obtained results, it was found that TLS produces a higher vertical accuracy than RTK-GPS in measuring control point networks. The RMS of TLS measurements was about 5cm. Moreover, TLS incidence angle is not preferable to be less than 45 degrees as the accuracy degrades significantly after this value. In steep-vertical cut measurements, TLS obtained RMS almost of 6mm discrepancies with a lower measurement period. Eventually, despite the fact that TLS is more expensive than traditional surveying techniques, it is more beneficial in terms of time and effort saving. In addition, it can figure out acceptable accuracy ranges with more detailed surveyed data.

Lee, H.S.; Kim, I.H., and Kim, H.G., 2016. Application of terrestrial 3D laser scanning to monitor changes of beach landforms. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 173–177. Coconut Creek (Florida), ISSN 0749-0208. Monitoring changes in the beach morphology of coastal landforms is important when considering coastal management measures. In this paper, to create a changing beach surface, point cloud data of the beach are obtained using three-dimensional (3D) terrestrial laser scanning (TLS), and a beach surface model is analysed based on 3D point data. The 3D point cloud is generated from the scanned beach, including breakwaters, and these points are registered and merged through a reference point (scan origin and ball target) surveyed by RTK-GPS. Noise elements and unnecessary points are eliminated to yield better surface modelli...

Tree architecture reflects a hierarchical growth pattern shaped by the interplay between genetics and the environment. Environmental variation leads to unique resource availability, resulting in each tree developing distinct structural features, akin to the uniqueness of a human fingerprint. In this study, we propose a nondestructive method for quantifying this architectural uniqueness using terrestrial laser scanning for tree identification. While tree identification is commonly based on their precise geospatial location, this information may not always be available. Instead, we hypothesized that a tree’s stem profile (diameters along the stem) and branching arrangement (locations of branch origins on the stem surface) could distinguish individuals within a population. The experimental setup included 65 Scots pine (Pinus sylvestris L.) trees in a managed boreal forest stand, scanned with terrestrial laser scanning in September 2021 (T1) and November 2022 (T2). We investigated whether individual trees could be identified based on architectural similarities between their point cloud reconstructions from T1 and T2. In total, 52 trees (80.0%) were identified based on their architectural characteristics. The results supported our hypothesis, showing that identifying ≥10 branch origins from independent reconstructions was sufficient to establish architectural uniqueness, resulting in 100% identification accuracy (n = 20 trees). These findings suggest that the complex three-dimensional tree architecture can be condensed into a two-dimensional pattern of points representing branch arrangement, which we term the “tree fingerprint.” These architectural characteristics, which can be reconstructed from the lower half of the tree, are well suited for acquisition via ground-based sensing techniques such as terrestrial or mobile laser scanning. If point cloud data capable of characterizing individual branches is acquired during forest operations, the proposed methodology can facilitate tree identification for applications such as wood tracking, even without geospatial coordinates.

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (&lt;2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.