Terrestrial Light Detection And Ranging Research Articles

Quantifying Asian chestnut gall wasp (Dryokosmus kuriphilus Yasumatsu) impact on fruit yield and on tree growth using terrestrial LiDAR

The Asian chestnut gall wasp is an invasive pest that causes ecological and economic losses in the management of the sweet chestnut (Castanea sativa Mill.). The pest therefore needs to be monitored in order to assess its impact on tree growth and fruit yield. As part of this work, we propose an infestation level classification based on the number of galls per branch. For two sweet chestnut orchards infested by the wasp, terrestrial light detection and ranging (LiDAR) data were used to calculate tree growth and fruit yield was quantified in terms of nuts and burrs. The tree growth and fruit yield variables were statistically analysed to determine the impact of different infestation levels on these variables, and differences between pairs of infestation levels were tested for significance. Negative correlations were found for the fruit yield variables with infestation indicators, while positive correlations were found for the tree growth variables. Significant differences were observed in fruit yield and tree growth variables associated with different infestation levels.

MAPPING 3D STRUCTURE OF URBAN TREES USING AIRBORNE LIDAR DATA FOR TOPOGRAPHIC SURVEY

Abstract. The designing of urban spaces utilizing trees is a promising approach to alleviate the severe thermal environment. Three-dimensional (3D) information on individual trees is important for their maintenance and for evaluating their microclimatic effects. Methods have been developed for estimating the 3D distribution of leaf area density (LAD) using high-density airborne light detection and ranging (LiDAR) point cloud. However, the coverage of high-density data is limited. This study sought to develop a method for estimating LAD distribution using existing low-density LiDAR data that was obtained for the nationwide topographic survey. A point cloud with the same density as the existing data was generated from the high-density data. LAD was estimated using the generated point cloud and was compared with LAD derived from the high-density data and terrestrial LiDAR to clarify the spatial scale of information obtained from the existing data. A method to improve the spatial scale was also examined. These methods were applied to the actual existing LiDAR data to generate a 3D information map at an urban scale.

A new method for voxel‐based modelling of three‐dimensional forest scenes with integration of terrestrial and airborne LiDAR data

Abstract Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied. We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared. Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06. We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests.

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

High-resolution topographic information of landslide-prone areas plays an important role in accurate prediction and characterization of potential landslides and mitigation of landslides-associated hazards. This study presents an advanced geomorphological surveying system that integrates the light detection and ranging (LiDAR) with an unmanned aerial vehicle (UAV), a multi-rotor aerial vehicle in specific, for prediction, monitoring and forensic analysis of landslides, and for maintenance of debris-flow barriers. The test-flight over a vegetated area demonstrates that the integrated UAV-LiDAR system can provide high-resolution, three-dimensional (3D) LiDAR point clouds below canopy and vegetation in forest environments, overcoming the limitation of aerial photogrammetry and terrestrial LiDAR platforms. An algorithm is suggested to delineate the topographic information from the acquired 3D LiDAR point clouds, and the accuracy and performance of the developed UAV-LiDAR system are examined through field demonstration. Finally, two field demonstrations are presented: the forensic analysis of the recent Gokseong landslide event, and the sediment deposition monitoring for debris-flow barrier maintenance in South Korea. The developed surveying system is expected to contribute to geomorphological field surveys in vegetated, forest environments, particularly in a site that is not easily accessible.

Accuracy of Structure-from-Motion/Multiview Stereo Terrain Models: A Practical Assessment for Applications in Field Geology

We assess the accuracy of Structure-from-Motion/Multiview stereo (SM) terrain models acquired ad hoc or without high-resolution ground control to analyze their usage as a base for inexpensive 3D bedrock geologic mapping. Our focus is on techniques that can be utilized in field projects without the use of heavy and/or expensive equipment or the placement of ground control in logistically challenging sites (e.g., steep cliff faces or remote settings). We use a Terrestrial Light Detection and Ranging (LiDAR) survey as a basis for the comparison of two types of SM models: (1) models developed from images acquired in a chartered airplane flight with ground control referenced by natural objects located on Google Earth scenes; and (2) drone flights with a georeference established solely from camera positions located by conventional, differentially corrected Global Navigation Satellite systems (GNSS). We find that all our SM models are indistinguishable in scale from the LiDAR reference model. The SM models do, however, show rigid body translations and rotations, with translations generally within the 1–5 m size of the natural objects used for ground control, the resolution of the GNSS receivers, or both. The rigid body rotations can be attributed to a poor imaging plan, which can be avoided with survey planning. Analyses of point densities in various models show a limitation of Terrestrial LiDAR point clouds as a mapping base due to the rapid falloff of resolution with distance. In contrast, SM models are characterized by relatively uniform point densities controlled by camera optics, the numbers of images, and the distance from the target. This uniform density is the product of the Multiview stereo step in SM processing that fills areas between key points and is important for bedrock geologic mapping because it affords direct interpretation on a point cloud at a relatively uniform scale throughout a model. Our results indicate that these simple methods allow SM model construction to be accurate to the range of conventional GNSS with resolutions to the submeter, even cm, scale depending on data acquisition parameters. Thus, SM models can, and should, serve as a base for high-resolution geologic mapping, particularly in a steep terrain where conventional techniques fail. Our SM models appear to provide accurate visualizations of geologic features over km scales that allow detailed geologic mapping in 3D with a relative accuracy to the decimeter or centimeter level and absolute positioning in the 2–5 m precision of GNSS; a geometric precision that will allow unprecedented new studies of any geologic system where geometry is the fundamental data.

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Flooding in urban streams can occur suddenly and cause major environmental and infrastructure destruction. Due to the high amounts of impervious surfaces in urban watersheds, runoff from precipitation events can cause a rapid increase in stream water levels, leading to flooding. With increasing urbanization, it is critical to understand how urban stream channels will respond to precipitation events to prevent catastrophic flooding. This study uses the Prophet time series machine learning algorithm to forecast hourly changes in water level in an urban stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Machine learning was highly accurate in predicting changes in water level for five locations along the stream with R2 values greater than 0.9. Yet, it can be challenging to understand how these water level prediction values will translate to water volume in the stream channel. Therefore, this study collected terrestrial Light Detection and Ranging (LiDAR) data for Hunnicutt Creek to model these areas in 3D to illustrate how the predicted changes in water levels correspond to changes in water levels in the stream channel. The predicted water levels were also used to calculate upstream flood volumes to provide further context for how small changes in the water level correspond to changes in the stream channel. Overall, the methodology determined that the areas of Hunnicutt Creek with more urban impacts experience larger rises in stream levels and greater volumes of upstream water during storm events. Together, this innovative methodology combining machine learning, terrestrial LiDAR, 3D modeling, and volume calculations provides new techniques to understand flood-prone areas in urban stream environments.

Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data

ABSTRACT Accurate quantification of individual tree parameters is vital for precise forest inventory and sustainable forest management. However, in dense forests, terrestrial laser scanning (TLS), which can provide accurate and detailed forest structural measurements, is limited to capturing the complete tree structure due to the lack of upper canopy views, resulting in an underestimation of tree height. Combining TLS with unmanned aerial vehicle laser scanning (ULS) is an effective way to overcome this limitation. Thus, it is vital to register multi-platform Light Detection and Ranging (LiDAR) data for various forestry applications. This study proposed three automated and nearly parameter-free optimized coarse-to-fine algorithms (i.e. FPFH-based optimized ICP (F-OICP), RANSAC-based optimized ICP (R-OICP), and NDT-based optimized ICP (N-OICP)) to accurately register TLS and ULS point data for individual tree crown delineation and parameters (diameter at breast height (DBH) and tree height) estimations in different forest types (i.e. coniferous, mixed broadleaf-coniferous, and broadleaf). Results showed that the proposed optimized algorithms had a good registration performance, with an average RMSE of about 8.3 cm for the transformation error; and obtained stable and high accuracies of individual tree crown delineation (ITCD) (F-score: 0.7), DBH (R2: 0.9, RMSE <1.85 cm), and tree height (R2: 0.8, RMSE <0.37 m) estimates for three forest types. F-OICP performed the best in tree height estimation, reducing the RMSE by 48%, 12%, and 12% compared to iterative closest point (ICP), R-OICP, and N-OICP, respectively. Stand type significantly impacted ITCD and individual tree parameter estimations. The ITCD and DBH estimation accuracy of coniferous forests were marginally higher than those of broadleaf forests (F-score: 0.78 vs. 0.78, DBH RMSE: 1.57 vs. 1.74), while those of mixed broadleaf-coniferous forests were the lowest (F-score: 0.71, DBH RMSE: 2.19). The accuracies of tree height estimates in coniferous forests were the highest (R2: 0.87, RMSE: 0.21 m), followed by mixed broadleaf-coniferous (R2: 0.84, RMSE: 0.37 m) and broadleaf (R2: 0.84, RMSE: 0.44 m) forests. This work developed automated, nearly parameter-free, and effective registration algorithms and recommended F-OICP to be the most appropriate for dense forests (i.e. natural secondary forests). The optimized registration algorithms facilitate the ability for the synergistic use of multi-platform LiDAR and offer appealing and promising approaches for future accurate quantification of individual tree parameters, efficient forest inventories, and sustainable forest management.

An exploration, analysis, and correction of the distance effect on terrestrial hyperspectral LiDAR data

Terrestrial hyperspectral light detection and ranging (LiDAR) (HSL), as one novel LiDAR system integrated with more wavelengths, has supplied enhanced possibilities in many fields by delivering the geographic coordinate and the hyperspectral backscattered intensity simultaneously. However, like the traditional single-wavelength LiDAR, it is faced with the distance effect, which restricts the accurate acquisition of the hyperspectral intensities and the quantitative retrieval of characteristics of the targets. The previously reported distance effect correction models mainly concentrated on the distance effect correction for the single-wavelength LiDAR system, and some normalization methods with the standard reflectance panel were also provided to calibrate the data measured by multispectral LiDAR and HSL, producing the reflectance-related value. Nevertheless, it is valuable to systematically explore the distance effect of HSL over long distances and search for the corresponding correction methods. Along these lines, in this study, the homogeneous targets and standard reflectance panels with different reflectance values were employed to participate in the distance experiment to systematically explore and analyze the distance effect phenomenon on terrestrial HSL data. A novel practical piecewise fitting model was proposed to output realistic correction results and it was also compared with the classic inverse distance square law and polynomial fitting model. To further demonstrate the feasibility and good capability, the models were employed in the intensity correction for the various vegetation leaf species. A simple 3-D scene experiment was then carried out to demonstrate the intensity variation before and after the implementation of the distance effect correction. From the acquired result, it suggests that (1) the distance effect on terrestrial HSL data is not related to the wavelength and the scanned target. All the wavelengths share a common distance effect; (2) the concept of the ‘distance effect function’ could represent well the distance effect for the terrestrial HSL; (3) as far as the correction ability is concerned, the proposed piecewise fitting model achieved better performance than the others because it exhibited the smallest standard deviation (σ), coefficient of variation (CV), and the ratio of the coefficient of variation (ε) for the correction results of the homogeneous targets, and the leaf experiments confirmed it again; and (4) the further 3-D experiment result exhibited the intensity variation. It proves that the distance effect does exist in the 3-D scanning results and the correction is necessary. Besides, for small-scanned scenes, the incidence angle effect dominates and largely affects the intensity data instead of the distance effect. This study enriches the study of the distance effect mechanism on the terrestrial HSL data, which facilitates the development of research on the radiative effect correction of the HSL. At the same time, the proposed model can be adopted for the analysis and correction of the distance effect on terrestrial HSL data in actual scanning tasks.

Tree Segmentation and Parameter Measurement from Point Clouds Using Deep and Handcrafted Features

Accurate measurement of the geometric parameters of trees is a vital part of forest inventory in forestry management. Aerial and terrestrial Light Detection and Ranging (LiDAR) sensors are currently used in forest inventory as an effective and efficient means of forest data collection. Many recent approaches to processing and interpreting this data make use of supervised machine learning algorithms such as Deep Neural Networks (DNNs) due to their advantages in accuracy, robustness and the ability to adapt to new data and environments. In this paper, we develop new approaches to deep-learning-based forest point cloud analysis that address key issues in real applications in forests. Firstly, we develop a point cloud segmentation framework that identifies tree stem points in individual trees and is designed to improve performance when labelled training data are limited. To improve point cloud representation learning, we propose a handcrafted point cloud feature for semantic segmentation which plays a complementary role with DNNs in semantics extraction. Our handcrafted feature can be integrated with DNNs to improve segmentation performance. Additionally, we combine this feature with a semi-supervised and cross-dataset training process to effectively leverage unlabelled point cloud data during training. Secondly, we develop a supervised machine learning framework based on Recurrent Neural Networks (RNNs) that directly estimates the geometric parameters of individual tree stems (via a stacked cylinder model) from point clouds in a data-driven process, without the need for a separate procedure for model-fitting on points. The use of a one-stage deep learning algorithm for this task makes the process easily adaptable to new environments and datasets. To evaluate our methods for both the segmentation and parameter estimation tasks, we use four real-world datasets of different tree species collected using aerial and terrestrial LiDAR. For the segmentation task, we extensively evaluate our method on the three different settings of supervised, semi-supervised, and cross-dataset learning, and the experimental results indicate that both our handcrafted point cloud feature and our semi-supervised and cross-dataset learning framework can significantly improve tree segmentation performance under all three settings. For the tree parameter estimation task, our DNN-based method performs comparably to well-established traditional methods and opens up new avenues for DNN-based tree parameter estimation.

Suitable LiDAR Platform for Measuring the 3D Structure of Mangrove Forests

Investigating the three-dimensional structure of mangrove forests is critical for their conservation and restoration. However, mangrove forests are difficult to survey in the field, and their 3D structure is poorly understood. Light detection and ranging (LiDAR) is considered an accurate and dependable method of measuring the 3D structure of mangrove forests. This study aimed to find a suitable LiDAR platform for obtaining attributes such as breast height diameter and canopy area, as well as for measuring a digital terrain model (DTM), the base data for hydrological analysis. A mangrove forest near the mouth of the Oura River in Aza-Oura, Nago City, Okinawa Prefecture, Japan, was studied. We used data from terrestrial LiDAR scanning “TLS” and unmanned aerial vehicle (UAV) LiDAR scanning “ULS” as well as data merged from TLS and ULS “Merge”. By interpolating point clouds of the ground surface, DTMs of 5 cm × 5 cm were created. DTMs obtained from ULS could not reproduce the heaps of Thalassina anomala or forest floor microtopography compared with those obtained from TLS. Considering that ULS had a few point clouds in the forest, automatic trunk identification could not be used to segment trees. TLS could segment trees by automatically identifying trunks, but the number of trees identified roughly doubled that of the visual identification results. The number of tree crowns identified using TLS and ULS was approximately one quarter of those identified visually, and many of them were larger in area than the visually traced crowns. The accuracy of tree segmentation using the canopy height model (CHM) was low. The number of canopy trees identified using Merge produced the best results, accounting for 61% of the visual identification results. Results of tree segmentation by CHM suggest that combining TLS and ULS measurements may improve tree canopy identification. Although ULS is a promising new technology, its applications are clearly limited, at least in mangrove forests such as the Oura River, where Bruguiera gymnorhiza is dominant. Depending on the application, using different LiDAR platforms, such as airborne LiDAR scanning, UAV LiDAR scanning, and TLS, is important. Merging 3D point clouds acquired by different platforms, as proposed in this study, is an important option in this case.

Characterization of Operational Vibrations of Steel-Girder Highway Bridges via LiDAR

This research is motivated by the need for rapidly deployable technologies such as wireless, non-contact or remote sensing for evaluating bridges under operating conditions to minimize the data collection time, avoid the disruption of traffic and increase the inspector’s safety. The objective established for this research is to explore the use of remote sensing (e.g., Light Detection and Ranging (LiDAR)) for characterizing the structural vibration of bridges to support and improve bridge assessment practices. To satisfy this objective, a field study was performed on a 12-span steel stringer bridge in the Philadelphia region. This structure was subjected to extensive LiDAR scanning and conventional vibration data collection through the use of accelerometers for validation purposes. The analysis of the data collected in the field revealed LiDAR’s capability for detecting the structure’s vibration. The field data displayed an error for LiDAR vs. accelerometers of between 1.9 and 10 percent. Additionally, numerical modeling was performed on MATLAB to allow for a better understanding of the interaction between the scanner and the structure. The numerical model presents a vibrating plate to represent a simply supported single-span bridge and a terrestrial LiDAR sensor located under the plate which scans while it is vibrating constantly without attenuation. Finally, a set of recommendations were established for the use of LiDAR scanning to evaluate the structure’s frequency of vibration.

Monitoring of Water and Tillage Soil Erosion in Agricultural Basins, a Comparison of Measurements Acquired by Differential Interferometric Analysis of Sentinel TopSAR Images and a Terrestrial LIDAR System

Agricultural production, the main pillar of food security, is highly dependent on soil quality, and threatened by erosion processes that degrade soil quality. This article is part of a research to verify the usefulness of differential interferometric analysis on TopSAR (Terrain Observation with Progressive Scans SAR, Synthetic Aperture Radar) images to measure water and tillage erosion in small agricultural basins. For this, images from the Sentinel 1 mission are used, analyzing the deformations on the earth’s surface. The purpose of this research is to verify the accuracy of the proposed method by comparing its measures with the ones taken with the gold standard laser terrestrial LIDAR (Light Detection and Ranging) system, as well as to establish a basic step period framework that guarantees an admissible loss of coherence. The results on a pilot plot in El Molar (north of Madrid, Spain) showed that the differences lay within the range of the error associated with the very LIDAR system and showed that coherence losses correspond with the deformations measured. Given the economic and labor advantages of the differential interferometric analysis, this method could be regarded as an excellent alternative to the use of LIDAR in large-scale studies for measuring ground deformation caused by water and tillage erosion.

High-precision deformation analysis of yingxian wooden pagoda based on UAV image and terrestrial LiDAR point cloud

The monitoring of wooden pagodas is a very important task in the restoration of wooden pagodas. Traditionally, this labor has always been carried out by surveying personnel, who manually check all parts of the pagoda, which not only consumes huge manpower, but also suffers from low efficiency and measurement errors. This article evaluates the feasibility of combining portable 3D light detection and ranging (LiDAR) scanning and unmanned aerial vehicle (UAV) photogrammetry to perform these inspection tasks easily and accurately. The wooden pagoda's exterior picture and inside point cloud are acquired using a UAV and a LiDAR scanner, respectively. We propose a feature−based global alignment method to register the site point cloud. The error equation of the column of observed values is utilized as the beginning value of the feature constraint for global leveling. The beam method leveling model solves the spatial transformation parameters and the unknown point leveling values. Then, the Structure from Motion (SfM) algorithm of computer vision is used to realize the fusion of the dense point cloud of the exterior of the wooden pagoda generated from multiple non−measured images by global optimization and the LiDAR point cloud of the interior of the wooden pagoda to obtain the complete point cloud of the wooden pagoda, which makes the deformation monitoring of the pagoda more detailed and comprehensive. After experimental verification, the overall registration accuracy of the Yingxian wooden pagoda reaches 0.006 m. Compared with the scanning point cloud data in 2018, the model is more accurate and complete. By analyzing and comparing the data of the second floor of the wooden pagoda, we knew that the inclination of a second bright layer and a second dark layer is still developing steadily. Overall, the western outer trough inclines thoughtfully, and the column frame slopes from southwest to northeast. Some internal columns showed a negative offset in 2020, and the deformation analysis of a single column was realized by comparing it with the standard column model. The main contribution of this method lies in the effective integration of UAV images and point cloud data to provide accurate data sources for good modeling. This research will provide theoretical and methodological support for the digital protection of architectural heritage and GIS data modeling. The analysis results can provide a scientific basis for the restoration scheme design.

From Stationary to Mobile: Unleashing the Full Potential of Terrestrial LiDAR through Sensor Integration

This paper discusses a comprehensive methodology for transforming a static LiDAR (Light Detection and Ranging) system into a mobile mapping system. The initial step involves integrating various sensors, such as GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit), and odometry sensors, into the system’s architecture to accurately estimate the position and orientation of the LiDAR sensor at any given time. Specialized algorithms compensate for the vehicle’s motion, and procedural requirements must be followed to ensure safe operation. Hardware integration is critical, requiring proper calibration and validation. Data processing workflows, including algorithms and software tools, play a crucial role in mobile LiDAR mapping systems. The quality and accuracy of the data depend on various factors, and practical considerations such as cost and time are also important. The proposed methodology provides a clear and effective way to transform a terrestrial LiDAR sensor into a mobile mapping system, increasing its flexibility and usability for various applications. However, the detailed analysis, system validation, and data processing details fall outside this paper’s scope and will be discussed in future publications.

Habitat highs and lows: Using terrestrial and UAV LiDAR for modelling avian species richness and abundance in a restored woodland

Vegetation structure influences landscape use and habitat quality for many bird species. Owing to the difficulties associated with collecting structural data from traditional field measurements, numerous studies have investigated the utility of Light detection and ranging (LiDAR) for providing landscape-scale structural information that may be useful for exploring animal-habitat associations. Notably, almost all of these studies have involved the use of LiDAR from airborne rather than terrestrial platforms. However, vegetation metrics that might be important for explaining bird species occurrence and diversity, such as understory vegetation complexity and overall vegetation volume, may be partially obscured from airborne sensors by tree canopy cover. These challenges might be overcome by terrestrial and UAV LiDAR sensors that can provide detailed information of understory forest strata. For the first time, we collected terrestrial LiDAR (TLS) and unoccupied aerial vehicle LiDAR (ULS) data in a woodland landscape to compare the ability of both sensors to identify relationships among vegetation structural metrics and bird species richness and abundance. Overall, TLS and ULS models provided similar results based on the sampling methodology we used for LiDAR data collection in an open woodland landscape. Canopy roughness, ground vegetation vertical complexity, total vegetation volume and canopy height derived from these sensors were among the most common significant variables in explaining avian diversity and individual species abundance. Individual species abundance models provided better prediction power (up to R2 = 0.82 (TLS) and R2 = 0.83 (ULS)) than bird community abundance by functional guilds (up to R2 = 0.40 (TLS), R2 = 0.41 (ULS)) and overall bird abundance (R2 = 0.10 (TLS), R2 = 0.16 (ULS)), species richness (R2 = 0.14 (TLS), R2 = 0.14 (ULS)) and diversity (R2 = 0.17 (TLS), R2 = 0.16 (ULS)). Additionally, we found that several vulnerable bird species are strongly associated with LiDAR structural variables, which may assist with habitat assessment and conservation management.

The Application of Terrestrial Light Detection and Ranging to Forest Resource Inventories for Timber Yield and Carbon Sink Estimation

New technologies are utilized to improve forest management, but they require the collection and analysis of digital data. This study assessed the applicability of using light detection and ranging (LiDAR) devices for the examination of forest resource inventories to obtain digital forest resource information. Two terrestrial LiDARs, a backpack laser scanner (BPLS) and a terrestrial laser scanner (TLS) were used and compared with the traditional method to identify which was optimal. The findings showed that the TLS single-scan method was least accurate due to occlusion. The TLS multi-scan method and the BPLS showed high levels of accuracy for the height and diameter at breast height (DBH) estimates in most sample plots. However, the BPLS underestimated height to a greater extent than the other methods in a sloped plot (24°). Nevertheless, the efficiency of the BPLS was 2.8 times higher than that of the TLS when considering the amount of time spent on completing all indoor and outdoor tasks. Thus, these results prove that the utilization of LiDAR devices increases the efficiency of data collection and overcomes the limitations of existing methods. Moreover, they provide accurate information that will be a basis for forest management plans, the estimation of biomass, and the transaction of forest products.

Metaheuristics for the optimization of Terrestrial LiDAR set-up

3D point clouds have a significant impact on a wide range of applications, although their acquisition is frequently conditioned by the occlusion of the objects in the scene. To address this problem, this paper describes an approach for optimizing LiDAR (Light Detection and Ranging) surveys using metaheuristics such as local searches and genetic algorithms. The method generates a set of optimal scanning locations to densely cover the real-world environment represented through 3D synthetic models. Compared to previous research, this paper handles 3D occlusion by varying the height of the sensor. Also, previously used metrics are compressed into three functions to avoid multi-objective optimization. Regarding performance, a LiDAR scanning solution based on GPU (Graphics Processing Unit) hardware is used. Several tests were conducted to show that the combination of local searches and genetic algorithms generates a reduced set of locations capable of optimizing the scanning of buildings.

Tree stem volume estimation from terrestrial LiDAR point cloud by unwrapping

Estimating the volume of standing trees is a fundamental concern in forestry and is typically accomplished using one or more measurements of stem diameter along with formulae that assume geometric primitives. In contrast, technologies such as terrestrial Light Detection And Ranging (LiDAR) can record very detailed spatial information on the actual surface of an object, such as a tree bole. We present a method using LiDAR that provides accurate volume estimates of tree stems, as well as 2D rasters that display details of stem surfaces, which we call the “unwrapping method.” This method combines the concepts of cylinder fitting, voxelization, and digital elevation models. The method is illustrated and tested using a sample of standing trees, whereby we are able to generate accurate volume estimates from the point cloud, as well as accurate visualization of the scanned stem sections. When compared to volume estimates derived from Huber's, Smalian's, and Newton's formulae, the differences are consistent with previous studies comparing formula-derived volume estimates and water-displacement-derived volume estimates, suggesting the unwrapping method has comparable accuracy to water displacement.

Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud

Exploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote sensing method. This paper implements a method for extracting and estimating rapeseed leaves through agricultural robots based on LiDAR point cloud, taking leaf area (LA) measurement as an example. Firstly, the three-dimensional (3D) point cloud obtained with a terrestrial laser scanner (TLS) were used to extract crop phenotypic information. We then imported the point cloud within the study area into a custom hybrid filter, from which the rapeseed point cloud was segmented. Finally, a new LA estimation model, based on the Delaunay triangulation (DT) algorithm was proposed, namely, LA-DT. In this study, a crop canopy analyzer, LAI-2200C, was used to measure rapeseed LA in farmland. The measured values were employed as standard values to compare with the calculated results obtained using LA-DT, and the differences between the two methods were within 3%. In addition, 100 individual rapeseed crops were extracted, and the output of the LA-DT model was subjected to linear regression analysis. The R² of the regression equation was 0.93. The differences between the outputs of the LAI-2200C and LA-DT in these experiments passed the paired samples t-test with significant correlation (p &lt; 0.01). All the results of the comparison and verification showed that the LA-DT has excellent performance in extracting LA parameters under complex environments. These results help in coping with the complex working environment and special working objects of agricultural robots. This is of great significance for expanding the interpretation methods of agricultural 3D information.

Comparison of Ground Point Filtering Algorithms for High-Density Point Clouds Collected by Terrestrial LiDAR

Terrestrial LiDAR (light detection and ranging) has been used to quantify micro-topographic changes using high-density 3D point clouds in which extracting the ground surface is susceptible to off-terrain (OT) points. Various filtering algorithms are available in classifying ground and OT points, but additional research is needed to choose and implement a suitable algorithm for a given surface. This paper assesses the performance of three filtering algorithms in classifying terrestrial LiDAR point clouds: a cloth simulation filter (CSF), a modified slope-based filter (MSBF), and a random forest (RF) classifier, based on a typical use-case in quantifying soil erosion and surface denudation. A hillslope plot was scanned before and after removing vegetation to generate a test dataset of ground and OT points. Each algorithm was then tested against this dataset with various parameters/settings to obtain the highest performance. CSF produced the best classification with a Kappa value of 0.86, but its performance is highly influenced by the ‘time-step’ parameter. MSBF had the highest precision of 0.94 for ground point classification but the highest Kappa value of only 0.62. RF produced balanced classifications with the highest Kappa value of 0.75. This work provides valuable information in optimizing the parameters of the filtering algorithms to improve their performance in detecting micro-topographic changes.