Terrestrial Laser Scanning Research Articles

Detecting selective logging in tropical forests with optical satellite data: an experiment in Peru shows texture at 3 m gives the best results

AbstractSelective logging is known to be widespread in the tropics, but is currently very poorly mapped, in part because there is little quantitative data on which satellite sensor characteristics and analysis methods are best at detecting it. To improve this, we used data from the Tropical Forest Degradation Experiment (FODEX) plots in the southern Peruvian Amazon, where different numbers of trees had been removed from four plots of 1 ha each, carefully inventoried by hand and terrestrial laser scanning before and after the logging to give a range of biomass loss (∆AGB) values. We conducted a comparative study of six multispectral optical satellite sensors at 0.3–30 m spatial resolution, to find the best combination of sensor and remote sensing indicator for change detection. Spectral reflectance, the normalised difference vegetation index (NDVI) and texture parameters were extracted after radiometric calibration and image preprocessing. The strength of the relationships between the change in these values and field‐measured ∆AGB (computed in % ha−1) was analysed. The results demonstrate that: (a) texture measures correlates more with ∆AGB than simple spectral parameters; (b) the strongest correlations are achieved for those sensors with spatial resolutions in the intermediate range (1.5–10 m), with finer or coarser resolutions producing worse results, and (c) when texture is computed using a moving square window ranging between 9 and 14 m in length. Maps predicting ∆AGB showed very promising results using a NIR‐derived texture parameter for 3 m resolution PlanetScope (R2 = 0.97 and root mean square error (RMSE) = 1.91% ha−1), followed by 1.5 m SPOT‐7 (R2 = 0.76 and RMSE = 5.06% ha−1) and 10 m Sentinel‐2 (R2 = 0.79 and RMSE = 4.77% ha−1). Our findings imply that, at least for lowland Peru, low‐medium intensity disturbance can be detected best in optical wavelengths using a texture measure derived from 3 m PlanetScope data.

Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review

Landslides or slope failure pose a significant risk to human lives and infrastructures. The stability of slopes is controlled by various hydrological processes such as rainfall infiltration, soil water dynamics, and unsaturated soil behavior. Accordingly, soil hydrological monitoring and tracking the displacement of slopes become crucial to mitigate such risks by issuing early warnings to the respective authorities. In this context, there have been advancements in monitoring critical soil hydrological parameters and slope movement to ensure potential causative slope failure hazards are identified and mitigated before they escalate into disasters. With the advent of the Internet of Things (IoT), artificial intelligence, and high-speed internet, the potential to use such technologies for remotely monitoring soil hydrological parameters and slope movement is becoming increasingly important. This paper provides an overview of existing hydrological monitoring systems using IoT and AI technologies, including soil sampling, deploying on-site sensors such as capacitance, thermal dissipation, Time-Domain Reflectometers (TDRs), geophysical applications, etc. In addition, we review and compare the traditional slope movement detection systems, including topographic surveys for sophisticated applications such as terrestrial laser scanners, extensometers, tensiometers, inclinometers, GPS, synthetic aperture radar (SAR), LiDAR, and Unmanned Aerial Vehicles (UAVs). Finally, this interdisciplinary research from both Geotechnical Engineering and Computer Science perspectives provides a comprehensive state-of-the-art review of the different methodologies and solutions for monitoring landslides and slope failures, along with key challenges and prospects for potential future study.

Crack Detection and Feature Extraction of Heritage Buildings via Point Clouds: A Case Study of Zhonghua Gate Castle in Nanjing

Zhonghua Gate Castle is on the tentative list for Chinese World Cultural Heritage. Due to long-term sunshine, rain erosion, and man-made damage, its surface appears to have different degrees of cracks and other diseases. This paper centers on Zhonghua Gate Castle; terrestrial laser scanning is used to obtain the exterior wall point cloud data. A crack detection method based on point cloud data curved surface reconstruction is proposed. It involves data preprocessing, crack detection, and the analysis of crack features. This method initially uses data preprocessing techniques to improve data quality. These techniques include removing ground points and super-voxel segmentation. Subsequently, local surface reconstruction was employed to address the issue of missing point cloud data within cracks and the Euclidean clustering algorithm was used for precise crack identification. The article provides a detailed analysis of the geometric characteristics of cracks. They involve the calculation of length, width, and area. The results of the experiment demonstrate that the method could successfully identify cracks and extract geometric features and has millimeter-level accuracy compared to actual crack sizes.

Geometrization of a 3D numerical model of an underground facility based on the results of terrestrial laser scanning

Abstract The article proposes a method of combining CloudCompare, RHINO, and FLAC3D software, aimed at building numerical models of underground objects of natural or engineering origin, based on the results of measurements made using terrestrial laser scanning technology. This technology is one of the most advanced in mine survey as it enables accurate mapping of even the most complex geometries of underground facilities. This opens wide possibilities in the construction of more accurate numerical models of the behavior of the rock mass around such underground objects. The results of simulation of the behavior of the rock mass around the analyzed excavations, obtained by performing numerical calculations, allow predicting unfavorable phenomena that may occur as a result of the destruction of the rock mass and which may threaten the safety of users of underground facilities, for example, caves, tunnels, and mining excavations. In this work, we carried out measurements using a terrestrial laser scanner and obtained a “point cloud” that reproduced the geometry of the underground facility. An example is a fragment of the adit St. Johannes, which is part of the underground tourist route “Geopark” St. Johannes Mine in Krobica in Lower Silesia in Poland in the neighborhood of Gierczyn and Przecznica. In the next step, the measurement results were processed, so that it was possible to import the generated geometry into the FLAC3D software and use it to build a numerical model of the adit, based on “brick” zones. The aim of the article is to present in detail the methodology of geometrization of numerical models of underground objects with complex geometry. The author wanted the method to be as easy to use as possible, give full control over the surface structure, and not require many numerical modeling programs.

An evaluation of TLS-based glacier change assessment in the central Tibetan plateau

ABSTRACT Fragmented surfaces and harsh environments have always been the main obstacles hindering observation works of glaciers in central Tibetan Plateau (TP). The advent of Terrestrial Laser Scanner (TLS) technology offers a potential revolution in this context. While TLS has been effectively applied to smaller glaciers in the Alps and Tianshan, this study extends its use to the large and topographically complex Ganglongjiama (GLJM) glacier in the Tanggula Mountains. Over a 5-year period, TLS, with a precision of up to 0.012 m, has documented an accelerated melting trend, with the terminus retreating by 13.305 m and a total mass loss of 2.580 m water equivalent. The research also underscores the role of supraglacial channels and lakes in intensifying surface melting and glacier front instability. Despite challenges in data acquisition due to occlusions and logistical constraints at high altitudes, this first TLS survey of a TP glacier provides invaluable insights into glacier dynamics. Future research could integrate TLS with Unmanned Aerial Vehicle-Structure-from-Motion (UAV-SfM) data fusion to achieve more comprehensive coverage and improve the temporal resolution of observations for a detailed analysis of glacier features.

Classification of soft cliff dynamics using remote sensing and data mining techniques

Coastal soft cliffs are subject to changes related to both marine and subaerial processes. It is imperative to comprehend the processes governing cliff erosion and develop predictive models for effective coastal protection. The primary objective of this study was to bridge the existing knowledge gap by elucidating the intricate relationship between changes in cliff system morphology and the driving forces behind these changes, all within the context of ongoing climate change. Therefore in this study, we employed various quantitative numerical methods to investigate the factors influencing coastal cliffs and the adjacent beaches. Our analysis involved the extraction of several morphological indicators, derived from terrestrial laser scanning data, which were then used to assess how cliffs respond to extreme weather events. The data span two winter storm seasons (2016–2018) and encompass three soft-cliff systems situated along the southern Baltic Sea, each characterized by distinct beach and cliff morphology. We conducted a detailed analysis of short-term cliff responses using various data mining techniques, revealing intricate mechanisms that govern beach and cliff changes. This comprehensive analysis has enabled the development of a classification system for soft cliff dynamics. Our statistical analysis highlights that each study area exhibits a unique conditional dependency between erosion processes and hydrometeorological conditions, both during and between storm events. Furthermore, our findings underscore the vulnerability of cliff coastlines to extreme water levels and episodes of intense precipitation.

Geometric Quality Analysis of Terrestrial Laser Scanning Data for Industrial Usage

Geometric Quality Analysis of Terrestrial Laser Scanning Data for Industrial Usage

Evaluating airborne, mobile and terrestrial laser scanning for urban tree inventories: A case study in Ghent, Belgium

In urban tree inventories, structural measurements such as diameter at breast height (DBH), tree height (H), crown projection area (CPA) and crown volume (CV) are essential for diverse applications, including accurate ecosystem service estimation and management decisions. Traditionally, tree measurements are obtained using range finders and diameter tape. These measurements can be integrated into urban tree inventories through 3D laser scanning (also known as LiDAR). Multiple platforms for laser scanning exist, each with its own advantages and disadvantages, which have not yet been compared explicitly for urban tree inventories. We collected terrestrial laser scanning (TLS), mobile laser scanning (MLS) and airborne laser scanning (ALS) in leaf-on (TLS, MLS, ALS) and leaf-off conditions (TLS, MLS) in Ghent, Belgium. We evaluated the DBH, H, CPA and CV of 95 individual trees acquired from each acquisition platform, benchmarking against TLS. Our results show accurate DBH derivation from both TLS and MLS (bias < 2 cm, concordance correlation coefficient (CCC) ≈ 1). However, during leaf-on conditions, occlusion from shrubs and ivy is observed. For leaf-on MLS, point clouds of large trees exhibited occlusion in the top canopy, impacting crown volume (CV MLS leaf-on: bias = −116 m², CCC=0.85) and, to a lesser extent, tree height (H MLS leaf-on: bias = −0.38 m, CCC=0.99). Crown projected area was less affected (bias = 0.49 m², CCC=0.99), with differences more attributed to varying point precision among sensors. The difference between the metric and benchmark increased with tree size and the structural complexity of the surroundings (e.g. buildings), especially for MLS, for which limited GNSS coverage, traffic, and suboptimal walking patterns impeded ideal data collection. Our results will help city councils and tree managers choose the most optimal LiDAR platform for urban tree inventories, accounting for their purpose, site complexity and budget.

Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Integrating UAV Photogrammetry and Terrestrial Laser Scanning for the 3D surveying of the Fortress of Bashtova

Through the synergistic application of Aerial Photogrammetry using UAVs and Terrestrial Laser Scanning (TLS), this paper investigates how this combination can be used for conducting a 3D survey of the Fortress of Bashtova thereby demonstrating the effectiveness of such integrated methods in acquiring an all-encompassing image of this historical building. As the efforts towards preservation become intense, there arises the urgency of precise and detailed 3D documentation that will facilitate appropriate conservation processes and further studies. Therefore, combining TLS and UAV photogrammetry offers a powerful tool that can provide accurate architectural data for the documentation of heritage areas. Moreover, the TLS component acquires ground point-cloud data with laser scanners giving a complementary alternative for aerial perspective. The merging of these datasets ensures broad inclusion since it allows the production of accurate, detailed three-dimensional models of the Fortress of Bashtova. Thanks to the research on the case study of the Fortress of Bashtova in the article, it can be stated that the integration of data from aerial photogrammetry and TLS is seamless with the help of modern software while respecting the basic photogrammetric-geodetic rules and demonstrates the possibility of creating a complex 3D model, usable for further analyses for architects and conservation professionals, as well as for restorers and civil engineers. To estimate the accuracy of the point clouds derived from TLS and UAV, we compared the distances between the point clouds using CloudCompare software. We obtained a mean RMS of 2.199073 mm and std. dev was 7.356 mm. Research has shown that the difference between point clouds from TLS and UAV is within 1.7 centimeters.

Terrestrial laser scanning and low magnetic field digitization yield similar architectural coarse root traits for 32-year-old Pinus ponderosa trees

BackgroundUnderstanding how trees develop their root systems is crucial for the comprehension of how wildland and urban forest ecosystems plastically respond to disturbances such as harvest, fire, and climate change. The interplay between the endogenously determined root traits and the response to environmental stimuli results in tree adaptations to biotic and abiotic factors, influencing stability, carbon allocation, and nutrient uptake. Combining the three-dimensional structure of the root system, with root morphological trait information promotes a robust understanding of root function and adaptation plasticity. Low Magnetic Field Digitization coupled with AMAPmod (botAnique et Modelisation de l’Architecture des Plantes) software has been the best-performing method for describing root system architecture and providing reliable measurements of coarse root traits, but the pace and scale of data collection remain difficult. Instrumentation and applications related to Terrestrial Laser Scanning (TLS) have advanced appreciably, and when coupled with Quantitative Structure Models (QSM), have shown some potential toward robust measurements of tree root systems. Here we compare, we believe for the first time, these two methodologies by analyzing the root system of 32-year-old Pinus ponderosa trees.ResultsIn general, at the total root system level and by root-order class, both methods yielded comparable values for the root traits volume, length, and number. QSM for each root trait was highly sensitive to the root size (i.e., input parameter PatchDiam) and models were optimized when discrete PatchDiam ranges were specified for each trait. When examining roots in the four cardinal direction sectors, we observed differences between methodologies for length and number depending on root order but not volume.ConclusionsWe believe that TLS and QSM could facilitate rapid data collection, perhaps in situ, while providing quantitative accuracy, especially at the total root system level. If more detailed measures of root system architecture are desired, a TLS method would benefit from additional scans at differing perspectives, avoiding gravitational displacement to the extent possible, while subsampling roots by hand to calibrate and validate QSM models. Despite some unresolved logistical challenges, our results suggest that future use of TLS may hold promise for quantifying tree root system architecture in a rapid, replicable manner.

Traditional to technological advancements in Ganoderma detection methods in oil palm.

Ganoderma sp., the fungal agent causing basal stem rot (BSR), poses a severe threat to global oil palm production. Alarming increases in BSR occurrences within oil palm growing zones are attributed to varying effectiveness in its current management strategies. Asymptomatic progression of the disease and the continuous monoculture of oil palm pose challenges for prompt and effective management. Therefore, the development of precise, early, and timely detection techniques is crucial for successful BSR management. Conventional methods such as visual assessments, culture-based assays, and biochemical and physiological approaches prove time-consuming and lack specificity. Serological-based diagnostic methods, unsuitable for fungal diagnostics due to low sensitivity, assay affinity, cross-contamination which further underscores the need for improved techniques. Molecular PCR-based assays, utilizing universal, genus-specific, and species-specific primers, along with functional primers, can overcome the limitations of conventional and serological methods in fungal diagnostics. Recent advancements, including real-time PCR, biosensors, and isothermal amplification methods, facilitate accurate, specific, and sensitive Ganoderma detection. Comparative whole genomic analysis enables high-resolution discrimination of Ganoderma at the strain level. Additionally, omics tools such as transcriptomics, proteomics, and metabolomics can identify potential biomarkers for early detection of Ganoderma infection. Innovative on-field diagnostic techniques, including remote methods like volatile organic compounds profiling, tomography, hyperspectral and multispectral imaging, terrestrial laser scanning, and Red-Green-Blue cameras, contribute to a comprehensive diagnostic approach. Ultimately, the development of point-of-care, early, and cost-effective diagnostic techniques accessible to farmers is vital for the timely management of BSR in oil palm plantations.

Reverse Engineering Workflows for the Structural Assessment of Historical Buildings

ABSTRACT Supported by advancements in 3D scanning and parametric modeling tools within the architecture, engineering, and construction sectors, reverse engineering processes for cultural heritage (CH) have recently gained popularity. While many studies have focused on simple 3D reconstructions to create virtual environments, a specialized subarea of this research field has targeted more specific digitization objectives, including, among many, structural analysis of building components. This emerging field has not yet been systematically developed due to the intrinsic challenges associated with CH. Within this context, this paper proposes and describes a reverse engineering-based method that utilizes terrestrial laser scanning and visual programming (VP) to analyze displacements and deformations that occurred over time in historical masonry buildings and applied it to the timber trusses, masonry facades, and columns of two selected Italian case study buildings. This method allows for comparing the surveyed condition of these components, considered the “deformed state”, with their ideal configuration, reconstructed using VP algorithms, considered the “original state”. The outcomes of this comparison facilitate the investigation of the components’ structural behavior and support joint considerations to assess the overall condition of the investigated building, providing helpful knowledge for guiding structural improvement interventions.

Automated Phenotypic Trait Extraction for Rice Plant Using Terrestrial Laser Scanning Data.

To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds.

Vector-autoregressive based vibration monitoring of wind energy turbines using laser scanner profile measurements

Structural health monitoring of wind energy turbines (WET) is crucial due to the potential presence of an imbalanced rotor, leading to adverse centrifugal forces. In this research, non-contact vibration monitoring of a WET is conducted using a terrestrial laser scanner (TLS) of the type Zoller+Fröhlich IMAGER 5016 in a two-dimensional (2D) profile mode, and with a rotation speed of 55 revolutions per second. The TLS profile measurements cover the entire pillar up to a height of 100 meter. Therefore, the challenges imposed by conventional measurement systems, such as accelerometers or inductive displacement transducers, are tackled through the high spatial resolution of the TLS measurements and non-contact measurement methods. Additionally, both time and cost are reduced regarding the sensor installation. In general, the WET is measured in two different directions to account for significant movements, both in the direction of the wind and perpendicular to it. To initiate the analysis, time series are generated from the profile measurements for various positions covering the entire pillar. A robust time-domain modal parameter identification approach based on the Vector-autoregressive (VAR) process with multivariate t-distributed random deviations (VAR-RT-MPI) is proposed to estimate modal parameters, including eigenfrequencies, eigenforms, and modal damping. Besides, it allows estimating unknown auto- and cross-correlation coefficients of the VAR process, the cofactor matrix, and the degrees of freedom of the t-distribution. The VAR-RT-MPI algorithm enables to jointly estimate the eigenfrequencies and damping ratio coefficients considering multiple time series. Additionally, the spatio-temporal model of the pillar is characterised based on the estimates of the amplitudes (in a submillimetre range) and phase shifts at different positions. Vibration monitoring was conducted on a WET located in Hannover, Germany, and the results were compared with a well-known covariance driven stochastic subspace identification (SSI-COV) approach.

A framework for automated reconstruction of communication towers from terrestrial laser scanning point clouds

ABSTRACT Communication towers provide reliable wireless communication services and play a vital role in modern society. This paper presents an automatic 3D reconstruction framework for communication towers based on point clouds. The antenna is reconstructed by a model-driven method, which constructs an energy function including the data and smoothing terms to evaluate the model accuracy. The parameters of the antenna model are obtained by minimizing energy function with hybrid Genetic Algorithm (GA) and Simulated Annealing (SA). And the predefined model is transformed using these parameters to achieve antenna 3D reconstruction. For tower body reconstruction, the point cloud of the tower body is finely extracted with the region growing followed by piecewise fitting to obtain the model. The performance of the proposed method is evaluated on point clouds from six scenes. The average root-mean-square error (RMSE) of antenna reconstruction is 1.3 cm. The average precision, recall, and F1-score values of the tower body point extraction are 99.9%, 93.5%, and 96.6%, respectively. The average RMSE of tower body reconstruction is 1.2 cm. The experimental results indicate that the method can effectively reconstruct the antenna and tower body with point clouds, providing a feasible framework for the 3D reconstruction of communication towers.

Automatic detection of color markings and numbers on trees in point clouds from Personal Laser Scanning (PLS) and Terrestrial Laser Scanning (TLS)

In forestry, colored spray paint is used for different purposes, such as marking trees that are chosen for the next harvest or that will be promoted by future silvicultural activities, or for the demarcation of skid/cable roads. Numerous studies have demonstrated the application of Terrestrial Laser Scanners (TLS) and Personal Laser Scanners (PLS) for the acquisition of tree parameters (diameters, heights, and tree positions) in a forest inventory context. However, no studies have included classification based on spectral information by applying colored markings to trees. When a laser scanner is combined with a camera, the color information can be mapped onto the laser point cloud, allowing for additional analysis of spectral information. In this study, we have tested the analysis of joint information from imagery and 3D point clouds in two different settings: (i) PLS point cloud data from a GeoSLAM ZEB Horizon were complemented with spectral information from images collected with a NCTech iSTAR Pulsar 360-degree camera, (ii) TLS point cloud data from a RIEGL VZ-400i were combined with imagery from a Nikon D850 digital single lens reflex (DSLR) camera with 14 mm fisheye lens. The major objective was to automatically classify trees that were marked with colored sprays automatically identify the numbers sprayed on the trees. The marked trees could be classified correctly with a balanced accuracy of 77.26% for PLS and 98.01% for the data collected with the TLS system using a support vector machine (SVM) model. The numbers written on the trees were classified from the TLS data using a YOLOv8 model. The accuracy of the digit classification reached up to 97.30%. In conclusion, it is possible to automatically detect marked trees and to automatically recognize the numbers spayed on these trees from colored point cloud data. However, accuracy strongly depends on the selected combination of scanner and camera. The findings of future research on this subject might differ with the ongoing development of PLS systems and could be enhanced by creating a large dataset of handwritten numbers collected using laser scanning systems.

Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud

Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

A new point cloud simplification algorithm based on V-P container constraint and normal vector angle information entropy

Abstract Most point cloud simplification algorithms use k-order neighborhood parameters, which are set by human experience; thus, the accuracy of point feature information is not high, and each point is repeatedly calculated simultaneously. The proposed method avoids this problem. The first ordinal point of the original point cloud file was used as the starting point, and the same spatial domain was then described. The design method filters out points located in the same spatial domain and stores them in the same V-P container. The normal vector angle information entropy was calculated for each point in each container. Points with information entropy values that met the threshold requirements were extracted and stored as simplified points and new seed points. In the second operation, a point from the seed point set was selected as the starting point for the operation. The same process was repeated as the first operation. After the operation, the point from the seed point set was deleted. This process was repeated until the seed point set was empty and the algorithm ended. The simplified point set thus obtained was the simplified result. Five experimental datasets were selected and compared using the five advanced methods. The results indicate that the proposed method maintains a simplification rate of over 82% and reduces the maximum error, average error, and Hausdorff distance by 0.1099, 0.074, and 0.0062 (the highest values among the five datasets), respectively. This method has superior performance for single object and multi object point cloud sets, particularly as a reference for the study of simplified algorithms for more complex, multi object and ultra-large point cloud sets obtained using terrestrial laser scanning and mobile laser scanning.