Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird\'s-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R 2 = 0.95, RMSE = 7.6 cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPAR f ), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.

Nowadays, the use of advanced sensors, such as terrestrial, mobile 3D scanners and photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling (RM) and the digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team handling the laborious planning and time-consuming execution of the 3D scanning process tailored to each site\'s specific requirements and constraints. To minimize human intervention, this paper proposes a novel methodology for autonomous 3D Reality Modeling of CH monuments by employing autonomous robotic agents equipped with the appropriate sensors. These autonomous robotic agents are able to carry out the 3D RM process in a systematic, repeatable, and accurate approach. The outcomes of this automated process may also find applications in digital twin platforms, facilitating secure monitoring and the management of cultural heritage sites and spaces, in both indoor and outdoor environments. The main purpose of this paper is the initial release of an Industry 4.0-based methodology for reality modeling and the survey of cultural spaces in the scientific community, which will be evaluated in real-life scenarios in future research.

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.

A Leica RTC360 laser scanner was investigated using a linear horizontal comparator system with four targets of different reflectance. Several thousand panorama scans were conducted along the 30 m long comparator, basically in 40 mm steps. For a selected target, more detailed investigations were carried out with a 2 mm step width for a 2 m wide section. The absolute offset between the scanner and the relative interferometer measurements was determined with a calibrated total station. The investigations revealed several systematic effects like an offset in the distance measurement of about 1.3 mm. Furthermore, sections with stochastic behavior as well as sections with pseudo-cyclic parts were observed, depending on the reflectance of the target. The deterministic sections showed curved and striped patterns with some discontinuities of about 2 mm at 20 m, resulting in a saw-tooth like pattern along the distances. Within all the experiments, the distance deviations were below the manufacturer specifications of the 3D point accuracy. However, it was demonstrated that the distance measurements had clear systematic components. In using these new findings, the specification of the measurement \"noise\" in the data sheet has to be seen as critical.

Vegetation regulates microclimate stability through biophysical mechanisms such as evaporation, transpiration and shading. Therefore, thermal conditions in tree-dominated habitats will frequently differ significantly from standardized free-air temperature measurements. The ability of forests to buffer temperatures nominates them as potential sanctuaries for tree species intolerant to the increasingly challenging thermal conditions established by climate change. Although many factors influencing thermal conditions beneath the vegetation cover have been ascertained, the role of three-dimensional vegetation structure in regulating the understory microclimate remains understudied. Recent advances in remote sensing technologies, such as terrestrial laser scanning, have allowed scientists to capture the three-dimensional structural heterogeneity of vegetation with a high level of accuracy. Here, we examined the relationships between vegetation structure parametrized from voxelized laser scanning point clouds, air and soil temperature ranges, as well as offsets between field-measured temperatures and gridded free-air temperature estimates in 17 sites in a tropical mountain ecosystem in Southeast Kenya. Structural diversity generally exerted a cooling effect on understory temperatures, but vertical diversity and stratification explained more variation in the understory air and soil temperature ranges (30%-40%) than canopy cover (27%), plant area index (24%) and average stand height (23%). We also observed that the combined effects of stratification, canopy cover and elevation explained more than half of the variation (53%) in understory air temperature ranges. Stratification\'s attenuating effect was consistent across different levels of elevation. Temperature offsets between field measurements and free-air estimates were predominantly controlled by elevation, but stratification and structural diversity were influential predictors of maximum and median temperature offsets. Moreover, stable understory temperatures were strongly associated with a large offset in daytime maximum temperatures, suggesting that structural diversity primarily contributes to thermal stability by cooling daytime maximum temperatures. Our findings shed light on the thermal influence of vertical vegetation structure and, in the context of tropical land-use change, suggest that decision-makers aiming to mitigate the thermal impacts of land conversion should prioritize management practices that preserve structural diversity by retaining uneven-aged trees and mixing plant species of varying sizes, e.g., silvopastoral, or agroforestry systems.

The registration of bridge point cloud data (PCD) is an important preprocessing step for tasks such as bridge modeling, deformation detection, and bridge health monitoring. However, most existing research on bridge PCD registration only focused on pairwise registration, and payed insufficient attention to multi-view registration. In addition, to recover the overlaps of unordered multiple scans and obtain the merging order, extensive pairwise matching and the creation of a fully connected graph of all scans are often required, resulting in low efficiency. To address these issues, this paper proposes a marker-free template-guided method to align multiple unordered bridge PCD to a global coordinate system. Firstly, by aligning each scan to a given registration template, the overlaps between all the scans are recovered. Secondly, a fully connected graph is created based on the overlaps and scanning locations, and then a graph-partition algorithm is utilized to construct the scan-blocks. Then, the coarse-to-fine registration is performed within each scan-block, and the transformation matrix of coarse registration is obtained using an intelligent optimization algorithm. Finally, global block-to-block registration is performed to align all scans to a unified coordinate reference system. We tested our framework on different bridge point cloud datasets, including a suspension bridge and a continuous rigid frame bridge, to evaluate its accuracy. Experimental results demonstrate that our method has high accuracy.

The latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registration, which aligns raw point cloud data from separate local coordinate systems into a unified coordinate system, still relies on manual intervention. To address this practical issue, this study presents an automated point cloud registration approach optimized for a stop-and-go scanning system based on a quadruped walking robot. The proposed approach comprises three main phases: perpendicular constrained wall-plane extraction; coarse registration with plane matching using point-to-point displacement calculation; and fine registration with horizontality constrained iterative closest point (ICP). Experimental results indicate that the proposed method successfully achieved automated registration with an accuracy of 0.044 m and a successful scan rate (SSR) of 100% within a time frame of 424.2 s with 18 sets of scan data acquired from the stop-and-go scanning system in a real-world indoor environment. Furthermore, it surpasses conventional approaches, ensuring reliable registration for point cloud pairs with low overlap in specific indoor environmental conditions.

There is great potential for the use of terrestrial laser scanning (TLS) to quantify aspects of habitat structure in the study of animal ecology and behaviour. Viewsheds-the area visible from a given position-influence an animal\'s perception of risk and ability to respond to potential danger. The management and conservation of large herbivores and their habitats can benefit greatly from understanding how vegetation structure shapes viewsheds and influences animal activity patterns and foraging behaviour. This study aimed to identify how woodland understory structure influenced horizontal viewsheds at deer eye height. Mobile TLS was used in August 2020 to quantify horizontal visibility-in the form of Viewshed Coefficients (VC)-and understory leaf area index (LAI) of 71 circular sample plots (15-m radius) across 10 woodland sites in North Wales (UK) where fallow deer (Dama dama) are present. The plots were also surveyed in summer for woody plant size structure, stem density and bramble (Rubus fruticosus agg.). Eight plots were re-scanned twice in winter to compare seasonal VC values and assess scan consistency. Sample plots with higher densities of small stems had significantly reduced VC 1 m from the ground. Other stem size classes, mean percentage bramble cover and understory LAI did not significantly affect VC. There was no difference in VC between summer and winter scans, or between repeated winter scans. The density of small stems influenced viewsheds at deer eye height and may alter behavioural responses to perceived risk. This study demonstrates how TLS technology can be applied to address questions in large herbivore ecology and conservation.

Transmission pipelines belong to technical infrastructure, the condition of which is subject to periodic monitoring. The research was to verify whether aerial measurement methods, especially UAV laser scanning, could determine the geometric shape of pipelines with a precision similar to that of terrestrial scanning, adopted as a reference method. The test object was a section of a district heating pipeline with two types of surfaces: matte and glossy. The pipeline was measured using four methods: terrestrial scanning, airborne scanning, UAV scanning and the structure from motion method. Then, based on the reference terrestrial scanning data, pipeline models were created, with which all methods were compared. The comparison made it possible to find that only the UAV scanning yielded results consistent with those of the terrestrial scanning for all the pipes. The differences usually did not exceed 10 mm, sometimes reaching 20 mm. The structure from motion method yielded unstable results. For the old, matte pipes, the results were similar to those of the UAV scan; however, for the new, shiny pipes, the differences were up to 60 mm.

Intertidal artificial habitats are proliferating, but are generally simpler in structure and host lower biodiversity than natural rocky reefs. Eco-engineering aims to enhance the biodiversity of coastal infrastructure, often through physical structural modifications that mimic topographic properties of natural shores. Relationships between biotic assemblages and structural properties of natural and artificial reefs have been extensively studied at sampling scales of up to 1 m2. But evidence that quantified local structural variation has an appreciable influence on biotic assemblages, at a shore-wide scale across regional environmental gradients, is lacking. Here we addressed this knowledge gap with an observational study at 32 natural and artificial intertidal reef sites in Wales, UK. We used multivariate community analysis and permutation tests to examine associations between local physical structure, regional environmental variables and sessile biotic assemblages. A potential influence of local habitat structure on assemblage composition was evident across regional-scale environmental gradients. Compared to natural sites, artificial reefs had lower taxonomic richness, distinct and more variable assemblage composition, and different physical structure. After removing the effect of habitat (natural or artificial), canonical correspondence analysis showed that environmental variables (wave exposure, sea surface temperature and salinity variation), along with two metrics of physical structure (standard deviation in log-transformed detrended roughness and skewness of surface verticality, both at 0.5 m scale), explained 40 % of the variation in assemblage composition among sites. The two structural metrics independently explained 14.5 % of the variation. Associations identified between individual taxa and environmental variables indicated that sites with a higher proportion of horizontal surfaces hosted more canopy macroalgae, which in turn support other algae and invertebrates. Our findings provide evidence to inform scaling-up of structural eco-engineering interventions from experimental contexts to enhance the biodiversity of coastal infrastructure across regional extents.