Terrestrial Data Research Articles

Evaluating effects of silvicultural treatments on forest canopy structure outcomes

Traditional forest management can homogenize forests, and management strategies that can restore complexity and promote adaptive capacity to global change are needed. However, effects of different silvicultural practices on complexity of forest structural components, such as the canopy, are not well understood mechanistically. We conducted a coupled field and simulation-modeling study to evaluate: 1) how near-term effects of silviculture on canopy structure and complexity differ across treatment types, and 2) how outcomes of common silvicultural treatments compare to a spectrum of randomly implemented removals. Thirteen different silvicultural treatments replicated across 12 study plots were simulated within 3D models derived from terrestrial lidar data. Treatment types often differed in their multi-dimensional structural outcomes, including vertical heterogeneity and canopy structural complexity. Moderate intensity thinning treatments that preferentially removed smaller trees increased near-term canopy structural complexity, while diameter-limit cutting often reduced complexity. Silvicultural treatments collectively produced a wide range of residual canopy conditions; however, variability among structural outcomes within individual treatment types or categories was limited relative to the range of possible outcomes from random removals. Most treatments induced shifts in canopy structure outside the spectrum of random removal, suggesting ample space for adapting management to promote forest heterogeneity.

A New Combination Approach for Gibbs Phenomenon Suppression in Regional Validation of Global Gravity Field Model: A Case Study in North China

A global gravity field model (GGM) is essential to be validated with ground-based or airborne observational data for the accurate application of the GGM at a regional scale. Furthermore, accurately understanding the commission errors between the GGM and observational data are crucial for improving regional gravity fields. Taking the North China region as an example, to circumvent the omission errors, it is necessary to unify the spatial resolutions of the EIGEN-6C4 model and terrestrial gravity observational data to 110 km (determined by the distribution of gravity stations) by employing the spherical harmonic function for the EIGEN-6C4 model and the Slepian basis function for the gravity data, respectively. However, the application of spherical harmonic function expansions in the gravity model results in the Gibbs phenomenon, which may be a primary factor contributing to commission errors and impedes the accurate validation of the EIGEN-6C4 model with terrestrial gravity data. To effectively mitigate this issue, this study proposes a combination approach of window function filtering and regional eigenvalue constraint (based on the Slepian basis). Utilizing the EIGEN-6C4 gravity model to derive the gravity disturbance field at a resolution of 110 km (with spherical harmonic expansion up to the 180th degree and order), the combination approach effectively suppresses over 90% of high-degree (above the 120th degree) Gibbs phenomena. This approach also reduces signal leakage outside the region, thus enhancing the spatial accuracy of the regional gravity disturbance field. A subsequent comparison of the regional gravity disturbance field derived from the true model and terrestrial gravity data in North China indicates excellent consistency, with a root mean squared error (RMSE) of 0.80 mGal. This validation confirms that the combined approach of window function filtering and regional eigenvalue constraints effectively mitigates the Gibbs phenomenon and yields precise regional gravity fields. This approach is anticipated to significantly benefit scientific applications such as improving the accuracy of regional elevation benchmarks and accurately inverting the Earth’s internal structure.

Comprehensive Hydrological Analysis of the Buha River Watershed with High-Resolution SHUD Modeling

This study utilizes the Simulator of Hydrologic Unstructured Domains (SHUD) model and the China Meteorological Forces Dataset (CMFD) to investigate the hydrological dynamics of the Buha River watershed, a critical tributary of Qinghai Lake, from 1979 to 2018. By integrating high-resolution terrestrial and meteorological data, the SHUD model simulates streamflow variations and other hydrological characteristics, providing valuable insights into the region’s water balance and runoff processes. Key findings reveal a consistent upward trend in precipitation and temperature over the past four decades, despite minor deviations in daily precipitation intensity and relative humidity data. The SHUD model demonstrates high accuracy on a monthly scale, with Nash–Sutcliffe Efficiency (NSE) values of 0.72 for the calibration phase and 0.61 for the validation phase. The corresponding Kling–Gupta Efficiency (KGE) values are 0.73 and 0.49, respectively, underscoring the model’s applicability for hydrological forecasting and water resource management. Notably, the annual runoff ratios for the Buha River fluctuate annually between 0.11 and 0.21, with significant changes around 2007 correlating with a shift in Qinghai Lake’s water levels. The analysis of water balance indicates a net leakage over long-term periods, with spatial alterations in leakage and replenishment along the river. Furthermore, snow accumulation, which increases with altitude, significantly contributes to streamflow during the melting season. Despite the Buha River basin’s importance, research on its hydrology remains limited due to data scarcity and minimal human activity. This study enhances the understanding of the Buha River’s hydrological processes and highlights the necessity for improved dataset accuracy and model parameter optimization in future research.

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

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.

A regional approach for high-resolution gravity anomaly recovery from full airborne gravity gradient tensor

SUMMARY A regional approach is developed for high-resolution gravity anomaly recovery from the full airborne gravity gradient tensor (GGT) based on the radial basis function (RBF) technique. The analytical expressions that link the full GGT to the gravity anomaly based on Poisson wavelets are developed, where the closed formulae of the associated derivatives of Poisson wavelets are deduced. Based on this approach, the gravity anomalies at a mean resolution of ∼0.15 km over the Kauring Test Range in Australia are recovered by using the local airborne GGT. The results show that the solution computed from the vertical component provides the best quality when a single component is used, whereas the model computed from the curvature component performs the worst. Moreover, the incorporation of two components magnifies the gravity anomalies and further improves the fit with the terrestrial and airborne gravity data, compared with the solutions computed from individual components. However, the solutions calculated by additionally merging one or more components provide comparable qualities with the models calculated by fusing two components only. Finally, the solution is computed by merging the full airborne GGT, and the standard deviation of the misfits against the terrestrial gravity data is 0.788 mGal. Further comparisons with the Fourier transformation and equivalent source method demonstrate that the proposed approach has slightly better performance. The proposed method is numerically efficient and offers a better data adaptation, which is useful for high-resolution gravity data recovery in managing huge number of gravity gradient data.

Utilizing Euler poles for the evaluation of plate rigidity in numerical mantle convection models

SUMMARY Evidence that the Earth’s surface is divided into a tessellation of piece-wise rigidly translating plates is the primary observation supporting the solid-state creep-enabled convection paradigm, utilized to investigate evolution of the Earth’s mantle. Accordingly, identifying the system properties that allow for obtaining dynamically generated plates remains a primary objective in numerical global mantle convection simulations. The first challenge for analysing fluid dynamic model output for the generation of rigid plates is to identify candidate plate boundaries. Here, we utilize a previously introduced numerical tool for plate boundary detection which uses a user specified threshold (tolerance) to automatically detect candidate plate boundaries. The numerical tool is applied with different sensitivities, to investigate the nature of the surface velocity fields generated in three calculations described in earlier work. The cases examined differ by the values that they specify for the model yield stress, a parameter that can allow the formation of tightly focussed bands of surface deformation. The three calculations we examine include zones comprising possible plate boundaries that are characterized by convergence, divergence and strike-slip behaviour. Importance of the potential plate boundaries is assessed by examining the rigidity of the inferred model generated plates. The rigidity is measured by comparing the model velocities to the rigid rotation velocities implied by the statistically determined Euler poles for each candidate plate. We quantify a lack in rigidity by calculating a deformity field based on disagreement of actual surface velocity with rotation about the Euler pole. For intermediate yield stress and boundary detection threshold value, we find that the majority of the model surface can translate almost rigidly about distinct plate Euler poles. Regions that conform poorly to large-scale region rigid translation are also obtained but we find that generally these regions can be decomposed into subsets of smaller plates with a lower tolerance value. Alternatively, these regions may represent diffuse boundary zones. To clarify the degree to which the mantle convection model behaviour shows analogues with Earth’s current-day surface motion, we apply the plate boundary detection and Euler pole calculation methods to previously published terrestrial strain-rate data. Strong parallels are found in the response of the terrestrial data and mantle convection calculations to the threshold value, such that appropriate choice of that parameter results in very good agreement between observations and convection model character. We conclude that plates generated by fluid dynamic convection models can exhibit motion that is markedly rigid, and define statistics (plateness) and fields (deformity) by which the generation of self-consistently determined plate rigidity can be quantified, as well as describing how plate recognition might be optimized. We also note that in agreement with the Earth’s current state, we obtain a dozen dominant plates in the case exhibiting the most plate-like (rigid) surface, suggesting that this approximate number of plates is perhaps intrinsic to the geometry, surface area and physical properties of Earth’s mantle.

From Dawn to Dusk: High-Resolution Tree Shading Model Based on Terrestrial LiDAR Data

Light availability and distribution play an important role in every ecosystem as these affect a variety of ecosystem processes and functions. To estimate light availability and distribution, light simulations can be used. Many previous models were based on highly simplified tree models and geometrical assumptions about tree form, or were sophisticated and computationally demanding models based on 3D data which had to be acquired in every season to be simulated. The aim of this study was to model the shadow cast by individual trees at high spatial and temporal resolution without the need for repeated data collection during multiple seasons. For our approach, we captured trees under leaf-off conditions using terrestrial laser scanning and simulated leaf-on conditions for individual trees over the remainder of the year. The model was validated against light measurements (n=20,436) collected using 60 quantum sensors underneath an apple tree (Malus domestica Borkh.) on a sunny and cloudless summer day. On this day, the leaves and the shadow were simulated with a high spatial (1 cm) and temporal resolution (1 min). The simulated values were highly correlated with the measured radiation at r=0.84. Additionally, we simulated the radiation for a whole year for the sample apple tree (tree height: 6.6 m, crown width: 7.6 m) with a resolution of 10 cm and a temporal resolution of 10 min. Below the tree, an area of 49.55 m² is exposed to a radiation reduction of at least 10%, 17.74 m² to at least 20% and only 0.12 m² to at least 30%. The model could be further improved by incorporating branch growth, curved leaf surfaces, and gravity to take the weight of the foliage into account. The presented approach offers a high potential for modelling the light availability in the surroundings of trees with an unprecedented spatial and temporal resolution.

Threshold for increased liver weight is protective of other effects in Peromyscus exposed to PFNA.

Perfluorononanoic acid (PFNA) is a commercially relevant, long-chain (8 fully fluorinated carbon) perfluorinated carboxylic acid. PFNA has limited terrestrial ecotoxicity data and is detected in humans, animals, and the environment. This study is the fourth in a series with the objective of investigating the toxicity of a suite of per- and polyfluoroalkyl substances (PFAS) detected on military installations in a mammal indigenous to North America. Peromyscus leucopus (white-footed mice, ∼25/sex/dose) were exposed via oral gavage to either 0, 0.03, 0.14, 1, or 3 mg PFNA/kg-d for 112 consecutive days (4 wk premating exposure followed by an additional 12 wk of exposure after onset of mating). Parental generation animals were assessed for potential reproductive and developmental effects, organ weight changes, thyroid modulation, and immunotoxicity. Pup weight and survival were assessed at postnatal days 0, 1, 4, 7, and 10. Change in liver weight was determined to yield the most sensitive dose response according to benchmark dose analysis, and serves as the most protective point of departure (BMDL = 0.37 mg/kg-d PFNA). Other effects of PFNA exposure included reduced formation of plaque-forming cells, which are indicative of functional immune deficits (BMDL = 2.31 mg/kg-d); decreased serum thyroxine (BMDL = 0.93 mg/kg-d) without changes in some other hormones; and increased stillbirths (BMDL = 0.61 mg/kg-d PFNA). Pup weight and survival were not affected by PFNA exposure. Combined with data from previous studies, data from Peromyscus provide a One Health perspective on health effects of PFAS.

Investigating the Potential of Hyper-Temporal Terrestrial Laser Point Clouds for Monitoring Deciduous Tree Growth

Abstract. Monitoring tree growth processes is relevant for ecological research and understanding the intricate relationship between vegetation and the environment. Time series analyses have revealed a correlation between leaf emergence timing and climate change, with earlier leaf emergence attributed to global warming. While traditional forest inventory methods struggle to quantify growth processes on small scales, terrestrial laser scanning provides a powerful alternative for providing high-resolution 3D information. This study explores the use of high-frequency hyper-temporal terrestrial laser scanning data to quantitatively describe deciduous tree growth, tested on a pedunculate oak (Quercus robur). The research aims to address key questions about detecting leaf growth in hypertemporal terrestrial laser scanning data. Additionally, it explores how 3D tree parameters and point cloud comparisons capture leaf and tree growth throughout the year. Results from M3C2 point cloud analyses indicate that the temporary branch movements correlate with precipitation. Over the year, branch movements were detected to increase with growing distance from the trunk.

High-speed GaN-based laser diode with modulation bandwidth exceeding 5 GHz for 20 Gbps visible light communication

Visible light communication (VLC) based on laser diodes demonstrates great potential for high data rate maritime, terrestrial, and aerial wireless data links. Here, we design and fabricate high-speed blue laser diodes (LDs) grown on c-plane gallium nitride (GaN) substrate. This was achieved through active region design and miniaturization toward a narrow ridge waveguide, short cavity length, and single longitudinal mode Fabry–Perot laser diode. The fabricated mini-LD has a low threshold current of 31 mA and slope efficiency of 1.02 W/A. A record modulation bandwidth of 5.9 GHz (−3 dB) was measured from the mini-LD. Using the developed mini-LD as a transmitter, the VLC link exhibits a high data transmission rate of 20.06 Gbps adopting the bit and power loading discrete multitone (DMT) modulation technique. The corresponding bit error rate is 0.003, satisfying the forward error correction standard. The demonstrated GaN-based mini-LD has significantly enhanced data transmission rates, paving the path for energy-efficient VLC systems and integrated photonics in the visible regime.

Monitoring of Extreme Drought in the Yangtze River Basin in 2022 Based on Multi-Source Remote Sensing Data

The Yangtze River Basin experienced a once-in-a-century extreme drought in 2022 due to extreme weather, which had a serious impact on the local agricultural production and ecological environment. In order to investigate the spatial distribution and occurrence of the extreme drought events, this study used multi-source remote sensing data to monitor the extreme drought events in the Yangtze River Basin in 2022. In this study, the gravity satellite data product CSR_Mascon was used to calculate the GRACE Drought Intensity Index (GRACE-DSI), which was analyzed and compared with the commonly used meteorological drought indices, relative soil humidity, and soil water content data. The results show that (1) terrestrial water storage change data can well reflect the change in water storage in the Yangtze River Basin. Throughout the year, the average change in terrestrial water storage in the Yangtze River Basin from January to June is higher than the average value of 33.47 mm, and the average from July to December is lower than the average value of 48.17 mm; (2) the GRACE-DSI responded well to the intensity and spatial distribution of drought events in the Yangtze River Basin region in 2022. From the point of view of drought area, the Yangtze River Basin showed a trend of extreme drought increasing first, and then decreasing in the area of different levels of drought, and the range of drought reached a maximum in September with a drought area of 175.87 km2, which accounted for 97.71 per cent of the total area; at the same time, the area of extreme drought was the largest, with an area of 85.69 km2; (3) the spatial and temporal variations of the GRACE-DSI and commonly used meteorological drought indices were well correlated, with correlation coefficients above 0.750, among which the correlation coefficient of the SPEI-3 was higher at 0.937; (4) the soil moisture and soil relative humidity products from the CLDAS, combined with soil moisture products from the GLDAS, reflect the starting and ending times of extreme drought events in the Yangtze River Basin in 2022 well, using the information from the actual stations. In conclusion, gravity satellite data, analyzed in synergy with data from multiple sources, help decision makers to better understand and respond to drought.

MEASUREMENT AND ENHANCEMENT OF GRAVITY DATA FOR GEOPHYSICAL INVESTIGATIONS IN ABUJA, NIGERIA

The availability of gravity data is useful to individuals, governments and organizations that wish to carry out a geophysical investigation in Abuja. The data provides an accurate model for environmental monitoring and disaster studies. This research was aimed at measurement and prediction of gravity data in Abuja, Nigeria. The aim was achieved by observation of terrestrial gravity data at forty (40) gravity stations around the federal capital territory Abuja, prediction of the gravity data at other points and the evaluation of the predicted data. Gravity reduction was carried out on the measured data to account for drift correction, free air correction, bouguer correction latitude correction and terrain correction. The data was further enhanced by interpolation using the Kriging method. A total of 59 points were predicted from the 40 measured stations. The predicted gravity anomalies were validated by comparing with their corresponding gravity anomalies obtained from the Earth Gravity Model 2008 (EGM08) because of their well-established reliability. For the number of observations, n = 59, the RMSE and SE were computed as 4.797mGal (4.787x10-5 ms-2) and 4.880mGal (4.880 x10-5 ms-2) respectively. Also, the correlation value obtained was 0.991 which is significant at 0.01 level. This shows a strong relationship signifying a perfect possible agreement in the values obtained. It was therefore concluded that Kriging is a good interpolator that provides an estimate with high reliability and dependency. In this regard, considering the high cost of geophysical data collection, gravity prediction can make geophysical data collection more efficient and cost-effective.

Multi-Sensor 3D Survey: Aerial and Terrestrial Data Fusion and 3D Modeling Applied to a Complex Historic Architecture at Risk

This work is inscribed into a more comprehensive project related to the architectural requalification and restoration of Frinco Castle, one of the most significant fortified medieval structures in the Monferrato area (province of Asti, Italy), that experienced a structural collapse. In particular, this manuscript focuses on data fusion of multi-sensor acquisitions of metric surveys for 3D documenting this structural-risky building. The structural collapse made the entire south front fragile. The metric survey was performed by using terrestrial and aerial sensors to reach every area of the building. Topographically oriented Terrestrial Laser Scans (TLS) data were collected for the exterior and interior of the building, along with the DJI Zenmuse L1 Airborne Laser Scans (ALS) and Zenmuse P1 Photogrammetric Point Cloud (APC). First, the internal alignment in the TLS data set was verified, followed by the intra-technique alignments, choosing TLS as the reference data set. The point clouds from each sensor were analyzed by computing voxel-based point density and roughness, then segmented, aligned, and fused. 3D acquisitions and segmentation processes were fundamental for having a complete and structured dataset of almost every outdoor and indoor area of the castle. The collected metrics data was the starting point for the modeling phase to prepare 2D and 3D outputs fundamental for the restoration process.

Impact of incorporating Spire CubeSat GPS observations in a global GPS network solution

Commonly, Global Positioning System (GPS)-based precise orbit determination (POD) of a low Earth orbiting (LEO) satellite is conducted by introducing fixed GPS orbits and clock corrections that were derived in a previous independent network solution using ground-based GPS receivers only. There have been a number of studies showing that the integration of LEO GPS observations can be advantageous in global network solutions, particularly when it comes to estimating geodetic parameters, such as the Earth’s center-of-mass. There has been an increase in GPS data available from LEO CubeSats over the past few years, such as from the Spire satellites, which are equipped with dual-frequency GPS receivers. The goal of the present study is to determine how GPS observations collected by specific Spire satellites affect global network solutions when combined with GPS observations from ground stations of the International GNSS Service. To obtain a combined GPS-LEO solution, GPS observations collected from receivers of selected Spire satellites and additional scientific LEOs are used to obtain combined GPS-LEO solutions by performing one joint least-squares adjustment process. Using this method, LEO satellite orbit parameters are determined along with GPS orbit parameters and geodetic parameters, namely GNSS station coordinates, Earth rotation parameters (ERPs), and the Earth’s center-of-mass. The influence of the Spire LEOs is assessed by analyzing different solutions that use GPS observations from different Spire and LEO satellites. Besides comparing the different global network solutions with one another, a comparison to a solution using only terrestrial GPS data is performed as well. As part of the quality assessment of the GPS-LEO solution, quality characteristics are analyzed, which are the geodetic parameters, orbit overlaps at arc boundaries, and formal errors determined for the estimated parameters. The results show that the estimation of the Earth’s center-of-mass coordinates benefits from including GPS-LEO observations. The solution is more improved by integrating GPS-LEO observations from scientific LEOs than from GPS-Spire-included solutions. The analysis reveals that GPS orbits are improved using the LEO-integrated approach, while orbit misclosures of LEO and Spire trajectories currently indicate a decrease in LEO orbit precision due to LEO orbit modeling deficiencies.

Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data

Branch architecture plays an important role in forest physical and ecological processes, greatly affecting forest spatial structure and the accumulation, production and distribution of organic carbon. Very few studies have quantified the branch architecture of large-scale forest plots, due to the lack of high-resolution large-scale three-dimensional data. The emergence of unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) provides great potential to overcome this limitation. However, due to insufficient quality of UAV-LiDAR data for branch reconstruction, existing algorithms remain tremendously challenging. We propose an effective branch reconstruction algorithm for UAV-LiDAR data to quantify branch architecture. First, individual branches are extracted using a region growth method that is guided by the branch growth direction and local smoothness constraint. Second, incorrect branches are eliminated based on three pieces of branch features, i.e., specific angles between branches at the same whorl, specific height differences between branches at different whorls, and the growth pattern of branches from the stem outward, reaching maximum distances at tips. Finally, branches are reconstructed using polynomial fitting, and branch architecture parameters are extracted based on branch models. The proposed algorithm simplifies branch reconstruction by skipping the separation of photosynthetic and non-photosynthetic components. It also adapts well to UAV-LiDAR point clouds, generating more realistic reconstructions. The algorithm successfully identified non-photosynthetic components in experiments involving 240 trees, including Scots pine, Norway spruce, and Radiata pine. The average overall accuracy for these three species was 0.88, 0.76, and 0.74, respectively. The proposed algorithm was tested for branch reconstruction using UAV-LiDAR data from a two-hectare Larix principis-rupprechtii plot. The accuracy of branch identification and parameter extraction accuracy were evaluated branch by branch based on the individual branches manually identified in terrestrial laser scanning data. Results showed the F-score of branch and stem identification was 0.58 and 1. The relative RMSE of branch length and angle was 36.87% and 18.3%, and ones of stem length and diameter were 1.46% and 6.16%. The proposed algorithm outperformed the well-known reconstruction algorithms, including TreeQSM, AdTree and Laplacian-based algorithms.

Determining the trend of geometrical changes of a hydrotechnical object based on data in the form of LiDAR point clouds

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article.

Simulating cable corridors based on terrestrial LiDAR data

This article introduces a new basis for optimising cable corridor layouts in timber extraction on steep terrain by using a digital twin of a forest. Traditional approaches for generating cable corridor layouts rely on less accurate contour maps, which can lead to layouts which rely on infeasible supports, undermining confidence in the generated layouts. We present a detailed simulational approach which uses high-resolution tree maps and digital terrain models to compute realistic representations of all possible cable corridors in a given terrain. We applied established methods in forestry to compute feasible cable corridors in a designated area, including rope deflection, determining sufficient tree anchors and placing intermediate supports where necessary. The proposed individual cable corridor trajectories form the foundation for an optimised overall layout that enables a reduction of installation and operation costs and promotes sustainable timber extraction practices on steep terrain. As a next step we aim to mathematically optimise the layout of feasible cable corridors based on multiple criteria (cost, ergonomic aspects, ecological aspects), and integrate the results into an user-friendly workflow.

Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method

This study presents a Bayesian-driven ensemble learning framework to minimize errors in evapotranspiration (ET) data assimilation, thereby generating a continuous-scale ET estimation with improved accuracy for the data-scarce arid Central Asia (CA). Five state-of-the-art gridded ET products were extensively tested and utilized to reconstruct new long-term ET data at grid scale, including two Global Land Data Assimilation System (GLDAS)-Land Surface Models (LSM) (Catchment and NOAH), two reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications – ver. 2 (MERRA2)), along with the Global Land Evaporation Amsterdam (GLEAM) satellite ET product ver. 3.6b. Taylor Skill Score was employed to evaluate the input gridded ET datasets, using in situ measurements across four arid biomes — cropland, shrubland, farmland, and grassland —as benchmarks. Bayes-Markov Chain Monte Carlo (Bayes-MCMC)-derived logarithmically transformed predictors from the posterior ensemble anomalies were standardized to resolve the mismatches in the cumulative distribution functions of ET data relating to the in situ observations. We found that the constrained residual errors were more profound between 45th and 95th percentiles for the rescaled total ET anomalies across all the input ET datasets at 95 % confidence interval. Moreover, the weighted ensemble estimates derived from the Bayes-MCMC were instrumental in correcting ET anomalies, leading to the robust reconstruction of a more reliable ensemble mean ET data for Central Asia (CAETens) at monthly temporal and 0.25° spatial resolutions, spanning from 2003 to 2020. Overall, CAETens exhibited superior performance, achieving the highest averaged Kling-Gupta Efficiency of 0.75 and registering the lowest Root Mean Square Error of 6.85 mm/m compared to the standalone gridded ET products. Comparisons of spatial distributions of fine-scale features, and evaluations using standard metrics revealed that all ET products varied significantly (p < 0.05) by biome types. Remarkably, only CAETens exhibited a reasonable low positive bias and pronounced ability to resolve the complex spatiotemporal patterns associated with the input gridded ET products. The study bridges the gap in continuous ET monitoring for sustainable water resources management in CA.