Search Results (96)

The study aims to analyze morphological variations and spatial distribution patterns of Krascheninnikovia compacta (Losinsk.) Grubov communities across 12 sampling areas at different elevations in the Tibetan antelope breeding grounds of the western Kunlun Mountains. Additionally, it projected the future climatically suitabie habitats of K. compacta under climate change scenarios, aiming to elucidate its community characteristics, spatial distribution dynamics, and the impacts of global warming on its growth. Integrated GIS, remote sensing, and unmanned aerial vehicles (UAVs) were used to investigate K. compacta communities. The Pearson correlation analysis revealed significant correlations between crown diameter, as well as between plant height and environmental factors. The redundancy analysis (RDA) results indicated that multiple environmental factors jointly explained the variation in plant height and crown diameter of K. compacta. Point pattern analysis, using the g(r) function combined with two null models, demonstrated changes in plant distribution during scale transitions. Additionally, the MaxEnt model was employed to project the potential suitable habitats of K. compacta under future climate scenarios. Overall, as the elevational gradient increases, the plant height of K. compacta gradually decreases while the crown diameter expands. Mean annual temperature (MAT) dominates the morphological variations in crown diameter and plant height, with lower temperatures correlating to shorter plant height and larger crown diameter. The complete spatial randomness (CSR) model indicates that across all elevations, the distribution patterns of plants transition sequentially from uniform to random, then clustered, and back to random as spatial scale increases. The heterogeneous Poisson (HP) model suggests that habitat heterogeneity is the primary driver of shifts in plant distribution patterns at larger scales. The MaxEnt model revealed distinct changes in suitable habitat areas of K. compacta under future climate scenarios. During 2061 to 2080s, its suitable habitats under the SSP126 and SSP585 pathways significantly contracted and expanded markedly, respectively. Full article

Accurate estimates of aboveground biomass (AGB) are essential for forest policies to reduce carbon emissions. Unmanned aerial laser scanning (UAV-LS) offers unprecedented millimetric detail but is underutilized in monitoring broadleaf Mediterranean forests compared to coniferous ones. This study aims to design and evaluate a procedure for AGB estimates based on the predictive power of crown features. In the first step, we manually created Quantitative Structure Models (QSMs) for 320 trees using data from UAV laser scanning (UAV-LS), airborne laser scanning (ALS), and co-registered terrestrial laser scanning (TLS). This provided the most accurate non-destructive estimate of aboveground biomass (AGB) in the absence of destructive measurements. For each reference tree we also measured crown projection and crown volume to build two separated models relating AGB to such crown features. In the second phase, we evaluated the potential of UAV-LS for quantifying AGB in a pure European beech (Fagus sylvatica) forest and compared it with traditional ALS estimates, using fully automatic procedures. The two obtained tree-level AGB models were then tested using three datasets derived from 35 sampling plots over the same study area: (a) 1130 trees manually segmented (phase-2 reference); (b) trees automatically extracted from ALS data; and (c) trees automatically extracted from UAV-LS data. Results demonstrate that detailed UAV-LS data improve model sensitivity compared to ALS data (RMSE = 45.6 Mg ha⁻¹, RMSE% = 13.4%, R2 = 0.65, for the best ALS model; RMSE = 44.0 Mg ha⁻¹, RMSE% = 12.9%, R2 = 0.67, for the best UAV-LS model), allowing for the detection of AGB differences even in quite homogenous forest structures. Overall, this study demonstrates the combined use of both laser scanner data can foster non-destructive and more precise AGB estimation than the use of only one, in forested areas across hectare scales (1 to 100 ha). Full article

This article offers the architectural definition and interpretative keys to a unique project. It is a space shared by the three main Abrahamic faiths: the Jewish, Christian and Muslim religions. Although conceptually other religions could be accommodated. Its configuration is very elementary: a cubic volume, massive and almost blind, with a cylindrical space crowned by a simple skylight. Each of the religions is based on a scratching of the interior surfaces of the space, forming the ritual areas of each of them. And towards the center of the space there are other areas of prayer and celebration that could be shared among the believers of the different religions, from the conviction that they are addressed to the same God. In this configuration there is a will of invitation, of offering to all men of good will. The article, written by the architect of this space, mentions some plastic and conceptual references that have served as inspiration for the project and its presentation is intended to fuel the debate on the possibility of this space. Full article

Pacific crown-of-thorns starfish (Acanthaster solaris) outbreaks pose a significant threat to coral reef ecosystems, with climate change potentially exacerbating their distribution and impact. However, there remains only a small number of predictive studies on how climate change drives changes in the distribution patterns of A. solaris, and relevant assessments of the impact of these changes on coral reef areas are lacking. To address this issue, this study investigated potential changes in the distribution of A. solaris under climate change and its impact on Acropora coral habitats. Using a novel two-step framework, we integrated both abiotic and biological (Acropora distribution) predictors into species distribution modeling to project future shifts in A. solaris habitats. We created the first reliable set of current and future global distribution maps for A. solaris using a comprehensive dataset and machine learning approach. The results showed significant distribution shifts under three climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), with expanded ranges under all scenarios, and the greatest expansion occurring near 10° S. Asymmetry in the latitudinal shifts in habitat boundaries suggests that the Southern Hemisphere may face a more severe expansion of A. solaris. Regions previously unsuitable for A. solaris, such as parts of New Zealand, might experience new invasions. Additionally, our findings highlight the potential increase in predatory pressure on coral reefs under SSP2-4.5 and SSP5-8.5 scenarios, particularly in the Western Coral Triangle and Northeast Australian Shelf, where an overlap between A. solaris and Acropora habitats is significant. This study provides critical insights into the ecological dynamics of A. solaris in the context of climate change, and the results have important implications for coral reef management. These findings highlight the need for targeted conservation efforts and the development of mitigation strategies to protect coral reefs from the growing threat posed by A. solaris. Full article

Human hair, commonly considered a discarded organic waste, is a keratin-rich material with remarkable potential for sustainable agriculture as an innovative resource. This study systematically explored the potential of non-composted human hair hydrolysates as eco-friendly and nutrient-rich liquid fertilizers, emphasizing their ability to enhance agricultural sustainability and mitigate organic waste accumulation. Eight distinct hydrolysates prepared with alkaline solutions were evaluated for their effects on plant growth using red-hot chili pepper (Capsicum frutescens) as the primary model under greenhouse conditions. The present study introduces a novel approach by employing an advanced digital image analysis technique to quantitatively assess 37 distinct plant growth parameters, providing an unprecedented depth of understanding regarding the impact of liquid human hair hydrolysates on plant development. Additionally, the integration of pilot-scale field trials and multi-species evaluations highlights the broader applicability and scalability of these hydrolysates as sustainable fertilizers. Collectively, these features establish this research as a pioneering contribution to sustainable agriculture and bio-waste management. The top-performing hydrolysates (KCaMgN, KMgN, KCaN) demonstrated significant enhancements in plant growth metrics, with fresh weight reaching up to 3210 mg, projected leaf area of approximately 132 cm², and crown diameter of 20.91 cm for the best-performing formulations, outperforming a commercial organomineral fertilizer by 20–46% in overall growth performance. Furthermore, observational studies on various species (such as bird of paradise flower (Strelitzia reginae), avocado (Persea americana), lemon (Citrus limon L.), Mazafati date (Phoenix dactylifera L.), and red mini conical hot pepper (Capsicum annuum var. conoides) and field trials on long sweet green peppers (Capsicum annuum) confirmed the broad applicability of these hydrolysates. Toxicity assessments using shortfin molly fish (Poecilia sphenops) validated the environmental safety of plants cultivated with hydrolysates. These findings highlight that human hair hydrolysates offer a sustainable alternative to synthetic fertilizers, contributing to waste management efforts while enhancing agricultural productivity. Full article

This paper describes the investigation and findings from the root cause analysis (RCA) of a significant collapse that occurred in a hydropower tunnel at a confidential location. This collapse involved about 12,000 m³ of material being deposited in the tunnel from a narrow 20 m width failure zone encountered in the haunch and crown area of the main power tunnel. This paper describes contributing factors which include the following: (1) degradation of a highly zeolitized (laumontite-rich) zone of rock within a bedding concordant fault zone, termed the fault-damaged zone or FDZ; (2) relatively high in situ rock stresses concentrated in the haunch and crown area of the collapse zone in the tunnel; (3) large transient water pressure differences in the rock above the collapse zone and upstream and downstream of the collapse zone; (4) cyclical repetition of the above-described factors resulted in the propagation of crown and sidewall collapse in and around the FDZ. Lessons learnt on this project and other projects with similar durability problems in volcanic rock are distilled in this paper. It is hoped that advances made in the understanding of the failure mechanism at the unnamed tunnel can be included in future tunnel investigations and design in volcanic rocks. Full article

The world’s big cities, including Budapest, are becoming more crowded, with more and more people living in smaller and smaller spaces. There is an increasing demand for more green space and trees, with less vertical and less horizontal space. In addition, deteriorating environmental conditions are making it even more difficult for trees to grow and survive. Tree species in urban areas have multiple functions and high ecosystem services when in good health. Among taxa with diverse habits, sizes, crown shapes, growth vigor, longevity, urban tolerance, and canopy habit, our research aims to evaluate urban specimens of spherical species with smaller space requirements and sizes but have regular geometric crown shapes in public plantations in Budapest. In the restricted urban habitats, the city’s cadastral records include 4676 specimens with spherical crowns. Among the species examined, eight species with globular crowns (Acer platanoides ‘Globosum’, Catalpa bignonioides ‘Nana’, Celtis occidentalis ‘Globosa’, Fraxinus excelsior ‘Nana’, Fraxinus ornus ‘Mecsek’, Platanus × hispanica ‘Alphen’s Globe’, Prunus × eminens ‘Umbraculifera’ and Robinia pseudoacacia ‘Umbraculifera’) were evaluated in relation to age, health, wood type, crown size, and shade projection in order to show which species are or will be suitable in the future. Full article

Roadside tree segmentation and parameter extraction play an essential role in completing the virtual simulation of road scenes. Point cloud data of roadside trees collected by LiDAR provide important data support for achieving assisted autonomous driving. Due to the interference from trees and other ground objects in street scenes caused by mobile laser scanning, there may be a small number of missing points in the roadside tree point cloud, which makes it familiar for under-segmentation and over-segmentation phenomena to occur in the roadside tree segmentation process. In addition, existing methods have difficulties in meeting measurement requirements for segmentation accuracy in the individual tree segmentation process. In response to the above issues, this paper proposes a roadside tree segmentation algorithm, which first completes the scene pre-segmentation through unsupervised clustering. Then, the over-segmentation and under-segmentation situations that occur during the segmentation process are processed and optimized through projection topology checking and tree adaptive voxel bound analysis. Finally, the overall high-precision segmentation of roadside trees is completed, and relevant parameters such as tree height, diameter at breast height, and crown area are extracted. At the same time, the proposed method was tested using roadside tree scenes. The experimental results show that our methods can effectively recognize all trees in the scene, with an average individual tree segmentation accuracy of 99.07%, and parameter extraction accuracy greater than 90%. Full article

Terrestrial laser scanning (TLS) provides highly detailed 3D information of forest environments but is limited to small spatial scales, as data collection is time consuming compared to other remote sensing techniques. Furthermore, TLS data collection is heavily dependent on wind conditions, as the movement of trees negatively impacts the acquired data. Hardware advancements resulting in faster data acquisition times have the potential to be valuable in upscaling efforts but might impact overall data quality. In this study, we investigated the impact of the pulse repetition rate (PRR), or pulse frequency, which is the number of laser pulses emitted per second by the scanner. Increasing the PRR reduces the scan time required for a single scan but decreases the power (amplitude) of the emitted laser pulses commensurately. This trade-off could potentially impact the quality of the acquired data. We used a RIEGL VZ400i laser scanner to test the impact of different PRR settings on the point cloud quality and derived tree structural metrics from individual tree point clouds (diameter, tree height, crown projected area) as well as quantitative structure models (total branch length, tree volume). We investigated this impact across five field plots of different forest complexity and canopy density for three different PRR settings (300, 600 and 1200 kHz). The scan time for a single scan was 180, 90 and 45 s for 300, 600 and 1200 kHz, respectively. Differences among the raw acquired scans from different PRR replicates were largely removed by several necessary data processing steps, notably the removal of uncertain points with a low reflectance attribute. We found strong agreement between the individual tree structural metrics derived from each of the PRR replicates, independent of the forest complexity. This was the case for both point cloud-based metrics and those derived from quantitative structural models (QSMs). The results demonstrate that the PRR in high-end TLS instruments can be increased for data collection with negligible impact on a selection of derived structural metrics that are commonly used in the context of aboveground biomass estimation. Full article

This study compared hierarchical Bayesian, mixed-effects Gaussian process regression, and random forest models for predicting height to crown base (HCB) in Qinghai spruce (Picea crassifolia Kom.) forests using LiDAR-derived data. Both modeling approaches were applied to a dataset of 510 trees from 16 plots in northern China. The models incorporated tree-level variables (height, diameter at breast height, crown projection area) and plot-level spatial competition indices. Model performance was evaluated using leave-one-plot-out cross-validation. The Gaussian mixed-effects process model (with an RMSE of 1.59 and MAE of 1.25) slightly outperformed the hierarchical Bayesian model and the random forest model. Both models identified LiDAR-derived tree height, DBH, and LiDAR-derived crown projection area as primary factors influencing HCB. The spatial competition index (SCI) emerged as the most effective random effect, with the lowest AIC and BIC values, highlighting the importance of local competition dynamics in HCB formation. Uncertainty analysis revealed consistent patterns across the predicted values, with an average relative uncertainty of 33.89% for the Gaussian process model. These findings provide valuable insights for forest management and suggest that incorporating spatial competition indices can enhance HCB predictions. Full article

Hydraulic tunnels are prone to pass through faults and high-intensity earthquake areas, which will cause serious damage under fault dislocation and earthquake action. Fault dislocation and seismic excitation are often considered separately in previous studies. For tectonic earthquakes with higher frequency in seismic phenomena, fault dislocation and ground motion are often associated, and fault dislocation is usually the cause of earthquake occurrence, so it is limiting to consider the two separately. Moreover, strong earthquake records show that there will be significant differences in the mainland vibration within 50 m. The uniform ground motion inputs in previous studies are not suitable for long hydraulic tunnels. This paper begins with the simulation of non-uniform stochastic seismic excitations that consider spatial correlation. Based on stochastic vibration theory, multiple multi-point acceleration time-history curves that can reflect traveling wave effects, coherence effects, attenuation effects, and non-stationary characteristics are synthesized. Furthermore, a fault velocity function is introduced to account for the velocity effect of fault dislocation. Finally, numerical analyses of the response patterns of the tunnel lining under four different conditions are conducted based on an actual engineering project. The results indicate the following: (a) the maximum lining response values occur under the combined effects of fault dislocation and non-uniform seismic excitation, indicating its importance in the seismic resistance of the tunnel. (b) Compared to uniform seismic excitation, the peak displacement of the tunnel under non-uniform seismic excitation increases by up to 6.42%, and the peak maximum principal stress increases by up to 28%. Additionally, longer tunnels exhibit a noticeable delay effect in axial deformation during an earthquake. (c) Under non-uniform seismic excitation, the larger the fault dislocation magnitude, the greater the peak displacement and peak maximum principal stress at the monitoring points of the lining. The simulation results show that the extreme response values primarily occur at the crown and haunches of the tunnel, which require special attention. The research can provide valuable references for the seismic design of cross-fault tunnels. Full article

Hedgerow cultivation systems have revolutionized olive growing in recent years because of the mechanization of harvesting. Initially applied under irrigated conditions, its use has now extended to rainfed cultivation. However, there is limited information on the behavior of olive cultivars in hedgerow growing systems under rainfed conditions, which is a crucial issue in the context of climate change. To fill this knowledge gap, a rainfed cultivar trial was planted in 2020 in Southern Spain to compare ‘Arbequina’, ‘Arbosana’, ‘Koroneiki’, and ‘Sikitita’, under such growing conditions. One of the most important traits in low-water environments is the canopy growth. Because traditional canopy measurements are costly in terms of time and effort, the use of light detection and ranging (LiDAR) sensor onboard an uncrewed aerial vehicle (UAV) was tested. Statistical analyses of data collected in November 2022 and January 2023 revealed high correlations between UAV-LiDAR metrics and field measurements for height, projected area, and crown volume, based on validation with measurements from 36 trees. These results provide a solid basis for future research and practical applications in rainfed olive growing, while highlighting the potential of UAV-LiDAR technology to characterize tree canopy structure efficiently. Full article

This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed the dataset independently, for model training. The experimental results show that the F1-score of Sophora japonica, Pinus tabulaeformis, and Koelreuteria paniculata reached 87.4%, 85.7%, and 86.3%, respectively. Meanwhile, we tested for the study area with a total area of 146 ha, and 27,403 tree species were identified in nine categories, with a total crown projection area of 318,725 m². We also fitted a biomass calculation model for oil pine (Pinus tabulaeformis) based on field measurements and assessed 205,199.69 kg of carbon for this species across the study area. Additionally, we compared the model to U-net, and the results showed that BlendMask has strong crown-segmentation capabilities. This study demonstrates that BlendMask can effectively perform crown segmentation and forest inventory in large-scale complex forest areas, showing its great potential for forest resource management. Full article

In rainfed areas, precipitation variations directly impact wheat growth stages such as emergence, tillering, jointing and booting, and maturity. Evaluating the impact of climate change on precipitation patterns during these critical growth stages is crucial for adapting climate change and ensuring global food security. In this study, projections of five General Circulation models (GCMs) under two shared socioeconomic pathways (SSPs) were used to predict the changing characteristics of precipitation during four main growth stages of wheat in the rainfed region of the Pothwar Plateau, Pakistan. Historical datasets of daily precipitation at six weather stations were analyzed to check the past changes in the precipitation patterns. During the baseline period (1985–2014), the annual average precipitation decreased at a rate of −9.75 mm/decade, while the amount of precipitation during the rabi season (wheat-growing season) decreased at a rate of −20.47 mm/decade. An increase in the precipitation was found during the fourth (flowering) stage of crop growth, while the first three stages experienced a decrease in the precipitation amount. The multimodal ensembled data, under the SSP2-4.5 scenario, revealed a significant decline (at the rate of −16.63 mm/decade) in the future annual precipitation. However, it is projected that, under SSP2-4.5, there may be a slight increase (4.03 mm/decade) in the total precipitation amount during the future rabi season. Under the SSP5-8.5 scenario, average annual precipitation exhibited a slightly increasing trend, increasing by 1.0 mm/decade. However, during the rabi season, there was a possibility of a decrease in precipitation amount, with a rate of 11.64 mm/decade. It is also expected that the precipitation amount may vary significantly during the crown root initiation, jointing and booting, and flowering stages in the near future. These results provide a framework for the planning of wheat production in the Pothwar region of Pakistan, taking into account the potential impact of shifting weather patterns, particularly in terms of uneven precipitation. Full article

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. Full article