Search Results (305)

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

The structural analysis of shelterbelts forms the foundation of their planning and management, yet the scientific and effective quantification of shelterbelt structures requires further investigation. This study developed an innovative heterogeneous analytical framework, integrating three key methodologies: the LeWoS algorithm for wood–leaf separation, TreeQSM for structural reconstruction, and 3D alpha-shape spatial quantification, using terrestrial laser scanning (TLS) technology. This framework was applied to three typical farmland shelterbelts in the Ulan Buh Desert oasis, enabling the first precise quantitative characterization of structural components during the leaf-on stage. The results showed the following to be true: (1) The combined three-algorithm method achieved ≥90.774% relative accuracy in extracting structural parameters for all measured traits except leaf surface area. (2) Branch length, diameter, surface area, and volume decreased progressively from first- to fourth-order branches, while branch angles increased with ascending branch order. (3) The trunk, branch, and leaf components exhibited distinct vertical stratification. Trunk volume and surface area decreased linearly with height, while branch and leaf volumes and surface areas followed an inverted U-shaped distribution. (4) Horizontally, both surface area density (Scd) and volume density (Vcd) in each cube unit exhibited pronounced edge effects. Specifically, the Scd and Vcd were greatest between 0.33 and 0.60 times the shelterbelt’s height (H, i.e., mid-canopy). In contrast, the optical porosity (Op) was at a minimum of 0.43 H to 0.67 H, while the volumetric porosity (Vp) was at a minimum at 0.25 H to 0.50 H. (5) The proposed volumetric stratified porosity (Vsp) metric provides a scientific basis for regional farmland shelterbelt management strategies. This three-dimensional structural analytical framework enables precision silviculture, with particular relevance to strengthening ecological barrier efficacy in arid regions. Full article

Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m²/m²), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m²/m²). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI. Full article

While soil warming has been demonstrated to significantly alter the processes of the nitrogen cycle in forest ecosystems, how leaf-available nitrogen, representing the primary forms of nitrogen absorbed by plants, responds to such thermal alterations remains insufficiently understood. In the present study, a control (CK) group and a soil-warming treatment (W) were set up. The nitrogen contents of nitrate (NO₃⁻-N), ammonium (NH₄⁺-N), and amino acids (AA-N) in previous- and current-year leaves from the upper and lower canopy of Chinese fir were measured under both CK and W conditions. By comparing the differences in available nitrogen distribution across different canopy layers or leaf ages, we aimed to illustrate the effects of soil warming on the allocation of available nitrogen in leaves. It was shown that soil warming can alter the distribution of available nitrogen in Chinese fir leaves, and its impact on leaf AA-N was significantly greater than its impact on inorganic nitrogen. Additionally, the allocation of available nitrogen in Chinese fir under soil warming was also influenced by leaf position and leaf age. Soil warming altered the distribution patterns of available nitrogen in leaves of Chinese fir across different canopy layers or leaf ages, which provides a scientific basis for coniferous tree species to adapt to the thermal environment by regulating available nitrogen allocation. Full article

Canopy-forming algae play an important role in coastal ecosystems because these species are highly productive and provide habitats and shelter for numerous marine organisms. Sargassum is the main genus of canopy-forming algae in the western North Pacific, but despite the importance of their ecological role, studies on the changes in their distribution are still scarce. Based on the present distribution of four Sargassum species, this study predicted the geographic distribution of future habitats (2030s, 2060s, and 2090s) under three Shared Socioeconomic Pathway (SSP) scenarios. The environmental variables predicted from the sixth phase of the coupled model intercomparison project (CMIP6) had different impacts depending on the species, with current velocity and water temperature showing high contributions in all four species. According to the projections, three Sargassum species (S. horneri, S. macrocarpum, and S. patens) are expected to maintain a higher habitat suitability index (HSI) and suitable habitat (MAXENT ≥ 0.4) through the 2090s under the SSP1-1.9 scenario. However, under the SSP2-4.5 and SSP5-8.5 scenarios, the HSI of the species is projected to gradually decrease in the southern coastal waters of the Korean peninsula and increase in the East Sea (North Korea), with these results intensifying under the SSP5-8.5 scenario. On the other hand, S. piluliferum was found to increase its HSI and habitat under the highest emission scenarios. All Sargassum species are predicted to shift northward from 0.8° N to 3.8° N by the 2090s under the SSP5-8.5 scenario. Although many marine protected areas exist off the coasts of South Korea and Japan, suitable Sargassum habitats were found to be located within protected reserves between 47.1% and 61.2%, depending on the scenario. These findings on Sargassum provide distributional predictions for ecological conservation strategies and provide new evidence for the need for climate change efforts. Full article

Tree-lined spaces as informal communication areas and important pathways for pedestrians are the second largest zones on university campuses, and they have a large impact on the microclimate. At present, the effects of the spatial form for tree-lined boulevards on microclimates have not been investigated. Thus, this study applied experimental and simulation methods to investigate the effects of tree-lined boulevards on microclimates in hot summer and cold winter regions. The main meteorological parameters including air temperature, relative humidity, wind speed, and solar radiation of the boulevard were obtained by experiments. Furthermore, the experimental data as a boundary condition were input into ENVI-met software to investigate the effects of the aspect ratio and canopy diameter of double-row open-canopy boulevards on microclimate regulation. The results showed that when the aspect ratio was reduced from 1.5 to 0.9, the temperature and UTCI increased by 0.047 °C and 0.21 °C, while relative humidity decreased by 0.227%. Decreasing the aspect ratio can effectively improve the microenvironment. As the canopy diameter increased from 7 m to 11 m, the temperature and UTCI of the boulevard space decreased by 0.064 °C and 0.45 °C, while relative humidity increased by 0.245%. An increase in canopy diameter is unfavorable to the improvement of microclimates. This study aims to provide a scientific basis for the design and improvement of tree-lined boulevards on university campuses. Full article

As a form of green infrastructure, urban forests play a key role in the provision of hydrological ecosystem services (ESs) in cities. Understanding how urban forest structure and soil properties influence water regulation ESs is crucial for managing and planning green infrastructure in cities. We analysed two indicators—the runoff to precipitation (Q/P) and the evapotranspiration to precipitation (ETP/P) ratios—for five different urban forests. We used the hydrological model Brook90 over 16 years to simulate runoff, evapotranspiration, canopy interception, transpiration and soil evaporation. The results showed that mixed forests have the highest water retention capacity, with the lowest Q/P (0.41) and the highest ETP/P (0.59). In contrast, riparian deciduous forests had the lowest water retention capacity, with the highest Q/P (0.75) and the lowest ETP/P (0.25). Both indicators showed similar annual and seasonal results. However, Q/P showed strong inter-annual variation and a strong correlation with precipitation, while ETP/P remained consistent despite precipitation fluctuations in the observed years. In conclusion, the ETP/P ratio is better suited to assess the water regulation ES of urban forests. Full article

The objective of this work was to assess different concentrations of a novel formulation of 1-aminocyclopropane-1-carboxylic acid (ACC) on anthocyanin accumulation and color development, as well as on the physicochemical characteristics of the ‘Benitaka’ table grape grown in a subtropical region in two application forms. The trial was conducted on a commercial property located in a subtropical area in Brazil in 2022. Treatments included different concentrations of a new formulation containing 400 g kg⁻¹ of ACC, ranging from 0 to 125 g 100 L⁻¹, as well as a standard concentration of a formulation containing 100 g L⁻¹ of abscisic acid (S-ABA): 3.2 L ha⁻¹. The exogenous application of ACC was performed at the beginning of berry ripening (véraison), while that of S-ABA was performed twice: the first, at véraison, and the second, 7 days later. The concentration of total anthocyanins, berry color index, physicochemical characteristics, and sensory–visual analysis of color coverage of the bunches were evaluated weekly, while berry firmness was appraised at harvest. A single exogenous application of ACC or two applications of S-ABA resulted in daily increment rates that provided a high accumulation of total anthocyanins, as well as greater berry color development, regardless of the application method, directed to the canopy of the vines or only to the bunches. As a result, the new formulation of ACC at concentrations of 75 g to 100 g 100 L⁻¹ is a novel tool to stimulate the anthocyanins accumulation and berry color development in ‘Benitaka’ table grapes grown in subtropical regions without negative impact on bunches or vines. Full article

Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies. This research focuses on the Ha’erwusu open-pit coal mine in Inner Mongolia, China, using measured dust content on plant canopies and UAV-borne VNIR hyperspectral data as the data sources. The study employed five spectral transformation forms—first derivative (FD), second derivative (SD), logarithm transformation (LT), reciprocal transformation (RT), and square root (SR)—alongside the competitive adaptive reweighted sampling (CARS) method to extract characteristic bands associated with canopy dust. Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. The spatial distribution of canopy dust was then analyzed. The results demonstrate that the geometric and radiometric correction of the UAV-borne VNIR hyperspectral images successfully restored the true spatial information and spectral features. The spectral transformations significantly enhance the feature information for canopy dust. The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. Both feature extraction and model selection influence the inversion accuracy, with the LT-CARS and RF combination offering the best predictive performance. Canopy dust content decreases with increasing distance from the dust source. These findings offer valuable insights for canopy dust retention monitoring and offer a solid foundation for dust pollution management and the development of suppression strategies. Full article

Intensifying heat from warming climates regularly concentrates in urban areas lacking green infrastructure in the form of green space, vegetation, and ample tree canopy cover. Nature-based interventions in older U.S. city cores can help minimize the urban heat island effect, yet neighborhoods targeted for cooling interventions may remain outside the decisional processes through which change affects their communities. This translational research seeks to address health disparities originating from the absence of neighborhood-level vegetation in core urban areas, with a focus on tree canopy cover to mitigate human susceptibility to extreme heat exposure. The development of LiDAR-based imagery enables communities to visualize the proposed greening over time and across seasons of actual neighborhood streets, thus becoming an effective communications tool in community-engaged research. These tools serve as an example of how visualization strategies can initiate unbiased discussion of proposed interventions, serve as an educational vehicle around the health impacts of climate change, and invite distributional and participatory equity for residents of low-income, nature-poor neighborhoods. Full article

In the face of global warming, mitigating the urban heat island effect has become an important concern worldwide. This study applies the principle of buoyancy ventilation formed by sunlight in double skin façades (DSFs) to improve the thermal environment outside buildings by discharging heat through temperature and pressure differences. The study subject is a 15 × 30 × 40 m residential concrete building situated in a subtropical climate. The lower opening of the DSF faces the outdoor environment; heat is absorbed through this opening from the ground environment and then evacuated up to above the urban canopy layer heat island in order to cool pedestrian environments on the ground. We used numerical simulation to analyze the cooling potential of this DSF in summer daytime conditions. The results show that the DSF can successfully transport heat energy and discharge it above the urban canopy layer. Significant cooling effects were observed in both the horizontal and vertical spaces on the leeward side of the building DSF through the passage of surface heat, thereby reducing the load of indoor air conditioning. Full article

The generation of aerial and unmanned aerial vehicle (UAV)-based 3D point clouds in forests and their subsequent structural analysis, including tree delineation and modeling, pose multiple technical challenges that are partly raised by the calibration of non-metric cameras mounted on UAVs. We present a novel method to deal with this problem for forest structure analysis by photogrammetric 3D modeling, particularly in areas with complex textures and varying levels of tree canopy cover. Our proposed method selects various subsets of a camera’s interior orientation parameters (IOPs), generates a dense point cloud for each, and then synthesizes these models to form a combined model. We hypothesize that this combined model can provide a superior representation of tree structure than a model calibrated with an optimal subset of IOPs alone. The effectiveness of our methodology was evaluated in sites across a semi-arid forest ecosystem, known for their diverse crown structures and varied canopy density due to a traditional pruning method known as pollarding. The results demonstrate that the enhanced model outperformed the standard models by 23% and 37% in both site- and tree-based metrics, respectively, and can therefore be suggested for further applications in forest structural analysis based on consumer-grade UAV data. Full article

The combination of global climate change and the urban heat island effect has given rise to a deterioration in the livability of residential districts within cities, posing challenges to enhancing the health quality of urban environments. Meanwhile, the intensification of daytime changes in temperature and humidity in residential districts has rendered the sensory representation of the urban heat island effect more pronounced. This study selects the residential districts in Fuzhou City as the research case area, which have witnessed a discernible warming trend in recent years, and acquires temperature and humidity parameter data at three time periods (early morning, noon, and evening) to represent the daytime temperature and humidity change phase. Through aerial photography and field research, three types of spatial morphological indicators (buildings I, vegetation II, and the combination of buildings and vegetation II) of residential districts are quantified to represent the three-dimensional spatial form of the case study area. The analysis results show the following: ➀ Residential districts experience two phases of daytime changes in temperature and humidity: a warming and drying phase (WDP) in the morning and a cooling and humidifying phase (CHP) in the afternoon. The characteristics of changes in temperature and humidity show a spatial correlation with each other. ➁ The impact of urban three-dimensional morphology on changes in temperature and humidity in WDP is minor, whereas, in CHP, it is influenced by Class II and Class III indicators. The two types of urban morphology exert a synergistic regulatory effect on changes in temperature and humidity. ➂ Vegetation has a significant regulatory effect on temperature and humidity variations in residential areas through changes in its three-dimensional form. Enlarging the area of individual trees while reducing their canopy volume can restrain the warming and dehumidification of residential districts and promote cooling and humidification. In contrast to only planting trees, a vegetation configuration combining trees, shrubs, and grass can bring a more obvious cooling effect to residential districts. The research results can provide a reference for urban planners in the planning and design of residential areas as well as the optimization and improvement of urban living environments. Full article

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR. Full article

Crop monitoring can promptly reflect the growth status of crops. However, conventional methods of growth monitoring, although simple and direct, have limitations such as destructive sampling, reliance on human experience, and slow detection speed. This study estimated the fresh weight of lettuce (Lactuca sativa L.) in a plant factory with artificial light based on three-dimensional (3D) reconstruction technology. Data from different growth stages of lettuce were collected as the training dataset, while data from different plant forms of lettuce were used as the validation dataset. The partial least squares regression (PLSR) method was utilized for modeling, and K-fold cross-validation was performed to evaluate the model. The testing dataset of this model achieved a coefficient of determination (R²) of 0.9693, with root mean square error (RMSE) and mean absolute error (MAE) values of 3.3599 and 2.5232, respectively. Based on the performance of the validation set, an adaptation was made to develop a fresh weight estimation model for lettuce under far-red light conditions. To simplify the estimation model, reduce estimation costs, enhance estimation efficiency, and improve the lettuce growth monitoring method in plant factories, the plant height and canopy width data of lettuce were extracted to estimate the fresh weight of lettuce in addition. The testing dataset of the new model achieved an R² value of 0.8970, with RMSE and MAE values of 3.1206 and 2.4576. Full article