Search Results (500)

Tropical forests face increasing threats and are often replaced by secondary forests that regenerate after disturbances. In the Atlantic Forest, this creates fragments of different successional stages. The aim of this study is to understand how soil nutrients and light availability gradients influence the species composition and structure of trees and regenerating strata in remnants of lowland rainforest. We sampled 15 plots for the tree stratum (DBH ≥ 5 cm) and 45 units for the regenerating stratum (height ≥ 50 cm, DBH < 5 cm), obtaining phytosociological, entropy and equitability data for both strata. Canopy openness was assessed with hemispherical photos and soil samples were homogenized. To analyze the interactions between the vegetation of the tree layer and the environmental variables, we carried out three principal component analyses and two redundancy analyses and applied a linear model. The young fragments showed good recovery, significant species diversity, and positive successional changes, while the older ones had higher species richness and were in an advanced stage of succession. In addition, younger forests are associated with sandy, nutrient-poor soils and greater exposure to light, while mature forests have more fertile soils, display a greater diversity of dispersal strategies, are rich in soil clay, and have less light availability. Mature forests support biodiversity and regeneration better than secondary forests, highlighting the importance of preserving mature fragments and monitoring secondary ones to sustain tropical biodiversity. Full article

In recent years, the traditional orchard sprayer has had problems, such as waste of liquid agrochemicals, low target coverage, high manual dependence, and environmental pollution. In this study, an automatic swing-arm sprayer for orchards was developed based on the standardized pear orchard in Pinggu, Beijing. Firstly, the structural principles of a crawler-type traveling system and swing-arm sprayer were simulated using finite element software design. The combination of a diffuse reflection photoelectric sensor and Arduino single-chip microcomputer was used to realize real-time detection and dynamic spray control in the pear canopy, and the sensor delay compensation algorithm was used to optimize target recognition accuracy and improve the utilization rate of liquid agrochemicals. Through the integration of innovative structural design and intelligent control technology, a vertical droplet distribution test was carried out, and the optimal working distance of the spray was determined to be 1 m; the nozzle angle for the upper layer was 45°, that for the lower layer was 15°, and the optimal speed of the swing-arm motor was 75 r/min. Finally, a particle size test and field test of the orchard sprayer were completed, and it was concluded that the swing-arm mode increased the pear tree canopy droplet coverage by 74%, the overall droplet density by 21.4%, and the deposition amount by 23% compared with the non-swing-arm mode, which verified the practicability and reliability of the swing-arm spray and achieved the goal of on-demand pesticide application in pear orchards. Full article

Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. Aiming at overcoming the bottleneck issues of canopy gap identification in mountainous forest regions, we constructed a multi-task deep learning model (ES-Net) integrating an edge–semantic collaborative perception mechanism. First, a refined sample library containing multi-scale interference features was constructed, which included 2808 annotated UAV images. Based on this, a dual-branch feature interaction architecture was designed. A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. Results from selected areas in Yunnan Province (China) demonstrate that ES-Net outperforms U-Net, boosting the Intersection over Union (IoU) by 0.86% (95.41% vs. 94.55%), improving the edge coverage rate by 3.14% (85.32% vs. 82.18%), and reducing the Hausdorff Distance by 38.6% (28.26 pixels vs. 46.02 pixels). Ablation studies further verify that the synergy between SSM and EDM yields a 13.0% IoU gain over the baseline, highlighting the effectiveness of joint semantic–edge optimization. This study provides a terrain-adaptive intelligent interpretation method for forest disturbance monitoring and holds significant practical value for advancing smart forestry construction and ecosystem sustainable management. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

In a hypothetical climate change scenario, zoysiagrass species could be a good choice for turfgrass areas due to their adaptation to heat conditions and the great variability in species and cultivars. Knowledge of the root system’s characteristics is paramount for predicting cultivar adaptation to different heat–drought scenarios and therefore for designing proper turfgrass management programs, especially irrigation. A field experiment was conducted in the Mediterranean environment of Valencia (Spain) to study the root weight density (RWD), root length density (RLD), and average root diameter (RDI) at three different soil depths (0–5, 5–15, and 15–30 cm) of five new zoysiagrass genotypes (Zoysia matrella (L.) Merr. cultivars, Zoysia japonica Steud., and their hybrid), relating these parameters to the performance of these cultivars during their establishment. All the tested cultivars had a higher RWD and RLD in the upper soil layer (0–5 cm), while the RDI was higher in the lowest layer of the sampled soil profile (0.269 mm compared with 0.249 mm and 0.241 mm in the upper layers). All the tested cultivars showed the same RWD and RLD, while the Zoysia matrella cultivar ZG18003 showed a higher RDI value (0.2683 mm) than those for the Z. japonica cultivar (0.2369 mm) and the hybrid (0.2394 mm). This last finding could have influenced its more rapid establishment, although it was not linked to its NDVI values during autumn. In conclusion, different morphological root characteristics were detected among new zoysiagrass genotypes and soil depths, which could have affected their canopy performance, and they are expected to affect irrigation management in a possible future drought scenario. Full article

The Amazon rainforest hosts a diverse array of forest types, including those where Brazil nut (Bertholletia excelsa) occurs, which plays a crucial ecological and economic role. The Brazil nut is the second most important non-timber forest product in the Amazon, a symbol of development and sustainable use in the region, promoting the conservation of the standing forest. Understanding the vertical structure of these forests is essential to assess their ecological complexity and inform sustainable management strategies. We used terrestrial laser scanning (TLS) to assess the vertical structure of Amazonian forests with the occurrence of Brazil nut (Bertholletia excelsa) at regional (Amazonas, Mato Grosso, Pará, and Amapá) and local scales (forest typologies in Amapá). TLS allowed high-resolution three-dimensional characterization of canopy layers, enabling the extraction of structural metrics such as canopy height, rugosity, and leaf area index (LAI). These metrics were analyzed to quantify the forest vertical complexity and compare structural variability across spatial scales. These findings demonstrate the utility of TLS as a precise tool for quantifying forest structure and highlight the importance of integrating structural data in conservation planning and forest monitoring initiatives involving B. excelsa. Full article

Understanding temperature variations within the complex urban canopy layer (UCL) is challenging due to limitations and discrepancies between temperature measurements taken in urban canyons and on rooftops. The key question is how much these measurements differ and what factors contribute to these differences. According to the guidance by the World Meteorological Organization (WMO), rooftop observations are not encouraged for urban monitoring, due to potentially anomalous microclimatic conditions, whereas measurements within urban canyons are recommended. This is particularly relevant given the increasing number of rooftop sensors deployed through citizen science, raising questions about the representativeness of such data. This study aimed to address this knowledge gap by comparing temperatures within the UCL using two sensors: one located on a rooftop, and the other positioned within the canyon. The temperature difference between these two nearby locations followed a clear diurnal cycle, peaking at over 1 °C between 12:00 and 16:00 local time, with the canyon warmer than the rooftop. This daytime warming was primarily driven by solar radiation and, to a lesser extent, by wind speed, but only under clear-sky conditions. During the rest of the day, the temperature difference remained negligible. Full article

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article

(1) Background: Understanding how forest management practices regulate hydrological cycles is critical for sustainable water resource management and addressing global water crises. However, the effects of light-felling (selective thinning) on hydrological processes in temperate mixed forests remain poorly understood. This study comprehensively evaluated the impacts of light-felling intensity levels on three hydrological layers (canopy, litter, and soil) in mid-rotation Korean pine (Pinus koraiensis) forests managed under the “planting conifer and preserving broadleaved trees” (PCPBT) system on Changbai Mountain, China. (2) Methods: Hydrological processes—including canopy interception, throughfall, stemflow, litter interception, soil water absorption, runoff, and evapotranspiration—were measured across five light-felling intensity levels (control, low, medium, heavy, and clear-cutting) during the growing season. The stand structure and precipitation characteristics were analyzed to elucidate the driving mechanisms. (3) Results: (1) Low and heavy light-felling significantly increased the canopy interception by 18.9%~57.0% (p < 0.05), while medium-intensity light-felling reduced it by 20.6%. The throughfall was significantly decreased 10.7% at low intensity but increased 5.3% at medium intensity. The stemflow rates declined by 15.8%~42.7% across all treatments. (2) The litter interception was reduced by 22.1% under heavy-intensity light-felling (p < 0.05). (3) The soil runoff rates decreased by 56.3%, 16.1%, and 6.5% under the low, heavy, and clear-cutting intensity levels, respectively, although increased by 27.1% under medium-intensity activity (p < 0.05). (4) The monthly hydrological dynamics shifted from bimodal (control) to unimodal patterns under most treatments. (5) The canopy processes were primarily driven by precipitation, while litter interception was influenced by throughfall and tree diversity. The soil processes correlated strongly with throughfall. (4) Conclusions: Low and heavy light-felling led to enhanced canopy interception and reduced soil runoff and mitigated flood risks, whereas medium-intensity light-felling supports water supply during droughts by increasing the throughfall and runoff. These findings provide critical insights for balancing carbon sequestration and hydrological regulation in forest management. Full article

Parks play a crucial role in mitigating urban heat island effects, a key challenge for urban sustainability. Park cooling intensity (PCI) mechanisms across varying canopy-layer urban heat island (CUHI) gradients remain underexplored, particularly regarding interactions with meteorological, topographical, and socio-economic factors. According to the urban-suburban air temperature difference, this study classified the city into non-, weak, and strong CUHI regions. We integrated causal inference, machine learning and a geographical detector (Geodetector) to model and interpret PCI dynamics across CUHI gradients. The results reveal that surrounding impervious surface coverage is a universal driver of PCI by enhancing thermal contrast at park boundaries. However, the dominant drivers of PCI varied significantly across CUHI gradients. In non-CUHI regions, surrounding imperviousness dominated PCI and exhibited bilaterally enhanced interaction with intra-park patch density. Weak CUHI regions relied on intra-park green coverage with nonlinear synergies between water body proportion and park area. Strong CUHI regions involved systemic urban fabric influences mediated by surrounding imperviousness, evidenced by a validated causal network. Crucially, causal inference reduces model complexity by decreasing predictor counts by 79%, 25% and 71% in non-, weak and strong CUHI regions, respectively, while maintaining comparable accuracy to full-factor models. This outcome demonstrates the efficacy of causal inference in eliminating collinear metrics and spurious correlations from traditional feature selection, ensuring retained predictors reside within causal pathways and support process-based interpretability. Our study highlights the need for context-adaptive cooling strategies and underscores the value of integrating causal–statistical approaches. This framework provides actionable insights for designing climate-resilient blue–green spaces, advancing urban sustainability goals. Future research should prioritize translating causal diagnostics into scalable strategies for sustainable urban planning. Full article

Conventional and conservation tillage systems are applied differently in agricultural practices. Considering the current trends, the spread of tillage before denser crop cultures can also be observed in the case of other crops. These systems alter microclimatic conditions in the cultivated layer, soil surface, and crop canopy by physically modifying the soil environment. This greatly influences the occurrence and success of microbiome, plant, and animal organisms. At the same time, it has a decisive influence on the occurrence and damage caused to crops by harmful microorganisms and herbivorous pests. This review investigates how tillage systems influence the emergence and mass propagation of herbivores, based on their soil dependency. The impact of soil as a medium on pests will be analysed by grouping them according to their soil attachment and providing cultivation and agro-zoological examples. We highlight that selecting a tillage system should consider soil-dwelling pest ecology, as this knowledge is critical for optimizing both soil health and crop protection. 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

This study collected site and spatial morphological data from 63 typical aging community activity spaces and extracted 12 spatial types through statistical analysis. A parametric modeling tool was used to generate spatial models. Based on clearly defined design variables and constraints, the NSGA-II multi-objective optimization algorithm was applied to minimize summer thermal discomfort, maximize winter thermal comfort, and maximize annual average sunlight duration, resulting in 342 Pareto optimal solutions. The study first explored the linear relationships between spatial morphology and environmental performance using the Spearman method. It then integrated ensemble learning and the interpretable machine learning model SHAP to reveal nonlinear relationships and boundary effects. The results of the two methods complemented and reinforced each other. Based on a comparison of these two approaches, morphological indicators showing significant differences were selected for attribution and sensitivity analyses, clarifying the mechanisms by which spatial morphological parameters influence environmental performance and identifying their critical thresholds. Key findings include the following: (1) the UTCI-S exhibits significant negative linear correlations with the open space ratio (OSR) and spatial crowding density (SCD); the UTCI-W shows negative linear correlations with canopy coverage (CVH) and wind speed (WS); and a positive linear correlation exists between the sky view factor (SVF) and AV.SH. (2) Boundary effects and threshold intervals of critical morphological parameters were identified as follows. The open space ratio should be controlled to 10–15%, the shrub–tree layer coverage to 0.013–0.0165%, and the average building height to 3.1–3.8 m. (3) Spatial layout principles demonstrate that placing fully enclosed spaces (E-2) and semi-enclosed spaces (S-1/S-3) on the northern side, as well as semi-enclosed spaces (S-1/S-2) and circulation spaces (C-3) on the southern side, significantly enhance microclimatic performance. These findings provide quantitative guidelines for community space design in cold regions and offer data support for creating outdoor environments that meet the comfort needs of the elderly. Full article

Leaf mass per area (LMA) represents the allocation of carbon resources per unit leaf area, which is closely related to the photosynthetic capacity of tree leaves. Clarifying the distribution features of LMA is very useful in understanding nutrient and energy transmission and photosynthetic capacity in the canopy. To this aim, the leaf samples of varied forest types were collected, and LMA and related spectral data were measured. The Partial Least Squares (PLS), Linear Mixed Models (LMM), Support Vector Machine Regression (SVM), and Random Forest (RF) methods were used to establish a new model of three-dimension LMA prediction by using vegetation index, DBH, and the vertical and horizontal position of leaves in this study. The results found that the LMA varies significantly with the change in the spatial position of the leaves and horizontal distance to the tree trunk. Statistically speaking, changes in LMA were not significantly related to the direction where the leaves were located. The best model of 3D LMA estimation was RF with a 10-fold R² value of 0.939. Compared to the RF model, the maximum and minimum of R² of 10-fold testing of other models increased by 23.75% and 55.87%. The results indicated that RF has a strong generalization ability and can predict the LMA distribution in 3D with a high accuracy. This study showed a reference for LMA 3D feature distribution and is helpful in clarifying the photosynthetic capacity of the canopy. Full article