Search Results (3,070)

Row crops are regarded as a transitional type between continuous and discrete vegetation. Previous studies idealized row crops as periodic hedgerows with rectangular cross-sections. However, these models relied on oversimplified assumptions, failing to capture the intrinsic heterogeneity of canopy or its dynamic evolution over the life cycle. In 2020, we proposed a row crop model with a gradual decrease in leaf area volume density (LAVD) from the center of the row to the edge, partially overcoming these limitations. Building on this previous model, this paper introduces the leaf shape factor proposed by Mõttus et al. into the model. Three control parameters, a leaf width control parameter (β), leaf length control parameter (ψ), and leaf azimuth control parameter (e), are proposed to regulate the spatial distribution of LAVD. Additionally, an empirical exponential function from Watanabe et al. is adopted to describe the leaf zenith angle distribution, enabling the realistic calculation of the G-function and Γ-function in conjunction with leaf azimuth distribution. The LAVD is formulated at three hierarchical scales: individual scale, row scale, and scene scale. The model delivers two key advancements: enabling pronounced spatial heterogeneity and high tunability of the LAVD, and accurately simulating row crops throughout the life cycle, which bridges the row structure and continuous stages of row crops. Radiative transfer simulations are conducted to derive the bidirectional reflectance distribution function (BRDF), which is validated against the discrete anisotropic radiative transfer (DART) model. Comparisons across three growth stages demonstrated good consistency. Furthermore, this paper investigates the sensitivity of the BRDF to three control parameters (β, ψ, and e). The results indicate that changes in three parameters significantly affect the reflectance in the darkspot direction, leading to a maximum error of 22.6%. In carrying out remote sensing applications such as parameter inversion and yield estimation for row crops, the new model is recommended for more accurate BRDF simulations. Full article

The use of LiDAR in estimating tree growth is a current and practical research topic that is important from both an ecological and forest management perspective. The aim of this study was to assess the possibility of applying publicly available LiDAR data to assess the growth of forest stands. This study focused on forests in northern Poland, where pine trees dominate, but deciduous trees such as alders and birches are also partially present. The research used generally available point clouds from airborne LiDAR data from the years 2013 and 2022 with an average density of 4 pts/m² and an accuracy of 0.15–0.25 m. Inventory data were obtained for the same dates. A methodology was developed to determine height increments from these data, and 216 corresponding tree stands were compared. The Pearson correlation coefficient was 0.6, showing a moderate correlation between height increments determined from LiDAR and inventory data. Performing LiDAR measurements during the growing season could minimize errors in determining stand heights and increase the correction between airborne laser scanning data and inventory data. Our experiment confirms that it is possible to improve forest inventory and forest management using airborne LiDAR data. Full article

Although accurate quantification of forest soil CO₂ emissions is critical for improving global carbon cycle models, traditional chamber and gradient methods often underestimate fluxes under windy conditions. Based on long-term field observations in a subtropical maple forest, we quantified the interaction between canopy-level winds and soil pore air pressure fluctuations in regulating vertical CO₂ profiles. The results demonstrate that canopy winds, rather than subcanopy airflow, dominate deep soil CO₂ dynamics, with stronger explanatory power for concentration variability. The observed “wind-pumping effect” operates through soil pressure fluctuations rather than direct wind speed, thereby enhancing advective CO₂ transport. Soil pore air pressure accounted for 33%–48% of CO₂ variation, far exceeding the influence of near-surface winds. These findings highlight that, even in dense forests with negligible understory airflow, canopy turbulence significantly alters soil–atmosphere carbon exchange. We conclude that integrating soil pore air pressure into flux calculation models is essential for reducing underestimation bias and improving the accuracy of forest carbon cycle assessments. Full article

Climate change is reshaping how forests balance carbon uptake and water loss. This review aims to clarify how climate change alters forest carbon–water coupling. Using water-use efficiency (WUE) as a unifying lens, we synthesize mechanisms from leaves to ecosystems and evaluate evidence from studies screened in 2000–2025 spanning eddy covariance, tree-ring isotopes, remote sensing and models. Globally, tree-ring data indicate ~40% intrinsic WUE increases since 1901, yet ecosystem-scale gains are usually <20% after accounting for mesophyll conductance. Under drought, heat and high vapor-pressure deficit, photosynthesis declines more than evapotranspiration, producing partial carbon–water decoupling and lower WUE_e. Responses vary with hydraulic traits, forest type/age and site water balance, with notable tropical data gaps. We identify when WUE gains translate into true resilience: stomatal regulation and canopy structure jointly maintain GPP, prevent hydraulic failure and ensure post-event recovery. Management options include thinning, species/provenance choice, mixed stands and adaptive rotations to balance carbon storage with water yield. Key uncertainties stem from sparse long-term observations, tropical satellite biases and models that overestimate WUE or underplay extremes. We recommend integrating multi-source, multi-scale data with interpretable hybrid models, expanding tropical networks and strengthening MRV frameworks to support risk-aware, climate-smart forestry. Full article

Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana) with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management. Full article

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. To improve robustness, data augmentation was applied, generating six variations on each image and expanding the dataset from 150 to 900 images for training and testing. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as inputs to a Multilayer Perceptron, achieving 96.67% accuracy for overall stress detection, and 95.93% and 94.44% for water and nitrogen stress identification, respectively. A Random Forest model, using only the green band, achieved 92.22% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using a Scale-Invariant Feature Transform descriptor (SIFT) achieved 93.33% for water stress, while RF with RGB bands and canopy cover reached 85.56% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency. Full article

Landslides pose significant threats to sustainable development by causing infrastructure damage and ecosystem degradation, particularly in densely vegetated mountainous regions. To support sustainable land-use planning and disaster-resilient development, this study integrates three advanced vegetation metrics—Vegetation Formation Group (VFG), aboveground biomass (AGB), and forest canopy height (FCH)—into landslide susceptibility modeling. Using Yuanling County, a subtropical vegetated region in China, as a case study, we developed a novel ensemble model, AdaBoost-CB (AdaBoost-CatBoost), and compared its performance with mainstream machine learning models including RF, XGBoost, and LGB. The results show that AdaBoost-CB achieved the highest Area Under the Curve (AUC) value of 0.915. Furthermore, it yielded the highest landslide frequency ratio of 6.51 in the very-high-susceptibility zones. The dominant landslide-controlling factors—NDVI, elevation, slope gradient, slope aspect, and rainfall—were consistently identified across six models. These findings provide a scientific basis for sustainable land-use planning and disaster risk reduction strategies, contributing directly to the goals of sustainable development in vulnerable mountainous regions. Full article

Natural elements such as vegetation, water bodies, and sky, together with artificial elements including buildings and paved surfaces, constitute the core of urban visual environments. Their perception at the pedestrian level not only influences city image but also contributes to residents’ well-being and spatial experience. This study develops a hybrid 3D visibility assessment framework that integrates a city-scale LOD1 solid model with high-resolution mobile LiDAR point clouds to quantify five visual exposure indicators. The case study area is Yueya Lake Park in Nanjing, where a voxel-based line-of-sight sampling approach simulated eye-level visibility at 1.6 m along the southern lakeside promenade. Sixteen viewpoints were selected at 50 m intervals to capture spatial variations in visual exposure. Comparative analysis between the solid model (excluding vegetation) and the hybrid model (including vegetation) revealed that vegetation significantly reshaped the pedestrian visual field by reducing the dominance of sky and buildings, enhancing near-field greenery, and reframing water views. Artificial elements such as buildings and ground showed decreased exposure in the hybrid model, reflecting vegetation’s masking effect. The calculation efficiency remains a limitation in this study. Overall, the study demonstrates that integrating natural and artificial elements provides a more realistic and nuanced assessment of pedestrian visual perception, offering valuable support for sustainable landscape planning, canopy management, and the equitable design of urban public spaces. Full article

The use of UAVs to detect litchi in natural environments is imperative for rapid litchi yield estimation and automated harvesting systems. However, UAV-based lychee fruit detection bottlenecks arise from complex canopy architecture and leaf occlusion. This study proposed a collaborative optimization strategy integrating agronomic technique with deep learning. Three leaf thinning intensities (0, 6, and 12 compound leaves) were applied at the early stage of fruit to systematically evaluate their effects on fruit growth, canopy structure, and detection performance. Results indicated that moderate defoliation (six leaves) significantly enhanced canopy openness and light penetration without adversely impacting on yield and fruit quality. Subsequent UAV-based detection under moderate versus no defoliation treatment revealed that the YOLOv8-based model achieved significant performance gains: mean average precision (mAP) increased from 0.818 to 0.884, and the F1-score improved from 0.796 to 0.842. The study contributes a novel collaborative optimization strategy that effectively mitigates occlusion issues in fruit detection. This approach demonstrates that agronomic techniques can be strategically used to enhance AI perception, offering a significant step forward in the integration of agricultural machinery and agronomy for intelligent orchard systems. Full article

Dollar spot is a severe and widespread turfgrass disease. Ultraviolet-C (UV-C) light treatment offers a promising management strategy, and its integration into autonomous mowers could reduce fungicide use, promoting sustainable and efficient turfgrass management. To ensure effectiveness and optimize intervention timing, monitoring is essential and hyperspectral sensing could represent a valuable resource. This study aimed to develop an innovative approach for the early detection and integrated management of dollar spot in bermudagrass by evaluating (i) the efficacy of an autonomous mower equipped with UV-C lamps in mitigating infections, and (ii) the potential of full-range hyperspectral sensing (350–2500 nm) for disease detection and monitoring. The autonomous mower enabled UV-C treatment with a field capacity of 0.04 ha h⁻¹, requiring 1.3 machines to treat 1 ha day⁻¹, and a primary energy consumption of 55.06 kWh ha⁻¹ for a complete weekly treatment. Full-range canopy hyperspectral data (400–2400 nm) enabled rapid, non-destructive field detection. Permutational multivariate analysis of variance (PERMANOVA) detected significant effects of Clarireedia jacksonii (Cj; dollar spot pathogen) and the Cj × UV-C interaction. Partial least-squares discriminant analysis (PLS-DA) separated Cj+/UV+ and Cj+/UV− plots (Accuracy validation ≈ 0.73; K ≈ 0.69). Investigated spectral indices confirmed Cj × UV-C interactions. Future research should explore how to optimize UV-C application regimes, improve system scalability, and enhance the robustness of hyperspectral models across diverse turfgrass genotypes, growth stages, and environmental conditions. Full article

Many Global-South cities lack dense monitoring and suffer persistent cloud cover, hampering fine-scale trend detection. This study evaluates the potential of annual multi-sensor satellite embeddings from the AlphaEarth Foundations model in Google Earth Engine to predict and map major air pollutants in Quito, Ecuador, between 2017 and 2024. The 64-dimensional embeddings integrate Sentinel-1 radar, Sentinel-2 optical imagery, Landsat surface reflectance, ERA5-Land climate variables, GRACE terrestrial water storage, and GEDI canopy structure into a compact representation of surface and climatic conditions. Annual median concentrations of NO₂, SO₂, PM_2.5, CO, and O₃ from the Red Metropolitana de Monitoreo Atmosférico de Quito (REEMAQ) were paired with collocated embeddings and modeled using five machine learning algorithms. Support Vector Regression achieved the highest accuracy for NO₂ and SO₂ (R² = 0.71 for both), capturing fine-scale spatial patterns and multi-year changes, including COVID-19 lockdown-related reductions. PM_2.5 and CO were predicted with moderate accuracy, while O₃ remained challenging due to its short-term photochemical and meteorological drivers and the mismatch with annual aggregation. SHAP analysis revealed that a small subset of embedding bands dominated predictions for NO₂ and SO₂. The approach provides a scalable and transferable framework for high-resolution urban air quality mapping in data-scarce environments, supporting long-term monitoring, hotspot detection, and evidence-based policy interventions. Full article

This study provides the first comprehensive, field-inventory-based assessment of urban ecosystem services within a Russian university campus, focusing on the woody vegetation of the Lobachevsky State University of Nizhny Novgorod. Utilizing a detailed field tree inventory combined with the i-Tree framework (including i-Tree Eco, i-Tree Canopy, UFORE, and i-Tree Hydro models), we quantified the campus’s capacity for carbon storage and sequestration, air pollutant removal, and stormwater runoff mitigation. The campus green infrastructure, comprising 1887 trees across 32 species with a density of 145.5 stems per hectare, demonstrated significant ecological value. Results show a carbon storage density of 26.61 t C ha⁻¹ and an annual gross carbon sequestration of 11.43 tons. Furthermore, the campus trees removed 1213.7 kg of air pollutants annually (a deposition rate of 9.35 g m⁻²), with ozone, particulate matter, and sulfur dioxide showing the highest deposition. The campus also retained 956.1 m³ of stormwater annually. These findings, particularly the high carbon sequestration rates, are attributed to the dominance of relatively young, fast-growing tree species. This research establishes a critical baseline for understanding urban ecosystem services in a previously under-researched geographical context. The detailed, empirical data offers crucial insights for urban planners and policymakers in Nizhny Novgorod and beyond, advocating for the strategic integration of ecosystem services assessments into campus planning and broader urban green infrastructure development across Russian cities. The study underscores the significant role of university campuses as vital components of urban green infrastructure, contributing substantially to environmental sustainability and human well-being. Full article

Oak forests provide critical ecosystem services, but are being increasingly exposed to climate variability, drought, and insect outbreaks that threaten their long-term resilience. This study aims to integrate structural canopy indicators with climate-derived indices to detect early-warning signals of decline in temperate oak stands. We monitored eight Forest Management Units in western Romania between 2017 and 2021, combining field-based assessments of crown morphology, vitality traits, defoliation, and epicormic shoot frequency with hydroclimatic indices such as the Forest Aridity Index. Results revealed strong spatial and temporal variability: several stands showed advanced canopy deterioration characterized by increased defoliation, dead branches, and epicormic resprouting, while others maintained stable conditions, suggesting resilience and suitability as reference sites. Insect defoliators, particularly Geometridae, contributed additional stress, but generally at subcritical levels. By synthesizing these metrics into conceptual models and a risk scorecard, we identified the causal pathways linking climatic anomalies and biotic stressors to structural decline. The findings demonstrate that combining structural and climatic indicators offers a transferable framework for forest health monitoring, providing robust early-warning tools to guide adaptive silviculture and resilience-based management. Beyond the Romanian context, this integrative approach supports sustainability goals by strengthening conservation strategies for temperate forests under global change. Full article

Hurricane Maria crossed Puerto Rico with winds as high as 250 km/h, resulting in widespread damages and loss of weather station data, thus limiting direct weather measurements of wind variability. Here, we identified more than 155 million trees to estimate the distribution of wind speed over 9000 km² of land from island-wide LiDAR point clouds collected before and after the hurricane. The point clouds were classified and rasterized into the canopy height model to perform individual tree identification and perform change detection analysis. Individual trees’ stem diameter at breast height were estimated using a function between delineated crown and extracted canopy height, validated using the records from Puerto Rico’s Forest Inventory 2003. The results indicate that approximately 35.7% of trees broke at the stem (below the canopy center) and 28.5% above the canopy center. Furthermore, we back-calculated the critical wind speed, or the minimum speed to cause breakage, at individual tree level this was performed by applying a mechanical model using the estimated diameter at breast height, the extrapolated breakage height, and pre-Hurricane Maria canopy height. Individual trees were then aggregated at 115 km² cells to summarize the critical wind speed distribution of each cell, based on the percentage of stem breakage. A vertical wind profile analysis was then applied to derive the hurricane wind distribution using the mean hourly wind speed 10 m above the canopy center. The estimated wind speed ranges from 250 km/h in the southeast at the landfall to 100 km/h in the southwest parts of the islands. Comparison of the modeled wind speed with the wind gust readings at the few remaining NOAA stations support the use of tree breakages to model the distribution of hurricane wind speed when ground readings are sparse. Full article

Photosynthesis drives terrestrial carbon uptake, yet its diurnal dynamics remain poorly resolved due to the sparse availability of flux towers and the coarse spatial resolution of current satellite observations. Solar-induced chlorophyll fluorescence (SIF) provides a direct proxy of carbon uptake, but the existing global monthly mean diurnal total canopy SIF product is limited to 0.5° resolution. We developed a random forest-based downscaling framework to generate a global monthly mean hourly SIF dataset (SIF_{total_01}) at 0.1° resolution for 2000–2022. When validated against eddy-covariance-based gross primary productivity (GPP) data, SIF_{total_01} showed a strong correlation (R² = 0.81) and reduced root mean square error when compared with SIF_total (2.89→2.8 mW m⁻² nm⁻¹), providing notable gains in broadleaved forests (R²: 0.80→0.88 with a root mean square error of 2.32→1.81 mW m⁻² nm⁻¹). The SIF_{total_01} dataset revealed a distinct double-peak in the SIF_{total_01}–GPP slope, reflecting widespread afternoon depression of photosynthesis, with normalized slopes declining from 1.03 in the morning to 0.98 in the afternoon. Soil moisture modulated this depression pattern, as the afternoon–morning SIF_{total_01} difference increased from 0.02 to 0.10 mW m⁻² nm⁻¹ across dry to wet years. Under water stress, SIF yield was more sensitive than absorbed photosynthetic active radiation (APAR), with a doubling of the afternoon–morning SIF yield difference (0.5→1.1 10⁻³ nm⁻¹), while the afternoon–morning APAR difference showed a smaller change (−300→−180 kJ m⁻²). This study improves the potential for bridging observational gaps and constraining models offer valuable insights for fundamental and applied research in the analysis of ecosystem productivity, climate-carbon feedbacks, and vegetation stress. Full article