Search Results (345)

Accurate estimation of winter wheat canopy nitrogen concentration (CNC) supports crop diagnosis and precision nitrogen management, yet the relative value and redundancy of multispectral, structural, and chlorophyll-related variables remain unclear in small dat asets. Using 155 multi-temporal observations retained from the 2016–2017 and 2017–2018 growing seasons, seven prespecified feature combinations were evaluated with four traditional regression models under five-fold repeated season-stratified cross-validation. A stricter season-balanced, unit-level grouped five-fold cross-validation was added to prevent observations from the same field from occurring in both training and test partitions. Two fully connected neural networks were additionally assessed for the selected compact module. Spectral-only combinations yielded negative mean R² values, whereas LAI, vegetation cover, and chlorophyll content achieved a mean R² of 0.684. Combining these variables with four raw multispectral bands produced M5, which achieved mean RMSE, R², and RPD values of 0.328, 0.782, and 2.147, respectively, with 36.4% fewer variables than the full module. RF provided the best numerical performance under repeated sample-level cross-validation (R² = 0.796 ± 0.007), whereas M5 retained R² values of 0.736–0.778 under unit-grouped validation, with SVR performing best in that stricter setting. Parameter-removal analysis showed the largest incremental contribution for vegetation cover and limited additional value from LAI. Bidirectional cross-season validation remained direction- and model-dependent. Overall, controlled low-redundancy feature fusion was more beneficial than increased model complexity, while field-level and cross-season tests indicated that the strong within-dataset results should not be interpreted as broad generalization capability. Full article

Fine-scale identification of Spartina alterniflora (S. alterniflora) is essential for coastal wetland conservation. However, in tidal-flat environments, lodged S. alterniflora often occurs together with upright S. alterniflora and native vegetation. The two-dimensional spectral features of S. alterniflora are easily affected by senescence, canopy posture, tidal stage and mixed pixels, leading to unstable classification. The integration of UAV multispectral imagery and LiDAR data can effectively address this problem. The study focused on Shangsha Island within Jiuduansha Wetland in the Yangtze Estuary and constructed multidimensional spectral–structural features by integrating the two data sources. The separability of upright S. alterniflora, lodged S. alterniflora, Phragmites australis (P. australis) and Scirpus mariqueter (S. mariqueter) was characterized using point-cloud elevation distributions, vertical organization and canopy density. The results showed that P. australis had a multilayered point-cloud structure with broad vertical extent, S. mariqueter showed a compact and sparse structure, and S. alterniflora was characterized by a continuous and dense single-layer point-cloud structure. Lodged S. alterniflora further showed a more concentrated, single-layered point-cloud structure and stronger grass-layer continuity. Multisource classification achieved an overall accuracy of 98.15% and a Kappa coefficient of 0.97. In the lodging-area comparison experiment, point-cloud fusion increased overall accuracy from 95.18% to 97.99% and Kappa from 0.89 to 0.95, improving boundary continuity and discrimination stability. Experimental results demonstrate that the fusion of UAV multispectral imagery and LiDAR data can improve the identification of lodged S. alterniflora in complex tidal-flat environments. Accurate identification and spatial delineation of S. alterniflora can help reduce field-survey effort and associated costs while supporting more targeted and efficient removal operations. Full article

Lodging limits oat yield, forage value, silage quality, and mechanized harvesting, but resistance cannot be explained by plant height alone. This structured narrative review synthesizes literature available through 1 June 2026 using a tiered evidence framework. After duplicate removal, the review included 119 unique references in total. These comprised 12 Level I studies with direct oat evidence, 30 Level II oat-focused studies requiring further validation, and 77 Level III comparative sources. Overall, 42 sources (35.3%) focused on oat or Avena. The strongest direct evidence supports roles for basal-internode geometry and mechanics, cell-wall composition, canopy architecture, planting density, nitrogen management, and genotype-specific responses to plant growth regulators. However, the direction and magnitude of these effects depend strongly on genotype × management × environment interactions. Evidence for root lodging remains limited because root traits are rarely examined together with direct anchorage measurements and soil mechanical properties. Image-based phenotyping also lacks independently validated, multi-environment oat datasets for model development. Genome-wide association studies and multi-omics analyses have identified candidate loci, genes, and pathways, but independent, homoeolog-aware validation is still needed. We recommend standardized reporting of lodging type, field severity, basal-internode mechanics, root–soil conditions, and imaging protocols. We also propose resource-matched deployment and staged priorities, from phenotyping harmonization to multi-environment validation and causal analysis of belowground mechanisms. Full article

Tree-related microhabitats act as key links between forest structure and biodiversity, but these pathways may be altered in urban forests. In Mediterranean urban forests, we found that deadwood-related processes were weaker than hypothesized, while structural diversity remained the primary driver of microhabitat richness. We evaluated whether hierarchical pathways linking structure, diversity, and richness are maintained under urban conditions. A Structural Equation Model was applied to 180 plots across three Italian cities (Florence, Rome, Campobasso), assessing relationships among tree and deadwood volume, diversity, richness, and landscape variables (forest area, patch shape complexity, canopy cover). A consistent bottom-up hierarchy emerged, in which structural heterogeneity was associated with microhabitat richness through diversity-mediated pathways. However, deadwood-related pathways were positive but less consistent than living-tree-related pathways. Landscape context acted mainly as a secondary filter, although canopy cover positively influenced some richness components. Overall, urban forest microhabitat richness depended more on structural heterogeneity than biomass; patterns are consistent with the hypothesis that management, including deadwood removal, may constrain habitat continuity. Maintaining structurally diverse stands, retaining deadwood where compatible with public safety, and integrating microhabitat monitoring into spatial planning are essential to support biodiversity and ecosystem services in urban forests. Full article

Conventional methods of plant distance and volume measurements are limited by low efficiency, limited spatial coverage, and high measurement error. LiDAR and RGB-D imaging offer cost-effective, precise, and non-destructive techniques for plant distance and volume measurements. This study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging. The sensors were mounted on a 1.6 kW electric field scouting platform (EFSP) for data collection. Point cloud (PCD) data were collected using LiDAR, whereas data processing, visualization, and measurements were done using commercial software and open-source programming scripts. A total of 20 cabbage plants were analyzed. LiDAR data processing included data frame screening, outlier removal, denoising, voxelization, and generation of 3D PCD density maps. Depth image processing included importing raw data and metadata shaping using intrinsic camera parameters, visualization, extraction of depth points, and pixel-level measurements of distances and volume. RGB image processing involved image conversion, segmentation, normalization, binary masking, mask cleaning, region extraction of cabbages, separation of ROI and preparation of contours, Delaunay triangulation and convex hull preparation, ROI overlay, bounding box preparation, sharing boundary between two boxes, conversion to pixel distances, and for visualization, plant height, volume measurements, and center to center distance measurement for measuring the plant distance. LiDAR demonstrated higher measurement accuracy for cabbage plant height, circumferential volume (geometric canopy volume), and plant distance, followed by RGB-D imaging, while RGB imagery showed comparatively lower performance under the study field conditions. Overall, LiDAR and RGB-D imaging provided reliable and non-destructive approaches for cabbage geometric characterization under field conditions, although accurately capturing complex plant geometry remains challenging. Positive and negative values of bias represent the over- and under-estimated results, respectively. Future studies should include larger and more diverse plant datasets exhibiting diversified size, shape, and geometric structure to further improve the robustness and general applicability of the proposed sensing approaches. Full article

Local climate zones (LCZs) provide a robust framework for understanding Urban Heat Island dynamics and for supporting climate-sensitive urban planning. Although widely adopted since their introduction in 2012, LCZ mapping remains constrained by urban morphology description and spectral separability among built-up classes. This study aims to strengthen the methodology to produce a high-resolution LCZ map by integrating multispectral (Sentinel-2, 10 m spatial resolution) and hyperspectral data (PRISMA, 30 m spatial resolution) with a suite of urban canopy parameters that describe the morphological and surface characteristics of the urban fabric, using a machine learning classification approach at finer spatial resolutions. The proposed approach is tested in the urban area of Bologna, Italy. The digitization of representative training and validation sites—which is one of the key challenges in accurate LCZ mapping, especially for spectrally heterogeneous classes—was conducted in a GIS environment by visual interpretation of high-resolution imagery with the aid of the Technical Map of the Municipality of Bologna. With the aim of strengthening the methodology, the present work tests different outlier-removal techniques on the training data and evaluates their impact on LCZ mapping performance. Finally, the Random Forest classifier was selected, and the workflow was implemented in a Python environment using the scikit-learn library. The results show that the classification achieved overall accuracy values of 0.79 using Sentinel-2 and 0.82 using PRISMA. Overall, the results show that urban morphology parameters are among the most important features. Training-sample refinement helped interpret the effect of sample heterogeneity, but LCZ classification performance was ultimately controlled by feature discriminative power, spatial resolution, and the intrinsic separability of each class. Full article

Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C₄ carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment. Full article

LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture. Full article

Here, we present a single-operator push-cart platform equipped with a 16-beam LiDAR. A push-button interface controls data acquisition, and the data processing pipeline removes ground points, filters noise, performs 5-cm voxelization, and produces plot-level canopy metrics. We validated biomass estimation in hairy vetch (Vicia villosa) and corn (Zea mays) leaf- and whole-plant thinning experiments. In vetch, voxelized estimation of plant volume correlated strongly with destructively measured biomass (r² = 0.88), showing that the multi-beam LiDAR can produce biomass estimates comparable to previously reported methods. In corn, comparisons of perpendicular (0°) and multi-angle LiDAR beams showed significantly greater voxel counts in the upper canopy when angled beams were used (beam angle × height interaction, p < 0.001), demonstrating that multi-beam scanning provides greater penetration into the upper canopy than a single perpendicular scan plane. We also extended the suite of LiDAR-derived traits to include apparent leaf area index (LAI), mean tilt angle (MTA), persistent homology-based stand density, and plot-bounded foliage area density (FAD). The persistent homology algorithm distinguished between leaf-removal and plant-removal treatments (removal type × removal amount, p = 0.0039). LiDAR-derived LAI has been used to estimate canopy leaf area, but gap-fraction approaches do not fully exploit the ability of LiDAR to resolve distance. Plot-bounded FAD used ray length and interception distance within defined plot volumes and was more sensitive to plot-level treatments than apparent LAI or MTA, detecting differences associated with both the removal amount and removal type. These results show that a robust, portable, multi-beam LiDAR cart can reproduce plot-level canopy measurements and improve trait especially in research-sized plots. Full article

Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus (CLas) and vectored by Asian citrus psyllid (Diaphorina citri), remains a major constraint to sustainable citrus production. In Florida, individual protective covers (IPCs) have been adopted as an effective psyllid exclusion tool by shielding young trees from this vector of the phloem-dwelling bacterium CLas. Brassinosteroids (BRs), a class of plant steroid hormones, are being explored as a treatment to mitigate HLB and are approved for commercial use in the state. We investigated the effect of IPCs combined with homobrassinolide (HBr) applied as a foliar spray on CLas titer, canopy volume, tree growth, yield, fruit quality, and defense-related gene expression of the salicylic acid (SA) pathways in ‘Tango’ mandarin grafted on sour orange (SO) or US-942 rootstocks. After being covered with IPCs in the field for three years, trees were subjected to monthly foliar application of HBr upon IPC removal. The experiment included four treatment groups: trees with IPC and HBr spray (IPC HBr+), IPC without HBr (IPC HBr-), no-IPC with HBr (no-IPC HBr+), and no-IPC without HBr (no-IPC HBr-). IPCs effectively delayed bacterial infection for six to nine months after IPC removal, maintaining higher cycle threshold (Ct) values (lower CLas titers) than in no-IPC trees, confirming the protective effect of IPCs against early CLas colonization. The combination of IPCs and HBr spray significantly enhanced canopy volume, particularly in trees grafted on SO. This effect was sustained over one year and was consistently greater in IPC HBr+ trees than in IPC HBr- and no-IPC HBr+ or HBr- trees, suggesting a synergistic effect of the combined therapy on enhancing tree growth. The tree height and trunk diameter were primarily improved by IPC, regardless of HBr treatment. IPC-treated trees exhibited significantly greater height and trunk diameters (scion and rootstock) than no-IPC trees across one or both rootstocks, indicating that IPCs alone contribute to these horticultural growth improvements. IPC trees also showed reduced preharvest fruit drop compared to the no-IPCs trees, resulting in higher yields, with additional gains observed in IPC HBr+ trees on SO. Fruit quality attributes, including °Brix, titratable acidity, peel color, and size, did not differ significantly among treatments. Importantly, gene expression analysis revealed early and sustained upregulation of key SA pathway genes in IPC HBr+ trees, indicating that HBr effectively activated systemic acquired resistance (SAR), particularly on SO rootstock. This study highlights the complementary roles of IPCs and HBr in the management of HLB. While IPCs provided essential early protection against CLas and promoted long-term horticultural growth, HBr enhanced early canopy development, activated host defense mechanisms, and enhanced yield. The integration of both approaches offers a sustainable and effective strategy to protect young citrus trees, delay CLas infection, and improve tree health and productivity under endemic HLB. Full article

Micro-dwarf tomato cultivars are increasingly considered for urban and controlled-environment agriculture due to their compact architecture and suitability for high-density planting. In this study, we evaluated the effects of different leaf removal intensities on leaf-level physiological performance, fruit yield, and fruit quality in three micro-dwarf tomato cultivars (Mohamed, Hahms Gelbe Topftomate, and Red Robin) grown under contrasting seasonal light conditions. Plants were subjected to low (15%), moderate (30%), or severe (90%) leaf removal, and leaf-level gas exchange was measured across canopy layers, along with yield and fruit quality assessments. Severe leaf removal (90%) increased carbon assimilation, transpiration, and stomatal conductance in middle and lower canopy leaves by up to approximately twofold compared with control plants, indicating improved light availability at the leaf level. However, these physiological enhancements did not consistently translate into higher yield, reflecting reduced whole-plant source capacity under excessive leaf removal. Low to moderate leaf removal (15–30%) generally increased or maintained yield and fruit number, whereas severe leaf removal reduced yield in Hahms Gelbe and Red Robin, particularly under low seasonal radiation. Fruit quality was largely unaffected by leaf removal, except for total soluble solids, which declined by approximately 12% under severe leaf removal across cultivars, consistent with sugar dilution under source limitation. Overall, these results demonstrate that optimal leaf removal in micro-dwarf tomatoes requires balancing improved canopy light distribution with maintenance of sufficient leaf area for carbon assimilation. For the tested compact canopies, LR15–30% represented a generally safe, practical range, whereas LR90% posed a substantial risk of source limitation, particularly at lower radiation; the exact threshold, however, remained cultivar- and light-dependent. Full article

Shoot density is a key viticultural factor modulating canopy microclimate, berry composition, and wine quality, although yield–quality relationships are strongly influenced by environmental conditions. Saignée, a winemaking technique involving partial juice removal prior to fermentation, increases the skin-to-juice ratio and may enhance phenolic extraction. This study assessed the combined effects of shoot density (33 [T1], 20 [T2], and 15 [T3] shoots/m) and saignée (20% vs. control) on yield, grape composition, and wine chemical and sensory properties in Malbec across two vintages (2021–2022). At harvest, the pruning weight, yield components, general maturity parameters, and phenolic composition were measured. The wines were analysed for their phenolic and elemental composition, polysaccharides and volatile compounds, colour, and sensory attributes. T1 exhibited the highest yields and vegetative imbalance, whereas T2 and T3 achieved optimal Ravaz indices. The general grape maturity parameters were unaffected; however, T3 had increased berry phenolic content in 2022. T2 and T3 had enhanced wine tannins, total phenols, and polymeric pigments, particularly in 2022. Saignée increased the pH, potassium, total phenols, tannins, and acylated anthocyanins. Targeting yields near 4 kg/vine (≈10,500 kg/ha) improved vine balance and phenolic composition, although the responses were strongly modulated by interannual variability. Full article

Urban mangroves are increasingly recognized for their important blue-carbon functions, yet their long-term aboveground carbon dynamics under climate extremes and human disturbances remain poorly understood. Here, we developed an integrated framework that combines multi-source satellite observations, field survey and LiDAR-constrained modeling to reconstruct annual species composition, canopy structure, and aboveground carbon dynamics from 1990 to 2022 in Shenzhen Bay, which is the only mangrove ecosystem within a megacity in China. Total aboveground carbon increased from 1820 (95% CI: 1386–2199) Mg C in 1990 to 6006 (95% CI: 5280–6618) Mg C in 2022, with habitat expansion accounting for most of the increase. Aboveground carbon accumulation was affected by coastal reclamation, estuarine engineering, and management-driven removal of introduced stands. Species composition emerged as a key determinant of ecosystem response to disturbance and long-term carbon dynamics. Native mangroves remained dominant and exhibited relatively stable canopy greenness during the 2008 extreme cold event. But the introduced Sonneratia apetala experienced a 42.9% drop in greenness and then took about five years to return to the level before the disturbance. By linking long-term changes in species composition, canopy structure, and aboveground carbon storage, this study provides a transferable foundation for monitoring urban blue-carbon ecosystems and evaluating the long-term consequences of disturbance, restoration, and management under accelerating urbanization and climate change. Full article

Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1$\sigma$ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. Full article

Interest in light detection and ranging (LiDAR) for the precise monitoring of vegetative growth of grain crops has increased. The study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM). A commercial LiDAR system was used for data collection in the middle and late growth stages using static and dynamic scanning. A small number (ten) of data frames, consisting of a region of interest (ROI) of 1 m × 0.9 m for each frame, were selected as data samples. The data processing workflow consisted of data conversion, targeted data frame selection, visualization, region of interest (ROI) segmentation, outlier and untargeted point removal, downsampling, denoising, voxelization, preparation of the convex hull, and 3D PCD density map. To estimate the plant size and distance of wheat, the results obtained using CHM and VGM were compared with measured data results, and both methods were applied for the middle and late growth stages of wheat. The relative accuracy of LiDAR-estimated plant height, canopy volume, plant spacing, and row distances with respect to the measured results were 94%, 87%, 94%, and 87%, respectively, using CHM, and 76%, 72%, 62%, and 71% by VGM for static data scanning; for dynamic scanning, the estimated relative accuracy percentages were 87%, 91%, 94%, and 93%, respectively, using CHM, and 77%, 74%, 75%, and 74%, respectively, using VGM. The same methods were applied to the late growth stage data sets. Between the two methods, CHM provided higher accuracy for static and dynamic data-scanning approaches in the middle and late growth stages because the complex geometry of plants, thin and sparse leaf area, and structure complicated voxelization. Despite several challenges in PCD collection and processing, this study supports size and distance estimation for wheat and similar grains as non-destructive methods. Full article