Search Results (149)

Understanding the dynamics of carbon (C), nitrogen (N), and phosphorus (P) stoichiometry and C stocks along a stand development chronosequence has been extensively studied in forest ecosystems. However, despite the global economic and ecological importance of apple orchards, such knowledge remains limited for these intensively managed perennial agroecosystems. We examined C, N, and P concentrations and stoichiometric ratios in tree tissues (root, stem, branch, foliage) and soils (0–100 cm depth), as well as ecosystem C stocks, across a chronosequence of 4, 8, 12, and 16 yr old Malus pumila orchards in the eastern Yan Mountains, Hebei Province, North China. The results showed that C concentrations exhibited no consistent age-dependent trend in tree tissues. In contrast, N and P concentrations in all tree tissues decreased significantly with stand age, while their C:N and C:P ratios increased. The leaf N:P ratios suggested progressive P limitation as orchards aged. In soil, C, N, and P concentrations first decreased and then increased along the chronosequence, with the highest values observed in the 16 yr stands. This U-shaped trajectory reflected the dynamic interplay between stand development and anthropogenic management. Intercropping and intensive fertilization in the 4 yr orchards initially elevated soil nutrient levels, while the cessation of intercropping and nutrient removal via fruit harvesting in the 8 yr stands led to a decline. Thereafter, accumulation of litter decomposition and root turnover, combined with continued organic matter inputs, progressively replenished soil nutrient pools in the 12 and 16 yr stands. The total ecosystem C stocks ranged from 70.80 to 136.13 Mg ha⁻¹, initially declining from 4 to 8 years and then increasing at 12 and 16 years, with soil contributing 84.7–99.7% of the total. Plant and soil nutrient concentrations showed predominantly negative correlations, indicating weak coupling between tree and soil nutrient pools. Our findings demonstrated that stand age profoundly influenced C:N:P stoichiometry and C stocks in apple orchard ecosystems and that prolonged orchard development enhanced both tree biomass C and soil C stocks. These results provide a scientific basis for nutrient optimization and sustainable management of apple orchards in temperate regions. Full article

Seasonal forest phenology is commonly summarized as canopy greenness or phenophase timing, although leaf development also redistributes observed plant material through three-dimensional space. We evaluated whether repeated RGB-colorized handheld simultaneous localization and mapping (SLAM) can provide height-resolved trajectories of seasonal observed occupancy in adjacent Ailanthus altissima-dominated and native-dominated sectors of a young deciduous forest. We acquired 149 scans on 25 dates from March 2025 to March 2026 at six permanent locations. Point clouds were restricted to date-invariant common support, normalized to a March terrain model, and voxelized at 0.20 m. New occupancy was referenced to the union of two strict March leaf-off scans. A weakly supervised foliage likeness proxy combined geometry-first pseudo-labels with relative color, intensity, and local three-dimensional features; its outputs were interpreted as relative scores rather than leaf fraction, LAI, or biomass. The strongest and most persistent invaded positive signal was localized to 2–4 m. Continuous-time models supported the integrated 1–5 m contrast from late April through October, whereas formal support for the 5–12 m crown domain was limited to the late season invaded positive phase; the earlier native positive crown feature remained descriptive. Height-integrated SLAM showed broad seasonal concordance with intercepted PAR (r_rm = 0.881), GCP-linked Sentinel-2 EVI2 (r_rm = 0.670) and the five-date litterfall comparison (r_rm = 0.914). However, correlations of the invaded minus native trajectories were positive but imprecise. The independent observations therefore supported the broad seasonal cycle rather than the detailed sector-specific or height-specific pattern. The workflow provides a conservative means of localizing relative seasonal observed occupancy in three dimensions, but the resulting contrasts remain site-specific and hypothesis-generating. 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

Direct adoption of nationwide generalized biomass models often causes systematic prediction errors when applied to forest biomass estimation in Hunan Province. This study developed localized multi–component compatible biomass model systems for Chinese fir, poplar and oak based on 461 felled–tree specimens sampled from 14 administrative cities of Hunan Province during 2022–2023. Individual allometric equations were first fitted via weighted least squares, and hierarchical compatible model structures were further established by algebraic component summation and proportional scaling under simultaneous equation constraints. The final total–biomass compatible equations were: Chinese fir: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.065662D^{1.750420}{H}^{0.780381}+0.024760{(D}^2{H)}^{0.774387}$; Poplar: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.046471D^{2.171796}{H}^{0.462424}+0.013116·{(D}^2{H)}^{0.822310}$; Oak: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.0927573{\left(D^{2}H\right)}^{0.89013}+0.018969{\left(D^{2}H\right)}^{0.89162}$. R² values for total and aboveground biomass exceeded 0.92 and prediction accuracy exceeded 96%, with TRE within ±3% for all species. Root biomass models reached prediction accuracy higher than 78% with R² > 0.78. Stem, wood and bark sub–models achieved distinctly better fitting performance than branch and foliage sub–models, and strict hierarchical compatibility was guaranteed among total and component biomass predictions. Calibrated exclusively with local field data, these models effectively eliminate regional systematic estimation bias and only require readily measurable DBH and tree height as input variables, serving as reliable tools for regional forest biomass assessment and routine forest resource monitoring across Hunan Province. Full article

Ash-free net heat content (AF-NHC) represents the combustible heat content of plant biomass and is an important parameter in fire behavior and fire effects modeling. Despite its widespread use, little information exists regarding seasonal and regional variation in AF-NHC among common woody fuels of the southeastern US. This study quantified seasonal and regional variation in AF-NHC among five common woody species in eastern Texas: yaupon (Ilex vomitoria), greenbrier (Smilax spp.), eastern red cedar (Juniperus virginiana), Chinese privet (Ligustrum sinense), and escarpment live oak (Quercus fusiformis). Foliage samples were collected during the dormant and growing seasons across the Pineywoods, Post Oak Savannah, and Blackland Prairie ecoregions and were analyzed using oxygen bomb calorimetry. Linear mixed-effects models evaluated species, season, and species × season effects while accounting for regional variation. AF-NHC ranged from 17.35 to 19.92 MJ kg⁻¹ and differed significantly among species and seasons, with distinct species-specific seasonal trajectories (p < 0.05). Regional variation accounted for approximately 41% of total model variance, indicating that environmental conditions influence fuel thermal properties. AF-NHC was greatest in yaupon and red cedar, intermediate in privet and greenbrier, and lowest in live oak. Although AF-NHC likely exerts less influence on fire behavior than fuel consumption and the rate of spread, species-specific differences in combustible heat content may contribute to variation in potential heat release and fuel combustibility. These findings provide baseline AF-NHC values for common eastern Texas woody fuels and improve the understanding of spatial and temporal variation in fuel thermal properties relevant to fire effects and wildfire hazard assessment. Full article

Remote sensing contributes to characterizing forest structure across heterogeneous tropical regions, yet structural parameters used to compare Atlantic Forest phytophysiognomies remain limited, especially in fragmented landscapes affected by multiple drivers of forest loss and degradation. This study used Global Ecosystem Dynamics Investigation (GEDI) data to compare the structure of old-growth candidate forest polygons in four Brazilian Atlantic Forest phytophysiognomies: Dense Ombrophilous Forest (DOF), Mixed Ombrophilous Forest (MOF), Seasonal Semideciduous Forest (SSdF), and Seasonal Deciduous Forest (SDF). We analyzed canopy height (H), canopy cover (COVER), foliage height diversity (FHD), plant area index (PAI), and aboveground biomass density (AGBD) from GEDI L2B and L4A footprints acquired between 2019 and 2024. Structural differences among phytophysiognomies were significant for all variables (Kruskal–Wallis, p < 0.001), with small-to-moderate effect sizes (ε² ≈ 0.05–0.15). The strongest pairwise contrasts occurred for SDF–SSdF and SSdF–DOF, whereas MOF showed greater overlap with the other groups. Across variables, AGBD and H were the most consistent discriminators, and polygon-level summaries strengthened among-group separation. These findings show that GEDI-derived polygon-level metrics can support regional comparisons of forest structure among Atlantic Forest phytophysiognomies and help identify the strongest contrasts in fragmented landscapes. Full article

Accurately quantifying aboveground biomass (AGB) in tropical forest enrichment plantations remains challenging, particularly in managed regenerating stands where tree crown architecture, size structure, and species composition differ from the datasets used to calibrate classical allometric equations. Here, we assess whether AGB in tropical forest enrichment plantations can be estimated more accurately by combining tree-specific woody volume reconstructed from mobile laser scanning (MLS) with an explicit foliar-biomass component. We combined destructive measurements from 83 trees with high-resolution MLS point clouds to quantify biomass components, calibrate leaf-mass models, and assess the contribution of foliage to total AGB. Stems accounted for most of the biomass (65%), whereas leaves contributed only 3% on average. Among the models tested, Model 3, which included DBH, projected crown area, and wood density, showed the best performance (R² = 54.4%; RMSE = 2.43 kg). The main gain relative to regional (−20.4%) and pantropical (−25.6%) allometric equations came from the use of MLS-derived woody volume combined with species wood density, whereas the inclusion of predicted leaf biomass provided a moderate additional correction to the remaining bias. These results highlight the importance of canopy structure for biomass estimation in enrichment plantations and managed regenerating stands and support the use of LiDAR data as a robust alternative for AGB assessment in this context. Full article

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R² values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce. Full article

Using native tree species, the phytostabilisation of toxic metals at former mining and industrial sites can provide ways to prevent metal spread and leaching into the environment and bring the sites back into the economic circuit. In this study, mixed afforestations with young trees from seven Central European species showing contrasted autecology (Picea abies (L.) Karst, Fagus sylvatica L., Acer pseudoplatanus L., Alnus incana (L.) Moench, Populus tremula L., Salix viminalis L. and Betula pendula Roth) were exposed during five years to mixed soil contamination (Zn/Cu/Pb/Cd = 1349/317/70/8 mg kg⁻¹). The uptake and allocation of the metals in root and shoot tissues, various functional traits and nutrient responses were compared. Despite high metal availability, all tree species showed low metal uptake and similar metal concentrations in their roots. The mobile metals (Zn, Cd) accumulated in the shoot and foliage of early-successional species with acquisitive ecological strategy only, whereas the late-successional species blocked the transfer of all metals from the roots to the aboveground organs. All species showed good tolerance to metal contamination, with large interspecific differences regarding the biomass production and some nutrient concentrations, in apparent relation to the varying species’ ecological strategies and independent of the metal treatment. Zn allocation within fine root tissues could enhance transient spatial and temporal metal immobilisation, especially when associated with protective or defence structures, which also contributed to metal detoxification. Higher transfer of mobile metals to aboveground organs in pioneer tree species was clearly related to their acquisitive ecological strategies, in the context of higher nutrient demand in foliage and lesser defence and protection of vegetative organs. The implications of findings for phytostabilisation applications are discussed. Full article

Laser radiation constitutes a promising technological advancement within the integrated weed management toolbox but is hindered by low energy use efficiency. This study investigated the efficiency of a pulsed blue diode laser for controlling small weed seedlings and seeds under controlled conditions. Dose–response experiments were conducted on three grasses (Poa annua, Echinochloa crus-galli, Digitaria sanguinalis) and three dicotyledonous species (Solanum nigrum, Chenopodium album, Senecio vulgaris). For seedlings, the effects of species, growth stage (cotyledon, 2-leaf), and leaf wetness (dry, wet) were tested. For seeds, burial depth (0 mm, 2 mm) and imbibition status (non-imbibed, imbibed) were examined. Biological efficiency was assessed through plant survival, aboveground dry biomass, leaf area, and seed viability. Laser application caused significant, dose-dependent reductions in biomass accumulation and plant survival, with up to 100% mortality. Seedlings were most sensitive at the cotyledon stage and when foliage was dry, requiring up to 68 and 52% lower energy doses compared to older or wet targets, respectively. Species-specific responses were observed, with dicotyledonous species generally requiring 80 to 99% lower energy doses than grasses. Laser exposure was also effective in reducing the viability of non-imbibed, surface-exposed seeds, requiring up to 64 and 99% lower energy doses than imbibed or buried seeds, respectively. These results confirm that laser efficiency is strongly influenced by species traits, developmental stage, surface moisture, and seed water status. Optimising and tailoring laser parameters to these factors enhances weed control efficacy while maximising energy efficiency, improving the performance and sustainability of laser-based weeding. Full article

Understanding yield improvement in horticultural systems depends on elucidating how multiple plant traits operate in concert to sustain productivity. Mulberry (Morus alba L.) provides a suitable model for examining such whole-plant integration. Under cold-region field conditions, a modern high-yield cultivar (‘Nongsang 14’) was compared with a traditional cultivar (‘Lusang 1’). Measurements encompassed canopy architecture, biomass allocation between roots and shoots, leaf economic traits, and gas-exchange parameters, allowing trait coordination to be evaluated across structural and physiological dimensions. Multivariate profiling—Principal Component Analysis (PCA) and correlation networks—was used to characterise phenotypic integration. The modern cultivar’s superior productivity emerged as a coordinated “acquisitive” trait syndrome. This strategy couples a larger canopy (higher LAI) and nitrogen-rich foliage (higher LNC) with greater stomatal conductance (G_s), operating together with reduced root-to-shoot allocation. These features form a tightly connected network where structural investment and physiological upregulation are synchronised to maximise carbon gain. These findings provide a whole-plant framework for interpreting high productivity, offering guidance for breeding programmes that target trait integration rather than single-trait optimisation. Full article

Radiata pine (Pinus radiata D. Don) is New Zealand’s dominant plantation species, supporting carbon sequestration under the national Emissions Trading Scheme. However, existing stand-level carbon models cannot estimate individual tree carbon stocks which are often required for modern remote sensing-based forest inventories. This study developed comprehensive allometric equations for predicting tree-level carbon in radiata pine using an extensive dataset of 894 trees spanning ages 1–42 years across eight New Zealand locations. We fitted 12 models predicting stem wood, bark, branch, and foliage biomass from varying combinations of tree height, diameter at breast height, stand age, stand density and wood density. Models incorporating both height and diameter achieved excellent accuracy for stem wood and bark (R² > 0.99, log-transformed scale), while inclusion of age, stand density and wood density substantially improved crown component predictions (R² = 0.95 for branches and 0.93 for foliage). Biomass predictions were converted to carbon using component-specific and age-dependent carbon fractions derived from New Zealand radiata pine, avoiding biases from generic conversion factors. The resulting equations provide a tiered system accommodating different data availability levels and are directly compatible with LiDAR-derived tree attributes. These models provide a robust framework for accurate individual-tree carbon estimation, supporting both operational plantation management and robust carbon accounting across New Zealand’s radiata pine estate. Full article

The intensity of wildfires is projected to increase with the rising frequency of droughts due to climate change. Management practices following forest fires must include restoring the appropriate species composition. This study was performed within the wider context of the regeneration of soil properties, including the stock and soil organic matter (SOM) content, at the largest forest fire site in Poland (more than 9000 ha) in the Rudziniec Forest District, Upper Silesia. Research plots were established on sandy soils (Podzols and Arenosols) in pure stands of Scots pine (Pinus sylvestris L.), common birch (Betula pendula Roth), and black alder (Alnus glutinosa (L.) Gaertn.). The organic and mineral soil horizons were sampled from each research plot and control plots unaffected by the fire. The trees’ foliage was also sampled to determine the nutrient supply. Basic soil properties were determined, including the texture, pH, bulk density, organic carbon (C), macronutrient contents, soil microbial biomass, and labile C and nitrogen (N) fractions. We found that, 30 years after the fire, the post-fire soils had similar SOC stocks (34.80 Mg ha⁻¹) to the control plots (31.72 Mg ha⁻¹); however, they differed in their stocks of labile C and N fractions. The post-fire soils had a less stable C pool due to a higher stock of the fraction associated with particulate organic matter. In contrast, the N pool was more stable in the post-fire soils than in the control soils due to a lower contribution of the most labile fractions. The soils under Scots pine had the least stable SOM, which may have influenced the intensification of the podzolization process, whereas the highest biomass of soil microorganisms was observed under common birch. The soils under black alder had the highest acidity and lowest phosphorus (P) content. The C:N:P ratios in the post-fire soils and tree foliage indicated that P may have been the limiting factor in alder growth, and N for pine and birch. Our findings indicate that tree species composition is an important factor in the recovery of post-fire soil properties. However, the introduction of pure black alder stands to post-fire soils with low moisture and P availability showed little effectiveness in restoring the SOM content and N pool. Full article

Large-scale tree planting programs that store carbon provided by wood and non-wood products are being promoted to mitigate climate change. Assessing the biomass pool of plantations is thus an essential task in forest ecology. This study investigated biomass allocation and allometric equations for above- and belowground components along an age-sequence of Pinus tabuliformis plantations (8, 18, 32, and 46 years old) in northern Hebei Province, China. The biomass of each tree component (root, stem, branch, foliage) was quantified by destructive harvesting. Allometric equations and biomass conversion and expansion factors (BCEFs) were subsequently developed for each tree component. The mean above- and belowground biomass was 5.86, 20.05, 41.26, and 135.28 kg tree⁻¹ and 1.73, 3.42, 11.39, and 27.30 kg tree⁻¹ in the 8-, 18-, 32-, and 46-year-old stands, respectively. The proportion of stem biomass to total tree biomass increased from 28.7% for the 8-year-old stand to 55.8% for 46-year-old stand. In contrast, the contributions of foliage and branch decreased along the chronosequence. The root contribution to total tree biomass also showed a declining trend with stand age. Allometric models based on diameter at breast height showed a good fit (p < 0.001) and incorporating stand age as an additional variable improved the fit of allometric equations (higher R² and lower ACI) for branch, aboveground, root, and total tree biomass. BCEFs decreased for all tree components as stand age increased. These findings indicate that changes in tree biomass allocation and allometry across stand development must be considered to improve estimates of plantation biomass and carbon stocks at regional and national scales. Full article

Pyrolysis of 24 samples of foliage from three U.S. regions with frequent wildland fires (Southeastern U.S., northern Utah and Southern California) was studied in a fuel-rich flat-flame burner system at 765 °C (for Southeastern U.S. samples) and 725 °C (for northern Utah and Southern California species), with a heating rate of approximately 180 °C/s. These conditions were selected to mimic the conditions of wildland fires. Individual plant samples were introduced to the high temperature zone in a flat-flame burner and pyrolysis products were collected. Tar was extracted and later analyzed by GC/MS. Light gases were collected and analyzed by GC/TCD. The estimated range for the average yields of tar and light gases were 48 to 62 wt% and 18 to 31 wt%, respectively. Apart from Eastwood’s manzanita (Arctostaphylos glandulosa Eastw.), aromatics were the major constituents of tar. The variations in the concentrations of tar compounds likely resulted from differences in biomass composition and physical characteristics of the foliage. The four major components of light gases from pyrolysis (wt% basis) were CO, CO₂, CH₄ and H₂. Tar contributed more than 82% of the high heating value of volatiles. These data can be used to improve physical-based fire propagation models. Full article