Search Results (587)

As a vital carbon reservoir in terrestrial ecosystems, forest canopy height plays a pivotal role in determining the precision of biomass estimation and carbon storage calculations. Acquiring an accurate Canopy Height Map (CHM) is crucial for building carbon budget models at regional and global scales. A novel UNet++ deep-learning model was constructed using Sentinel-1 and Sentinel-2 multispectral remote sensing images to estimate forest canopy height data based on full-waveform LiDAR measurements from the Global Ecosystem Dynamics Investigation (GEDI) satellite. A 10 m resolution CHM was generated for Chaling County, China. The model was evaluated using independent validation samples, achieving an R² of 0.58 and a Root Mean Square Error (RMSE) of 3.38 m. The relationships between multiple Relative Height (RH) metrics and field validation data are examined. It was found that RH98 showed the strongest correlation, with an R² of 0.56 and RMSE of 5.83 m. Six different preprocessing algorithms for GEDI data were evaluated, and the results demonstrated that RH98 processed using the ‘a1’ algorithm achieved the best agreement with the validation data, yielding an R² of 0.55 and RMSE of 5.54 m. The impacts of vegetation coverage, assessed through Normalized Difference Vegetation Index (NDVI), and terrain slope on inversion accuracy are explored. The highest accuracy was observed in areas where NDVI ranged from 0.25 to 0.50 (R² = 0.77, RMSE = 2.27 m) and in regions with slopes between 0° and 10° (R² = 0.61, RMSE = 2.99 m). These results highlight that the selection of GEDI data preprocessing methods, RH metrics, vegetation density, and terrain characteristics (slope) all have significant impacts on the accuracy of canopy height estimation. Full article

Moso bamboo (Phyllostachys heterocyclas) has rapidly expanded in subtropical broadleaf forests of eastern China, raising concerns about biodiversity loss and community restructuring. We investigated how the expansion of this native bamboo influences species diversity and phylogenetic diversity across forest strata (trees, shrubs, herbs) by surveying 16 plots along a gradient from bamboo-free to bamboo-dominated stands. We measured soil properties, calculated multiple α-diversity indices, and constructed a community phylogeny to assess phylogenetic metrics. We also constructed a phylogenetically informed Resistance Index (RI) to evaluate species-specific responses to bamboo expansion. The results showed that overstory tree species richness and Faith’s phylogenetic diversity declined sharply with increasing bamboo cover, accompanied by significant losses of evolutionary lineages. In contrast, understory shrub and herb layers exhibited stable or higher species richness under bamboo expansion, although functional redundancy among new colonists suggests limited gains in ecosystem function. Soil conditions shifted substantially along the expansion gradient: pH increased by approximately 0.5 units, while total organic carbon and total nitrogen each decreased by about 30% (p < 0.01). Redundancy analysis and variance partitioning indicated that bamboo’s impacts on community diversity are mediated primarily through these soil changes. Species-level trends revealed that formerly dominant canopy trees (e.g., Schima superba, Pinus massoniana) were highly susceptible to bamboo, whereas certain shade-tolerant taxa (e.g., Cyclobalanopsis glauca, Rubus buergeri) showed resilience. In conclusion, the aggressive expansion of Moso bamboo drastically alters multi-layer forest diversity and community assembly processes. Our findings point to a need for targeted management (e.g., reducing bamboo density, soil restoration, and enrichment planting of native species) to mitigate biodiversity loss, underscoring the importance of considering phylogenetic diversity in expansion ecology and forest conservation. Full article

The prolonged retention of senescent branches and needles (canopy litter) in Cunninghamia lanceolata canopies is an evolutionary adaptation, yet its impacts on stand microenvironment and understory succession remain poorly quantified. To address this gap, we conducted a 5-year field experiment across six planting densities (1800, 2400, 3000, 3600, 4200, and 4800 trees·ha⁻¹), aiming to evaluate the effects of canopy litter removal on canopy structure, forest light environment, and understory biodiversity. Results demonstrated that leaf area index (LAI) and mean tilt angle of the leaf (MTA) significantly increased with density (p < 0.05), leading to marked reductions in photosynthetic photon flux density (PPFD) and light transmittance (T). Canopy litter removal significantly reduced LAI across all densities after 4–5 years (p < 0.05) and consistently enhanced PPFD and transmittance (p < 0.01). MTA and light quality parameters (red:blue and red:far-red ratios) both exhibited variable responses to litter removal, driven by density and time interactions, with effects diminishing over time. Understory vegetation diversity exhibited pronounced temporal dynamics and density-dependent responses to canopy litter removal, with increases in species richness (S), Simpson diversity (D), and Shannon–Wiener diversity (H), while Pielou Evenness (J) responded more variably. The most notable increase in species richness occurred in the 4th year, when 21 new species were recorded, largely due to the expansion of light-demanding bamboos (e.g., Indocalamus tessellatus and Pleioblastus amarus), heliophilic grasses (e.g., Lophatherum gracile) and pioneer ferns (e.g., Pteris dispar and Microlepia hancei). Correlation analyses confirmed PPFD as a key positive driver of all diversity indices (p < 0.01), whereas LAI was significantly negatively correlated with PPFD, light transmittance, and understory diversity (p < 0.01). These findings demonstrate that strategic management of canopy litter incorporating stand density regulation can improve understory light availability, thereby facilitating heliophilic species recruitment and biodiversity enhancement in subtropical coniferous plantations. Full article

The integration of unmanned aerial vehicle-based laser scanning (ULS) and mobile laser scanning (MLS) enables the detection of forest three-dimensional structure in high-density canopy areas and has become an important tool for monitoring and managing forest ecosystems. However, MLS faces difficulties in positioning due to canopy occlusion, making integration challenging. Due to the variations in observation platforms, ULS and MLS point clouds exhibit significant structural discrepancies and limited overlapping areas, necessitating effective methods for feature extraction and correspondence establishment between these features to achieve high-precision registration and integration. Therefore, we propose a registration algorithm that introduces a Feature Deviation Metric to enable feature extraction and correspondence construction for forest point clouds in complex regional environments. The algorithm first extracts surface point clouds using the hidden point algorithm. Then, it applies the proposed dual-threshold method to cluster individual tree features in ULS, using cylindrical detection to construct a Feature Deviation Metric from the feature points and surface point clouds. Finally, an optimization algorithm is employed to match the optimal Feature Deviation Metric for registration. Experiments were conducted in 8 stratified mixed tropical rainforest plots with complex mixed-species canopies in Malaysia and 6 structurally simple, high-canopy-density pure forest plots in anorthern China. Our algorithm achieved an average RMSE of 0.17 m in eight tropical rainforest plots with an average canopy density of 0.93, and an RMSE of 0.05 m in six northern forest plots in China with an average canopy density of 0.75, demonstrating high registration capability. Additionally, we also conducted comparative and adaptability analyses, and the results indicate that the proposed model exhibits high accuracy, efficiency, and stability in high-canopy-density forest areas. Moreover, it shows promise for high-precision ULS-MLS registration in a wider range of forest types in the future. Full article

Larix gmelinii is the fourth most important tree species in China and a typical zonal climax species in the cold temperate region, with high ecological and resource value. However, intensive logging, high-density afforestation, and insufficient scientific management have led to overly dense, homogeneous, and unstable plantations, severely limiting productivity. To clarify the mechanisms by which structural dynamics regulate productivity, we established a space-for-time sequence (T1–T3, T2-D, CK) under a consistent early-tending background. Using the “1 + 4” nearest-neighbor framework and six spatial structural parameters, we developed tree and forest spatial structure indices (TSSI and FSSI) and integrated nine structural–functional indicators for multivariate analysis. The results showed that TSSI and FSSI effectively characterized multi-level stability and supported stability classification. Along the stand-age gradient, structural stability and spatial use efficiency improved significantly, with FSSI and biomass per hectare (BPH) increasing by 91% and 18% from T1 to T3, though a “structural improvement–functional lag” occurred at T2. Moderate thinning markedly optimized stand configuration, reducing low-stability individuals from 86.45% in T1 to 42.65% in T2-D, while DBH, crown width, FSSI, and BPH (229.87 t·hm⁻²) increased to near natural-forest levels. At the tree scale, DBH, tree height, crown width, and TSSI were positive drivers, whereas a high height–diameter ratio (HDR) constrained growth. At the stand scale, canopy density, species richness, and mean DBH promoted FSSI and BPH, while mean HDR and stand density imposed major constraints. A critical management window was identified when DBH < 25 cm, HDR > 10, and TSSI < 0.25 (approximately 10–30 years post-planting). We propose a stepwise, moderate, and targeted thinning strategy with necessary underplanting to reduce density and slenderness, increase diameter and canopy structure, and enhance diversity, thereby accelerating the synergy between stability and productivity. This framework provides a practical pathway for the scientific management and high-quality development of L. gmelinii plantations. Full article

The aim of this study is the estimation of the biodiversity of pure oak stands within the jurisdiction of the Forest Service of Xanthi in northeastern Greece. Using a published graded biodiversity index that operates on management-plan description sheets, we scored five stand-level attributes (total wood stock, age of trees, canopy density, presence of regeneration, and stand aspect/orientation) for every eligible stand and classified biodiversity as low, moderate, or high. These data were sourced from the description sheets of pure oak stands found in the management plans of public forest complexes. Moderate biodiversity predominates (63.4% of stands), followed by low (33.5%), while high biodiversity is scarce (3.1%). Forest practice can influence all the factors which were used for the assessment of the biodiversity characterization of the stands except the aspect of the stand. From these factors the total amount of wood stock and the canopy density were the main factors which determined the low percentage of high-biodiversity stands. On the other hand, the age structure and the regeneration existence were the main factors which counterbalanced the negative influence of the total amount of wood stock and of the canopy density and thus led to the dominance of the stands characterized as having moderate biodiversity score. Full article

Bird–plant interactions are critical for maintaining biodiversity and ecosystem function, and represent a key research focus in modern ecology. Using the line transect method, we surveyed bird diversity and collected plant trait data in four habitat types in the southern zone of Fujian’s Meihuashan National Nature Reserve during October–December 2021 and July–August 2022. This study investigated how plant traits (tree height, diameter at breast height (DBH), canopy density fruit amount) influence the diversity of frugivorous birds (species richness, abundance, Shannon–Wiener, Pielou, Simpson) across four disturbed habitats—villages (residential areas), bamboo forests (economic plantations), unguarded broad-leafed forests (wild forests), and nurtured broad-leafed forests (managed forests)—during both summer (breeding season) and autumn–winter (fruiting season). The key findings revealed that (1) significant correlations between plant traits and bird diversity were exclusive to the fruiting season, with no associations found in summer; (2) during autumn–winter, the key plant traits driving bird diversity varied distinctively by habitat: tree height and canopy density were paramount in villages; both habitat structure (canopy density) and fruit amount were important in bamboo forests, whereas in both broad-leafed forests, a combination of tree structure (height, DBH, canopy density) and fruit amount determined bird abundance; (3) a significant interaction between season and habitat was detected for community evenness, indicating that habitat type modulates the seasonal effects on community composition. This study underscores that in human-modified landscapes, conserving habitat structural complexity and key resource plants is crucial for sustaining frugivorous bird diversity and its ecological functions. Conservation strategies must account for seasonal dynamics to be effective. Full article

Mangroves are critical blue carbon ecosystems, yet accurately estimating their aboveground carbon (AGC) stocks remains challenging due to structural complexity and spectral saturation in dense canopies. This study aims to develop a scalable AGC estimation framework by integrating high-resolution canopy height (CH) data from UAV-LiDAR with multi-source satellite features from Sentinel-1, Sentinel-2, and ALOS PALSAR-2. Using the Maowei Sea mangrove zone in Guangxi, China, as a case study, we extracted structural, spectral, and textural features and applied Random Forest regression with Recursive Feature Elimination (RFE) to optimize feature combinations. Results show that incorporating UAV-derived CH significantly improves model accuracy (R² = 0.75, RMSE = 14.18 Mg C ha⁻¹), outperforming satellite-only approaches. CH was identified as the most important predictor, effectively mitigating saturation effects in high-biomass stands. The estimated total AGC in the study area was 88,363.73 Mg, with a mean density of 53.01 Mg C ha⁻¹. This study highlights the advantages of cross-scale UAV–satellite data fusion for accurate, regionally scalable AGC mapping, offering a practical tool for blue carbon monitoring and coastal ecosystem management under global change. Full article

This study examined the structural and ecological drivers of burn severity during the March 2025 wildfire in Uiseong County, Republic of Korea, with a focus on developing a predictive framework using the differenced Normalized Burn Ratio (dNBR). Seventeen candidate variables were evaluated, among which the forest type, stand age, tree height, diameter at breast height (DBH), and Normalized Difference Vegetation Index (NDVI) were consistently identified as the most influential predictors. Burn severity increased across all forest types up to the 4th–5th age classes before declining in older stands. Coniferous forests exhibited the highest severity at the 5th age class (mean dNBR = 0.3069), followed by mixed forests (0.2771) and broadleaf forests (0.2194). Structural factors reinforced this pattern, as coniferous and mixed forests recorded maximum severity within the 5–11 m height range, while broadleaf forests showed relatively stable severity across 3–21 m but declined thereafter. In the final prediction model, NDVI emerged as the dominant variable, integrating canopy density, vegetation vigor, and moisture conditions. Notably, NDVI exhibited a positive correlation with burn severity in coniferous stands during this early-spring event, diverging from the generally negative relationship reported in previous studies. This seasonal anomaly underscores the need to interpret NDVI flexibly in relation to the forest type, stand age, and phenological stage. Overall, the model results demonstrate that mid-aged stands with moderate heights and dense canopy cover are the most fire-prone, whereas older, taller stands show reduced susceptibility. By integrating NDVI with structural attributes, this modeling approach provides a scalable tool for the spatial prediction of wildfire severity and supports resilience-based forest management under climate change. Full article

Crown fire hazard assessment and behavior prediction in dwarf pine (Pinus pumila) forests are dictated by the amount of canopy fuel available, topography, and weather. In this study, we collected data on CFL (available canopy fuel load), CBD (canopy bulk density), and CBH (canopy base height) through the destructive sampling of dwarf pine trees in the Greater Khingan Mountains of Northeast China. Allometric equations were developed for estimating the canopy’s available biomass, CFL, and CBD to support the assessment of canopy fuel. Three burning scenarios were designed to investigate the impact of various environmental parameters on fire behavior. Our findings indicated that the average CFL of a dwarf pine was 0.36 kg·m⁻², while the average CBD was measured at 0.17 kg·m⁻³. The vertical variation trends of both CFL and CBD exhibited consistency, with values increasing progressively from the bottom to the top of the tree crown. Fire behavior simulations indicated that the low CBH of dwarf pine trees increased the likelihood of crown fires. Various factors, including wind speed, slope, and CBH, exerted considerable influence on fire behavior, with wind speed emerging as the most critical determinant. Silvicultural treatments, such as thinning and pruning, may effectively reduce fuel loads and elevate the canopy base height, thereby decreasing both the probability and intensity of crown fires. Full article

Maize (Zea mays L.) is a key global cereal crop with significant relevance to food security. Maize yield prediction is challenged by cultivar diversity and varying management practices. This preliminary study was conducted at Youyi Farm, Heilongjiang Province, China. Three maize cultivars (Songyu 438, Dika 1220, Dika 2188), two fertilization rates (700 and 800 kg·ha⁻¹), and three planting densities (70,000, 75,000, and 80,000 plants·ha⁻¹) were evaluated across 18 distinct cropping treatments. During the V6 (Vegetative 6-leaf stage), VT (Tasseling stage), R3 (Milk stage), and R6 (Physiological maturity) growth stages of maize, multi-temporal canopy spectral images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor. In situ measurements of key agronomic traits, including plant height (PH), stem diameter (SD), leaf area index (LAI), and relative chlorophyll content (SPAD), were conducted. The optimal vegetation indices (VIs) and agronomic traits were selected for developing a maize yield prediction model using the random forest (RF) algorithm. Results showed the following: (1) Vegetation indices derived from the red-edge band, particularly the normalized difference red-edge index (NDRE), exhibited a strong correlation with maize yield (R = 0.664), especially during the tasseling to milk ripening stage; (2) The integration of LAI and SPAD with NDRE improved model performance, achieving an R² of 0.69—an increase of 23.2% compared to models based solely on VIs; (3) Incorporating SPAD values from middle-canopy leaves during the milk ripening stage further enhanced prediction accuracy (R² = 0.74, RMSE = 0.88 t·ha⁻¹), highlighting the value of vertical-scale physiological parameters in yield modeling. This study not only furnishes critical technical support for the application of UAV-based remote sensing in precision agriculture at the field-plot scale, but also charts a clear direction for the synergistic optimization of multi-dimensional agronomic traits and spectral features. Full article

Longleaf pine forests are economically important habitats that stabilize and enrich the soil and store carbon over long periods. When mixed with oaks, these forests provide an abundance of lichen habitats. The tree canopy lichens promote greater moisture capture and retention and encourage canopy insects. Ground lichens limit some vascular plant germination and growth, promoting a more open and healthy pine community. There is a longstanding mutualistic relationship between longleaf pine habitat and lichens. Longleaf pine habitat has a long history of natural summer burning, which promotes a diverse understory and limits tree densities. Lichen diversity exceeds vascular plant diversity in many mature longleaf pine habitats, yet information on the impacts of prescribed fire on lichen species in these habitats is limited. We assessed lichen diversity and abundance before and after a prescribed ground fire in a longleaf pine/wiregrass habitat near Ocala, Florida. Pre-burn, we found greater lichen abundance and diversity on hardwoods, primarily oak species, than on pines. Post-burn, lichen abundance on hardwoods dropped overall by 28%. Lichen abundance on conifers dropped overall by 94%. Ground lichen species were basically eliminated, with a 99.5% loss. Our study provides insights into retaining lichen diversity after a prescribed burn. Hardwood trees, whether alive or standing dead, help retain lichen biodiversity after burning, whereas conifer trees do not support as many species. Landscapes may need to be actively managed by raking pine needle litter away from ground lichen beds, moistening the ground, or removing some lichen material before the burn and returning it to the site post-fire. Based on these results, we suggest retaining some oaks and conducting burns in a mosaic pattern that retains unburned areas. This will allow for lichens to recover between burns, significantly enhancing biodiversity and the ecological health of these longleaf pine communities. Full article

Bonellia nervosa is an understory tree with reverse phenology in tropical dry forests (TDFs), where seasonal water and temperature stress typically shape plant phenology and trait expression. This species is heliophytic and phreatophytic, relying on high light availability and deep-water access during the dry season. However, the role of dry-season light variation in influencing leaf traits of species with inverted phenology remains poorly understood. We examined how plant size, reproductive stage, and canopy structure influence trait variation in B. nervosa during the dry season. We measured plant height and diameter, reproductive status, and canopy structure using hemispherical photographs to estimate canopy openness, leaf area index, and transmitted light. Leaf structural traits included specific leaf area (SLA), thickness, water content, and stomatal density, while photochemical performance was assessed via chlorophyll fluorescence and rapid light curves. Principal component analysis and linear regression were used to examine trait–environment relationships. Photosynthetic efficiency was not affected by plant size or reproductive status. No strong trait correlations were observed for leaf water content and stomatal density. A negative relationship between canopy openness, transmitted light, and SLA indicates structural leaf adaptation to light conditions, with lower SLA values occurring under reduced light. In B. nervosa, leaf traits are driven more by light than by water availability during the dry season. This suggests that reverse phenology in phreatophytic species is functionally decoupled from seasonal water stress. Full article

Drought is a critical factor influencing the distribution of forest species in both present and future global terrestrial ecosystems. Therefore, to investigate the sensitivity of typical afforestation tree species on the Loess Plateau to drought and its influencing factors, we conducted field experiments to measure the sap flow, soil moisture content, fine root density, leaf water potential, and xylem hydraulic characteristics of three deciduous trees: apple (Malus domestica), black locust (Robinia pseudoacacia), and jujube (Ziziphus jujube). We found that the canopy conductance (Gc) of black locust and apple trees was highly sensitive to VPD variations. Their transpiration (T) was also sensitive to soil moisture variation, especially for black locust. In contrast, the Gc and T sensitivity of jujube trees was low. The differences in their drought sensitivities can primarily be attributed to variations in xylem hydraulic conductivity and embolism vulnerability. Our results demonstrate that both mature black locust and apple trees on the Loess Plateau have strong drought sensitivity, especially black locust. Therefore, alterations in precipitation patterns driven by climate change may significantly influence the community distribution of black locusts trees on the Loess Plateau. Full article

Accurate and scalable estimation of forest production is essential for quantifying carbon sequestration, forecasting timber yields, and guiding climate change mitigation strategies. While prior studies established a positive linkage between net primary production (NPP) and canopy structural complexity (CSC) metrics derived from terrestrial LiDAR, the spatial coverage of ground-based surveys is limited. Airborne laser scanning (ALS) could offer a rapid and spatially extensive alternative to terrestrial scanning, but the predictive capacity of ALS-derived CSC metrics for estimating forest production remains insufficiently explored. To address this gap, we derived a suite of three-dimensional (3D) CSC metrics from small-footprint, high-density ALS data collected by the National Ecological Observatory Network’s Airborne Observation Platform. We evaluated relationships between CSC metrics and the NPP of plots nested within seven deciduous and evergreen temperate forests. Optimal metric combinations for predicting NPP within and across forest types were identified using partial least squares regression coupled with recursive feature elimination. ALS-derived CSC metrics explained 77% (RMSE = 11%) and 76% (RMSE = 13%) of the variance in deciduous and evergreen forest plot NPP, respectively. Our findings demonstrate that 3D CSC metrics derived from high-density ALS are robust predictors of plot-level NPP, offering performance comparable to terrestrial scanners while enabling greater scalability and more efficient data acquisition. Full article