Search Results (467)

Individual tree segmentation and diameter at breast height (DBH) estimation are fundamental to precision forest inventory. Light Detection and Ranging (LiDAR) technology has become an essential tool for achieving these objectives at the single-tree level. Different LiDAR platforms—notably unmanned aerial vehicle (UAV) and Mobile Laser Scanning (MLS)—each offer distinct advantages in capturing forest structural information. Multi-source LiDAR fusion has been proposed as a strategy to combine these complementary strengths. However, how to effectively select segmentation algorithms across different LiDAR data sources remains insufficiently understood, particularly for subtropical coniferous plantations with heterogeneous canopy structure. This study systematically compared four individual tree segmentation algorithms (Donager2021, Dalponte2016, Silva2016, and marker-controlled watershed segmentation [MCWS]) across three LiDAR data sources (UAV-only, MLS-only, and fused UAV–MLS) in Pinus massoniana plantations in subtropical China. DBH estimation models were then developed based on the best-performing segmentation results to examine whether data fusion simultaneously improves both detection and DBH retrieval accuracy. The main findings are as follows: (1) the three canopy height model (CHM)-based algorithms achieved a mean overall accuracy (OA) for individual-tree detection of approximately 64% on UAV data but fell below 30% on MLS data, failing to support effective detection; (2) The Donager2021 algorithm, which directly exploits trunk structure from three-dimensional point clouds, achieved the highest OA of 93.15% with MLS data and further improved to 94.82% with fused data; (3) DBH estimation reached a mean R² of 0.96 for both MLS and fused datasets, yet MLS LiDAR alone produced a lower RMSE (2.31 cm; rRMSE = 5.91%) than fused LiDAR (RMSE = 2.40 cm; rRMSE = 6.24%); and (4) higher detection accuracy did not necessarily lead to better DBH estimation, revealing a trade-off between the two objectives. These findings indicate that fusion does not universally improve all downstream tasks, and that the choice of LiDAR configuration and segmentation algorithm should be guided by specific inventory objectives. Full article

Facioscapulohumeral muscular dystrophy (FSHD) is a genetically and epigenetically complex autosomal dominant myopathy that presents formidable challenges to molecular diagnosis and reproductive intervention. The disease is caused by aberrant derepression of the DUX4 retrogene within the D4Z4 macrosatellite repeat array at chromosome 4q35, triggered either by pathological contraction of the array on a permissive 4qA haplotype (FSHD1, ~95% of cases) or by mutations in epigenetic modifier genes SMCHD1, DNMT3B, and LRIF1 that lead to global D4Z4 hypomethylation (FSHD2, ~5% of cases). Traditional approaches (Southern blotting, optical genome mapping, bisulfite sequencing) are discontinuous and labor-intensive. Nanopore ultra-long read sequencing spans the entire D4Z4 array in single reads, simultaneously resolving repeat number, haplotype, and allele-specific CpG methylation without bisulfite conversion. With the telomere-to-telomere (T2T-CHM13) reference genome, long-range haplotype phasing enables preimplantation genetic testing for monogenic conditions (PGT-M) for families with de novo pathogenic variants and somatic mosaicism, groups previously excluded from reproductive genetic intervention. This review systematically examines FSHD molecular mechanisms, the Nanopore diagnostic workflow integrated with T2T-CHM13, Nanopore-based PGT-M clinical data, and future perspectives including R11 pore chemistry, AI-driven bioinformatics, CRISPR-targeted enrichment, and multi-omics integration. Full article

Spaceborne LiDAR provides essential observations of forest vertical structure, yet canopy height retrievals remain vulnerable to terrain-related errors in steep mountainous forests, where terrain heterogeneity complicates attribution of error to footprint geolocation and ground elevation representation. To distinguish these effects, we used 1 m airborne laser scanning (ALS)-derived digital terrain and canopy height models (DTM and CHM) as local references in Jiuzhaigou, China. A 2 × 2 diagnostic design independently varied footprint geolocation and ground reference across four slope classes. For 2102 strictly filtered footprints, geolocation refinement slightly increased RMSE from 13.36 to 13.47 m, whereas median-based ground reference replacement reduced RMSE to 9.98 m, a 25.3% reduction. The GEDI-implied ground was closest to the footprint median below 35° but shifted toward P20–P30 in the steepest terrain. Vertical reference sensitivity showed that the exact ground-diagnostic optimum shifted between P20 and P30, whereas the footprint median consistently minimized corrected-RH98 RMSE against ALS CHM P98. The ATL03 comparison likewise showed substantially improved canopy height agreement when ALS DTM replaced Copernicus DEM as the terrain support input under otherwise identical photon processing. These results identify ground elevation representation, rather than the tested horizontal geolocation refinement, as the dominant terrain-related factor shaping GEDI–ALS canopy height disagreement in steep terrain and show that ground diagnosis and RH98 correction require different terrain-reference choices. Full article

Rivers and riparian zones support a variety of woody plant species and play an important role in riverine ecosystems and fluvial processes. Rapid establishment of herbaceous and woody vegetation occurs in unvegetated river channels, mainly because of dam construction and hydrological alterations. However, repeated monitoring of the three-dimensional structure over broad river corridors remains challenging. We aimed to assess the applicability of RGB-derived canopy-height models (CHMs) inferred using Depth Anything V2 for monitoring riparian woody vegetation. A monocular depth-estimation framework was trained using the National Agriculture Imagery Program-CHM dataset and applied to three Korean riverine environments. The inferred CHMs were evaluated against the LiDAR-derived CHMs and further tested using two application-oriented assessments: individual tree detection (ITD) and woody vegetation area classification. The RGB-derived CHMs reproduced the overall spatial patterns of the riparian canopy height, although the accuracy varied with vegetation structure and image acquisition conditions. The mean absolute error between RGB- and LiDAR-derived CHMs was approximately 0.96 m across the study sites. In the ITD assessment, the RGB-derived CHM achieved an F1 score of 0.73, compared with 0.80 for the LiDAR-derived CHM. For woody vegetation area classification, the RGB-derived CHM showed high pixel-level accuracy, while Dice coefficient and IoU varied depending on the canopy-height threshold and study site. These results suggest that the RGB-derived CHMs can serve as supplementary data for monitoring riparian woody vegetation when LiDAR acquisition is limited. Full article

Biodiversity monitoring is essential for evaluating mine rehabilitation success. Traditionally, assessments have relied on ground-based plot measurements, but advances in remote sensing offer opportunities to complement or replace plot-based surveys with spatially continuous monitoring approaches. We evaluated drone-derived multispectral imagery, the Soil Adjusted Vegetation Index (SAVI), and canopy height models (CHM) for mapping plant species used in mine rehabilitation in central Queensland, Australia. Ten classification models tested four combinations of spectral and structural data. Incorporating CHM improved overall accuracy by up to 10%, with notable gains for vegetation classes containing Eucalyptus and Acacia species. Species-level accuracy ranged from 79% to 100% for Eucalyptus and 74% to 100% for Acacia species. Misclassification was greatest among closely related red gums (Eucalyptus tereticornis Sm. and Eucalyptus camaldulensis Dehnh.) and Corymbia citriodora (Hook.) K.D.Hill & L.A.S.Johnson. These results demonstrate that structural information substantially improves species discrimination and has the potential to enhance biodiversity monitoring across plot (500 m²), block (1–100 ha), and landscape (100–1000 ha) scales. Full article

Monitoring forest regeneration is vital for sustainable management, yet traditional ground surveys and early aerial imagery methods face cost, labor, and resolution limitations. Unmanned Aerial Vehicles (UAVs) offer cost-effective, flexible platforms for acquiring ultra-high-resolution data to address these gaps. This paper reviews recent research on UAV applications in forest regeneration surveys (FRS), tracing the evolution from field-based surveys and conventional aerial approaches to current UAV practices, and synthesizing developments in data acquisition, processing workflows, and analysis. It contrasts established Canopy Height Model (CHM) and point-cloud approaches with the growing use of deep learning, particularly Convolutional Neural Networks (CNNs) for seedling detection, crown delineation, density, height estimation, and species classification. Particular attention is given to accuracy assessment, examining sampling design, reference data, prediction-to-reference matching, and evaluation metrics, and highlighting the disconnect between traditional map-validation principles and standard deep-learning metrics that often neglect background classes. The case study applying Mask R-CNN to red pine seedlings in an Adirondack Park plantation achieved stand-level recall of 70.3% and precision of 98.7%, while plot-level DL detections represented only 36.8% of the field-observed seedling count. These results demonstrate the potential of DL for reliably identifying visible red pine seedlings while highlighting its limitations for complete regeneration inventories, particularly when seedlings have small crown sizes. Full article

Accurate precipitation data are critical for hydrological and climatic studies in Guangdong Province, where complex terrain and frequent extreme rainfall pose substantial challenges. However, the performance of gridded precipitation products is still not well understood. This study evaluates nine products, including gauge-based (CHM_PRE, CN05.1, GMCP, NOAA CPC), satellite-based (IMERG-E, IMERG-F, TMPA RT, TMPA 3B42), and ERA5 reanalysis against NCDC observations from 2001 to 2019 using metrics including trend significance, correlation (R), root mean square error (RMSE), categorical statistics (POD, FAR, ETS), and relative bias across rainfall intensities. The results indicate that, based on validation against NCDC observations, CHM_PRE performs the best across all temporal scales, capturing significant increasing trends (p < 0.05) and achieving the highest consistency with observations at the annual (R = 0.99), monthly (R = 0.99), and daily (R = 0.89) scales. Using CHM_PRE as the reference, CN05.1 shows the highest spatial consistency with it, especially for extreme events. NOAA CPC exhibits the best performance in monthly event detection (ETS = 0.42; BIAS ≈ 1). Satellite products show acceptable performance at the monthly scale but exhibit intensity-dependent biases and high daily variability, with pronounced “light rain overestimation and heavy rain underestimation.” ERA5 shows limitations, particularly in its severe underestimation of extreme precipitation. CHM_PRE is thus identified as the most suitable dataset for Guangdong based on its agreement with NCDC observations. With CHM_PRE as the reference, CN05.1 provides a reliable alternative for spatial analyses; NOAA CPC performs the best in monthly event detection. Satellite products suit monthly use but require daily-scale caution; ERA5 shows a relatively poor performance. Full article

Accurate stem volume estimation is fundamental to forest resource inventory and carbon stock assessment. Traditional methods rely on destructive sampling, whereas terrestrial laser scanning (TLS) offers a non-destructive alternative. However, the accuracy of individual tree segmentation in structurally complex subtropical natural forests is constrained by crown overlap, species mixing, and vertical stratification. In this study, we developed a semantic voxel-based framework for individual tree segmentation and robust stem volume estimation from plot-level TLS point clouds. The method integrates 3D-CNN-based voxel semantic classification with bottom-up tree growth segmentation, followed by parameter extraction, taper equation fitting, and volume estimation using the sectional measurement method. Evaluation across 18 plots (1451 trees) in Guangxi, Southern China, demonstrated that the proposed method achieved an F-score of 0.881 for individual tree segmentation, significantly outperforming conventional CHM-based (0.533) and geometric voxel-based (0.794) approaches. Optimal taper equations were established for Chinese fir (Zeng Weisheng model, validation R² = 0.943) and Eucalyptus (Yan Ruohai model, validation R² = 0.987). TLS-based volume estimates yielded R² values of 0.94–0.97 and RMSE of 0.022–0.037 m³ per tree, with negligible systematic bias. These findings demonstrate that the proposed semantic voxel framework enables accurate and non-destructive stem volume estimation in complex subtropical forests, providing a practical technological pathway for modernizing forest inventory practices. Full article

Extreme precipitation can disproportionately enhance rainfall erosivity in complex mountainous terrain, yet its spatial amplification and topographic differentiation remain poorly understood. Focusing on the Hengduan Mountains, this study evaluated three precipitation products (ChinaMet, CHM_PRE, and IMERG) against station observations and assessed their ability to capture precipitation extremes. Using the best-performing product, rainfall erosivity associated with total (PRCPTOT), heavy (R95p), and extreme (R99p) precipitation was estimated for 2005–2024, and its spatial patterns, amplification effects, topographic differentiation, and hotspots were analyzed. CHM_PRE showed the best overall performance, with a correlation coefficient (CC) of 0.83 and a Kling–Gupta efficiency (KGE) of 0.74, together with the highest probability of detection (POD = 0.95), accuracy (ACC = 0.83), and critical success index (CSI = 0.78) for extreme precipitation. Precipitation and the corresponding rainfall erosivity exhibited a pronounced southeast-to-northwest decreasing gradient. Although R95p and R99p accounted for only 9.61% and 2.43% of total precipitation, they contributed 14.84% and 4.33% of total rainfall erosivity, yielding erosivity amplification factors (AFs) of 1.52 and 1.73, respectively. This indicates a disproportionate contribution of precipitation extremes to rainfall erosivity, with stronger amplification under R99p. Rainfall erosivity also exhibited pronounced topographic differentiation, and high-level hotspots were consistently concentrated along the southeastern and southern margins. Extreme hotspots under PRCPTOT and R95p occurred at mean elevations of 2735.19–2791.92 m and mean slopes of 14.79–15.09°, whereas R99p intense hotspots occurred at a mean elevation of 2374.15 m and a mean slope of 12.28°. Strongly undulating mid-high mountains were the dominant geomorphic units within PRCPTOT and R95p extreme hotspots, while moderately and strongly undulating mid-high mountains dominated R99p intense hotspots. Moreover, hotspots became increasingly localized as precipitation extremity increased. These findings highlight the disproportionate erosive significance and spatial selectivity of precipitation extremes and provide a basis for identifying priority areas for soil and water conservation and rainfall-related hazard management in the Hengduan Mountains under climate change. Full article

Peat swamp forests play a critical role in maintaining the ecological integrity of tropical peatlands. The conservation and restoration efforts on these ecosystems have gained considerable attention considering their vulnerability. Accurate spatial mapping of aboveground biomass (AGB) is important to support such measures, and it can be accurately implemented using airborne LiDAR. However, the high operational cost of LiDAR surveys typically restricts their spatial coverage. This study estimated AGB of peat swamp forests by combining two remote sensing datasets, i.e., Planet NICFI imagery (2023–2024) and partially covered airborne LiDAR (10.56% of the total area), in the Katingan–Mentaya peat swamp forest, Central Kalimantan, Indonesia. Two workflows were proposed and compared. The first, DL-PowerReg, estimated canopy height model (CHM) using a U-Net deep learning model trained on Planet imagery, followed by AGB mapping through power regression. The second, StepwiseReg-DL, derived LiDAR-based AGB through stepwise regression of LiDAR metrics, then upscaled it to the full study area using U-Net with Planet imagery as input. DL-PowerReg (MAE = 52.24 t/ha) outperformed StepwiseReg-DL (MAE = 62.08 t/ha) and additionally produced an intermediate CHM map, providing complementary information on forest structure. The two approaches estimated total AGB storage in the study area at 47.75 Mt (mean = 239.91 t/ha) and 40.07 Mt (mean = 201.30 t/ha), respectively. This study demonstrates a methodological framework for leveraging spatially incomplete LiDAR data in high-resolution wall-to-wall forest biomass mapping. Full article

Soil moisture (SM) remote sensing is widely used for agricultural drought monitoring, yet most applications still emphasize static moisture states rather than event-scale wetting responses. We developed an interpretable Earth observation (EO) framework to evaluate precipitation–SM product consistency and diagnose wetting processes across China’s croplands. Multi-source precipitation and SM products, including ERA5-Land, Soil Moisture Active Passive (SMAP), Soil Moisture of China by in situ data (SMCI), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Grid-based Precipitation dataset for Mainland China (CHM_PRE), were assessed using lagged consistency between rainfall forcing and relative soil moisture increments. The selected pairing was then used to model daily wetting increments at three depths with eXtreme Gradient Boosting (XGBoost), Shapley additive explanations (SHAPs), generalized additive models (GAMs), and quantile regression (QR). ERA5-Land precipitation paired with ERA5-Land SM showed the strongest reanalysis-constrained event-scale consistency (peak mean r = 0.43 at a 1-day lag), providing an internal-consistency baseline for comparison with independent satellite-derived combinations rather than an absolute accuracy ranking. EO-derived wetting signals showed depth-dependent lags, with a 1-day surface response and an approximately 2-day delayed profile signal at 28–100 cm; this pattern should not be interpreted as direct evidence of rapid physical infiltration to 100 cm. Precipitation transition thresholds followed a U-shaped dependence on antecedent wetness, with higher rainfall requirements under extremely dry and near-saturated states. These findings indicate that event-scale EO diagnostics can characterize product consistency, lagged wetting responses, and state-dependent precipitation thresholds, while same-system and deep-layer interpretations remain constrained by reanalysis coupling and model-assisted root-zone products. Full article

Accurate quantification of individual-tree parameters is essential for improving the quality of natural secondary forests; however, conventional measurements remain challenging because of canopy overlap and interference from tall shrubs. Despite the broad use of Light Detection and Ranging (LiDAR) in forest inventory, achieving precise tree segmentation and parameter estimation in complex forest stands remains difficult. This study utilized unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) data to systematically evaluate the impact of single-source point clouds versus fused point clouds, different segmentation methods (CHM, treeX, and CSP), and different estimation approaches on the estimation of individual tree parameters, and proposed the RSQ-Tree framework for robust parameter extraction. Comparative analysis of seven experimental schemes across 473 sample trees in six plots showed that the “stem denoising + fused data + treeX + RSQ-Tree” scheme performed best, with R² values of 0.96, 0.84, and 0.75 for estimates of diameter at breast height, tree height, and crown width, respectively, and substantially reduced RMSE. These results indicate that multi-source LiDAR fusion, combined with robust segmentation and parameter modelling, can effectively improve the accuracy and stability of individual-tree parameter estimation in complex secondary forests. Full article

Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of “time-series perception–dynamic simulation–feature identification–early prediction”, providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research. Full article

This study investigated the influence of airborne laser scanning (ALS) pulse density on the accuracy of total tree height estimates and the quality of raster products commonly used in individual tree detection (ITD) workflows. Using a high-density (36 pulses per square meter (PPM)) ALS dataset, we generated lower-density subsets and compared derived outputs using a standardized processing pipeline. Tree height estimates were validated against field measurements, and elevation products—digital elevation models (DEMs), digital surface models (DSMs), and canopy height models (CHMs)—were assessed across pulse densities. DSM and CHM quality improved with increasing density, showing reduced bias and tighter variation. In contrast, DEM accuracy remained relatively stable across densities, indicating lower-density ALS may suffice for ground modeling in forested environments. The results also revealed a strong positive relationship between pulse density and total tree height accuracy. Higher-density datasets consistently produced more accurate and less biased tree height estimates, while sparser datasets exhibited systematic underestimation due to missed canopy peaks. These findings emphasize the importance of aligning ALS pulse density with project objectives. While low-density data may be adequate for terrain modeling, higher-density acquisitions are critical for reliable canopy representation and accurate ITD outputs. This study provides operational guidance for forestry practitioners and highlights the value of investing in higher-resolution ALS data for modern forest inventories. Full article

Forest aboveground live biomass carbon storage (hereafter referred to as “forest carbon storage”) is an important indicator of forest vegetation carbon sequestration, and its spatial patterns and associated factors are highly heterogeneous. Identifying these variations can improve the understanding of carbon accumulation in complex mountain forests and support fine-scale carbon assessment in similar ecosystems. The results showed the following. (1) The total forest carbon storage in the study area was 3.75 × 10⁶ t C, with a carbon density of 44.83 t C·hm⁻². Pinus massoniana and hard broad-leaved tree species were the main contributors, and carbon storage peaked at the mature forest stage. (2) Carbon storage exhibited significant spatial clustering (Moran’s I = 0.312), with high-value areas concentrated within the national nature reserve and low-value areas distributed in regions with frequent human activities. (3) The GWR model outperformed the ordinary least squares model, with R² increasing to 0.88 and residual spatial autocorrelation reduced by 42.22%. (4) The positive effect of stand volume increased from northeast to southwest, the effect of stand age differed between eastern and western areas, and shrub layer height, soil depth, and slope exhibited region-specific positive and negative effects, with significant interactions among factors. Full article