Search Results (74)

Backpack and handheld LiDAR simultaneous localization and mapping (SLAM) systems have become an important solution for large-scale 3D data acquisition. Since Global Navigation Satellite System (GNSS) positioning is not always available in many LiDAR SLAM systems, point clouds acquired from different surveying projects or devices are represented in independent local coordinate systems and require coarse registration to fuse all data into a unified coordinate system. Existing coarse registration approaches based on local feature descriptors often depend on locally estimated surface normals or reference directions, whose repeatability can be affected by measurement noise and non-uniform sampling. To address this issue, this paper proposes a Global-Prior Augmented Fast Point Feature Histogram (GA-FPFH) descriptor. The proposed method constructs a Z-axis-augmented local reference frame (Z-LRF) using the gravity-aligned vertical direction provided by the SLAM system. Three new geometric components are proposed based on the Z-LRF and combined with conventional FPFH features to form a six-component and 66-dimensional descriptor. Experiments on 12 real-world point-cloud pairs show that GA-FPFH increases the inlier ratio by 26.9–148.9% and, across five registration algorithms, reduces the rotation error, translation error, and RMSE by 31.5–87.9%, 29.6–97.8%, and 42.4–97.6%, respectively, while increasing the overall registration success rate from 71.7% to 88.3%. The significant error reductions are partly attributable to the higher registration success rate and fewer failure cases. Full article

Integrated qiaopi office–residence heritage preserves the material setting of remittance-letter operations together with domestic, educational, and ritual activities. In Lingnan’s hot–humid and disaster-prone environment, condition records need to be repeatable, spatially traceable, and continuously updatable. Taking Jingzu Jiashu and Mingde Jiashu, two former qiaopi office sites in Chaoshan, as case studies, this research develops an evidence-traceable digital conservation workflow integrating multi-source documentation; an adopted YOLOv8 surface-deterioration baseline; qualitative Grad-CAM visualization; structured deterioration records; and semi-automatic, human-confirmed Revit/HBIM association. UAV and terrestrial photography, mobile LiDAR/scanning, handheld measurement, measured drawings, point-cloud and reality-based products, and geometric models were organized into case-specific HBIM environments. The adopted deterioration dataset comprised 362 original images at 512 × 512 pixels and 2024 bounding-box annotations for five visually identifiable categories: spalling, staining, plants, saltpetering, and crack. The original images were divided into 253 training, 72 validation, and 37 independent-test images, while augmentation was restricted to the training subset, increasing the training pool to 1600 images. The previously established YOLOv8 baseline achieved a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58. Grad-CAM heatmaps were used as qualitative aids to examine model-emphasized image regions. Retained detections associated with Jingzu Jiashu and Mingde Jiashu were converted into versioned records containing source-image identifiers, deterioration classes, detector confidence, survey information, spatial references, verification states, and revision histories. Candidate spatial associations were generated through case identifiers, façade or space zones, element identifiers, and available spatial evidence, while final M1–M3 associations required human confirmation. By preserving source provenance, spatial uncertainty, and record histories, the workflow provides an auditable information basis for routine inspection, post-event review, maintenance prioritization, repair interpretation, and resilience-oriented preventive conservation. The workflow supports screening-level deterioration recognition and information management but does not provide causal diagnosis, structural assessment, exact affected-area measurement, building-independent generalization, or automatic repair recommendations. Full article

Integrating ground-based and aerial remote sensing for individual tree-level stem volume modeling remains underexplored in Mediterranean mixed forests, despite the growing need for cost-effective, automated forest inventory approaches. This study evaluated the combined use of Handheld Laser Scanning (HLS) and Unmanned Aerial Vehicle (UAV)-based Structure from Motion (SfM) photogrammetry for individual-tree stem volume modeling in a mixed stand in Castilla y León, Spain, dominated by Pinus halepensis, Pinus pinea, Quercus faginea, and Cupressus sempervirens. Two open-source HLS processing tools; the Forest Structural Complexity Tool (FSCT) and 3D Forest Inventory (3DFin), were compared for individual tree attribute extraction, with FSCT outperforming 3DFin across all species. Reference stem volumes were derived by applying species-specific Spanish National Forest Inventory (SNFI) allometric equations to FSCT-extracted diameter and height values. Random Forest models were then built using UAV-SfM crown metrics as predictors, testing two image overlap configurations: 80 × 80 F (80% front and side overlap) and 80 × 60 CF (80% front, 60% side, cross-flight). The 80 × 80 F configuration produced the best-performing model (R² = 0.730), with 80 × 60 CF achieving comparable accuracy (R² = 0.688), results confirmed by spatially independent leave-one-plot-out cross-validation (LOPO-CV R² = 0.627 and 0.613, respectively). These results show that combining HLS and UAV-SfM through a predominantly open-source workflow offers a viable, reproducible approach to stem volume modeling in structurally complex Mediterranean mixed forests. Full article

Exploiting the complementary views of ground-based and aerial laser scanning requires co-registering their point clouds, which is difficult in closed tropical forest where Global Navigation Satellite System (GNSS) reception fails. We co-registered handheld Simultaneous Localization and Mapping (SLAM) LiDAR and UAV LiDAR without per-tree GNSS, using girth-identity anchors inherited from a companion inventory, over an open dry dipterocarp forest (DDF) and a closed dry evergreen forest (DEF) at the SERS, northeastern Thailand. Coarse-to-fine registration reduced the residual to a mean absolute distance of 0.17–0.18 m, and the clouds sampled complementary strata-stems and understory to about 25–30 m, emergent canopy to about 37 m. The co-registered data give each tree a ground-measured stem diameter and an aerially measured height, which we used to test whether diameter can instead be inferred from the canopy. It cannot: both a height-based and a best-case crown-and-height model saturated near 50 cm while measured stems reached about 100 cm, so UAV-only individual-tree carbon did not track the field reference; supplying the ground-measured diameter restored agreement (R² = 0.91), validated against reflective-tape anchor trees. The horizontal offset between a stem base and its crown apex further explains why position-only stem-to-crown matching fails and why girth-identity linkage is needed. We conclude that credible individual-tree carbon in closed tropical forest requires ground measurement of stem diameter, made possible by GNSS-independent SLAM-UAV co-registration; the aerial platform contributes height and coverage, not diameter. 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

Reliable and scalable landmine detection technologies are essential for humanitarian mine action (HMA), yet standardized benchmarks for Unmanned Aerial Vehicle (UAV)-based sensing in operationally relevant environments remain limited. This study presents a comprehensive evaluation of 34 multimodal datasets acquired over a standardized seeded test site for landmine and unexploded ordnance detection. Nine sensing modalities, including RGB, thermal, multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar (SAR), are evaluated using the Anomaly, Identifiable Anomaly, Unique Identifiable Anomaly (AIU) index to establish a unified framework for quantifying detection fidelity. Results indicate that RGB imagery achieves the highest surface detection rate (94.8%), with 45.4% of targets classified as uniquely identifiable, reducing false-positive risk. For sub-surface detection, handheld electromagnetic induction (EMI) and magnetometry exceed 95% detection for ferrous items but fall below 10% for plastic ordnance. Ground-penetrating radar (GPR) is the only modality capable of detecting buried plastic targets (55.6% for cart-based systems), whereas UAV-mounted GPR remains limited (18.2%) at current operational flight heights. Based on the comparative analysis, we discuss the gaps in current detection capabilities, compare false-positive rates across modalities, and perform a cost–benefit analysis fitting contamination scenarios with the most cost-effective detection method. All datasets are publicly released, along with an interactive web-map, to support reproducible benchmarking and cross-modality comparison in UAV-enabled explosive hazard detection. Full article

Per-tree geolocation in closed tropical canopies has typical uncertainties of 5–15 m with GNSS receivers, preventing automated linking of field inventories to point-cloud stem data. We propose an anchor-based matching framework that does not require per-tree GNSS. A handheld SLAM LiDAR scanner maps stems and girths within ≈40 min; field crews record species, girth, and serial numbers without physical markers or tools. Dataset linkage uses a small subset of reflective-tape anchor trees (35 and 43 per hectare, roughly one per 400–500 m²) with approximate GNSS locations. Species identity is transferred using median-based GNSS bias correction and quadrant-partitioned Hungarian matching with global deduplication; accuracy is validated by leave-one-anchor-out (LOAO) tests and exact binomial statistics. Tested in two 1-ha plots of open Dry Dipterocarp Forest (DDF; 280 trees/ha) and dense Dry Evergreen Forest (DEF; ~1054 trees/ha) at the Sakaerat Biosphere Reserve, Thailand, SLAM girth matched tape data with R² = 0.997, RMSE = 1.82 cm in DDF; in DEF, after correcting a 6.38 m GNSS bias, R² = 0.986 and RMSE = 7.01 cm, with ≥99% detection for stems ≥30 cm girth (99.2% DDF; 100% DEF). LOAO accuracy was 35/35 in DDF and 40/43 in DEF. Retroreflective-tape anchors were additionally detected automatically from the SLAM intensity channel in 71.4% of DDF anchors (95% CI 53.7–85.4) and 76.7% of DEF anchors (95% CI 61.4–88.2) at intensity ≥ 150 DN, with up to 59-fold enrichment over matched non-anchor controls at I ≥ 250 in DEF (Fisher’s exact p < 1 × 10⁻¹⁵), enabling a fully automated anchor-detection pipeline. Full article

Mobile laser scanning (MLS) enables rapid, high-resolution measurement of forest structure, yet its reliability for estimating stem volume and aboveground biomass (AGB) in dense plantations and its sensitivity to tree height uncertainty remain insufficiently quantified. This study evaluates handheld MLS for tree-level stem volume and AGB estimation in a mature Sitka spruce (Picea sitchensis (Bong.) Carr.) plantation in Ireland, using destructive sampling (n = 12) as a reference. MLS-derived diameter measurements were used to reconstruct stem profiles, with merchantable volume calculated by frustum integration to a 7 cm top-end diameter. The central objective was to quantify how uncertainty in tree height propagates through MLS-derived stem reconstruction and affects volume and AGB estimates. On average, 68.2% of merchantable stem volume was directly measured before upper-stem reconstruction. Under ideal validation conditions using true felled-stem height, MLS-derived merchantable volume and total AGB were estimated with RMSE values of 5.6% and 10.9%, respectively. Across practical height-input scenarios, error increased moderately, indicating that direct measurement of the lower stem constrained the propagation of height uncertainty. Compared with the nationally applied spruce allometric benchmark, the MLS-based workflow showed lower sensitivity to height-input uncertainty under the conditions evaluated. These findings demonstrate the potential of handheld MLS as a tree-level validation and calibration tool for measurement-based biomass assessment while highlighting the need for broader testing across stand types, species and operational plot-level workflows. Full article

Forest digital twins play a crucial role in modern precision forestry by supporting biomass estimation and carbon cycle monitoring. However, existing 3D reconstruction methods struggle to simultaneously achieve metric-level structural accuracy and visual realism in complex understory environments. This study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery. First, a multi-modal fusion mechanism is constructed to extract geometric anchors from registered LiDAR data for precise 3DGS spatial initialization, which mitigates rendering artifacts and geometric drift caused by poor initialization in purely visual methods. Second, a semantic regularization optimization strategy is proposed to realize differentiated modeling of tree trunks and canopies, effectively balancing the structural accuracy of rigid trunks and the photorealistic rendering of non-rigid canopies. Experiments conducted on three study plots demonstrate that the proposed approach achieves an average PSNR of 24.94 dB, SSIM of 0.773, and LPIPS of 0.231 across all plots, outperforming standard NeRF and baseline 3DGS, while enabling DBH estimation with R² = 0.848 and RMSE = 2.705 cm. This method provides a solution for high-fidelity forest digital twin construction in open-canopy forest environments such as urban and campus forests. Full article

Traditional field surveys of urban park biodiversity lack efficiency and scale, whereas LiDAR offers precise 3D vegetation quantification. This study investigates how 3D vegetation structural complexity impacts urban park plant diversity. We integrated Unmanned Aerial Vehicle (UAV) and handheld LiDAR data with ground-based quadrat surveys to capture comprehensive vegetation structures. Using six key 3D structural metrics, we modeled their relationship with plant diversity via Random Forest. Results indicate canopy height standard deviation (Hstd) primarily influences cultivated plant diversity, while the vegetation density index (VDI) drives spontaneous diversity. The model predicted species richness better (30.63% variance explained) than the Shannon index (7.63%). These drivers exhibited significant non-linear effects and potential ecological thresholds. A strong synergy emerged: when the vertical structure is complex and the 3D spatial density is high, the predicted plant diversity initially exhibits a trend of saturation and stabilization. Ultimately, multi-dimensional 3D vegetation structure proves to be a robust indicator of plant diversity. Our proposed multi-platform LiDAR fusion framework enables rapid, precise ecological assessments, providing methodological references and support for the transition from 2D to 3D green quality evaluation in the fine-scale management of similar cities. Full article

Forest canopy water storage capacity is a critical component of ecohydrological research. However, because most current studies focus on the plot or stand scale, upscaling these fine-scale measurements to regional spatial scales remains a major challenge. Taking the forest in southern Jiangxi province as a case study, we integrated water immersion experiments, Handheld Laser Scanning (HLS), Unmanned Aerial Vehicle LiDAR (UAV-LiDAR), and optical remote sensing data to construct a spatial upscaling model. This model aims to quantify regional canopy water storage capacity and delineate its spatial patterns. The results indicate that: (1) the water storage capacity of branches and leaves per unit surface area of coniferous trees was significantly higher than that of broad-leaved trees, and the water storage capacity of branches was 6.0–10.7 times that of leaves. The mean canopy water storage capacities of coniferous forests, mixed coniferous and broad-leaved forests, and broad-leaved forests were 1.41 ± 0.27 mm, 1.30 ± 0.45 mm, and 1.26 ± 0.36 mm, respectively. (2) The canopy water storage capacity was significantly positively correlated with canopy volume ($V_C$) and average canopy area ($A_C$) extracted from UAV-LiDAR data, and vegetation structure factors such as normalized difference vegetation index (NDVI) and vegetation cover (FVC) extracted from optical remote sensing, and significantly negatively correlated with altitude and slope. Among them, canopy closure (C), average canopy area ($A_C$), and altitude were key factors affecting canopy water storage capacity. (3) The upscaling prediction models based on UAV-LiDAR data and optical remote sensing factors, respectively, show reliable prediction performance, with R² values of 0.884 and 0.815, RMSE of 0.951 and 0.116 mm, respectively. (4) The canopy water storage in the study area ranged from 0 to 1.76 mm, with a prediction uncertainty ranging from 0.12 to 0.49 mm. Canopy water storage is higher in the continuous middle and low mountain and hill areas within the region, while it is relatively lower in the high elevation ridge areas along the western, eastern, and southern margins. The results provide baseline structural information for understanding the spatial patterns of regional forest canopy interception potential. Full article

This study presents a comparative evaluation of three modern surveying techniques—UAV photogrammetry, static tripod-based LiDAR scanning, and handheld mobile LiDAR—applied in the context of historic monument restoration. The focus is on analysing workflow efficiency, data accuracy, and adaptability to complex architectural features, including interior wall paintings, which are integral to the monument’s heritage value. Particular attention is given to how each technique captures surface texture, color fidelity, and material deterioration. The study also examines performance around intricate architectural elements such as vaulted ceilings, apses, cornices, columns, and carved stone portals, where occlusions, tight clearances, and fine ornamentation challenge coverage and resolution. By evaluating the strengths and limitations of each approach, the research highlights methodological considerations relevant for conservation professionals. The results indicate that the Static TLS is the most demanding workflow, requiring complex total station integration for control and station points. It produced the highest data density, with acquisition rates of one million points per second, making it the most hardware-intensive and difficult to manipulate. UAV photogrammetry provided a balanced middle-ground; it required minimal physical effort during acquisition and produced datasets that were significantly easier to manage. Handheld SLAM LiDAR emerged as the most productive solution for rapid coverage. While the handheld scanner’s image quality was lower than the photogrammetry, it still provided enough detail for the structural assessment and documentation needed. Although the point cloud lacked the extreme geometric detail provided by the TLS, the FARO Connect software made georeferencing and data manipulation significantly more efficient. Full article

Understanding the interplay between tree structure and seasonal dynamics, particularly cross-species, is crucial for managing urban forest ecosystems. However, balancing fine-scale inventory of trees with large-area mapping of forest ecosystems is a challenge. This endeavor integrates multi-temporal Sentinel-2 satellite remote sensing (RS) imagery with high-density handheld light detection and ranging (LiDAR) point clouds to launch exploratory analyses of cross-species phenological–structural relationships (CSPSRs) in urban park trees. We derived plot-level phenological metrics (e.g., start of growing season, SOS) and quantified fine-scale three-dimensional (3D) tree structural attributes (e.g., tree height and trunk curvature), respectively. Then, we investigated how the 3D structural attributes of urban park trees covary with their phenological traits. The results revealed the underlying CSPSRs, e.g., a weak but significant negative correlation between SOS and tree height in the study area. The derived CSPSRs demonstrate that tree structure is a key predictor of its phenology, even across species. Overall, the integrated RS approach can provide a robust framework for associating the structure and phenology of trees, offering valuable insights for the ecological management of urban forests. Full article

The accurate and efficient extraction of individual tree phenotypic traits for seabuckthorn (Hippophae rhamnoides L.) in natural forests is crucial for germplasm exploration, precision silviculture, and ecological restoration. This study extracted structural and biochemical traits of seabuckthorn in Tibet’s Lhasa valley using Unmanned aerial vehicle (UAV) LiDAR, multispectral imagery, and the N-PROSAIL model. Firstly, building on a classification conducted through multi-scale spatial analysis and hierarchical clustering with dynamic thresholds, shrub interference was effectively reduced, thereby improving the accuracy of individual tree segmentation. Tree height and crown width were derived from the segmentation results, and a DBH estimation model was developed using handheld LiDAR data. Finally, leaf nitrogen content was mapped within canopies using random forest combined with the N-PROSAIL model and nitrogen reference data. The results demonstrated that the optimized segmentation method successfully extracted structural traits (F1 = 84.21%). Tree height was accurately estimated (R² = 0.814, RMSE = 0.580 m), and the DBH prediction model performed satisfactorily (R² = 0.779, RMSE = 1.725 cm). The random forest model also effectively estimated leaf nitrogen content (R² = 0.680, RMSE = 2.074 mg/g). Full article

Simultaneous online mapping and semantic segmentation using handheld scanners supports various environmental inspection and measurement tasks. For such scanners, combing visual and LiDAR data is beneficial for improving the segmentation performance. But the direct fusion of multi-modal and multi-view features faces challenges in terms of both real-time performance and robustness. To address these challenges, this paper proposes a multi-view and cross-modal knowledge distillation method for supporting runtime LiDAR-only semantic segmentation. The proposed method hierarchically compacts multi-view and cross-model priors and distills them into two branches to improve segmentation accuracy. In addition, we design an improved data augmentation technique based on PolarMix for rendering more realistic point cloud scenes. The experimental results on the SemanticKITTI and nuScenes datasets demonstrate that the mIoU of our approach outperforms the state-of-the-art knowledge-distillation-based methods. In addition, mapping experiments using a handheld scanner demonstrate the proposed method’s superior real-time performance and accuracy. Full article