Aboveground-canopy and belowground-root-trait correlations contribute to root system characteristics estimation: Insights from ground penetrating radar data

Ecological Indicators, Год журнала: 2025, Номер 173, С. 113354 - 113354

Опубликована: Март 22, 2025

Язык: Английский

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Combining hand-held and drone-based lidar for forest carbon monitoring: insights from a Mediterranean mixed forest in central Portugal

European Journal of Forest Research, Год журнала: 2025, Номер unknown

Опубликована: Март 26, 2025

Язык: Английский

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Mapping Individual Tree- and Plot-Level Biomass Using Handheld Mobile Laser Scanning in Complex Subtropical Secondary and Old-Growth Forests

Remote Sensing, Год журнала: 2025, Номер 17(8), С. 1354 - 1354

Опубликована: Апрель 10, 2025

Forests are invaluable natural resources that provide essential ecosystem services, and their carbon storage capacity is critical for climate mitigation efforts. Quantifying this would require accurate estimation of forest structural attributes deriving aboveground biomass (AGB). Traditional field measurements, while precise, labor-intensive often spatially limited. Handheld Mobile Laser Scanning (HMLS) offers a rapid alternative building inventories; however, its effectiveness accuracy in diverse subtropical forests with complex canopy structure remain under-investigated. In study, we employed both HMLS traditional surveys within structurally plots, including old-growth (Fung Shui Woods) secondary forests. These characterized by dense understories abundant shrubs lianas, as well high stem density, which pose challenges Light Detection Ranging (LiDAR) point cloud data processing. We assessed tree detection rates extracted attributes, diameter at breast height (DBH) height. Additionally, compared tree-level plot-level AGB estimates using allometric equations. Our findings indicate successfully detected over 90% trees types precisely measured DBH (R2 > 0.96), although exhibited relatively higher uncertainty 0.35). The derived from were comparable to those obtained measurements. By producing highly demonstrates potential an effective non-destructive method inventory forests, making it competitive option aiding estimations environments.

Язык: Английский

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Comparison of LiDAR Operation Methods for Forest Inventory in Korean Pine Forests

Forests, Год журнала: 2025, Номер 16(4), С. 643 - 643

Опубликована: Апрель 7, 2025

Precise forest inventory is the key to sustainable management. LiDAR technology widely applied tree attribute extraction. Therefore, this study compared DBH and height derived from Handheld Mobile Laser Scanning (HMLS), Airborne (ALS), Integrated ALS HMLS determined applicability of integrating scanning methods estimate individual attributes such as diameter at breast (DBH) in pine forests South Korea. There were strong correlations for level (r > 0.95; p < 0.001). ALS-HMLS achieved high accuracy estimations, showing Root Mean Squared Error (RMSE) 1.46 cm (rRMSE 3.7%) 1.38 3.5%), respectively. In contrast, obtained was lower than expected, an RMSE 2.85 m (12.74%) along with a bias −2.34 m. data enhanced precision achieving 1.81 −1.24 However, resulted most precise estimations reduced 1.43 biases −0.3 its advantages are beneficial solution accurate inventory, which turn supports management planning.

Язык: Английский

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Terrain and individual tree vertical structure-based approach for point clouds co-registration by UAV and Backpack LiDAR

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104544 - 104544

Опубликована: Май 1, 2025

Язык: Английский

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Estimating canopy leaf angle from leaf to ecosystem scale: a novel deep learning approach using unmanned aerial vehicle imagery

New Phytologist, Год журнала: 2025, Номер unknown

Опубликована: Май 10, 2025

Summary Leaf angle distribution (LAD) impacts plant photosynthesis, water use efficiency, and ecosystem primary productivity, which are crucial for understanding surface energy balance climate change responses. Traditional LAD measurement methods time‐consuming often limited to individual sites, hindering effective data acquisition at the scale complicating modeling of canopy variations. We present a deep learning approach that is more affordable, efficient, automated, less labor‐intensive than traditional estimating LAD. The method uses unmanned aerial vehicle images processed with structure‐from‐motion point cloud algorithms Mask Region‐based convolutional neural network. Validation single‐leaf using manual measurements across three species confirmed high accuracy proposed ( Pachira glabra : R 2 = 0.87, RMSE 7.61°; Ficus elastica 0.91, 6.72°; Schefflera macrostachya 0.85, 5.67°). Employing this method, we efficiently measured leaf angles 57 032 leaves within 30 m × plot, revealing distinct among four representative tree species: Melodinus suaveolens (mean inclination 34.79°), Daphniphyllum calycinum (31.22°), Endospermum chinense (25.40°), Tetracera sarmentosa (30.37°). can estimate scales, providing critical structural information vegetation modeling, including species‐specific strategies their effects on light interception photosynthesis in diverse forests.

Язык: Английский

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Adaptive fusion of different platform point cloud with improved particle swarm optimization and supervoxels

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 130, С. 103934 - 103934

Опубликована: Май 30, 2024

Fusion of point cloud from different platforms is crucial for enhancing spatial information completeness in large-scale scenes, particularly urban 3D modeling. To address redundancy, noise, and accuracy degradation direct registration across platforms, we propose an adaptive fusion method utilizing supervoxels. Initially, a high-precision selected as the reference (RPC),and apply coarse-to-fine approach to unify RPC target (TPC). Registration parameters are optimized using Improved Particle Swarm Optimization (IPSO), automation precision fine registration. Subsequently, supervoxels constructed registered TPC. Finally, within each corresponding supervoxel, redundancy noise eliminated by applying alpha-shape Laplacian, considering data quality density distribution RPC. Experimental validation was conducted with acquired three distinct platforms. The proposed significantly enhances precision. Compared RANSAC-ICP, our reduced average RMSE 36.35%, MAE 34.85%, Frobenius Norm 84.48% experimental groups. improves completeness, reducing count about 30% compared Moreover, it effectively preserves detailed features fused cloud, serving accurate sources constructing models.

Язык: Английский

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UAV-Spherical Data Fusion Approach to Estimate Individual Tree Carbon Stock for Urban Green Planning and Management

Remote Sensing, Год журнала: 2024, Номер 16(12), С. 2110 - 2110

Опубликована: Июнь 11, 2024

Due to ever-accelerating urbanization in recent decades, exploring the contributions of trees mitigating atmospheric carbon urban areas has become one paramount concerns. Remote sensing-based approaches have been primarily implemented estimate tree-stand stock (CS) for parks and streets. However, a convenient yet high-accuracy computation methodology is hardly available. This study introduces an approach that tested small area. A data fusion based on three-dimensional (3D) was applied calibrate individual tree CS. photogrammetry-based technique employed unmanned aerial vehicle (UAV) spherical image compute total height (H) diameter at breast (DBH) each tree, consequently estimating regression analysis conducted compare results with ones obtained high-cost laser scanner data. Our demonstrates applicability this method, highlighting its advantages even large city contrast other are often more expensive. could serve as efficient tool assisting planners ensuring proper utilization available green space, especially complex environment.

Язык: Английский

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Modeling a new taper curve and form factor of tree branches using terrestrial laser scanning

Forestry An International Journal of Forest Research, Год журнала: 2024, Номер unknown

Опубликована: Янв. 11, 2024

Abstract Modeling branch taper curve and form factor contributes to increasing the efficiency of tree crown reconstructions: taper, defined as sequential measure diameters along course branch, is pivotal accurately estimate key variables such biomass volume. Branch or volumes have commonly been estimated from terrestrial laser scanning (TLS) based on automatized voxelization cylinder-fitting approaches, given whole length sufficiently covered by reflections. The results are, however, often affected ample variations in point cloud characteristics caused varying density, occlusions, noise. As these TLS can hardly be controlled eliminated techniques, we proposed a new model factor, which employed directly reflections under variable characteristics. In this paper, approach demonstrated primary branches using set TLS-derived datasets sample 20 trees (six species). showed an R2 0.86 mean relative absolute error 1.03 cm (29%) when validated with field-measured diameters. improved accuracy diameter estimates for fine scales (&lt;10 cm) compared quantitative structural (QSM). Our also allowed estimation relatively larger number manually recognized (&gt;85%) clouds panoramic images acquired simultaneously scanning. Frequently used reconstructions QSM, other hand, were gaps due obstruction, crown-tops finer being most critical. reports factors across examined species 0.35 0.49, determined at 5% 10% total length, respectively. may potential produce volume information reasonable only knowing respective each branch. delivers first approximation but was developed small samples. We believe that our holds improve assessment data. extended orders. This could expand horizon volumetric calculations non-destructive proxies crowns.

Язык: Английский

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Assessment of Carbon Sink and Carbon Flux in Forest Ecosystems: Instrumentation and the Influence of Seasonal Changes

Remote Sensing, Год журнала: 2024, Номер 16(13), С. 2293 - 2293

Опубликована: Июнь 23, 2024

Accurate measurement and estimation of forest carbon sinks fluxes are essential for developing effective national global climate strategies aimed at reducing atmospheric concentrations mitigating change. Various errors arise during monitoring, especially instability due to seasonal variations, which require be adequately addressed in ecosystem research applications. Seasonal fluctuations temperature, precipitation, aerosols, solar radiation can significantly impact the physical observations mapping equipment or platforms, thereby data’s accuracy. Here, we review technologies used monitoring across different remote sensing including ground-based, airborne, spaceborne sensing. We further investigate uncertainties introduced by variations observing equipment, compare strengths weaknesses various technologies, propose corresponding solutions recommendations. aim gain a comprehensive understanding on accuracy map data, improving fluxes.

Язык: Английский

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