Evaluating the potential of handheld mobile laser scanning for an operational inclusion in a national forest inventory – A Swiss case study

Remote Sensing of Environment, Год журнала: 2025, Номер 321, С. 114685 - 114685

Опубликована: Март 4, 2025

Язык: Английский

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Analysis of UAS-LiDAR Ground Points Classification in Agricultural Fields Using Traditional Algorithms and PointCNN

Remote Sensing, Год журнала: 2023, Номер 15(2), С. 483 - 483

Опубликована: Янв. 13, 2023

Classifying bare earth (ground) points from Light Detection and Ranging (LiDAR) point clouds is well-established research in the forestry, topography, urban domains using acquired by Airborne LiDAR System (ALS) at average densities (≈2 per meter-square (pts/m2)). The paradigm of cloud collection has shifted with advent unmanned aerial systems (UAS) onboard affordable laser scanners commercial utility (e.g., DJI Zenmuse L1 sensor) unprecedented repeatability UAS-LiDAR surveys. Therefore, there an immediate need to investigate existing methods, develop new ground classification UAS-LiDAR. In this paper, for first time, traditional algorithms modern machine learning methods were investigated filter high-density data (≈900 pts/m2) over five agricultural fields North Dakota, USA. To end, we tested frequently used algorithms: Cloth Simulation Function (CSF), Progressive Morphological Filter (PMF), Multiscale Curvature Classification (MCC), ArcGIS along PointCNN deep model trained. We two aspects PointCNN: (a) accuracy optimized (i.e., fine adjustment user-defined parameters) training site, (b) transferability potential four yet diverse test fields. evaluation metrics omission error, commission total kappa coefficients showed that outperforms both aspects: overall accuracy,

Язык: Английский

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Robust characterisation of forest structure from airborne laser scanning—A systematic assessment and sample workflow for ecologists

Methods in Ecology and Evolution, Год журнала: 2024, Номер 15(10), С. 1873 - 1888

Опубликована: Сен. 6, 2024

Abstract Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of obtained using airborne laser scanning (ALS). CHMs widely used monitoring dynamics, mapping biomass calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses. Here, we high‐quality ALS data from nine sites in Australia, ranging semi‐arid shrublands to 90‐m tall Mountain Ash canopies, comprehensively assess algorithms. This included testing their sensitivity point cloud degradation quantifying the propagation errors derived metrics structure. We found that varied both predictions (differences up 10 m, or 60% height) characteristics (biases 5 40% height). Impacts properties on CHM‐derived varied, robust inference percentiles, considerable above‐ground estimates (~50 Mg ha −1 , 10% total) volatility quantify spatial associations canopies (e.g. gaps). However, also two algorithms—a variation a ‘spikefree’ algorithm adapts local pulse densities simple Delaunay triangulation first returns—allowed characterisation should thus create secure foundation comparisons space time. show choice has strong previously been largely overlooked. To address this, provide sample workflow best‐practice guidelines minimise biases uncertainty downstream In doing so, our study paves way more rigorous large‐scale dynamics scanning.

Язык: Английский

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UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Remote Sensing, Год журнала: 2024, Номер 16(4), С. 699 - 699

Опубликована: Фев. 16, 2024

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection ranging (LiDAR) has been proven to be the most effective tool crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable onboard unmanned aerial systems (UASs) have available commercial applications. UAS (ULSs) recently introduced, their operational procedures are not well investigated particularly an agricultural context multi-temporal point clouds. To acquire seamless quality clouds, ULS parameter assessment, flight altitude, pulse repetition rate (PRR), number of return echoes, becomes non-trivial concern. This article therefore aims investigate DJI Zenmuse L1 practices traditional density, canopy height modeling (CHM) techniques, comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed flights were conducted over experimental research site Fargo, North Dakota, USA, three dates. The altitudes varied from 50 m 60 above ground level (AGL) along scanning modes, repetitive/non-repetitive, frequency modes 160/250 kHz, echo (1n), (2n), (3n), assessed diverse dry corn, green sunflower, soybean, sugar beet, near harvest yet changing phenological stages. Our results showed that mode (2n) captures better than (1n) (3n) whereas provides highest penetration at 250 kHz compared 160 kHz. Overall, CHM heights correlated situ measurements R2 (0.99–1.00) root mean square error (RMSE) (0.04–0.09) m. Among all crops, soybeans lowest correlation (0.59–0.75) RMSE (0.05–0.07) We weaker occurred due selective underestimation short crops influenced by phonologies. explained mode, PRR, analysis unable completely decipher impact acquired For first time context, we phenology meaningful clouds revealed WF analyses. Nonetheless, present study established state-of-the-art benchmark framework optimization datasets.

Язык: Английский

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Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 30, 2024

Abstract Forests display tremendous structural diversity, shaping carbon cycling, microclimates, and terrestrial habitats. One of the most common tools for forest structure assessments are canopy height models (CHMs): maps obtained at high resolution large scale from airborne laser scanning (ALS). CHMs can be computed in many ways, but little is known about robustness different CHM algorithms how they affect ecological analyses. Here, we used high-quality ALS data nine sites Australia, ranging semi-arid shrublands to 90-m tall Mountain Ash canopies, comprehensively assess algorithms. This included testing their sensitivity point cloud degradation quantifying propagation errors derived metrics structure. We found that varied widely both predictions (differences up 10 m, or 60% height) characteristics (biases ∼5 m 40% height). Impacts properties on CHM-derived varied, robust inference percentiles, considerable aboveground biomass estimates (∼50 Mg ha −1 , 10% total), volatility quantify spatial associations canopies (e.g., gaps autocorrelation). In some cases, biases exceeded variation across by a factor 2. However, also two – “spikefree” algorithm adapts local pulse densities simple Delaunay triangulation first returns allowed characterization should thus create secure foundation comparisons space time. Canopy tool ecology, derivation not trivial. Our study provides best-practice guideline sample workflow minimize uncertainty downstream doing so pave way global-scale complexity scanning.

Язык: Английский

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Assessing Methods to Measure Stem Diameter at Breast Height with High Pulse Density Helicopter Laser Scanning

Remote Sensing, Год журнала: 2025, Номер 17(2), С. 229 - 229

Опубликована: Янв. 10, 2025

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods estimate diameter. Stem horizontal size was estimated every 25 cm living crown, cubic spline used where there were gaps. Individual diameter at breast height (DBH) for 77% of field-measured trees. The root mean square error (RMSE) DBH estimates 7–12 circle fitting. Adapting approach use an existing taper model reduced RMSE (<1 cm). In contrast, produced from previously estimation (PREV) could be achieved 100% stems (DBH 6 cm), but only after location-specific corrected. required comparatively little development statistical models provide estimates, which ultimately had similar level accuracy (RMSE < 1 cm) PREV. HALS datasets can measure broad-scale plantations reduce field efforts should considered important tool aiding in inventory creation decision-making within management.

Язык: Английский

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Integration of UAS and Backpack-LiDAR to Estimate Aboveground Biomass of Picea crassifolia Forest in Eastern Qinghai, China

Remote Sensing, Год журнала: 2025, Номер 17(4), С. 681 - 681

Опубликована: Фев. 17, 2025

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements Diameter at Breast Height with height (DBH H), optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity AGB are labor-intensive. Recent advancements technologies, predominantly Light Detection Ranging (LiDAR), offer potential improvements accurate Nonetheless, there limited research on the combined use UAS (Uncrewed Aerial System) Backpack-LiDAR technologies forest biomass. Thus, our study aimed to estimate plot level Picea crassifolia eastern Qinghai, China, by integrating UAS-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed segment point clouds from Backpack-LiDAR, detect seed points calculate DBH individual trees. After that, using these initial files, we segmented trees data employing Point Cloud Segmentation (PCS) method measured tree heights, which enabled calculation observed/measured across three specific areas. Furthermore, advanced regression models, Random Forest (RF), Multiple Linear Regression (MLR), Support Vector (SVR), used integrated both sources (UAS Backpack-LiDAR). Our results show that: (1) extracted compared shows about (R2 = 0.88, RMSE 0.04 m) whereas achieved accuracy 0.91, 1.68 m), verifies reliability abstracted obtained LiDAR (2) Individual Tree (ITS) a file X Y coordinates Backpack UAS-LiDAR, attaining total F-score 0.96. (3) Using allometric equation, ranges 9.95–409 (Mg/ha). (4) RF model demonstrated superior coefficient determination (R2) 89%, relative Root Mean Square Error (rRMSE) 29.34%, (RMSE) 33.92 Mg/ha MLR SVR models prediction. (5) combination enhanced ITS forests. This work highlights advance monitoring, can be very important climate change mitigation environmental monitoring practices.

Язык: Английский

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Automated registration of forest point clouds from terrestrial and drone platforms using structural features

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2025, Номер 223, С. 28 - 45

Опубликована: Март 12, 2025

Язык: Английский

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Occlusion mapping reveals the impact of flight and sensing parameters on vertical forest structure exploration with cost-effective UAV based laser scanning

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104493 - 104493

Опубликована: Апрель 6, 2025

Язык: Английский

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UAV‐Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate

Journal of Ecology, Год журнала: 2023, Номер 111(7), С. 1411 - 1427

Опубликована: Апрель 18, 2023

Abstract Widespread forest loss and fragmentation dramatically increases the proportion of areas located close to edges. Although detrimental, precise extent mechanisms by which edge proximity impacts remnant forests remain be ascertained. By combining unmanned aerial vehicle laser scanning (UAV‐LS) with field data from 46 plots distributed at varying distances interior in a fragmented New‐Caledonia, we investigated influence on structure, composition, function, above‐ground biomass (AGB) microclimate. Using simple linear regressions, structural equation modelling variance partitioning, analysed direct indirect relationships between distance edge, UAV‐LS‐derived canopy metrics, understorey microclimate, AGB, taxonomic functional composition while accounting for potential fine‐scale variation topography. We found that closest was strongly correlated structure better microclimate than edge. This suggests is mediated changes structure. Plots near exhibited lower more gaps, higher extremes, biomass, diversity as well denser wood specific leaf area. metrics were relevant predictors composition. Overall, topography marginal compared effects. Synthesis . Accounting captured UAV‐LS provides insights multiple key properties related diversity, microenvironmental conditions. Integrating can foster our understanding cascading interacting anthropogenic tropical ecosystems should help improve conservation strategies landscape management policies.

Язык: Английский

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