Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests, Journal Year: 2021, Volume and Issue: 12(2), P. 250 - 250

Published: Feb. 22, 2021

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing overlap making acquisitions at lower altitudes improve structure motion point clouds represents canopies. However, only limited testing has evaluated resolution cloud filtering the individual locations heights. We evaluate Agisoft Metashape’s build dense Quality (image resolution) depth map filter settings influence canopy height models in ponderosa pine forests. Finer imagery with minimal provided best visual representation vegetation detail trees all sizes. These same maximized F-score >0.72 overstory (>7 m tall) >0.60 understory trees. Additionally, bias precision as becomes finer. Overstory open-canopy conifer might be optimized using finest that computer hardware enables, while applying filtering. The extended time data storage demands high-resolution must balanced against small reductions performance when down-scaling allow greater extents.

Language: Английский

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Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(2), P. 295 - 295

Published: Jan. 11, 2022

Detecting and mapping individual trees accurately automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods deep learning techniques, have been developed applied tree crown detection images. However, few studies evaluated the accuracy different (ITD) algorithms their data processing requirements. This study explored ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation with test Manually delineated crowns UAV imagery were used assessment three methods, followed by an evaluation application requirements to detect trees. Overall, R-CNN can best use information multi-band input detecting The results showed that model combination produced higher than single-band image, RGB band achieved highest (F1 score = 94.68%). Moreover, models are capable providing accuracies LM MCWS algorithms. algorithm also promising when canopy height (CHM) was as image 87.86% F1 85.92% algorithm). easy lower computer computational requirements, but they unable identify species limited parameters, which need be adjusted each classification. It highlighted its end-to-end-learning approach very efficient deriving multi-layer images, additional training set needed training, robust resources required, large number accurate samples necessary. provides valuable forestry practitioners select optimal

Language: Английский

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Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 263, P. 112540 - 112540

Published: June 5, 2021

Language: Английский

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Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(2), P. 298 - 298

Published: Jan. 10, 2022

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, which most appropriate segmentation method optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple types. This article compared applicability methods based on a canopy height model (CHM) normalized point cloud (NPC) obtained from data. The watershed algorithm, local maximum method, cloud-based cluster layer stacking were used segment trees extract parameters nine plots three results evaluated experimental field data, sensitivity was analyzed. Among all plots, overall accuracy F between 0.621 1, average RMSE extraction 1.175 m, RMSE% 12.54%. indicated that with CHM-based methods, NPC-based exhibited better performance segmentation; additionally, type complexity influence is preferred scheme while should supplement multilayer forests extremely complex heterogeneous forests. research provides guidance use accurately obtain structure perform In addition, this paper can employed vegetation indices, such height, leaf area index, coverage.

Language: Английский

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Comparison of Aerial and Ground 3D Point Clouds for Canopy Size Assessment in Precision Viticulture

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(5), P. 1145 - 1145

Published: Feb. 25, 2022

In precision viticulture, the intra-field spatial variability characterization is a crucial step to efficiently use natural resources by lowering environmental impact. recent years, technologies such as Unmanned Aerial Vehicles (UAVs), Mobile Laser Scanners (MLS), multispectral sensors, Apps (MA) and Structure from Motion (SfM) techniques enabled possibility characterize this with low efforts. The study aims evaluate, compare cross-validate potentiality limits of several tools (UAV, MA, MLS) assess vine canopy size parameters (thickness, height, volume) processing 3D point clouds. Three trials were carried out test different in vineyard located Chianti Classico area (Tuscany, Italy). Each was made UAV flight, an MLS scanning over MA acquisition 48 geo-referenced vines. Leaf Area Index (LAI) also assessed taken reference value. results showed that analyzed able correctly discriminate between zones characteristics. particular, R2 volumes acquired higher than 0.7, being highest value = 0.78 RMSE 0.057 m3 for vs. comparison. correlations found height data, 0.86 0.105 m For thickness weaker, lowest 0.48 0.052 correlation LAI moderately strong all 0.74 V_MLS data 0.69 V_UAV data.

Language: Английский

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UAV-enabled evaluation of forestry plantations: A comprehensive assessment of laser scanning and photogrammetric approaches

Published: Jan. 1, 2025

Language: Английский

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The Digital Forest: Mapping a Decade of Knowledge on Technological Applications for Forest Ecosystems

Earth s Future, Journal Year: 2021, Volume and Issue: 9(8)

Published: July 7, 2021

Abstract Forest ecosystem resilience is of considerable interest worldwide, particularly given the climate crisis, biodiversity loss, and recent instances zoonotic diseases linked to deforestation forest loss. Novel, digital‐based technologies are also increasingly ubiquitous. We provide a more comprehensive understanding how these new being used for management in different sectors contexts, discuss potential implications future research needs forestry researchers, managers, policymakers. carried out literature database search scoping review collect peer‐reviewed articles from 2010 2020, developed forest‐technology classification identify hardware and/or software techniques, methodology used, application(s), spatial temporal context, subsequent challenges limitations, opportunities. A qualitative analysis revealed strong emphasis on remote sensing‐based innovations monitoring, planning, management, where machine‐learning techniques play an important role data collection, processing, analysis. Data fusion approaches becoming common, enabled by open‐source sets sharing practices. More emerging applications include virtual/augmented environments human‐nature relationships behavior patterns, automated workflows operations, urban green infrastructure mapping services assessments via social media mobile tracking applications. The continued adoption tools will likely bring about questions ecosystems as dynamic social, ecological, technological landscapes, work should closely examine stakeholders can anticipate adapt both environmental uncertainty change forest‐ecosystem context.

Language: Английский

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Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (case study: Hyrcanian mixed forest)

Canadian Journal of Forest Research, Journal Year: 2021, Volume and Issue: 51(7), P. 962 - 971

Published: Jan. 26, 2021

Tree height and crown diameter are two common individual tree attributes that can be estimated from unmanned aerial vehicle (UAV) images thanks to photogrammetry structure motion. This research investigates the potential of low-cost UAV estimate diameter. Two successful flights were carried out in different seasons corresponding leaf-off leaf-on conditions generate a digital terrain model surface model, which further employed calculation canopy (CHM). The CHM was used using low pass local maximum filters, based on an inverse watershed segmentation algorithm. UAV-based estimates validated against field measurements resulted 3.22 m (10.1%) 0.81 (7.02%) root mean square errors, respectively. results showed high agreement between our measurements, with R 2 0.808 for 0.923 Generally, accuracy considered acceptable confirmed usefulness this approach estimating heights

Language: Английский

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Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(11), P. 2062 - 2062

Published: May 24, 2021

Estimation of urban tree canopy parameters plays a crucial role in forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide means to produce 3D point clouds using photogrammetric Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also very dense clouds. While derived from both LiDAR allow the accurate estimation critical parameters, so far comparison techniques is missing. Point these sources vary according differences data collection processing, detailed terms accuracy completeness, relation necessary. In this research, produced UAV-photogrammetry -LiDAR over park along with estimated are compared, results presented. The show that highly correlated R2 99.54% higher than 95%.

Language: Английский

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Teaching Innovation in STEM Education Using an Unmanned Aerial Vehicle (UAV)

Education Sciences, Journal Year: 2022, Volume and Issue: 12(3), P. 224 - 224

Published: March 18, 2022

The use of unmanned aerial vehicles (UAVs) has increased in the science, technology, engineering, and mathematics (STEM) professions. This means there is a growing need to integrate UAV training into STEM education. study aimed develop evaluate education module laboratory exercise for natural resource science students. used series reusable learning objects (RLOs) assess students’ prior knowledge remote sensing UAVs. Students were taught steps data acquisition processing through lectures simulation videos. applied this by completing that previously collected data. Student retention understanding evaluated using an online quiz determine effectiveness module. average score was 92%, indicating effectively students about research. Overall, expressed positive opinions feedback indicated engaging, but some would have preferred hands-on experience parts exercise. However, in-person instruction may not be accessible all educators because cost or lack instructor training. provides with crucial recommendations designing exercises improve access UAV-related educational content. indicates can introduce Given wide range uses across fields, many disciplines benefit from

Language: Английский

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