Effects of Differences in Structure from Motion Software on Image Processing of Unmanned Aerial Vehicle Photography and Estimation of Crown Area and Tree Height in Forests

Kameyama, Shohei; Sugiura, Katsuaki

doi:10.3390/rs13040626

Cited by 25 publications

(13 citation statements)

References 53 publications

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“…Agisoft Metashape and Pix4Dmapper were found to have better performance than Con-textCapture, which had a worse accuracy of point cloud models reconstruction than other software due to limitations in the performance of the image matching algorithm [51,52]. In addition, Pix4Dmapper was found to have similar performance [51,[53][54][55] with Agisoft Metashape in accuracy of point cloud models reconstruction and have better density [51] than Agisoft Metashape.…”

Section: Point Cloud Reconstructionmentioning

confidence: 93%

Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry

Shan

Zhu

2023

Structural Control and Health Monitoring

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Camera-enabled unmanned aerial vehicles (UAVs) provide a promising technique to considerably speed up the inspection and visual data collection from regions that may otherwise be inaccessible. In addition, the technology of image-based 3D reconstruction can generate a point cloud model using images captured by UAVs. However, the performance of the point cloud modeling may be affected by multiple factors, such as the modeling software, ground control points (GCPs), and UAV flight modes. In this study, three common software packages were compared, and Pix4Dmapper was considered a suitable software for point cloud modeling for earthquake-damaged buildings. The accuracy and resolution of point cloud models are usually evaluated by root mean square error (RMSE) and ground sampling distance (GSD). The effects of the main factors, including the number of GCPs, distribution of GCPs, flight manner of the UAV, and distance from the UAV to the target, were investigated on the basis of two real-world multistory earthquake-damaged structures. The influence rules of the main factors revealed that a close range, automatic flight mode of the UAV, a large number of GCPs, and a relatively wide distribution of the GCPs may generate a point cloud model with low computational costs, high accuracy, and high resolution. In the particular illustration example here, the RMSE is 6.78 mm while the GSD is 1.60 mm. Finally, rapid structural damage inspection was demonstrated using an accurate point cloud model and compared with the inspection results of a total station and terrestrial laser scanner point cloud models. The comparison of different inspection results showed that the relative errors were relatively acceptable within 4%.

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Section: Point Cloud Reconstructionmentioning

confidence: 93%

Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry

Shan

Zhu

2023

Structural Control and Health Monitoring

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“…Individual DBH and stand-level estimates of basal area, stand density, and canopy cover (CC) are commonly used as forest mensuration metrics. Kameyama and Sugiura [ 58 ] estimated the CA and TH using different SfM software such as Terra Mapper (version 2.5.1), PhotoScan (version 1.3.2.4205), and Pix4Dmapper (version 4.5.6) on aerial image processing by UAV at different altitudes. The UAV has been used successfully in several recent studies to predict the DBH distribution of trees [ 59 ], mean TH [ 60 ], and aboveground CST [ 10 ].…”

Section: Review Of Literaturementioning

confidence: 99%

UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

Karthigesu,

Owari,

Tsuyuki

et al. 2023

Sensors

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Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8–0.87) and TH (R2: 0.76–0.77; RMSE: 1.45–1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering.

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“…Inter-sensor comparisons reveal differences in surface reconstruction and reflectance values between common consumer-grade UAS platforms (Sona et al, 2014;Yanagi and Chikatsu, 2016). Moreover, other studies have found differences in mapping products produced by different SfM software packages for forested (Fraser and Congalton, 2018;Kameyama and Sugiura, 2021) and unvegetated (Sona et al, 2014;Yanagi and Chikatsu, 2016) areas. Variability introduced by UAS flight protocols and UAS-SfM workflows for mapping dynamic coastal wetland environments has not yet been well explored.…”

Section: Introductionmentioning

confidence: 95%

Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

DiGiacomo¹,

Giannelli²,

Puckett³

et al. 2022

Front. Remote Sens.

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Coastal wetlands of the Southeastern United States host a high abundance and diversity of critical species and provide essential ecosystem services. A rise in threats to these vulnerable habitats has led to an increased focus on research and monitoring in these areas, which is traditionally performed using manual measurements of vegetative characteristics. As these methods require substantial time and effort, they are often limited in scale and infeasible in areas of dense or impassable habitat. Unoccupied Aircraft Systems (UAS) provide an advantage over traditional ground-based methods by serving as a non-invasive alternative that expands the scale at which we can understand these ecosystems. While recent interest in UAS-based monitoring of coastal wetland habitats has grown, methods and parameters for UAS-based mapping lack standardization. This study addresses variability introduced by common UAS study techniques and forms recommendations for optimal survey designs in vegetated coastal habitats. Applying these parameters, we assess alignment of computed estimations with manually collected measurements by comparing UAS-SfM mapping products to ground-based data. This study demonstrates that, with careful consideration in study design and analysis, there exists great potential for UAS to provide accurate, large-scale estimates of common vegetative characteristics in coastal salt marshes.

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Effects of Differences in Structure from Motion Software on Image Processing of Unmanned Aerial Vehicle Photography and Estimation of Crown Area and Tree Height in Forests

Cited by 25 publications

References 53 publications

Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry

Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry

UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

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