Analysis of Crop Dynamics through Close-Range UAS Photogrammetry

Arriola-Valverde, Sergio; Villagra-Mendoza, Karolina; Méndez-Morales, Maikel; Solórzano-Quintana, Milton; Gómez-Calderón, Natalia; Rimolo-Donadío, Renato

doi:10.1109/iscas45731.2020.9181285

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Meanwhile, UAS-SfM applications are well documented in the literature. For example, UAS-SFM has been involved in various assessment applications, including roadways (e.g., [25][26][27]), railroads (e.g., [28,29]), structures (e.g., [5,7,30]), geotechnical slopes (e.g., [31,32]), and agricultural crops (e.g., [33][34][35]), and deep learning-based structural damage classification following natural hazard events (e.g., [36,37]).…”

Section: Literature Reviewmentioning

confidence: 99%

Discrete and Distributed Error Assessment of UAS-SfM Point Clouds of Roadways

Yi-jun

Wood

2020

Infrastructures

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Perishable surveying, mapping, and post-disaster damage data typically require efficient and rapid field collection techniques. Such datasets permit highly detailed site investigation and characterization of civil infrastructure systems. One of the more common methods to collect, preserve, and reconstruct three-dimensional scenes digitally, is the use of an unpiloted aerial system (UAS), commonly known as a drone. Onboard photographic payloads permit scene reconstruction via structure-from-motion (SfM); however, such approaches often require direct site access and survey points for accurate and verified results, which may limit its efficiency. In this paper, the impact of the number and distribution of ground control points within a UAS SfM point cloud is evaluated in terms of error. This study is primarily motivated by the need to understand how the accuracy would vary if site access is not possible or limited. In this paper, the focus is on two remote sensing case studies, including a 0.75 by 0.50-km region of interest that contains a bridge structure, paved and gravel roadways, vegetation with a moderate elevation range of 24 m, and a low-volume gravel road of 1.0 km in length with a modest elevation range of 9 m, which represent two different site geometries. While other studies have focused primarily on the accuracy at discrete locations via checkpoints, this study examines the distributed errors throughout the region of interest via complementary light detection and ranging (lidar) datasets collected at the same time. Moreover, the international roughness index (IRI), a professional roadway surface standard, is quantified to demonstrate the impact of errors on roadway quality parameters. Via quantification and comparison of the differences, guidance is provided on the optimal number of ground control points required for a time-efficient remote UAS survey.

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Section: Literature Reviewmentioning

confidence: 99%

Discrete and Distributed Error Assessment of UAS-SfM Point Clouds of Roadways

Yi-jun

Wood

2020

Infrastructures

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“…The proliferation of small unmanned aircraft systems (UAS) has provided new opportunities to optimize crop production through remote sensing. UAS-based remote sensing holds the promise of delivering highly precise measurements of crop status at spatiotemporal resolutions that far exceed what is achievable using conventional aircraft or satellite imagery [1][2][3][4][5][6][7]. However, there are tradeoffs that generally come from using smaller and less sophisticated instrumentation suitable for UAS deployment [8], which limit the accuracy of imagery for large-scale vegetation modeling [9].…”

Section: Introductionmentioning

confidence: 99%

Automating ground control point detection in UAS imagery using matrix barcodes

Ladino,

Sama

2024

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX

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Small unmanned aircraft systems (UAS) are increasingly used for remote sensing applications in precision agriculture due to their ability to collect high-resolution imagery. However, spatial calibration of UAS imagery is often a manual process that requires extensive planning and post-processing, presenting bottlenecks for automating image analysis workflows. This study seeks to address bottlenecks in the photogrammetry workflow that arise from manually tagging ground control points (GCPs) by automating the process. The main objectives included investigating (1) the application of data compression techniques for global navigation satellite system (GNSS) coordinates in generating matrix barcode representations and (2) the recovery of GNSS coordinates from matrix barcodes using a small UAS. GNSS coordinates were compressed using a base-36 encoding schema and encoded into QR code GCPs to reduce the number of alphanumeric characters required. Preliminary in-field testing demonstrated the reliability of recovering QR code GCPs from aerial imagery across various altitudes and exposure settings, with adjustments in exposure compensation mitigating altituderelated recoverability issues. Moreover, results indicated that the processing of aerial imagery into orthomosaic images did not compromise QR code recoverability. Further in-field testing identified QR code GCP background color as a key factor influencing recoverability, with darker colors generally improving recoverability. Statistical analysis validated altitude and background color as significant predictors of QR code GCP recoverability. Future research avenues include incorporating environmental factors such as solar radiation to improve statistical model fit. Overall, QR code GCPs offer a potential approach for automating photogrammetry workflows, reducing both time and labor associated with manual tagging.

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