Impact of flight altitude and cover orientation on Digital Surface Model (DSM) accuracy for flood damage assessment in Murcia (Spain) using a fixed-wing UAV

Anders, Niels; Smith, Mike; Suomalainen, Juha; Cammeraat, Erik; Valente, João; Keesstra, Saskia

doi:10.1007/s12145-019-00427-7

Cited by 28 publications

(14 citation statements)

References 43 publications

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“…In addition, UAV Remote Sensing (UAV-RS) is being gradually achieved by employing UAVs as carriers with imaging or non-imaging sensors to capture high-resolution remote sensing images, SAR images, as well as highprecision laser point clouds [1][2][3]. Since low-altitude and small UAVs exhibit several advantages (e.g., mobility, flexibility, low-cost, and rich data results) [4], they have been Sensors 2021, 21, 8109 2 of 17 adopted to generate 3D scenes by integrating Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms [5] for landslide disaster monitoring [6], flood hazard assessment [7], geomorphological evolution analysis [8], as well as crop monitoring [9].…”

Section: Introductionmentioning

confidence: 99%

Studies on Three-Dimensional (3D) Accuracy Optimization and Repeatability of UAV in Complex Pit-Rim Landforms As Assisted by Oblique Imaging and RTK Positioning

Gan

Yuan

et al. 2021

Sensors

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Unmanned Aerial Vehicles (UAVs) are a novel technology for landform investigations, monitoring, as well as evolution analyses of long−term repeated observation. However, impacted by the sophisticated topographic environment, fluctuating terrain and incomplete field observations, significant differences have been found between 3D measurement accuracy and the Digital Surface Model (DSM). In this study, the DJI Phantom 4 RTK UAV was adopted to capture images of complex pit-rim landforms with significant elevation undulations. A repeated observation data acquisition scheme was proposed for a small amount of oblique-view imaging, while an ortho-view observation was conducted. Subsequently, the 3D scenes and DSMs were formed by employing Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms. Moreover, a comparison and 3D measurement accuracy analysis were conducted based on the internal and external precision by exploiting checkpoint and DSM of Difference (DoD) error analysis methods. As indicated by the results, the 3D scene plane for two imaging types could reach an accuracy of centimeters, whereas the elevation accuracy of the orthophoto dataset alone could only reach the decimeters (0.3049 m). However, only 6.30% of the total image number of oblique images was required to improve the elevation accuracy by one order of magnitude (0.0942 m). (2) An insignificant variation in internal accuracy was reported in oblique imaging-assisted datasets. In particular, SfM-MVS technology exhibited high reproducibility for repeated observations. By changing the number and position of oblique images, the external precision was able to increase effectively, the elevation error distribution was improved to become more concentrated and stable. Accordingly, a repeated observation method only including a few oblique images has been proposed and demonstrated in this study, which could optimize the elevation and improve the accuracy. The research results could provide practical and effective technology reference strategies for geomorphological surveys and repeated observation analyses in sophisticated mountain environments.

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Section: Introductionmentioning

confidence: 99%

Studies on Three-Dimensional (3D) Accuracy Optimization and Repeatability of UAV in Complex Pit-Rim Landforms As Assisted by Oblique Imaging and RTK Positioning

Gan

Yuan

et al. 2021

Sensors

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“…While there is a clear tradeoff in product resolution with flight altitude (see Table 2), vertical accuracy does not demonstrate a clear trend with altitude. A similar study using a consumer-grade UAS to survey a shrubland area revealed an increase in vertical error with altitude (altitude range: 126-235 m) at the first site and consistent vertical error across altitudes at the second site (Anders et al, 2020). The range of low altitudes (25-120 m) used in this study are those most commonly used by United States UAS pilots given that flight altitudes above 120 m are restricted by the Federal Aviation Administration (FAA).…”

Section: Vertical Errormentioning

confidence: 77%

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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“…Very low-altitude UAV flight generates images with a very high spatial resolution; however, this results in limited area coverage and a safety problem. Very high altitude UAV flight reduces the spatial resolution and is likely to lose important information [24,25]. Therefore, these images were captured at an altitude of about 20 m to enable the ground resolution is about 1.3 cm/pixel, providing appropriate spatial information for yellow rust disease detection.…”

Section: Uav Multispectral Imaging Systemmentioning

confidence: 99%

Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

Zhang

et al. 2021

Remote Sensing

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Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales.

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Impact of flight altitude and cover orientation on Digital Surface Model (DSM) accuracy for flood damage assessment in Murcia (Spain) using a fixed-wing UAV

Cited by 28 publications

References 43 publications

Studies on Three-Dimensional (3D) Accuracy Optimization and Repeatability of UAV in Complex Pit-Rim Landforms As Assisted by Oblique Imaging and RTK Positioning

Studies on Three-Dimensional (3D) Accuracy Optimization and Repeatability of UAV in Complex Pit-Rim Landforms As Assisted by Oblique Imaging and RTK Positioning

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

Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

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