Assessing the Accuracy of Unmanned Aerial Vehicles Photogrammetric Survey

Adjidjonu, Denise; Burgett, Joseph M.

doi:10.1080/15578771.2020.1717683

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…This terrain finding process enables the isolation of canopyheight from topography. While it is possible to achieve sub-centimeter DTM accuracy from UAS-SfM for bare ground conditions [46], increasing vegetation cover heavily impacts DTM accuracy [47][48][49]. Through two rounds of ground finding parameter optimization, we achieved a site wide DTM RMSE of 2.12 m. However, 8% of DTM coverage in forest areas contained errors over 4 m. This height threshold represents the upper limit of the ladder fuel metric from Green et al [45], so errors of this magnitude connote an inability to detect surface or ladder fuels.…”

Section: Dtm and Chm Generation Capability Comparisonsmentioning

confidence: 99%

The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests

et al. 2021

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Wildfire shapes vegetation assemblages in Mediterranean ecosystems, such as those in the state of California, United States. Successful restorative management of forests in-line with ecologically beneficial fire regimes relies on a thorough understanding of wildfire impacts on forest structure and fuel loads. As these data are often difficult to comprehensively measure on the ground, remote sensing approaches can be used to estimate forest structure and fuel load parameters over large spatial extents. Here, we analyze the capabilities of one such methodology, unoccupied aerial system structure from motion (UAS-SfM) from digital aerial photogrammetry, for mapping forest structure and wildfire impacts in the Mediterranean forests of northern California. To determine the ability of UAS-SfM to map the structure of mixed oak and conifer woodlands and to detect persistent changes caused by fire, we compared UAS-SfM derived metrics of terrain height and canopy structure to pre-fire airborne laser scanning (ALS) measurements. We found that UAS-SfM was able to accurately capture the forest’s upper-canopy structure, but was unable to resolve mid- and below-canopy structure. The addition of a normalized difference vegetation index (NDVI) ground point filter to the DTM generation process improved DTM root-mean-square error (RMSE) by ~1 m with an overall DTM RMSE of 2.12 m. Upper-canopy metrics (max height, 95th percentile height, and 75th percentile height) were highly correlated between ALS and UAS-SfM (r > +0.9), while lower-canopy metrics and metrics of density and vertical variation had little to no similarity. Two years after the 2017 Sonoma County Tubbs fire, we found significant decreases in UAS-SfM metrics of bulk canopy height and NDVI with increasing burn severity, indicating the lasting impact of the fire on vegetation health and structure. These results point to the utility of UAS-SfM as a monitoring tool in Mediterranean forests, especially for post-fire canopy changes and subsequent recovery.

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Section: Dtm and Chm Generation Capability Comparisonsmentioning

confidence: 99%

The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests

et al. 2021

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“…The software capabilities that allow for object recognition allow for creating the largescale model in the first place. Agisoft Metashape © (see https://www.agisoft.com accessed 6 November 2021) and Bentley™ ContextCapture frequently receive the photos and metadata as inputs, and outputs fully functional SfM 3D models; the models are processed by readymade standard analyses as well as additional capabilities to manipulate and understand the data/models [35,42,47,58,[73][74][75][76][77]. CloudCompare also streamlines analysis of models and point clouds [42,47,57,58,[78][79][80].…”

Section: Specific Software (Machine Learning) Methodologymentioning

confidence: 99%

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Berrett

Vernon

Beckstrand

et al. 2021

Drones

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Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic COVID-19 pandemic during a mandatory and rare campus closure and details a large scale modeling workflow and best practice data acquisition and processing techniques. The model utilizes 80,384 images and high accuracy GPS surveying points to create a 1.65 trillion-pixel textured structure-from-motion (SfM) model with an average ground sampling distance (GSD) near structures of 0.5 cm and maximum of 4 cm. Separate model segments (31) taken from data gathered between April and August 2020 are combined into one cohesive final model with an average absolute error of 3.3 cm and a full model absolute error of <1 cm (relative accuracies from 0.25 cm to 1.03 cm). Optimized and automated UAV techniques complement the data acquisition of the large-scale model, and opportunities are explored to archive as-is building and campus information to enable historical building preservation, facility maintenance, campus planning, public outreach, 3D-printed miniatures, and the possibility of education through virtual reality (VR) and augmented reality (AR) tours.

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“…The map comprises several landmarks and at some timesteps, the sensors can see a part of the landmarks and record their observations [122]. Let there be L landmarks in the map denoted by l 1 , l 2 , • • • , l L [123].…”

Section: Slam In Uav: Motivation and Requirementsmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta

Fernando

2022

Drones

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This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

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Assessing the Accuracy of Unmanned Aerial Vehicles Photogrammetric Survey

Cited by 8 publications

References 11 publications

The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests

The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

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