Measuring Height Characteristics of Sagebrush (Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS)

Howell, Ryan G.; Jensen, Ryan R.; Petersen, Steven L.; Larsen, Randy T.

doi:10.3390/drones4010006

Cited by 12 publications

(13 citation statements)

References 38 publications

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“…In this study, we did not include structural data as computing the structure‐for‐motion point cloud can be computational and time intensive, which may be a limitation for user groups who wish to apply these drone imagery protocols. Nevertheless, UAS‐derived structural measurements can accurately represent plant height and aboveground biomass in dryland ecosystems (Cunliffe et al, 2016; Howell et al, 2020) and would likely improve classification accuracy of PFTs.…”

Section: Discussionmentioning

confidence: 99%

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

et al. 2022

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The structure and composition of plant communities in drylands are highly variable across scales, from microsites to landscapes. Fine spatial resolution field surveys of dryland plants are essential to unravel the impact of climate change; however, traditional field data collection is challenging considering sampling efforts and costs. Unoccupied aerial systems (UAS) can alleviate this challenge by providing standardized measurements of plant community attributes with high resolution. However, given widespread heterogeneity in plant communities in drylands, and especially across environmental gradients, the transferability of UAS imagery protocols is unclear. Plant functional types (PFTs) are a classification scheme that aggregates the diversity of plant structure and function. We mapped and modeled PFTs and fractional photosynthetic cover using the same UAS imagery protocol across three dryland communities, differentiated by a landscape-scale gradient of elevation and precipitation. We compared the accuracy of the UAS products between the three dryland sites. PFT classifications and modeled photosynthetic cover had highest accuracies at higher elevations (2241 m) with denser vegetation. The lowest site (1101 m), with more bare ground, had the least agreement with the field data. Notably, shrub cover was well predicted across the gradient of elevation and precipitation ($230-1100 mm/year). UAS surveys captured the heterogeneity of plant cover across sites and presented options to measure leaf-level composition and structure at landscape levels. Our results demonstrate that some PFTs (i.e., shrubs) can readily be detected across sites using the same UAS imagery protocols, while others (i.e., grasses) may require site-specific flight protocols for best accuracy. As UAS are increasingly used to monitor dryland vegetation, developing protocols that maximize information and efficiency is a research and management priority.

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Section: Discussionmentioning

confidence: 99%

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

et al. 2022

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“…As previously mentioned, in the agricultural context, it was reported that the error of the plant height measured using drones is 88.39 mm [31] or approximately 100 mm [32], and further improvement is required. From the rst experiment, it was con rmed that high-precision growth measurement of plants was possible using the proposed method.…”

Section: Second Experimentsmentioning

confidence: 99%

“…Remote sensing with a xed camera enables high-precision measurement of plant height [28,29] ; however, the accuracy of measurement decreases when the position and attitude of the camera, such as drone measurement, need to be speci ed. It was reported that the error in the plant height measured using drones is 88.39 mm [30] or approximately 100 mm [31] , and further improvement in accuracy is required.…”

Section: Limitations Of Sfm (Agricultural Context)mentioning

confidence: 99%

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

Matsuura

Zhang

Nakao

et al. 2022

Preprint

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Conventional crop height measurements performed using aerial drone images require the 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation times and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths during flight by linking the RTK-GNSS and aerial image capture points. A new calibration method is proposed to further improve the accuracy and stereo matching speed. Throught the comparison between the proposed method and conventional methods in natural world environments, wherein it reduced the error rates by 62.2% and 69.4%, at flight altitudes of 10 and 20 m. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the errors were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.

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“…Additionally, the capabilities and speed of computational SfM data processing are steadily increasing. This collection method has facilitated advances for many scientific investigations, including object height detection [17,18], geomorphic change [19,20], geologic change [3], glacial change [21], and cliff stability [22].…”

Section: Introductionmentioning

confidence: 99%

“…Much of the recent research in UAS-based topographic change measurement methodologies has been focused on increasing the accuracy [23][24][25] and efficiency [26][27][28] of digital elevation model (DEM) production from SfM photogrammetry. Some methodological studies have focused on reducing the above ground level (AGL) flight height of the UAS, to achieve a lower ground sample distance (GSD; the real-world space contained within a pixel of data) and therefore increase accuracy [17,29]. Other studies have focused on the methods for the optimization of survey control alignment [26,30,31].…”

Section: Introductionmentioning

confidence: 99%

A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA

Crawford

Swanson

Schultz-Fellenz

et al. 2021

Drones

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The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.

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Measuring Height Characteristics of Sagebrush (Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS)

Cited by 12 publications

References 38 publications

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA

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