Assessing the accuracy of vegetative roughness estimates using unmanned aerial vehicles [UAVs]

Brignoli, Lorenzo; Annable, W. K.; Plumb, Benjamin

doi:10.1016/j.ecoleng.2018.01.005

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…UAVs are operationally more flexible and less expensive than aerial surveys (planes) and, because they can fly at lower altitudes, the surveys produce data with higher resolution [8,9]. Of particular note for this paper, there has been interest in using UAV data for hydraulic roughness estimates for use in hydrodynamic modeling [10,11].…”

Section: Introductionmentioning

confidence: 99%

Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

et al. 2021

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Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance of the SfM-derived vegetation heights/roughness values if we were using a SfM DEM, although further work is needed. These results will help improve hydrodynamic modeling efforts, which are becoming increasingly important for management and planning in response to climate change, specifically in regions were high flow events are increasing.

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Section: Introductionmentioning

confidence: 99%

Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

et al. 2021

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“…Other studies have concluded that low-altitude mapping using drones is a feasible solution for vegetation mapping in streams [24,42] using visual and/or NIR-bands for classification. Regarding vegetation mapping the collected data indicate that the coverage estimation of submersed and emergent vegetation is underestimated using the standard DFI method.…”

Section: Discussionmentioning

confidence: 99%

Physical stream quality measured by drones and image analysis versus the traditional manual method

Svane

Flindt

Petersen

et al. 2020

Environmental Technology

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Information of the physical and ecological state of streams along with an overview of the need for maintenance is traditionally a time-consuming manual field task with subsequent limitations in area coverage. Here we propose a novel approach to stream monitoring and management using a low-cost Unmanned Aerial Vehicle (UAV) platform to collect data comparable to that from traditional monitoring schemes. This technology provides high resolution imagery while being easy to implement at a low cost along with providing data that represent the stream in both fine scale and at landscape scale. The results show a significant correlation between results obtained by the two methods, with the largest difference in DFI-values being ten, but in many cases being < five. The UAV-method is especially strong in supporting geographical measurements of stream width and course along with certain stream parameters such as physical variation, water flow and gravel coverage. The results indicate that UAV mapping of streams is a feasible alternative or support to the traditional mapping of certain open stream types with the possibility of covering more area with the same time-use.

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Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review

et al. 2022

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Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized.

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Assessing the accuracy of vegetative roughness estimates using unmanned aerial vehicles [UAVs]

Cited by 3 publications

References 27 publications

Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

Physical stream quality measured by drones and image analysis versus the traditional manual method

Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review

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