scholarly journals Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

2018 ◽  
Vol 12 (11) ◽  
pp. 3535-3550 ◽  
Author(s):  
Richard Fernandes ◽  
Christian Prevost ◽  
Francis Canisius ◽  
Sylvain G. Leblanc ◽  
Matt Maloley ◽  
...  
Keyword(s):  
Free Surface ◽  
Unmanned Aerial Vehicle ◽  
Structure From Motion ◽  
Snow Depth ◽  
Low Cost ◽  
Horizontal Resolution ◽  
Absolute Difference ◽  
Flat Surfaces ◽  
Aerial Vehicle

Abstract. Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼2 and ∼15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.

scholarly journals Monitoring snow depth change across a range of landscapes with ephemeral snow packs using Structure from Motion applied to lightweight unmanned aerial vehicle videos

2018 ◽  
Author(s):  
Richard Fernandes ◽  
Christian Prevost ◽  
Francis Canisius ◽  
Sylvain G. Leblanc ◽  
Matt Maloley ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Structure From Motion ◽  
Snow Depth ◽  
A Priori ◽  
Horizontal Resolution ◽  
Flat Surfaces ◽  
Alpine Regions ◽  
Surface Models ◽  
Order Of Magnitude ◽  
Aerial Vehicle

Abstract. Snow depth (SD) can vary by more than an order of magnitude over length scales of metres due to topography, vegetation and microclimate. Differencing of digital surface models derived from Structure from Motion (SfM) processing of airborne imagery has been used to produce SD maps with between ∼2 cm to ∼15 cm horizontal resolution and accuracies on the order of ±10 cm over both relatively flat surfaces with little or no vegetation and over alpine regions. Studies indicate that accuracy is lower in the presence of vegetation above or below the snowpack and in rough topography; suggesting that some biases may be temporally persistent. Moreover, flight and image parameters vary across studies but they are typically not related a priori to an expected uncertainty in SD. This study tests two hypotheses: i) that SD change can be more accurately estimated when differencing snow covered elevation surfaces rather than the absolute snow depth based on differencing a snow covered and snow free surface and ii) the vertical accuracy of SfM processing of imagery acquired by commercial light weight unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory. Moreover, these hypotheses are tested over areas with ephemeral snow pack conditions and across a range of micro-topography and vegetation cover. Weekly SD maps with

scholarly journals A new rapid, low-cost and GPS-centric unmanned aerial vehicle incorporating in-situ multispectral oil palm trees health detection.

2018 ◽  
Vol 02 (04) ◽  
Author(s):  
Bryan D. See ◽  
Shaiful J. Hashim ◽  
Helmi Z. M. Shafri ◽  
Syaril Azrad ◽  
Mohd. Roshdi Hassan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Oil Palm ◽  
Low Cost ◽  
Palm Trees ◽  
Aerial Vehicle

scholarly journals Citizen-Science with off-the-shelf UAV for Coastal Monitoring

2021 ◽  
Vol 930 (1) ◽  
pp. 012001
Author(s):  
S M Beselly ◽  
M A Sajali
Keyword(s):  
Citizen Science ◽  
Structure From Motion ◽  
Coastal Management ◽  
Low Cost ◽  
Coastal Monitoring ◽  
Shoreline Position ◽  
The Public ◽  
Promising Tool ◽  
Aerial Vehicle

Abstract Accurate and repetitive observation and quantification of the shoreline position and the coastal feature are essential aspects of coastal management and planning. Commonly, the dataset associated with coastal observation and quantification is obtained with in-situ coastal surveys. The current methods are mostly quite expensive, time-consuming, and require trained individuals to do the task. With the availability of the off-the-shelf low cost, lightweight, and reliable Unmanned Aerial Vehicle (UAV) with the advances of the algorithms such as structure-from-motion (SfM), UAV-based measurement becomes a promising tool. Open SfM initiative, open topographical database, and UAV communities are the enablers that make it possible to collect accurate and frequent coastal monitoring and democratize data. This paper provides a review and discussions that highlight the possibility of conducting scientific coastal monitoring or collaborating with the public. Literature was examined for the advances in coastal monitoring, challenges, and recommendations. We identified and proposed the use of UAV along with the strategies and systems to encourage citizen-led UAV observation for coastal monitoring while attaining the quality.

scholarly journals Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yunsheng Wang ◽  
Antero Kukko ◽  
Eric Hyyppä ◽  
Teemu Hakala ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
High Efficiency ◽  
Low Cost ◽  
Seamless Integration ◽  
Aerial Vehicle ◽  
Individual Trees ◽  
Mobile Mapping Systems ◽  
Aerial Perspective

Abstract Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.

scholarly journals ACCURACY OF SNOW DEPTH ESTIMATION IN MOUNTAIN AND PRAIRIE ENVIRONMENTS BY AN UNMANNED AERIAL VEHICLE

2016 ◽  
Author(s):  
Phillip Harder ◽  
Michael Schirmer ◽  
John Pomeroy ◽  
Warren Helgason
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Laser Scanning ◽  
Low Cost ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
Small Scale ◽  
Canadian Prairie ◽  
Aerial Vehicle ◽  
Mean Square Errors

Abstract. The quantification of the spatial distribution of snow is crucial to predict and assess snow as a water resource and understand land-atmosphere interactions in cold regions. Typical remote sensing approaches to quantify snow depth have focused on terrestrial and airborne laser scanning and recently airborne (manned and unmanned) photogrammetry. In this study photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snowcovers at a cultivated agricultural Canadian Prairie and a sparsely-vegetated Rocky Mountain alpine ridgetop site using Structure from Motion (SfM). The ability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from the DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided meaningful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to noise-ratio. The application of SfM to UAV photographs can estimate snow depth in areas with snow depth > 30 cm – this restricts its utility for studies of the ablation of shallow, windblown snowpacks. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 6 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover. Relative to surfaces having greater contrast and more identifiable features, snow surfaces present unique challenges when applying SfM to imagery collected by a small UAV for the generation of DSMs. Regardless, the low cost, deployment mobility and the capability of repeat-on-demand flights that generate DSMs and orthomosaics of unprecedented spatial resolution provide exciting opportunities to quantify previously unobservable small-scale variability in snow depth and its dynamics.

scholarly journals Monitoring of compliance with fuel sulfur content regulations through unmanned aerial vehicle (UAV) measurements of ship emissions

2019 ◽  
Vol 12 (11) ◽  
pp. 6113-6124 ◽  
Author(s):  
Fan Zhou ◽  
Shengda Pan ◽  
Wei Chen ◽  
Xunpeng Ni ◽  
Bowen An
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Sulfur Content ◽  
Measurement System ◽  
Yangtze River Delta ◽  
Exhaust Gas ◽  
Ship Emissions ◽  
Chemical Examination ◽  
Airborne Measurements ◽  
Aerial Vehicle

Abstract. Air pollution from ship exhaust gas can be reduced by the establishment of emission control areas (ECAs). Efficient supervision of ship emissions is currently a major concern of maritime authorities. In this study, a measurement system for exhaust gas from ships based on an unmanned aerial vehicle (UAV) was designed and developed. Sensors were mounted on the UAV to measure the concentrations of SO2 and CO2 in order to calculate the fuel sulfur content (FSC) of ships. The Waigaoqiao port in the Yangtze River Delta, an ECA in China, was selected for monitoring compliance with FSC regulations. Unlike in situ or airborne measurements, the proposed measurement system could be used to determine the smoke plume at about 5 m from the funnel mouth of ships, thus providing a means for estimating the FSC of ships. In order to verify the accuracy of these measurements, fuel samples were collected at the same time and sent to the laboratory for chemical examination, and these two types of measurements were compared. After 23 comparative experiments, the results showed that, in general, the deviation of the estimated value for FSC was less than 0.03 % (m/m) at an FSC level ranging from 0.035 % (m/m) to 0.24 % (m/m). Hence, UAV measurements can be used for monitoring of ECAs for compliance with FSC regulations.

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