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 Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

2016 ◽  
Vol 10 (6) ◽  
pp. 2559-2571 ◽  
Author(s):  
Phillip Harder ◽  
Michael Schirmer ◽  
John Pomeroy ◽  
Warren Helgason
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Laser Scanning ◽  
Surface Characteristics ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
Canadian Prairie ◽  
Aerial Vehicle ◽  
Accuracy And Repeatability ◽  
Mean Square Errors

Abstract. Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability 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 differencing snow-covered and non-snow-covered 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 useful 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 returns meaningful information in areas with mean snow depth  >  30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds  < 10 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover.

Performance Analysis in Object Diameter Estimation based on Single and Multiple Non-Destructive and Discrete Infrared Sensors

2014 ◽  
Vol 67 (1) ◽  
Author(s):  
Norashikin M. Thamrin ◽  
Norhashim Mohd. Arshad ◽  
Ramli Adnan ◽  
Rosidah Sam ◽  
Noorfazdli Abd. Razak ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Pass Rate ◽  
Small Scale ◽  
Infrared Sensors ◽  
Scanning Method ◽  
Average Percentage ◽  
Multiple Sensors ◽  
Localization And Mapping ◽  
Aerial Vehicle

In Simultaneous Localization and Mapping (SLAM) technique, recognizing and marking the landmarks in the environment is very important. Therefore, in a commercial farm, rows of trees, borderline of rows as well as the trees and other features are mostly used by the researchers in realizing the automation process in this field. In this paper, the detection of the tree based on its diameter is focused. There are few techniques available in determining the size of the tree trunk inclusive of the laser scanning method as well as image-based measurements. However, those techniques require heavy computations and equipments which become constraints in a lightweight unmanned aerial vehicle implementation. Therefore, in this paper, the detection of an object by using a single and multiple infrared sensors on a non-stationary automated vehicle platform is discussed. The experiments were executed on different size of objects in order to investigate the effectiveness of this proposed method. This work is initially tested on the ground, based in the lab environment by using an omni directional vehicle which later will be adapted on a small-scale unmanned aerial vehicle implementation for tree diameter estimation in the agriculture farm.  In the current study, comparing multiple sensors with single sensor orientation showed that the average percentage of the pass rate in the pole recognition for the former is relatively more accurate than the latter with 93.2 percent and 74.2 percent, respectively. 

Designing an Unmanned Aerial Vehicle for Specific Aerial Applications of Insecticides and Herbicides

2016 ◽  
Author(s):  
Mohammed S. Mayeed ◽  
Gabriel Darveau
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Lift Force ◽  
State Of The Art ◽  
Low Cost ◽  
Von Mises Stress ◽  
Small Scale ◽  
Aerial Application ◽  
Bottom Line ◽  
Flight Duration ◽  
Aerial Vehicle

In this study a gasoline powered hexa-copter unmanned aerial vehicle (UAV) has been designed as a solution to farmers’ need for a low cost, easy to maintain, long flight duration, and multi-purpose means of specific aerial applications for insecticides and herbicides. Application of herbicides and pesticides by airplane is an example of how farmers have used technology to improve their bottom line and overall quality of life. Fields can now be sprayed in under an hour instead of consuming an entire day. However, if a producer has noxious weeds in only a small area, fixed-wing aerial application cannot be used as it is only accurate enough to do an entire field. Currently there is no solution for small scale, accurate, aerial herbicide application to meet this need. The currently available Yamaha Rmax UAV costs a tremendous amount of money and also requires a lot of money to maintain. Though it may be useful in large scale aerial spraying on the farm land, it would not be used in targeted specific areas as it is not efficient in specific applications. The gasoline powered hexacopter UAV designed in this study is a low cost solution to farmers’ need for specific aerial applications of insecticides and herbicides. The UAV design can carry 2–3 gallons of herbicide (16.7–25.0 lbs.) for a flight time of more than 30 minutes without refueling. The design could be transported in a 60.3in × 56.7in pickup bed. Structural and fatigue analyses are performed on the complete structure using state of the art software SolidWorks Simulation. The minimum factor of safety is obtained to be 10 based on maximum von Mises stress failure criteria. Under normal conditions with an estimated commercial use of 100 cycles per day it is observed that the design would survive for about 13 years without any fatigue failure. A drop test analysis is performed to ensure the design can survive a 5 feet freefall and a frequency analysis is also performed to observe the critical natural frequency of the structure. Flow simulations are performed on the 6 propellers/blades model using state of the art software SolidWorks Flow Simulation to observe the effect of vorticity interactions on the lift force. The design has been reasonably optimized based on maximizing the lift force. With this new UAV design small scale and substantial farmers could afford a personal UAV for aerial applications with a small amount of capital whose absence hindered efficient and effective specific aerial application for many years.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5555 ◽  
Author(s):  
Ying Quan ◽  
Mingze Li ◽  
Zhen Zhen ◽  
Yuanshuo Hao ◽  
Bin Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Low Cost ◽  
Pearson Correlation ◽  
Point Clouds ◽  
Larix Olgensis ◽  
Individual Tree ◽  
Profile Fitting ◽  
Aerial Vehicle ◽  
Individual Trees

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923 ◽  
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

2020 ◽  
Vol 13 (1) ◽  
pp. 24
Author(s):  
Yuanshuo Hao ◽  
Faris Rafi Almay Widagdo ◽  
Xin Liu ◽  
Ying Quan ◽  
Lihu Dong ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Random Effects ◽  
Forest Inventory ◽  
Point Cloud ◽  
Laser Scanning ◽  
Small Scale ◽  
Individual Tree ◽  
Scanning Angle ◽  
Tree Diameter ◽  
Aerial Vehicle

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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 Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry

2020 ◽  
Vol 12 (20) ◽  
pp. 3352
Author(s):  
Jiachun An ◽  
Pan Deng ◽  
Baojun Zhang ◽  
Jingbin Liu ◽  
Songtao Ai ◽  
...  
Keyword(s):  
Snow Depth ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
The Arctic ◽  
Noise Power ◽  
Snow Surface ◽  
Depth Sensing ◽  
Three Stages ◽  
The Impact ◽  
Mean Square Errors

Snow plays a critical role in hydrological monitoring and global climate change, especially in the Arctic region. As a novel remote sensing technique, global navigation satellite system interferometric reflectometry (GNSS-IR) has shown great potential for detecting reflector characteristics. In this study, a field experiment of snow depth sensing with GNSS-IR was conducted in Ny-Alesund, Svalbard, and snow depth variations over the 2014–2018 period were retrieved. First, an improved approach was proposed to estimate snow depth with GNSS observations by introducing wavelet decomposition before spectral analysis, and this approach was validated by in situ snow depths obtained from a meteorological station. The proposed approach can effectively separate the noise power from the signal power without changing the frequency composition of the original signal, particularly when the snow depth changes sharply. Second, snow depth variations were analyzed at three stages including snow accumulation, snow ablation and snow stabilization, which correspond to different snow-surface-reflection characteristics. For these three stages of snow depth variations, the mean absolute errors (MAE) were 4.77, 5.11 and 3.51 cm, respectively, and the root mean square errors (RMSE) were 6.00, 6.34 and 3.78 cm, respectively, which means that GNSS-IR can be affected by different snow surface characteristics. Finally, the impact of rainfall on snow depth estimation was analyzed for the first time. The results show that the MAE and RMSE were 2.19 and 2.08 cm, respectively, when there was no rainfall but 5.63 and 5.46 cm, respectively, when it was rainy, which indicates that rainfall reduces the accuracy of snow depth estimation by GNSS-IR.

scholarly journals Drone-Based High-Resolution Tracking of Aquatic Vertebrates

Drones ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 37 ◽  
Author(s):  
Vincent Raoult ◽  
Louise Tosetto ◽  
Jane Williamson
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Trajectory Analysis ◽  
Low Cost ◽  
Movement Patterns ◽  
Small Scale ◽  
Lemon Shark ◽  
Mobile Species ◽  
Aquatic Vertebrates ◽  
Aerial Vehicle ◽  
Marine Vertebrates

Determining the small-scale movement patterns of marine vertebrates usually requires invasive active acoustic tagging or in-water monitoring, with the inherent behavioural impacts of those techniques. In addition, these techniques rarely allow direct continuous behavioural assessments or the recording of environmental interactions, especially for highly mobile species. Here, we trial a novel method of assessing small-scale movement patterns of marine vertebrates using an unmanned aerial vehicle that could complement longer-term tracking approaches. This approach is unlikely to have behavioural impacts and provides high accuracy and high frequency location data (10 Hz), while subsequently allowing quantitative trajectory analysis. Unmanned aerial vehicle tracking is also relatively low cost compared to single-use acoustic and GPS tags. We tracked 14 sharks for up to 10 min in a shallow lagoon of Heron Island, Australia. Trajectory analysis revealed that Epaulette sharks (Hemiscyllium ocellatum) displayed sinusoidal movement patterns, while Blacktip Reef Sharks (Carcharhinus melanopterus) had more linear trajectories that were similar to those of a Lemon shark (Negaprion acutidens). Individual shark trajectory patterns and movement speeds were highly variable. Results indicate that Epaulette sharks may be more mobile during diurnal low tides than previously thought. The approach presented here allows the movements and behaviours of marine vertebrates to be analysed at resolutions not previously possible without complex and expensive acoustic arrays. This method would be useful to assess the habitat use and behaviours of sharks and rays in shallow water environments, where they are most likely to interact with humans.

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