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Unmanned Aerial Systems for Surface Hydrology

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 50651

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Dr. Flavia Tauro

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Guest Editor

University of Tuscia, Via San Camillo de Lellis, Italy
Interests: surface hydrology; environmental sensings; image analysis; unmanned aerial systems; hillslope hydrology

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) are bridging the gap between ground-based and satellite remote sensing by enabling rapid and affordable observations in difficult-to-access environments at high spatial and temporal resolution. In the realm of surface hydrology, UAVs have the potential to augment our comprehension of complex and multifaceted processes occurring at largely heterogeneous scales.

This Special Issue welcomes contributions that involve the use and development of UAV technology for the advancement of our comprehension of surface hydrological processes. More specifically, submitted manuscript may cover the following topics:

UAV-based measurement in diverse compartments of the water cycle, including application to rainfall, surface water, river bathymetry, soil moisture, vegetation, temperature, and evapotranspiration measurements
Development and integration of sensors onboard UAV platforms for advanced hydrological measurements
Establishment of procedures and protocols for UAV-based hydrological observations
Assessment and comparison of UAV-based measurements to more established technologies
Development of algorithms for UAV-based data extraction
Analysis and assimilation of UAV-based measurements in hydrological models
Development of advanced multisensor UAV platforms for surface hydrology
Integration of UAV technology within collaborative projects and citizen scientists

Dr. Flavia Tauro
Guest Editor

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Keywords

Surface hydrology
Unmanned aerial vehicle
Multisensor platforms
Measurement protocol
Measurement assimilation
Citizen science

Published Papers (10 papers)

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Research

20 pages, 5600 KiB

Open AccessArticle

UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

by Mauro Maesano, Sacha Khoury, Farid Nakhle, Andrea Firrincieli, Alan Gay, Flavia Tauro and Antoine Harfouche

Remote Sens. 2020, 12(20), 3464; https://doi.org/10.3390/rs12203464 - 21 Oct 2020

Cited by 26 | Viewed by 4411

Abstract

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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23 pages, 1642 KiB

Open AccessArticle

QCam: sUAS-Based Doppler Radar for Measuring River Discharge

by John W. Fulton, Isaac E. Anderson, C.-L. Chiu, Wolfram Sommer, Josip D. Adams, Tommaso Moramarco, David M. Bjerklie, Janice M. Fulford, Jeff L. Sloan, Heather R. Best, Jeff S. Conaway, Michelle J. Kang, Michael S. Kohn, Matthew J. Nicotra and Jeremy J. Pulli

Remote Sens. 2020, 12(20), 3317; https://doi.org/10.3390/rs12203317 - 12 Oct 2020

Cited by 25 | Viewed by 3850

Abstract

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternative discharge algorithms with sensors capable of measuring surface velocity, streamgage networks can be established in regions where data collection was previously impractical or impossible. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from sUAS or fixed platforms. QCam is a Doppler (velocity) radar mounted and integrated on a 3DR^© Solo sUAS. It measures the along-track surface velocity by spot dwelling in a river cross section at a vertical where the maximum surface velocity is recorded. The surface velocity is translated to a mean-channel (mean) velocity using the probability concept (PC), and discharge is computed using the PC-derived mean velocity and cross-sectional area. Factors including surface-scatterer quality, flight altitude, propwash, wind drift, and sample duration may affect the radar-returns and the subsequent computation of mean velocity and river discharge. To evaluate the extensibility of the method, five science flights were conducted on four rivers of varying size and dynamics and included the Arkansas River, Colorado (CO), USA (two events); Salcha River near Salchaket, Alaska (AK), USA; South Platte River, CO, USA; and the Tanana River, AK, USA. QCam surface velocities and river discharges were compared to conventional streamgaging methods, which represented truth. QCam surface velocities for the Arkansas River, Salcha River, South Platte River, and Tanana River were 1.02 meters per second (m/s) and 1.43 m/s; 1.58 m/s; 0.90 m/s; and 2.17 m/s, respectively. QCam discharges (and percent differences) were 9.48 (0.3%) and 20.3 cubic meters per second (m³/s) (2.5%); 62.1 m³/s (−10.4%); 3.42 m³/s (7.3%), and 1579 m³/s (−18.8%). QCam results compare favorably with conventional streamgaging and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and river discharge, if cross-sectional area is available. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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21 pages, 11904 KiB

Open AccessArticle

Low-Cost Unmanned Aerial Multispectral Imagery for Siltation Monitoring in Reservoirs

by Diogo Olivetti, Henrique Roig, Jean-Michel Martinez, Henrique Borges, Alexandre Ferreira, Raphael Casari, Leandro Salles and Edio Malta

Remote Sens. 2020, 12(11), 1855; https://doi.org/10.3390/rs12111855 - 08 Jun 2020

Cited by 33 | Viewed by 5448

Abstract

The recent and continuous development of unmanned aerial vehicles (UAV) and small cameras with different spectral resolutions and imaging systems promotes new remote sensing platforms that can supply ultra-high spatial and temporal resolution, filling the gap between ground-based surveys and orbital sensors. This work aimed to monitor siltation in two large rural and urban reservoirs by recording water color variations within a savanna biome in the central region of Brazil using a low cost and very light unmanned platform. Airborne surveys were conducted using a Parrot Sequoia camera (~0.15 kg) onboard a DJI Phantom 4 UAV (~1.4 kg) during dry and rainy seasons over inlet areas of both reservoirs. Field measurements of total suspended solids (TSS) and water clarity were made jointly with the airborne survey campaigns. Field hyperspectral radiometry data were also collected during two field surveys. Bio-optical models for TSS were tested for all spectral bands of the Sequoia camera. The near-infrared single band was found to perform the best (R²: 0.94; RMSE: 7.8 mg L⁻¹) for a 0–180 mg L⁻¹ TSS range and was used to produce time series of TSS concentration maps of the study areas. This flexible platform enabled monitoring of the increase of TSS concentration at a ~13 cm spatial resolution in urban and rural drainages in the rainy season. Aerial surveys allowed us to map TSS load fluctuations in a 1 week period during which no satellite images were available due to continuous cloud coverage in the rainy season. This work demonstrates that a low-cost configuration allows dense TSS monitoring at the inlet areas of reservoirs and thus enables mapping of the sources of sediment inputs, supporting the definition of mitigation plans to limit the siltation process. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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20 pages, 4852 KiB

Open AccessArticle

Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers

by Silvano Fortunato Dal Sasso, Alonso Pizarro and Salvatore Manfreda

Remote Sens. 2020, 12(11), 1789; https://doi.org/10.3390/rs12111789 - 01 Jun 2020

Cited by 30 | Viewed by 4368

Abstract

River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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21 pages, 4984 KiB

Open AccessArticle

Identification and Analysis of Microscale Hydrologic Flood Impacts Using Unmanned Aerial Systems

by Jamie L. Dyer, Robert J. Moorhead and Lee Hathcock

Remote Sens. 2020, 12(10), 1549; https://doi.org/10.3390/rs12101549 - 13 May 2020

Cited by 7 | Viewed by 2728

Abstract

The need for accurate and spatially detailed hydrologic information is critical due to the microscale influences on the severity and distribution of flooding, and new and/or updated approaches in observations of river systems are required that are in line with the current push towards microscale numerical simulations. In response, the aim of this project is to define and illustrate the hydrologic response of river flooding relative to microscale surface properties by using an unmanned aerial system (UAS) with dedicated imaging, sensor, and communication packages for data collection. As part of a larger project focused on increasing situational awareness during flood events, a fixed-wing UAS was used to overfly areas near Greenwood, MS before and during a flood event in February 2019 to provide high-resolution visible and infrared imagery for analysis of hydrologic features. The imagery obtained from these missions provide direct examples of fine-scale surface features that can alter water level and discharge, such as built structures (i.e., levees and bridges), natural storage features (low-lying agricultural fields), and areas of natural resistance (inundated forests). This type of information is critical in defining where and how to incorporate high-resolution information into hydrologic models and also provides an invaluable dataset for eventual verification of hydrologic simulations through inundation mapping. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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15 pages, 4086 KiB

Open AccessFeature PaperArticle

The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling

by Liguang Jiang, Filippo Bandini, Ole Smith, Inger Klint Jensen and Peter Bauer-Gottwein

Remote Sens. 2020, 12(7), 1171; https://doi.org/10.3390/rs12071171 - 06 Apr 2020

Cited by 12 | Viewed by 3314

Abstract

Water level or water surface elevation (WSE) is an important state variable of rivers, lakes, and wetlands. Hydrodynamic models of rivers and streams simulate WSE and can benefit from spatially distributed WSE observations, to increase model reliability and predictive skill. This has been partially addressed by satellite radar altimetry, but satellite altimetry is unable to deliver useful data for small rivers. To overcome such limitations, we deployed a radar altimetry system on an unmanned aerial vehicle (UAV), to map spatially distributed WSE. We showed that UAV altimetry can provide observations of WSE with a very high spatial resolution (ca. 0.5 m) and accuracy (ca. 3 cm), in a time-saving and cost-effective way. Furthermore, we investigated the value of this dataset for the calibration and validation of hydrodynamic models. Specifically, we introduced spatially distributed roughness parameters in a hydrodynamic model and estimated these parameters, using the observed WSE profiles along the stream as input. A case study was conducted in the Åmose stream, Denmark. The results showed that UAV-borne WSE can identify significant variations of the Manning–Strickler coefficients, along this small and highly vegetated stream and over time. Moreover, the model performed extremely well using distributed roughness coefficients, but it could not reproduce WSE satisfactorily using uniform roughness. We concluded that distributed roughness coefficients should be considered, especially for small vegetated rivers, to improve model performance, both locally and globally. Spatially distributed parameterizations of the effective channel roughness could be constrained with UAV-borne WSE. This study demonstrated for the first time that UAV-borne WSE can help to understand the variations of hydraulic roughness, and can support efficient river management and maintenance. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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20 pages, 4532 KiB

Open AccessArticle

A Drone-Based Bioaerosol Sampling System to Monitor Ice Nucleation Particles in the Lower Atmosphere

by Paul Bieber, Teresa M. Seifried, Julia Burkart, Jürgen Gratzl, Anne Kasper-Giebl, David G. Schmale III and Hinrich Grothe

Remote Sens. 2020, 12(3), 552; https://doi.org/10.3390/rs12030552 - 07 Feb 2020

Cited by 20 | Viewed by 6698

Abstract

Terrestrial ecosystems can influence atmospheric processes by contributing a huge variety of biological aerosols (bioaerosols) to the environment. Several types of biological particles, such as pollen grains, fungal spores, and bacteria cells, trigger freezing processes in super-cooled cloud droplets, and as such can contribute to the hydrological cycle. Even though biogenic particles are known as the most active form of ice nucleation particles (INPs), the transport to high tropospheric altitudes, as well as the occurrence in clouds, remains understudied. Thus, transport processes from the land surface into the atmosphere need to be investigated to estimate weather phenomena and climate trends. To help fill this knowledge gap, we developed a drone-based aerosol particles sampling impinger/impactor (DAPSI) system for field studies to investigate sources and near surface transport of biological INPs. DAPSI was designed to attach to commercial rotary-wing drones to collect biological particles within about 100 m of the Earth’s surface. DAPSI provides information on particulate matter concentrations (PM₁₀ & PM_2.5), temperature, relative humidity, and air pressure at about 0.5 Hz, by controlling electrical sensors with an onboard computer (Raspberry Pi 3). Two remote-operated sampling systems (impinging and impacting) were integrated into DAPSI. Laboratory tests of the impinging system showed a 96% sampling efficiency for standardized aerosol particles (2 µm polystyrene latex spheres) and 84% for an aerosol containing biological INPs (Betula pendula). A series of sampling missions (12 flights) were performed using two Phantom 4 quadcopters with DAPSI onboard at a remote sampling site near Gosau, Austria. Fluorescence microscopy of impactor foils showed a significant number of auto-fluorescent particles < 0.5 µm at an excitation of 465–495 nm and an emission of 515–555 nm. A slight increase in ice nucleation activity (onset temperature between −27 °C and −31 °C) of sampled aerosol was measured by applying freezing experiments with a microscopic cooling technique. There are a number of unique opportunities for DAPSI to be used to study the transport of bioaerosols, particularly for investigations of biological INP emissions from natural sources such as birch or pine forests. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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24 pages, 19694 KiB

Open AccessFeature PaperArticle

An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems

by Sophie Pearce, Robert Ljubičić, Salvador Peña-Haro, Matthew Perks, Flavia Tauro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Dariia Strelnikova, Salvatore Grimaldi, Ian Maddock, Gernot Paulus, Jasna Plavšić, Dušan Prodanović and Salvatore Manfreda

Remote Sens. 2020, 12(2), 232; https://doi.org/10.3390/rs12020232 - 09 Jan 2020

Cited by 74 | Viewed by 7104

Abstract

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s

^{- 1}

, Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s

^{- 1}

image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s

^{- 1}

of the ADCP measurements, on average. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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12 pages, 2628 KiB

Open AccessArticle

Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory

by Guy J.-P. Schumann, Joseph Muhlhausen and Konstantinos M. Andreadis

Remote Sens. 2019, 11(8), 982; https://doi.org/10.3390/rs11080982 - 24 Apr 2019

Cited by 28 | Viewed by 5177

Abstract

Unmanned Aerial Vehicle (UAV) platforms have rapidly developed as tools for remote mapping at very high spatial resolutions. They have recently gained in popularity in many application fields owing to the versatility of platforms and sensors, ease of deployment, and a steady increase in computational power. Obtaining highly detailed topography data over very small scales is one of the more typical application domains. Here, we demonstrate this application using Structure from Motion (SfM) processing over a small river floodplain in Howard County (Maryland, USA). Evaluation of the derived bare-earth terrain model with state-of-the art LiDAR shows a trivial bias of 1.6 cm and a root mean square deviation (RMSD) of 39 cm. We then applied this terrain model to extract floodplain and river cross-section geometries of a small stream, important during high-magnitude urban flash flood events, with the aim to assess its value for floodplain inundation mapping and first order characterization of in-channel hydraulics. Initial findings agree with traditional stream and floodplain classification theory and thus show very promising results for this type of UAV usage. We expect this type of application to gain more momentum in the near future with the ever-growing importance of more detailed data in order to increase resilience to flood risk, especially in urban areas. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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18 pages, 8744 KiB

Open AccessArticle

Identification of a Threshold Minimum Area for Reflectance Retrieval from Thermokarst Lakes and Ponds Using Full-Pixel Data from Sentinel-2

by Pedro Freitas, Gonçalo Vieira, João Canário, Diogo Folhas and Warwick F. Vincent

Remote Sens. 2019, 11(6), 657; https://doi.org/10.3390/rs11060657 - 18 Mar 2019

Cited by 15 | Viewed by 6328

Abstract

Thermokarst waterbodies caused by permafrost thawing and degradation are ubiquitous in many subarctic and Arctic regions. They are globally important components of the biogeochemical carbon cycle and have potential feedback effects on climate. These northern waters are mostly small lakes and ponds, and although they may be mapped using very high-resolution satellites or aerial photography, these approaches are generally not suitable for monitoring purposes, due to the cost and limited availability of such images. In this study we evaluated the potential use of widely available high-resolution imagery from Sentinel-2 (S2) for the characterization of the spectral reflectance of thermokarst lakes and ponds. Specifically, we aimed to define the minimum lake area that could be reliably imaged, and to identify challenges and solutions for remote sensing of such waters in the future. The study was conducted in subarctic Canada, in the vicinity of Whapmagoostui-Kuujjuarapik (Nunavik, Québec), an area in the sporadic permafrost zone with numerous thermokarst waterbodies that vary greatly in size. Ground truthing lake reflectance data were collected using an Unmanned Aerial System (UAS) fitted with a multispectral camera that collected images at 13 cm resolution. The results were compared with reflectance from Sentinel-2 images, and the effect of lake area on the reflectance response was assessed. Our results show that Sentinel-2 imagery was suitable for waterbodies larger than 350 m² once their boundaries were defined, which in the two test sites would allow monitoring from 11% to 30% of the waterbodies and 73% to 85% of the total lake area. Challenges for remote sensing of small lakes include the confounding effects of water reflection (both direct radiation and diffuse), wind and shadow. Given the small threshold area and frequent revisit time, Sentinel-2 provides a valuable approach towards the continuous monitoring of waterbodies, including ponds and small lakes such as those found in thermokarst landscapes. UASs provide a complementary approach for ground truthing and boundary definition. Full article

(This article belongs to the Special Issue Unmanned Aerial Systems for Surface Hydrology)

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