Unmanned aerial system based spectroradiometer for monitoring harmful algal blooms: A new paradigm in water quality monitoring

Journal of Great Lakes Research 45 (2019) 444–453

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Unmanned aerial system based spectroradiometer for monitoring harmful algal blooms: A new paradigm in water quality monitoring Richard H. Becker a,⁎, Michael Sayers b, Dustin Dehm a, Robert Shuchman b, Kaydian Quintero a,c, Karl Bosse b, Reid Sawtell b a b c

Department of Environmental Sciences, University of Toledo, Toledo, OH 43606, United States of America Michigan Tech Research Institute (MTRI), Ann Arbor, MI 48105, United States of America St. Mary's University, San Antonio, TX 78228, United States of America

a r t i c l e

i n f o

Article history: Received 30 May 2018 Accepted 14 March 2019 Available online 10 April 2019 Communicated by Joseph Ortiz Keywords: UAV Hyperspectral Water quality HABs Lake Erie Maumee River

a b s t r a c t Water quality issues continue to impact recreational and resource use of waterways in the Great Lakes and across smaller inland water bodies in their watersheds. With the advancements in small unmanned aerial systems (sUAS) and in small sensor production, sUAS offer flexibility to overcome several of the shortcomings of satellite and airborne systems, and complement their measurements in this environment. In this study, we deployed two different low-cost, boat launchable sUAS configurations instrumented with Ocean Optics STS hyperspectral VisNIR spectroradiometers capable of making measurements over an approximately 2.5 km2 area, below the cloud deck in the nearshore and open lake environment. Flights of these systems were conducted at ten locations over Lake Erie and the Maumee River. Measured spectra compared well with at-surface based Analytical Spectral Devices (ASD) Fieldspec measured spectra, and derived parameters were consistent with in-water FluoroProbe measured water quality parameters and field observations. Using flight data, we constructed transect maps of derived CI products which show the variability in algae abundance in open water. These systems provide high quality, low cost, very high spatial resolution (cm to m scale measurements) hyperspectral data in the nearshore environment, can be consistently flown at low altitude (minimizing atmospheric effects) below cloud cover and can be deployed on extremely short notice. With the continued advancement in sensor development, automated flight capabilities, and increased flight duration from vertical take off and landing (VTOL) platforms, water quality observation platforms such as these will soon be a common tool in resource manager's toolboxes. © 2019 The Authors. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction and background At least 40 million people in the U.S. and Canada rely on the Great Lakes basin for drinking water, as well as food, work, and recreation (Shear et al., 1995). Water quality issues such as harmful algal blooms (HABs) can present a hazard to the substantial number of communities which draw water from the Great Lakes, tributaries, and smaller nearby reservoirs, or use any of them for recreation (Dyble et al., 2008; Watson et al., 2016). Mapping and monitoring water quality, including algal blooms, sediment, and contaminants across the Great Lakes, tributaries, and smaller reservoirs is a necessary component in protecting these resources (Graham et al., 2009). Monitoring water quality in large water bodies or a large set of small water bodies spatially and temporally using traditional in situ based methods of sampling at discreet stations can be expensive due to the high costs for boat time and for analysis

⁎ Corresponding author. E-mail address: [email protected] (R.H. Becker).

of collected samples. In addition, in situ methods only provide point sample assessment and do not provide a detailed assessment of the nearby spatial heterogeneity of a bloom at the local scale. Remote sensing techniques have long held great promise for making synoptic measurements of the near surface properties for such extensive systems. By virtue of their capabilities for repeatedly conducting measurements with the same observational parameters, remote sensors have been shown to be able to provide consistent measurements of selected physical properties for surface water systems on regional scales. Until recently, remote sensing approaches to algal bloom monitoring have been focused on satellite (e.g. MODIS, MERIS, Sentinel-3 (Becker et al., 2009; Binding et al., 2018; Clark et al., 2017; Kutser et al., 2006; Sayers et al., 2016; Stumpf et al., 2012)), and airborne imagers (CASI, AVIRIS, HSI (Beck et al., 2016; Kudela et al., 2015; Lekki et al., 2017; Ortiz et al., 2017)). We present a way to incorporate small unmanned aerial systems (sUAS, which is a class of unmanned aerial vehicle, UAV) to complement these monitoring techniques, which is particularly appropriate for smaller lakes, reservoirs, and rivers that are not resolvable at satellite spatial resolution.

https://doi.org/10.1016/j.jglr.2019.03.006 0380-1330/© 2019 The Authors. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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UAS systems have been used to complement or replace traditional satellite and manned aircraft acquired data in geologic and environmental problems. This research has included photogrammetric applications deriving point clouds used to generate topographic (Colomina and Molina, 2014) and structural information (e.g. Bemis et al., 2014; Bistacchi et al., 2015). It has also included a large number of terrestrial multispectral imaging studies for vegetation (Aasen et al., 2015; Torres-Sánchez et al., 2014), soil (Sona et al., 2016) and lithologic mapping (Cruden et al., 2016; Gallay et al., 2016). With the advent of miniaturized thermal systems, studies involving animal counts (Chrétien et al., 2016), and groundwater discharge and mixing (Lee et al., 2016) have also become possible. In addition to terrestrial studies, UAS based instruments have begun to be deployed successfully for a variety of water quality assessments. Flynn and Chapra (2014) used a standard commercial off-the-shelf (COTS) red, green, blue (RGB) camera to map submerged aquatic vegetation in non-turbid river waters. Murfitt et al. (2017) used a similar COTS RGB camera to map intertidal reef systems in Australia. Su and Chou (2015) utilized a RGB and near infra-red (NIR) camera to determine correlations between images and chlorophyll-a (chl-a) and total phosphorous. Larson et al. (2018) used multispectral imagery to assess turbidity in highly turbid river water. Van der Merwe and Price (2015) used a modified camera to obtain Blue/NIR images to calculate a Blue Normalized Difference Vegetation Index (BNDVI), which they related to cyanobacteria concentrations. Extending this type of work to hyperspectral analysis, Shang et al. (2017) deployed a fixed wing UAS (TOPRS LT-150) with a 2.5 m wingspan with a hyperspectral spectroradiometer to study algae in an estuary near the Taiwan Strait. NASA Glenn Research Center has successfully deployed their HSI2 hyperspectral imager on an Altavian Nova F6500 fixed wing sUAS system (2.75 m wingspan) (Tokars, personal communication). In addition, UAS systems have been used to deploy in situ sensors for water quality parameters (Koparan et al., 2018). SUAS flight vehicles (UAS systems less than 55lbs, (FAA, 2016)) come in many varieties, and choice of vehicle should be made to suit the nature of the survey. Fixed wing aircraft can cover great distances in a single flight, where single- or multi-rotor aircraft are more agile (Floreano and Wood, 2015). For example, larger, fixed wing systems such as the Altavian Nova Block 3 and the TOPRS LT-150 provide needed lift for larger payloads, but sacrifice ease of deployment and ability to operate from a small boat platform to achieve this. Smaller sUAS systems, such as small quadcopters (e.g. 450 mm size, such as the DJI phantom series) have far fewer limitations about where they can be deployed from (small boats, docks, banks, shorelines) because they don't require runway surfaces, but sacrifice payload capacity and flight time for this flexibility (Klemas, 2015). Flight capabilities may be specified by vehicle manufacturers, but they are also a function of environmental conditions. Flight times are determined by power supplies whose rate of consumption is affected by air temperatures and wind speeds. Payload size and air humidity also affect flight performance. As electronics and controllers continue to become smaller, more configurations can be built specific to certain applications and price points. For remote sensing of water quality, sUAS are capable of flying traditional aerial survey patterns or transects to sample wide areas of interest. This technique is used to capture spatial variations and generate maps (Oliver, 2017). SUAS are also proficient at manual or ad-hoc flight paths where data is desired at a specific target area. While virtually no consumer-grade sUAS platforms have the power plant facilities required for persistent surveillance, this technique is useful for making observations of transient targets, or where a measurement with a longer integration time requires loitering over a target. SUAS excel in providing rapid, temporary access with minimal environmental disturbance (Oliver, 2017). They are particularly well-suited to remote sensing tasks where observations are made quickly and from a distance. Many of the sensors that are required to localize remotelysensed data are included in the flight vehicle. A large amount of

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information can therefore be synthesized after a relatively short time spent in the field. However, planning is still critical when using sUAS to collect scientific data. Current satellite and airborne remote sensing techniques have many shortcomings that can be addressed by appropriate sUAS based sensor methods. The acquisition of a targeted series of measurements with minimal atmospheric interference over an algal bloom of interest is necessary for proper understanding of the temporal and spatial distribution of the bloom. Monitoring the distribution of algal blooms in the Laurentian Great Lakes area using satellite data is often hindered by cloud cover (Becker et al., 2009; Shuchman et al., 2013b). The acquisition of a series of satellite images of sufficient quality over Great Lakes locations is problematic. Multispectral satellite systems (e.g. MODIS, Sentinel-3) provide a time series for cloud free days, but have a lower spatial resolution for water quality products (300 m–1000 m). This means that observations of nearshore, riverine and small lake environments (all areas with high recreational use) are not always practical with these platforms. Airborne hyperspectral imagery has the advantage of the ability to fly below the cloud base most of the time, with higher spatial resolution (frequently 2-5 m pixel resolution), and to image large swaths of area. However, the deployment costs of monitoring systems that are based on airborne (AVIRIS, CASI, HSI) hyperspectral imagers that utilize reflectance algorithms (Kudela et al., 2015; Ortiz et al., 2017; Simis et al., 2005) can be high. Algal blooms present in the Maumee River, in Northwest Ohio, in the summer of 2017 showed both a very high spatial and temporal variability (Fig. 1). As a result, to properly investigate these blooms, an instrument that has a high spatial resolution, has a rapid deployment capability and can operate under any cloud cover conditions is highly desirable. The bloom in Fig. 1 had a significant surface scum which was photographed September 25, 2017. Returning only 2 days later, on September 27 the bloom was fully mixed in the water column at the same location as a result of increased winds and other factors. SUAS systems offer flexibility to overcome many of the shortcomings of satellite and airborne systems. This approach addresses many of the issues with current satellite systems for coastal and inland process studies identified by Mouw et al. (2015). With the advancement in small sensor production, multispectral and hyperspectral instruments can be flown on sUAS platforms. Small multiband cameras, such as the micasense red-edge 5 band multispectral imager (https:// www.micasense.com/rededge-m/), can be flown in sub 5-kg payload capacity sUAS systems. Ocean Optics STS series spectroradiometers provide hyperspectral measurements using an instrument in the sub 100 g

Fig. 1. Photograph of the algae bloom on the Maumee River at Toledo, September 25, 2018, showing the high spatial variability of the surface scum component of the algae bloom. This scum was a nuisance to recreational users of the river, including to a crew regatta two days prior.

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class. Flown from a sUAS platform, these can provide extremely high spatial resolution (cm to m scale measurements), and can be consistently flown under cloud cover. Because of the low elevation, atmospheric effects are negligible. Deployment can be rapid (the time it takes to travel to a site) and on demand, and data turnaround can be very quick as well (e.g. a contaminant index transect map produced within a few hours at the end of a flight). The logistics of sUAS flights over and around water do take some extra consideration. In large lakes, much of the lake is beyond line of sight from any available landbased launch point. This necessitates the use of a sUAS which can be launched and retrieved from the water. The simplest way to do this is to launch from and land directly on a boat. For boat-based launch and landing, the small multi-copters used in this study are ideal. With the recent advent of vertical takeoff and landing (VTOL) fixed wing sUAS platforms, VTOL (launch as a quadcopter, fly as a fixed wing) may prove to be the next advancement (Saeed et al., 2018). An additional option is a boat-launched fixed wing (catapult launch) system which is then water landed, and retrieved from the water. Recent changes in FAA regulations in the United States, with FAA Part 107 regulations coming into force in 2016, have made it far more practical to deploy a sUAS for water quality measurements in US airspace. For a system less than 55lbs (24.95 kg), operators need to have the sUAS registered and have a Part 107 sUAS pilots license. Flights within 5 miles (8 km) of an airport need to have advanced permission through the appropriate control tower. Operations over water from a boat (including a moving boat provided the sUAS pilot is not the boat captain) are permissible, allowing flights over large water areas which otherwise would have been impractical (FAA, 2016). In this study, we successfully demonstrate the utility of low cost sUAS systems, instrumented with hyperspectral spectroradiometers to measure water quality parameters that include chlorophyll, suspended minerals, cyanobacteria index, and surface scums. The UAS systems, flight control and configurations, sensor suites, collection protocols, and data processing algorithms are described. Two different low-cost sUAS configurations were deployed in the western basin of Lake Erie and the Maumee River in order to assess potential algal blooms in near-real time. The systems are capable of measurements over an approximately 2.5 km2 area, below the cloud deck in specific areas of interest. This combination of low cost, boat launchable and retrievable sUAS platforms with a high spectral resolution spectroradiometer capable of acquiring high spatial resolution measurements gives us the ability to address a variety of water quality issues.

that can carry up to 2.3 kg. The cost of the MTRI system including the sensors is under $7000. SUAS flight control UT's quadcopter is controlled by a Pixhawk PX4 flight controller. The open source QGroundControl flight control software allows either preprogrammed GPS controlled flights or stabilized free flight. Live telemetry and video are displayed on an Android tablet. MTRI's Bergen hexacopter is controlled by a Wookong M A2 autopilot which allows for customizable flight modes and flight system configurations. DJI ground station software can be used to upload flight paths and waypoints. Both flight systems are controlled by 2.4GHz RC remote control, used to issue flight commands. Both autopilots keep comprehensive flight logs of all aeronautical data reported by onboard flight sensors, and those logs can be used to supplement observation recorded by the sensor payload if desired. In the UT system, flight logs are synchronized with spectroradiometer measurements to provide sampling location for each spectral retrieval. The systems are also outfitted with a 5.8 Ghz video downlink that can be viewed by as many first-person-view monitors as are within the broadcasting range. This video downlink is also annotated with a heads-up-display readout of various flight parameters, including battery voltage and altitude. SUAS instrument: ocean optics STS spectroradiometer An Ocean Optics STS-vis minispectroradiometer system (https:// oceanoptics.com/product-category/sts-series/) was deployed on both the UT and MTRI systems with slightly varying configurations. The STS-VIS mini-spectroradiometer is a lightweight and compact instrument (40 × 42 × 24 mm, 60 g), with a spectral range of 350–800 nm. As deployed, with a 25 nm slit, the optical resolution is 1.5 nm. The instrument is connected to a Raspberry PI computer (https://www. raspberrypi.org/) via USB, or directly to a windows computer via USB, and data collection is initiated via a Wi-Fi web interface. Two optics were deployed with the system. An adjustable focus lens was used to measure upwelling irradiance. An RCR optic was used to measure downwelling irradiance. In the UT system, a single STS was deployed at a fixed nadir looking position in the sUAS frame. In the MTRI system, a pair of spectroradiometers was deployed in a watertight container in the 2-axis gimbal, with fiber-optic cabling connecting optics. Ground based instrument: ASD

Methods SUAS configuration For this investigation two separate systems were deployed. The group based at The University of Toledo (UT) deployed an in-house built 550 mm class sUAS, based on an Aquacopter Bullfrog quadcopter frame (Fig. 2a) (http://www.aquacopters.com/). This inexpensive, waterproof system was built using a Pixhawk flight controller, Quanum MT4108–370 motors, powering 14 × 5.5 propellers. It is powered by single or dual 6S 6000 mAh Li\\Po batteries. The fully built-up weight of the quadcopter and sensor is 2.1 kg. The Ocean Optics spectroradiometer is flown from a fixed position in the hull, and live video feed is provided via a Runcam FPV camera. The cost of this system as built was under $2000. The sUAS deployed by Michigan Tech Research Institute (MTRI) uses a Bergen RC multicopter flight vehicle to fly its Ocean Optics spectroradiometer. This sUAS is a 5.4 kg vehicle driven by six OMA3825-750 W motors with 14 × 5.5 in. (36 × 14 cm) propellers (Fig. 2b). This hexacopter is 61 cm tall and 99 cm wide. It is powered by four 4-cell 14.8-V 8000-mAh Li\\Po battery packs, with each battery pack weighing 0.45 kg. This hexacopter is equipped with a 2-axis gimbal

At-water-based radiometric data was collected using ASD FieldSpec III and Fieldspec III Hi-res spectroradiometers. This instrument has a spectral range of 350–2500 nm, a spectral resolution of 3 nm and a maximum sampling rate of 10 Hz. Spectral data was collected through the RS3 software program (v6.4; ASD, Inc.) and processed using the ViewSpecPro software program (v6.2; ASD, Inc.). On-water ASD protocol Two methods were utilized to measure surface reflectance at water. In the first, downwelling spectral plane irradiance (Ed, W/m2/nm) was measured by attaching a remote cosine receptor (RCR) to the FieldSpec III fiberoptic cable. Pointing the RCR straight up and avoiding any potential shadows, 10 spectral measurements were taken and averaged together. The upwelling spectral radiance (Lu, W/m2/sr−1/nm) of the water's surface was measured following the NASA standard protocol (Mueller et al., 2003). An 8-degree foreoptic was attached to the fiberoptic cable and pointed at the water at approximately a 40 degree angle off-nadir to reduce specular reflection (Mobley, 1999). Radiance was measured approximately 1 m from the target at approximately 135 degrees from the sun's position while avoiding shadows, sun

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Fig. 2. a) University of Toledo quadcopter used in this study. The spectroradiometer and flight controls are enclosed in the waterproof chassis, and the sUAS can be water landed without damaging the components. The Ocean Optics STS spectroradiometer optic is seen underneath. b) MTRI sUAS used in this study. The Ocean Optics STS spectroradiometer is enclosed in the waterproof case attached to the 2-axis gimbal below the sUAS. Fiberoptic cabling connects the RCR optic (top, on mast) with the spectroradiometer in the watertight case.

glint, and floating debris (Mobley, 1999). The diffuse sky radiance, Lsky, reflected by the water surface was measured by pointing the 8-degree foreoptic at a 40-degree upward angle 135 degrees from the sun azimuth. A water reflectivity value of 0.028 was used to estimate the amount of Lsky reflected into the Fieldspec field of view (Mobley, 1999). Remote sensing reflectance (Rrs, sr−1) was calculated as (LuLsky(0.028))/Ed. Sets of 20–30 Rrs spectra were collected and averaged together for each target. In the second method, a 10 × 10 cm white Spectralon target with well-known spectral characteristics was set up free of shadows or obstruction. An 8-degree foreoptic was attached to the fiberoptic cable and pointed at the Spectralon target, approximately 1 m from the target. Ten spectral measurements of the white reference were taken and averaged together. Downwelling irradiance (Ed) was estimated from this, assuming that the panel acted as a Lambertian reflector (Moore et al., 2017). Upwelling radiance from the water was measured as described

above. To calculate Rrs a ratio of upwelling radiance to downwelling irradiance was calculated. SUAS radiometer protocol The MTRI sUAS based spectroradiometer system was designed to simultaneously measure the downwelling spectral plane irradiance (Ed, W/m2/nm) and upwelling spectral radiance (Lu, W/m2/sr−1/nm) to derive remote sensing reflectance (Rrs, sr−1) as the ratio of Lu/Ed. It should be noted the diffuse sky surface reflectance was not measured or removed from these measurements. Irradiance and radiance measurements were made with two independent Ocean Optics STS spectroradiometers controlled by a Raspberry Pi computer. Irradiance was measured with a cosine collector foreoptic connected to a fiber optic cable which plugged into one of the radiometers. The cosine collector was attached to the sUAS GPS mast above the rotors to provide

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an unobstructed view of the downwelling light field. When the sUAS is hovering in a level position the irradiance sensor is positioned normal to the water surface. Upwelling radiance was measured with a 25-degree field-of-view (FOV) foreoptic also connected to a fiber optic cable plugged into the other spectroradiometer. The upwelling radiance sensor was mounted below the sUAS chassis and was pointed 40 degrees off-nadir and oriented at approximately 135 degrees from sun azimuth so as to minimize the water surface specular reflection (Mobley, 1999) and shadow from the platform itself. Both the irradiance and radiance spectroradiometers were relatively calibrated against an ASD Fieldspec III spectroradiometer which was recently radiometrically calibrated by the manufacturer. The MTRI sUAS was flown to a height of five meters above the water surface where it hovered in place while measurements were collected. With the 25-degree FOV pointed at 40 degrees off nadir at a height of five meters, the ground sample resolution was an ellipse with a major axis of 3.9 m and a minor axis of 2.2 m and a total viewing area of 6.8 m2. A total of six spectra were recorded at each site. Spectra were recorded at CP01-CP04 shown on Fig. 3 on Oct 3, 2017. To reduce weight, the UT system deploys a single STS spectroradiometer on the sUAS, with a second deployed at the base station (boat). The sUAS based spectroradiometer is deployed at a nadir position, and uses a 25-degree foreoptic to measure upwelling spectral

radiance. Prior and following each flight, calibration spectra were acquired with the sUAS sensor aimed at the white Spectralon target from approximately one meter above the target. Downwelling irradiance was estimated from the Spectralon target, assuming that the panel acted as a Lambertian reflector (Moore et al., 2017). Prior to flight, integration time is optimized for illumination conditions (typically 500–1000 ms) and a white reference measurement was taken using the Spectralon target. Irradiance was measured at the base station with a RCR foreoptic connected directly to the base station STS, and fixed on the boat above any obstructions. Irradiance was recorded on a laptop on the boat at 1 Hz. Flights of the UT sUAS were conducted either from UT's 8.5 m North River research vessel or from the shoreline at between 5 m and 10 m above the water's surface at locations marked on Fig. 3 on September 8 (8 M), 11 (7 M, WE6, EW5), 25 (MR1) and September 27(MR1, MR2), 2017. Flights were free flown at constant altitude at a maximum velocity of 1 m/s, spatial footprint was between 2 and 3 m radius, depending on the flight altitude. During each flight of the UT sUAS, the STS records upwelling radiance at 1 Hz. Following flights, flight tracks were extracted from the sUAS flight logs. Spectroradiometer measurements and flight track records were synchronized based on takeoff and landing events to obtain position estimates for every sUAS spectroradiometer measurement. All sUAS flights of each sUAS were

Fig. 3. Sample locations for sUAS flights over the Maumee River and the western basin of Lake Erie. Orange diamonds indicate flights by MTRI sUAS, yellow crosses indicate flights by UT sUAS.

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conducted by a FAA Part 107 licensed operator, observing all relevant FAA regulations. In-water Fluoroprobe measurements A FluoroProbe III (bbe-Moldaenke GmbH), hereafter referred to as the FluoroProbe, was used to collect in situ estimates of chlorophyll-a concentrations corresponding to sUAS overflights. The FluoroProbe uses six light-emitting diodes (at 370, 470, 525, 570, 590, and 610 nm) which interact with phytoplankton particles in the water. The instrument estimates abundances of different nominal phytoplankton groups (green algae, bluegreen algae, diatoms, and cryptophyta) by comparing the retrieved fluorescence excitation spectra to groupspecific standard curves. CDOM or “yellow substance” fluorescence is estimated using the 370-nm fluorescence excitation band to remove CDOM fluorescence contamination from algal chlorophyll concentrations (Beutler et al., 2002). The FluoroProbe was deployed by hand over the side of MTRI's research vessel. After warming up just below the water's surface, the instrument was held horizontally approximately 0.5 m below the surface for 1–2 min. Data was imported into the bbe++ software (version 2.1; bbe-Moldaenke GmbH) where standard calibrations were applied in order to derive chlorophyll-a concentrations for each phytoplankton group. Algorithms The Cyanobacterial Index (CI) identifies the presence and concentration of cyanobacteria using the spectral shape of the reflectance spectra around 681 nm. The spectral shape, is determined as a nominal second derivative around the wavelength of interest (681 nm) (Wynne et al., 2008; Wynne et al., 2010). The CI has been widely used to indicate the presence of a cyanobacterial bloom (Sayers et al., 2016; Stumpf et al., 2012; Wynne and Stumpf, 2015). The Colour Producing Agent Algorithm (CPA-A) is a bio-optical inversion model that retrieves concentrations of chlorophyll-a, suspended mineral (non-algal particles), CDOM absorption, and dissolved organic carbon from observations of water leaving reflectance (Shuchman et al., 2013a). The CPA-A relates the concentrations of retrieved parameters to observed reflectance through the knowledge of the spectral shape of the inherent optical properties (IOPs) of the water. The CPAA has been used extensively to generate water quality parameters in the Great Lakes (Fahnenstiel et al., 2016; Sayers et al., 2016; Shuchman et al., 2013b). The Surface Scum Index (SSI) is a simple normalized band ratio algorithm that identifies the presence of algal biomass floating on the water surface. The SSI is of the same form as the Normalized Difference Vegetation Index (NDVI) but uses spectral bands located at optimal wavelengths (red and near-infrared (NIR)), depending on sensor characteristics, to produce consistent retrievals between observations (Sayers et al., 2016). The SSI exploits the significant NIR reflectance from algal biomass relative to the strong NIR absorption of the underlying water mass. Positive values of the SSI have been shown to strongly correlate with the presence of cyanobacteria surface scums in Lake Erie (Sayers et al., 2016). In total, four open lake sUAS flight transects and six nearshore point acquisition flights were conducted. At each of these locations sUAS reflectance (point or transect), ASD reflectance (at a point under the transect) and fluorometric algae abundance were acquired. Results Point reflectance measurements were made hovering the MTRI sUAS at 5 m above locations CP01-CP04 at Cullen Park (Fig. 3). Boat based ASD measurements were acquired directly under the same location after each flight. Fig. 4 shows comparison of MTRI sUAS and ASD observed irradiance, radiance and computed reflectance values at site

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CP01 near Cullen Park. This comparison reveals good agreement between the near-simultaneous sUAS and ASD measured irradiance, radiance and remote sensing reflectance. The reflectance spectra measured by both systems indicates a water body with complex algal composition including blue-green algae, green algae, diatoms, and cryptophytes as confirmed by the Fluoroprobe measurements shown in Fig. 4 (bottom right panel). At sites EW5, WE6, 7 M and 8 M open water transects between 5 and 10 m above lake surface from 50 to 600 m in total length were flown using the UT sUAS system. Single location on-lake ASD measurements were made prior to flights adjacent to the UT research vessel, and these locations were overflown as part of the sUAS flight. Additionally, the UT sUAS was flown 5 m above site MR1 and ASD boat-based samples were acquired at the same location immediately after. The UT sUAS acquired spectra over surface scum 5 m above MR2, but the water was too shallow to allow boat access. Fig. 5 shows additional comparisons of reflectance values between ASD at-boat measurements and near-simultaneous sUAS observations at all 10 sampling locations across 3 sampling dates. Table 1 shows calculated R2 comparisons between ASD and sUAS-derived reflectances across the spectral range from 350 to 800 nm for stations where simultaneous measurements were collected. In addition, CPA-A, CI and SSI indices were calculated using the sUAS reflectance spectra for each location. The indices presented in Fig. 5 (bottom right panel) show a wide range of water constituents including scum and non-scum. Stations WE06, MR01–02 and CP01–04 showed a high proportion of cyanobacterial blooms, with scum present in varying proportions at MR02 and CP02–04. Moving further into the lake, EW5 and 7 M show lower concentrations of bloom with similar characteristics. MR01 (well-mixed river site) shows high bloom concentrations that are well-mixed through the water column. Site 8 M shows clear water with little cyanobacterial contribution, but with diatoms present. Fig. 6 shows spectra acquired at MR1 two days apart. The September 25 spectra reflects a thin surface scum (left panel, also seen in Fig. 1), while the September 27 spectra shows that same bloom well mixed in the water (right panel). This increased scum component is seen in the elevated reflectance beyond 720 nm relative to the 625–650 nm region. For the open water flight segments, the cyanobacterial index (CI) was computed for each measurement along the track (Wynne et al., 2010). Cyanobacterial index is proportional to cell count of cyanobacteria and is a measure of algal bloom intensity. These were overlain on their flight tracks to create a spatial transect map of each location. Fig. 7 is an example showing the derived CI map from the flight near location WE06 on September 11, 2017. This map shows the spatial variability of algae abundances on the scale of tens of meters. The sUASderived transect is overlain on a 300 m Sentinel 3/MERIS pixel footprint (magenta), showing the mismatch in scale of variability of the bloom with the pixel size of common satellite water quality instruments. Discussion The sUAS based spectroradiometers acquired high quality spectral data over open water, and were comparable in quality with the ASD spectroradiometer data, often considereds the gold standard reference for field spectroradiometer measurements. Comparisons between the sUAS based Ocean Optics and the at-water level ASD measurements at locations CP01-CP04 generally exhibited very similar spectra in upwelling radiance, downwelling irradiance, and the computed reflectance. For the sites CP03 and CP04, where conditions were more uniform, R2 between the sUAS and ASD acquired spectra were 0.978 and 0.998 (Table 1). When in a highly heterogeneous surface scum area (CP01, CP02), R2 decreased to 0.594 and 0.798, respectively, due to discrepancies in the wavelengths beyond 710 nm (R2 increased to 0.956 and 0.998 when only the spectral region below 710 nm was considered). This set of measurements (CP01, CP02) was made across water surface areas that showed at least partial surface accumulations of algae to a

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Fig. 4. Summary of measurements made at CP01, showing irradiance, radiance and reflectance from sUAS based Ocean Optics STS spectroradiometer flown at 5 m and ASD Fieldspec III measurements made from the MTRI boat. Fluoroprobe data from the same location show a dense cyanobacteria bloom. Note the overall agreement between the sensors, with slightly more variation in the ASD measured irradiance (likely due to changing illumination conditions across aquisition times). The strong absorption shoulder at 625 nm, and the increased reflectance beyond 725 nm are characteristic of a cyanobacterial (blue-green algal) dominated bloom with some surface scum formation.

Staon CHL EW5

SM

17.21 9.97

WE06 40.79 10.87 8.2

CI

SSI

0.018

N

0.026

N

0.0072

N

7M

14.4

8M

20.75 0.62 -0.00036

N

MR1

75.38 2.09

N

MR2

0.027

27.08 8.06

0.032

Y

CP01 100.5 7.44

0.0076

N

CP02 64.56 7.03

0.020

Y

CP03 73.81 6.65

0.021

Y

CP04 60.85 6.54

0.023

Y

Fig. 5. Reflectance measurements from at-water based ASD Fieldspec III (gray) and sUAS based Ocean Optics STS (red) in different bloom environments. With derived products of chl-a and SM, CI and SSI for each location. WE06, MR01–02 and CP01–04 show a high proportion cyanobacterial blooms, with scum present in varying proportions at MR02 and CP02–04. Moving further into the lake, EW5 and 7 M show lower concentrations of bloom with similar characteristics. MR01 (well mixed river site) shows high bloom concentrations that are well mixed through the water column. Site 8 M shows clear water with little cyanobacterial contribution, but with diatoms present.

R.H. Becker et al. / Journal of Great Lakes Research 45 (2019) 444–453 Table 1 R2 correlation coefficients for linear relationship between ASD and sUAS-derived spectra over same location. Site

R2

7M EW5 WE06 CP01 CP02 CP03 CP04 MR1

0.992 0.987 0.941 0.594 0.798 0.978 0.998 0.977

complete coverage in algae scum. This variability likely reflects the heterogeneity in surface scum, as the cyanobacteria in this area was distinctly streaky, and the inability to measure the same spot in the water as from the drone, due to disturbances in the water caused by the presence of the boat. There is closer agreement between ASD and sUAS measurement at EW5 and 7 M (R2 = 0.987, 0.982) in the open lake areas, and in the river at MR1 (R2 = 0.977), three sites where there was no surface accumulation of algae. At WE06, a similar discrepancy is noted beyond 700 nm (R2 = 0.941), and again this area had more surface algae accumulation. This is consistent with observations at numerous sites that were used to develop the SSI (Sayers et al., 2016), in that the greater the proportion of surface area demonstrating surface accumulation within the field of view, the higher the absorption beyond 700 nm. The sites CP01-CP04 represent different proportions of scum in the sensor field of view, from minimal (CP01) to partial (CP02 and CP03) to near complete (CP04). In summary, overall agreement between the ASD based reflectance and the sUAS acquired measurements is high, though there are small magnitude shifts which likely reflect variability in solar illumination between the two measurement times. The quality of this agreement varies slightly in the blue end of the spectra (below 425 nm), likely due to the lack of diffuse scattering correction in any of the calculations. A significant advantage of utilizing a hyperspectral instrument is that a wide variety of derived products can be generated. As examples, Fig. 5 shows derived CI (Wynne et al., 2008), chl-a, SM from CPA-A (Shuchman et al., 2013a), and SSI (Sayers et al., 2016). In addition, any of the satellite algorithms can be tested, as the spectroradiometer results can readily be resampled to simulate satellite bands using the different band response functions. In making comparisons between remotely sensed observations of water quality and in situ measurements, it is far easier to use high spatial resolution sUAS acquired derived product data than lower resolution satellite products because the measurements are made on the same scale. As shown in Fig. 6, the spatial heterogeneity on the scale of a 300 × 300 m Sentinel-3 pixel is clear, making field validation of satellite algorithms difficult. This mismatch of sampling scales is far less of an issue in the sUAS case.

451

The sampling scale of satellites causes further problems in the nearshore environment, where mixed land/water pixels make it extremely difficult to measure water quality values adjacent to the coastline. Because this is the environment where most recreational users interact with the water, and there are frequently algae scums at the surface, this area is very important to resource managers. Due to the higher spatial resolution, the sUAS is less likely to produce mixed pixels in the nearshore environment. There are issues inherent in the sampling that have the potential to affect the quality of comparisons. For sites CP03 and CP04, care had to be taken to exclude sampling of the dock in the integrating area of the sUAS spectroradiometer. To avoid this, simultaneous RGB images were acquired from the sUAS, and based on the optics used, the footprint was projected onto the RGB image during the quality control phase to ensure that only water was in the spectroradiometers field. For open water sampling, the presence of the boat disturbs algae at the surface of the water. This impact was evident in the CI calculations near the boat (seen on Fig. 7 where the transect line crosses itself), which were far lower than in the surrounding water (0.002 vs 0.03, respectively). Though the ASD and sUAS measurements were comparable immediately adjacent to the boat, suggesting no fundamental issues with comparing instruments, the bloom intensity increased as the sUAS moved away from the boat, suggesting that either the bloom is highly variable at the scale of 20–30 m, or that the presence of the boat modified the surface optical properties. The time sensitivity of the sampling, especially when comparing with other sensors is also shown to be very important. Fig. 6 shows the variability in spectra at the same location only 2 days apart – one during calm wind (b1.5 m/s) conditions, the later with 7.7 m/s winds which likely dispersed the surface accumulation throughout the water column. The ability to measure these fast-changing blooms using this type of easily deployable sUAS based instrument is highlighted by this rapid change in Rrs at the water surface. It is worth noting that the results between the two sUAS systems were highly comparable despite two different spectral acquisition protocols being used (reflectance from paired upwelling and downwelling sensors and reflectance calculated based on downwelling irradiance estimated from white panel). For example, in Fig. 5, at site MR1, the sUAS reflectance values were calculated based on irradiance derived from a Spectralon panel, and the ASD were based on paired measurements of upwelling radiance and downwelling irradiance, and are directly comparable. As seen on the figure, the sUAS transects can provide better insight into the variability found in a single MERIS or Sentinel-3 pixel (Fig. 7). The sUAS footprint is similar in size to that of a high resolution airborne imager flown at 7000′ (2133 m), such as the NASA GRC HSI2 system (Lekki et al., 2017). The advantage of the HSI2 over the inexpensive sUAS described in this study is the ability to create high resolution images over a wide swath as compared to a profile at a specific location. The major advantage of these sUAS systems is that they can obtain measurements at a similar spatial and spectral resolution to the HSI2 at a much lower price point.

Fig. 6. Repeated measurements at the same location on the Maumee River two days apart show the time sensitivity of measurement and need for rapid deployment of sensors in a river or nearshore environment. The spectral reflection changes at the same location over two days with more concentrated surface bloom (left, bloom shown Fig. 1) and well mixed bloom (right). This increased scum component is seen in the elevated reflectance beyond 720 nm relative to the 625–650 nm region.

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Fig. 7. Derived CI map from sUAS on lake flight at WE06 shows the spatial variability of algae abundances on the scale of tens of meters. The sUAS derived transect is overlain on a Sentinel 3/MERIS pixel footprint, showing the mismatch in scale of variability of the bloom with the pixel size of common satellite water quality instruments.

Summary We were able to deploy two different sUAS platform configurations, each utilizing Ocean Optics STS spectroradiometers, to measure optical properties of river and lake water undisturbed by a boat. These sUAS spectroradiometers have been shown to provide high quality observations for water monitoring. Because of the small form factor and flight ranges, these systems measure at an ideal scale for reservoir and river monitoring, as well as water intakes and other areas of high anthropogenic activity. When flight GPS records are synchronized with spectroradiometer measurements, it is possible to generate high spatial resolution sampling profiles, which can provide a mapping capability beyond simple point observations. The cost of both sUAS platforms was extremely low for the high quality data they produce. The total costs for the UT system including spectroradiometers was less than $2000, and the MTRI system total cost was under $7000. This low initial investment and the low operational costs can translate to the widespread implementation of this type of system. The high quality, repeatable datasets acquired make it possible to extract meaningful water quality information from these systems. Their low flight altitude, below cloud cover (b400′ (b122 m)), with negligible atmospheric contributions, high spatial resolution and rapid deployment make these ideal systems for higher frequency monitoring which can complement data acquired by aircraft and satellite sensors. Finally, these systems (unlike boats) are able to measure without disturbing the water surface, and are not impacted by the act of sampling itself. With the continued advancement in sensor development, automated flight capabilities, and increased flight duration, these sUAS-based water quality observations will soon be a common tool in resource manager's toolboxes. Acknowledgements This work was supported by the NASA Glenn Research Center with funding under contract # NNC15MF73P to The University of Toledo and contract #NNC15VA51P to Michigan Tech Research Institute. Additional support was provided by the NSF (National Science Foundation)

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