We describe a waterproof, lightweight (1.3 kg) low power (∼1.1 W average power) fluorometer operating on 5 VDC deployed on a small Uncrewed Aircraft System (sUAS) to measure chlorophyll and used for triggering environmental water sampling by the sUAS. The fluorometer uses a 450 nm laser modulated at 10 Hz for excitation and a standard photodiode and transimpedance amplifier for the detection of fluorescence. Additional detectors are available for measuring laser intensity and light scattering. Control of the fluorometer and communication between the fluorometer and the Raspberry Pi 4B computer controlling the sampler were provided by an Arduino microcontroller using the Robot Operating System (ROS). Calibrations were based on standards of dissolved chlorophyll extracted from Chlorella powder (a widely available dietary supplement). The detection limit for chlorophyll from these calibrations was found to be 0.2 micrograms per liter of water for a single 0.1 sec differential measurement. The detection limit decreases with the square root of the integration time as expected. Detection limits increase by a factor of 2 to 3 when mounted in the sUAS due to electrical noise; sUAS acoustic noise and vibration do not appear to contribute significantly.

We describe the control and interfacing of a fluorometer designed for aerial drone-based measurements of chlorophyll- a using an Arduino Nano 33 BLE Sense board. This 64 MHz controller board provided suitable resolution and speed for analog-to-digital (ADC) conversion, processed data, handled communications via the Robot Operating System (ROS) and included a variety of built-in sensors that were used to monitor the fluorometer for vibration, acoustic noise, water leaks and overheating. The fluorometer was integrated into a small Uncrewed Aircraft System (sUAS) for automated water sampling through a Raspberry Pi master computer using the ROS. The average power consumption was 1.1 W. A signal standard deviation of 334 µV was achieved for the fluorescence blank measurement, mainly determined by the input noise equivalent power of the transimpedance amplifier. An ADC precision of 130 µV for 10 Hz chopped measurements was achieved for signals in the input range 0-600 mV.

No abstract

Mission planning for small uncrewed aerial systems (sUAS) as a platform for remote sensors goes beyond the traditional issues of selecting a sensor, flying altitude/speed, spatial resolution, and the date/time of operation. Unlike purchasing or contracting imagery collections from traditional satellite or manned airborne systems, the sUAS operator must carefully select launching, landing, and flight paths that meet both the needs of the remote sensing collection and the regulatory requirements of federal, state, and local regulations. Mission planning for aerial drones must consider temporal and geographic changes in the environment, such as local weather conditions or changing tidal height. One key aspect of aerial drone missions is the visibility of the aircraft and communication with the aircraft. In this research, a visibility model for low-altitude aerial drone operations was designed using a GIS-based framework supported by high spatial resolution LiDAR data. In the example study, the geographic positions of the visibility of an aerial drone used for water sampling at low altitudes (e.g., 2 m above ground level) were modeled at different levels of tidal height. Using geospatial data for a test-case environment at the Winyah Bay estuarine environment in South Carolina, we demonstrate the utility, challenges, and solutions for determining the visibility of a very low-altitude aerial drone used in water sampling.

This article is the first of a 3-part series that gives a procedure to avoid common errors in spectrofluorometry using an example case. Part 2 will focus on throughput and polarization correction of spectrofluorometers for the purpose of inter-laboratory comparisons, while Part 3 will focus on non-ideal sample behavior.