Using multispectral drones to predict water quality in a subtropical estuary.

Drones are revolutionising earth system observations, and are increasingly used for high resolution monitoring of water quality. The objective of this research was to test whether drone-based multispectral imagery could predict important water quality parameters in an ICOLL (intermittently closed and opened lake or lagoon). Three water quality sampling campaigns were undertaken, measuring temperature, salinity, pH, dissolved oxygen (DO), chlorophyll (CHL), turbidity, total suspended sediments (TSS), coloured dissolved organic matter (CDOM), green algae, crytophyta, diatoms, bluegreen algae and total algal concentrations. DistilM statistical analyses were conducted to reveal the bands accounting for the most variation across all water quality data, then linear correlations between specific band/band ratios and individual water quality parameters were performed. DistilM analyses revealed the NIR band accounted for most variation in March, the Green band in April and the RE band in May, and showed that the most important contributors varied significantly among campaigns and variables. Significant linear correlations with R2 > 0.4 were obtained for eleven of the water quality parameters tested, with the strongest correlation obtained for CHL and the green band (R2 = 0.72). The relative importance of predictor bands and observed water quality parameters varied temporally. We conclude that drones with a multispectral sensor can produce useful 'snapshot' prediction maps for a range of water quality parameters, such as chlorophyll, bluegreen algae and dissolved oxygen. However, a single model was insufficient to reproduce the temporal variation of water parameters in dynamic estuarine systems.

Evaluating the effectiveness of drones for quantifying invasive upside-down jellyfish (Cassiopea sp.) in Lake Macquarie, Australia.

Upside-down jellyfish (Cassiopea sp.) are mostly sedentary, benthic jellyfish that have invaded estuarine ecosystems around the world. Monitoring the spread of this invasive jellyfish must contend with high spatial and temporal variability in abundance of individuals, especially around their invasion front. Here, we evaluated the utility of drones to survey invasive Cassiopea in a coastal lake on the east coast of Australia. To assess the efficacy of a drone-based methodology, we compared the densities and counts of Cassiopea from drone observations to conventional boat-based observations and evaluated cost and time efficiency of these methods. We showed that there was no significant difference in Cassiopea density measured by drones compared to boat-based methods along the same transects. However, abundance estimates of Cassiopea derived from scaling-up transect densities were over-inflated by 319% for drones and 178% for boats, compared to drone-based counts of the whole site. Although conventional boat-based survey techniques were cost-efficient in the short-term, we recommend doing whole-of-site counts using drones. This is because it provides a time-saving and precise technique for long-term monitoring of the spatio-temporally dynamic invasion front of Cassiopea in coastal lakes and other sheltered marine habitats with relatively clear water.