Mapping fine-scale seagrass disturbance using bi-temporal UAV-acquired images and multivariate alteration detection.

Seagrasses provide critical ecosystem services but cumulative human pressure on coastal environments has seen a global decline in their health and extent. Key processes of anthropogenic disturbance can operate at local spatio-temporal scales that are not captured by conventional satellite imaging. Seagrass management strategies to prevent longer-term loss and ensure successful restoration require effective methods for monitoring these fine-scale changes. Current seagrass monitoring methods involve resource-intensive fieldwork or recurrent image classification. This study presents an alternative method using iteratively reweighted multivariate alteration detection (IR-MAD), an unsupervised change detection technique originally developed for satellite images. We investigate the application of IR-MAD to image data acquired using an unoccupied aerial vehicle (UAV). UAV images were captured at a 14-week interval over two seagrass beds in Brisbane Water, NSW, Australia using a 10-band Micasense RedEdge-MX Dual camera system. To guide sensor selection, a further three band subsets representing simpler sensor configurations (6, 5 and 3 bands) were also analysed using eight categories of seagrass change. The ability of the IR-MAD method, and for the four different sensor configurations, to distinguish the categories of change were compared using the Jeffreys-Matusita (JM) distance measure of spectral separability. IR-MAD based on the full 10-band sensor images produced the highest separability values indicating that human disturbances (propeller scars and other seagrass damage) were distinguishable from all other change categories. IR-MAD results for the 6-band and 5-band sensors also distinguished key seagrass change features. The IR-MAD results for the simplest 3-band sensor (an RGB camera) detected change features, but change categories were not strongly separable from each other. Analysis of IR-MAD weights indicated that additional visible bands, including a coastal blue band and a second red band, improve change detection. IR-MAD is an effective method for seagrass monitoring, and this study demonstrates the potential for multispectral sensors with additional visible bands to improve seagrass change detection.

Long-term perspective on the relationship between phytoplankton and nutrient concentrations in a southeastern Australian estuary.

Sixteen years (1997-2013) of physicochemical, nutrient and phytoplankton biomass (Chlorophyll-a (Chl-a)) data and a decade (2003-2013) of phytoplankton composition and abundance data were analyzed to assess how the algal community in a temperate southeastern Australian estuary has responded to decreased chronic point source nitrogen loading following effluent treatment upgrade works in 2003. Nitrogen concentrations were significantly lower (P<0.05) following enhanced effluent treatment and Chl-a levels decreased (P<0.05) during the warmer months. Temperature and nutrient concentrations significantly influenced temporal changes of Chl-a (explaining 55% of variability), while salinity, temperature, pH and nutrient concentrations influenced phytoplankton abundance and composition (25% explained). Harmful Algal Bloom (HAB) dynamics differed between sites likely influenced by physical attributes of the estuary. This study demonstrates that enhanced effluent treatment can significantly decrease chronic point source nitrogen loading and that Chl-a concentrations can be lowered during the warmer months when the risk of blooms and HABs is greatest.