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

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

Blog Article

Abstract 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 trailmaster challenger 200x 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 neflintw-r6mpw 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.