Species level mapping of a seagrass bed using an unmanned aerial vehicle and deep learning technique

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.

LWDS: lightweight DeepSeagrass technique for classifying seagrass from underwater images

In many coastal areas around the world, the seagrasses provide an essential source of livelihood for many civilizations and support high levels of biodiversity. Seagrasses are highly valuable, as they provide habitat for numerous fish, endangered sea cows, Dugong dugon, and sea turtles. The health of seagrasses is being threatened by many human activities. The process of seagrass conservation requires the annotation of every seagrass species within the seagrass family. The manual annotation procedure is time-consuming and lacks objectivity and uniformity. Automatic annotation based on lightweight DeepSeagrass (LWDS) is proposed to solve this problem. LWDS computes combinations of various resized input images and various neural network structures, to determine the ideal reduced image size and neural network structure with satisfactory accuracy and within a reasonable computation time. The main advantage of this LWDS is it classifies the seagrasses quickly and with lesser parameters. The DeepSeagrass dataset is used to test LWDS's applicability.

Potential of seagrass habitat restorations as nature-based solutions: Practical and scientific implications in Indonesia

Seagrasses offer diverse ecosystem services, yet, they are among the most threatened ecosystems. When degraded or destroyed, their services are lost or reduced in the process, affecting, for instance, local communities directly dependent on their livelihood provision. The Intergovernmental Panel on Climate Change (IPCC) reported that climate change is projected to worsen over time; thus, there is an urgent need for mitigation strategies in practice and also in the longer term. This work aims to provide an alternative perspective of seagrass restoration as a nature based solution (NbS) on a global scale, yet, giving an emphasis on tropical regions such as Indonesia. We focused on seagrass restorations which are not yet well established in comparison with other restoration programs (e.g., mangroves) despite their critical roles. We present in this work how restoring seagrass meadows fits the global standard of NbS published by the International Union for Conservation of Nature (IUCN). The results of this study can serve as a basis for promoting seagrass restorations as NbS against climate change particularly in countries with a wide extent of seagrass coverage.