Automatic Seagrass Disturbance Pattern Identification on Sonar Images

Natural and anthropogenic disturbances are causing degradation and loss of seagrass cover, often in the form of bare patches (potholes) and propeller-scaring from vessels. Degradation of seagrass habitat has increased significantly in recent years with losses totaling some 110 km 2 per year. With seagrass habitat disappearing at historically unprecedented rates, development of new tools for mapping these disturbances is critical to understanding habitat distribution and seagrass abundance. Current methods for mapping seagrass coverage rely on appropriate meteorological conditions (satellite imagery), are high in cost (aerial photography), or lack resolution ( in situ point surveys). All of these methods require low turbidity, and none is capable of automatically detecting bare patches (potholes) in seagrass habitat. Sonar-based methods for mapping seagrass can function in high turbidity, and are not affected by meteorological conditions. Here, we present an automatic method for detecting and quantifying potholes in sidescan sonar images collected in a very shallow, highly disturbed seagrass bed. Acoustic studies of shallow seagrass beds (<2 m) are scarce due to traditional approaches being limited by reduced horizontal swath in these depth ranges. The main challenges associated with these sidescan sonar images are random ambient noise and uneven backscatter intensity across the image. Our method combines adaptive histogram equalization and top-hat mathematical morphology transformation to remove image noises and irregularities. Then, boundaries of potholes are detected using optimum binarization as well as closing and erosion mathematical morphology filters. This method was applied to several sonar images taken from the Lower Laguna Madre in Texas at less than 2-m depth. Experimental results in comparison with ground-truthing demonstrated the effectiveness method by identifying potholes with 97% accuracy.