  Monitoring underwater habitats is a vital part of observing the condition of
the environment. The detection and mapping of underwater vegetation, especially
seagrass has drawn the attention of the research community as early as the
nineteen eighties. Initially, this monitoring relied on in situ observation by
experts. Later, advances in remote-sensing technology, satellite-monitoring
techniques and, digital photo- and video-based techniques opened a window to
quicker, cheaper, and, potentially, more accurate seagrass-monitoring methods.
So far, for seagrass detection and mapping, digital images from airborne
cameras, spectral images from satellites, acoustic image data using underwater
sonar technology, and digital underwater photo and video images have been used
to map the seagrass meadows or monitor their condition. In this article, we
have reviewed the recent approaches to seagrass detection and mapping to
understand the gaps of the present approaches and determine further research
scope to monitor the ocean health more easily. We have identified four classes
of approach to seagrass mapping and assessment: still image-, video data-,
acoustic image-, and spectral image data-based techniques. We have critically
analysed the surveyed approaches and found the research gaps including the need
for quick, cheap and effective imaging techniques robust to depth, turbidity,
location and weather conditions, fully automated seagrass detectors that can
work in real-time, accurate techniques for estimating the seagrass density, and
the availability of high computation facilities for processing large scale
data. For addressing these gaps, future research should focus on developing
cheaper image and video data collection techniques, deep learning based
automatic annotation and classification, and real-time percentage-cover
calculation.
