A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data's spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

Automated summary

Submerged aquatic vegetation (SAV) is a critical but understudied component of aquatic ecosystems, which is rapidly changing due to global climate change and human disturbances. Remote sensing (RS) offers efficient and accurate large-scale monitoring for proper SAV management, though its application to underwater ecosystems is complicated by the water column effect and requires careful consideration of sensor selection and data processing methods. Successful use of RS for SAV identification and detection depends on factors such as data quality, resolution, and the specific research goals.