Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Abstract

Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas. In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems.

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

“…Similar is the case with the location circled next to station 26, with an SAV canopy depth from 2.6 m to 3.6 m (i.e., water depth 4.2 m, SAV stand height 0.6-1.6 m, SAV coverage 68%), where SAV could not be detected. Furthermore, the discrimination and identification of submerged vegetation become more difficult with the increase in water depth as the proportion of reflectance reaching the remote sensing device, diminishes with water depth [71]. Thus, apart from coverage area, the SAV canopy depth is also an important factor which substantially affects the detection of SAV.…”

“…Thus, apart from coverage area, the SAV canopy depth is also an important factor which substantially affects the detection of SAV. In previous studies, the seagrass of a coastal zone was mapped with an accuracy of only 59% by multispectral imagery (i.e., Landsat) whereas the high accuracy of 85-90% was obtained using the hyperspectral images (such as IKONOS and CASI) [71,72]. This further indicates the limitation of mapping deep water SAV species using the multispectral image which offers the information in 4-8 broad spectral bands compared to the hyperspectral images which provide the information at many more narrow spectral wavelengths, located around typical absorption points [71].…”

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

“…For example, the patchy mortality characteristic of seagrass die-off is very different from the gradual thinning and loss of seagrasses due to decreased water clarity [41]. Despite these benefits, the potential for using remote sensing to monitor environmental factors influencing seagrass loss has, thus far, been little explored [8]. Our assessment suggests that pixel-based classifiers of benthic habitats developed for specific images can be generalized and extended to images from other years, expediting the time series mapping that is required to adequately inform coastal resource management.…”

“…Despite their environmental and economic significance, seagrass populations are threatened worldwide by coastal development and eutrophication and may be nearing a crisis with respect to global sustainability [7]. The land-water interaction along coasts is influenced by freshwater networks where hydrologic impacts to seagrass ecosystems include increases in nutrients such as nitrogen and phosphorus occurring from industrial or agricultural sources [8], sediment discharge from watershed deforestation and mangrove clearing [8], and disruption of the natural salinity regime [2,9]. Without appropriate management, widespread loss of seagrass habitats is predicted to continue [10], especially given continued development of coastal lands.…”

Remote Sensing of Shallow Coastal Benthic Substrates: In situ Spectra and Mapping of Eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada

“…With increasing depth, eelgrass and surrounding substrate become less spectrally separable, as the optical properties of the water column constituents tend to dominate the signal [19,20]. The degree of this dominance is wavelength and constituent dependent.…”