Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Abstract

Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; and (ii) estimation of seagrass total aboveground biomass (STAGB). Firstly, the substrate leaving radiances is determined by compensating the water column correction of the pre-processed data because of the inherent errors associated with the geometric and radiometric fidelities including atmospheric perturbations. Secondly, the seagrass leaving radiances were correlated to the corresponding in situ STAGB to predict seagrass biomass. The relationship between STAGB and cover percentage was then established for seagrass meadows occurring in Merambong, Straits of Johor, Malaysia. By applying the above-mentioned approach on Landsat Thematic Mapper (TM) acquired in 2009 and Operational Land Imager (OLI) data acquired in 2013, the resulting maps indicated that submerged STAGB in less clear water can be successfully quantified empirically from Landsat data, and can be utilized in STAGB change detection over time. Data validation showed a good agreement between in situ STAGB and Landsat TM (R 2 = 0.977, p < 0.001) and OLI (R 2 = 0.975, p < 0.001) derived water leaving radiances for the studied seagrass meadows. The STAGB was estimated as 803˘0.47 kg in 2009, while it was 752.3˘0.34 kg in 2013, suggesting a decrease of 50.7 kg within the four-year interval. This could be mainly due to land reclamation in the intertidal mudflat areas performed, with a view to increase port facilities and coastal landscape development. Statistics on dugong sightings also supports changes in STAGB.

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

“…Recently, several change detection approaches (see Table 5) have been applied to seagrass environments for various time scales, considering the change in spatial distribution, coverage, and above-ground biomass in the range of 5–40 years [172,173,174,175]. However, no single technique has emerged as superior across diverse marine environments [10].…”

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

“…However, the methods are more diverse than those in Table 1. Change detection algorithms have been used in many applications such as arid environment monitoring [100], submerged biomasses in shallow coastal water [101], phragmites australis distribution [102], etc. We will mention some other applications in the following paragraphs.…”

Preface: Remote Sensing in Coastal Environments

“…Misbari and Hashim [17] used a two-step procedure to estimate seagrass total aboveground biomass (STAGB) using a 2009 Landsat TM and a 2013 Landsat OLI image in Straits of Johor, Malaysia. They demonstrated that Landsat data can be used to accurately estimate submerged STAGB for a multi-year change detection project.…”