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Seagrass meadows provide vital services like carbon storage and habitat provisioning. Accurately estimating aboveground biomass (AGB) is essential for monitoring ecosystem health and guiding conservation efforts. While remote sensing offers a non-destructive method for large-scale AGB assessment, the performance of different vegetation indices (VIs) for seagrass, particularly Zostera marina, is not well understood. Building on previous research for Zostera noltei, we evaluated the predictive accuracy of 32 VIs, derived from field-based remote sensing reflectance, for estimating Z. marina AGB at Poel Island, Germany. Linear regression and hyperbolic Michaelis–Menten models were applied to assess the relationship between VIs and AGB. ND800/700 and mND705 were the best AGB predictors, with strong relationships (R2 = 0.82 and 0.81) and high saturation points (74.22 ± 11.69 and 82.38 ± 13.41 gDWm−2). Commonly used NDVI indices (NDVI650, NDVI680 and NDVI) also performed well (R2 = 0.80), but saturated at lower biomass range (32.41 to 48.79 gDWm−2). Percentage cover explained 81% of AGB variability, supporting its use as a rapid proxy for biomass. Overall, this study demonstrates the strong potential of VIs for non-destructive Z. marina biomass estimation, supporting the development of remote sensing tools for the effective seagrass monitoring and management.
Kaharuddin et al. (Fri,) studied this question.