Quantifying spatial complexity in submerged aquatic vegetation landscapes using remote sensing: Lessons from simulated and real landscapes

The spatial organization of vegetation has been shown to be a strong indicator of ecological state in multiple ecosystems. In this study, we analyze the relationships between spatial complexity metrics in submerged aquatic vegetation (SAV) landscapes, and we explore the potential of satellite remote sensing to quantify these metrics in submerged environments. To do so, we estimated an array of complexity metrics over both simulated and real SAV landscapes of contrasted spatial organization. All these landscapes were artificially manipulated to (i) simulate remote sensing noise associated with the low signal-to-noise ratio (SNR) of aquatic environments and environmental noise generated by wind and waves, and (ii) reduce their spatial resolution from very high (2 m) to medium (30 m). Among these treatments, spatial resolution and low SNR (represented by sensor noise) had the strongest impacts on the perceived spatial complexity of the landscapes, while the impact of environmental noise was highly dependent on resolution. Although single metrics were deemed insufficient to characterize the spatial complexity of a landscape, a combination of informational complexity metrics such as the clumpy index, mean information gain, landscape shape index, and edge density provided a robust explanation of variation in the real and simulated datasets. These findings suggest that remote sensing has a strong potential for the ecological monitoring of SAV by contributing to establishing the link between SAV spatial structure and ecological status.