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.

中文翻译：

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利用遥感量化水下植被景观的空间复杂性：模拟和真实景观的经验教训


植被的空间组织已被证明是多个生态系统中生态状态的有力指标。在本研究中，我们分析了水下水生植被（SAV）景观中空间复杂性指标之间的关系，并探索了卫星遥感在水下环境中量化这些指标的潜力。为此，我们评估了对比空间组织的模拟和真实 SAV 景观的一系列复杂性指标。所有这些景观都经过人为操纵，以（i）模拟与水生环境的低信噪比（SNR）相关的遥感噪声以及风和波浪产生的环境噪声，以及（ii）将其空间分辨率从非常高的水平降低(2 m) 到中等 (30 m)。在这些处理中，空间分辨率和低信噪比（以传感器噪声为代表）对景观的感知空间复杂性影响最大，而环境噪声的影响高度依赖于分辨率。尽管单一指标被认为不足以表征景观的空间复杂性，但信息复杂性指标（例如丛集指数、平均信息增益、景观形状指数和边缘密度）的组合为真实和模拟数据集的变化提供了可靠的解释。这些发现表明，遥感有助于建立 SAV 空间结构和生态状况之间的联系，因此在 SAV 生态监测方面具有强大的潜力。