Sabellaria alveolata is a sedentary gregarious tube-building species widely distributed from southwest Scotland to Morocco. This species builds what are currently considered the largest European biogenic reefs in the bay of Mont-Saint-Michel (France). As an ecosystem engineer, S. alveolata generates small to large scale topographic complexity, creating numerous spatial and trophic niches for other species to colonize. Sabellaria reefs are also under anthropogenic pressures, leading locally to massive degradation. However, stakeholders lack spatially explicit measures of reef ecological status, at adapted spatial resolution to provide key management information for this protected habitat. Traditional field surveys are extremely time-consuming and rely on expertise for visual ecological status assessment. The present study aims at using an automatic processing approach based on optical airborne data to (i) assess the potential of hyperspectral imagery to discriminate Sabellaria bioconstructions and its main ecosystem associated habitats, including different types of substrate as well as biological components and (ii) to use the combination of the hyperspectral and LiDAR signals to estimate the spatial structure of the different bioconstruction types (veneers vs. hummocks and platforms) and ecological phases (retrograding and prograding). A reef from Mont-Saint-Michel was used as a test site. We built a processing chain based on supervised classification using the Mahalanobis distance to generate an accurate distribution map (overall accuracy of 88% and a Kappa of 0.85) of 10 Sabellaria-related benthic features, including large reef developing on sand and smaller veneers encrusting rocky shore areas. Specific spectral indices were used to define the spatial distribution of the main primary producers, in particular the microphytobenthos. Joining the hyperspectral and LiDAR data led characterizing the distribution of S. alvealata’s ecological status (prograding and retrograding phases) with an overall classification accuracy and Kappa coefficient that can respectively amount to up to 93 and 0.86. In our study site, the Sabellaria reef area (between 5.52 and 6.76 ha) was dominated by retrograding phases (between 53 and 58%). Our results showed that this automatic processing chain could be relevant for the spatial characterization of other Sabellaria reef sites. Study perspectives tend toward a quantitative estimation of their ecological status index.

中文翻译：

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高光谱和激光雷达：识别底栖特征和评估 Sabellaria alveolata 珊瑚礁生态状况的补充工具

Sabellaria alveolata 是一种久坐的群居管材物种，广泛分布于从苏格兰西南部到摩洛哥。该物种在圣米歇尔山（法国）湾建造了目前被认为是欧洲最大的生物礁。作为生态系统工程师，S. alveolata 产生小到大尺度的地形复杂性，为其他物种的殖民创造了许多空间和营养生态位。Sabellaria 珊瑚礁也受到人为压力，导致当地大规模退化。然而，利益相关者缺乏对珊瑚礁生态状况的空间明确测量，以适应​​的空间分辨率为这个受保护的栖息地提供关键的管理信息。传统的实地调查非常耗时，并且依赖于视觉生态状态评估的专业知识。本研究旨在使用基于光学机载数据的自动处理方法 (i) 评估高光谱图像区分 Sabellaria 生物结构及其主要生态系统相关栖息地的潜力，包括不同类型的基质和生物成分，以及 (ii)使用高光谱和 LiDAR 信号的组合来估计不同生物构造类型（贴面与小丘和平台）和生态阶段（退行和进退）的空间结构。圣米歇尔山的一块礁石被用作试验场。我们构建了一个基于监督分类的处理链，使用 Mahalanobis 距离生成 10 个与 Sabellaria 相关的底栖特征的准确分布图（总体准确度为 88%，Kappa 为 0.85），包括在沙子上发育的大礁石和覆盖岩石海岸区域的较小单板。特定的光谱指数被用来定义主要初级生产者的空间分布，特别是底栖微型植物。结合高光谱和 LiDAR 数据，以总分类精度和 Kappa 系数分别高达 93 和 0.86 来表征 S. alvealata 的生态状态（退化和退化阶段）的分布。在我们的研究地点，Sabellaria 礁区（5.52 和 6.76 公顷之间）主要是退化阶段（53 和 58% 之间）。我们的结果表明，这种自动处理链可能与其他 Sabellaria 珊瑚礁地点的空间特征相关。研究视角倾向于对其生态状况指数进行定量估计。