Although emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to this habitat type; however, methods for deriving robust monitoring metrics, such as coral cover, are not well developed for drone imagery. To address this knowledge gap, we compare the effectiveness of two remote sensing methods in quantifying broad substrate groups, such as coral cover, on a lagoon bommie, namely a pixel‐based (PB) model versus an object‐based (OB) model. For the OB model, two segmentation methods were considered: an optimized mean shift segmentation and the fully automated Segment Anything Model (SAM). Mean shift segmentation was assessed as the preferred method and applied in the final OB model (SAM exhibited poor identification of coral patches on the bommie). While good cross‐validation accuracies were achieved for both models, the PB had generally higher overall accuracy (mean accuracy PB = 75%, OB = 70%) and kappa (mean kappa PB = 0.69, OB = 0.63), making it the preferred method for monitoring coral cover. Both models were limited by the low contrast between Coral features and the bommie substrate in the drone imagery, causing indistinct segment boundaries in the OB model that increased misclassification. For both models, the inclusion of a drone‐derived digital surface model and multiscale derivatives was critical to predicting coral habitat. Our success in creating emergent reef habitat models with high accuracy demonstrates the niche role drones could play in monitoring these habitat types, which are particularly vulnerable to rising sea surface and air temperatures, as well as sea level rise which is predicted to outpace reef vertical accretion rates.

中文翻译：

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绘制新兴珊瑚礁地图：基于像素和基于对象的方法的比较


尽管新兴珊瑚礁占整个珊瑚礁栖息地的很大一部分，但由于其浅层和暴露的环境使得它们难以进入，因此它们经常被排除在监测项目之外。使用无人机调查新兴珊瑚礁克服了进入这种栖息地类型的问题；然而，用于无人机图像的可靠监测指标（例如珊瑚覆盖率）的方法尚未得到很好的开发。为了解决这一知识差距，我们比较了两种遥感方法在量化泻湖波米的广泛基质组（例如珊瑚覆盖）方面的有效性，即基于像素（PB）模型与基于对象（OB）模型。对于 OB 模型，考虑了两种分割方法：优化的均值平移分割和全自动分割任意模型 (SAM)。均值漂移分割被评估为首选方法并应用于最终的 OB 模型（SAM 对 bommie 上的珊瑚斑块的识别效果很差）。虽然两种模型都取得了良好的交叉验证精度，但 PB 通常具有更高的总体精度（平均精度 PB = 75%，OB = 70%）和 kappa（平均 kappa PB = 0.69，OB = 0.63），使其成为首选监测珊瑚覆盖的方法。这两种模型都受到无人机图像中珊瑚特征和珊瑚基底之间的低对比度的限制，导致 OB 模型中的分段边界不清晰，从而增加了错误分类。对于这两个模型，包含无人机衍生的数字表面模型和多尺度衍生模型对于预测珊瑚栖息地至关重要。 我们成功地创建了高精度的新兴珊瑚礁栖息地模型，这证明了无人机在监测这些栖息地类型方面可以发挥的利基作用，这些栖息地类型特别容易受到海面和气温上升以及海平面上升的影响，预计海平面上升的速度将超过珊瑚礁垂直增长的速度费率。