We introduce a multiscale approach to population estimation of maldanid polychaetes on intertidal sandflats. Animals that live in marine sediments play important roles in ecosystem processes and are frequently the target of environmental monitoring but are notoriously time consuming to sample. Our goal was to enable the mapping of patterns of abundance of estuary lifeforms over long tracks at spatial scales and temporal frequencies not amenable by traditional quantitative methods. At each of four study sites of 9 m${}^2$, we used several forms of imagery at increasing resolution, obtained with a low-cost camera system and UAVs, to estimate the presence of the maldanid polychaete macroclymenella stewartensis. This involved defining the micro-scale topographic features on the sediment surface linked to the worms feeding activity. At each site, counts of polychate faecal mounds were estimated by quadrat counts. High resolution imagery was used to construct 3D depth maps using Structure from Motion, which were then segmented to obtain a local count of M.stewartensis faecal mounds. Texture features were then extracted from the high resolution imagery and used to create a linear model using the local counts. Low resolution imagery was then scaled, texture features extracted and passed into the linear model to provide count estimates with an error of 4% at best and 35% at worst for best orientation. The results demonstrated the potential of our multiscale texture based approach to model data at scale on sediment topographic features linked to the activity of functionally important species. These methods in turn help to advance ecological research and estimate ecosystem services potential as well as provide an additional tool for large scale rapid ecological assessment and estuarine management.

我们引入了一种多尺度方法来估算潮间带滩涂上的马尔代夫多毛羚种群。生活在海洋沉积物中的动物在生态系统过程中起着重要作用，通常是环境监测的目标，但是采样非常耗时。我们的目标是在传统的定量方法无法适应的空间尺度和时间频率上绘制长途航迹丰富形式的映射。在每个9 m的四个研究站点中${}^2$，我们使用了低成本相机系统和无人飞行器获得的分辨率不断提高的几种形式的图像，来估算马尔代夫多毛大巨macro斯图尔特氏菌的存在。这涉及在与蠕虫的摄食活动相关的沉积物表面上定义微观尺度的地形特征。在每个站点，通过quadrat计数来估算多排粪便丘的计数。高分辨率图像用于使用Motion的“结构”构建3D深度图，然后对其进行分段以获得Stewartensis的局部计数粪便堆。然后从高分辨率图像中提取纹理特征，并使用局部计数将其用于创建线性模型。然后对低分辨率图像进行缩放，提取纹理特征并将其传递到线性模型中，以提供计数估计值，最佳方向的误差最大为4％，最差为35％。结果证明了我们的基于多尺度纹理的方法在与功能重要物种的活动有关的沉积物地形特征上按比例建模数据的潜力。这些方法反过来有助于推进生态学研究和估计生态系统服务潜力，并为大规模快速生态学评估和河口管理提供额外的工具。