ABSTRACT: Increasingly, drone-based photogrammetry has been used to measure size and body condition changes in marine megafauna. A broad range of platforms, sensors, and altimeters are being applied for these purposes, but there is no unified way to predict photogrammetric uncertainty across this methodological spectrum. As such, it is difficult to make robust comparisons across studies, disrupting collaborations amongst researchers using platforms with varying levels of measurement accuracy. Here we built off previous studies quantifying uncertainty and used an experimental approach to train a Bayesian statistical model using a known-sized object floating at the water’s surface to quantify how measurement error scales with altitude for several different drones equipped with different cameras, focal length lenses, and altimeters. We then applied the fitted model to predict the length distributions and estimate age classes of unknown-sized humpback whales Megaptera novaeangliae, as well as to predict the population-level morphological relationship between rostrum to blowhole distance and total body length of Antarctic minke whales Balaenoptera bonaerensis. This statistical framework jointly estimates errors from altitude and length measurements from multiple observations and accounts for altitudes measured with both barometers and laser altimeters while incorporating errors specific to each. This Bayesian model outputs a posterior predictive distribution of measurement uncertainty around length measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty, which allows one to make probabilistic statements and stronger inferences pertaining to morphometric features critical for understanding life history patterns and potential impacts from anthropogenically altered habitats.

中文翻译：

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用于预测无人机形态测量中摄影测量不确定性的贝叶斯方法

摘要：越来越多地，基于无人机的摄影测量已被用于测量海洋巨型动物的大小和身体状况变化。广泛的平台、传感器和高度计正用于这些目的，但没有统一的方法来预测整个方法范围内的摄影测量不确定性。因此，很难在研究之间进行强有力的比较，从而破坏了使用具有不同测量精度水平的平台的研究人员之间的合作。在这里，我们建立了先前量化不确定性的研究，并使用实验方法训练贝叶斯统计模型，使用漂浮在水面上的已知大小的物体来量化配备不同相机、焦距镜头的几种不同无人机的测量误差如何随高度变化，和高度计。Megaptera novaeangliae，以及预测南极小须鲸Balaenoptera bonaerensis 的讲台到气孔距离与全身长度之间的种群水平形态关系. 这个统计框架联合估计了来自多次观测的高度和长度测量的误差，并考虑了使用气压计和激光高度计测量的高度，同时结合了各自的特定误差。这种贝叶斯模型输出围绕长度测量的测量不确定性的后验预测分布，并允许构建最高后验密度区间来定义测量不确定性，这允许人们做出概率陈述和更强的推断，与形态特征有关，这对于理解生活史模式和人为改变的栖息地的潜在影响。