Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones
Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones
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DOI：
10.3354/meps13814
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发表时间：
2021-09-02
影响因子：
2.5
通讯作者：
Johnston, D. W.
中科院分区：
文献类型：
--
作者：
Bierlich, K. C.;Schick, R. S.;Johnston, D. W.
基于无人机的摄影测量越来越多地被用来测量海洋大型动物的大小和身体状况的变化。广泛的平台、传感器和高度计正被应用于这些目的，但没有统一的方法来预测这种方法光谱中的摄影测量不确定性。因此，很难在不同研究之间进行强有力的比较，扰乱研究人员使用不同测量精度水平的平台之间的合作。在这里，我们建立在之前量化不确定性研究的基础上，并使用一种实验方法来训练贝叶斯统计模型，该模型使用一个已知大小的漂浮在水面上的物体来量化几架配备了不同相机、焦距镜头和高度计的不同无人机的测量误差随高度的变化。然后，我们应用该模型预测了未知体型的座头鲸的长度分布和估计的年龄类别，以及预测了南极小须鲸的吻部到喷孔距离和总体长之间的种群水平形态关系。这一统计框架联合估计了来自多个观测的高度和长度测量的误差，并考虑了气压计和激光高度计测量的高度，同时结合了各自特有的误差。该贝叶斯模型输出关于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，这使得人们能够对形态测量特征做出概率陈述和更有力的推断，这对于理解生活史模式和人类改变的生境的潜在影响至关重要。
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.