Quantifying Population Movement Using a Novel Implementation of Digital Image Correlation in the ICvectorfields Package

R J. Pub Date : 2022-05-18 DOI:10.32614/rj-2022-028
D. Goodsman
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Abstract

Movements in imagery captivate the human eye and imagination. They are also of interest in variety of scientific disciplines that study spatiotemporal dynamics. Popular methods for quantifying movement in imagery include particle image velocimetry and digital image correlation. Both methods are widely applied in engineering and materials science, but less applied in other disciplines. This paper describes an implementation of a basic digital image correlation algorithm in R open source software as well as an extension designed to quantify persistent movement velocities in sequences of three or more images. Algorithms are applied in the novel arena of landscape ecology to quantify population movement and to produce vector fields for easy visualization of complex movement patterns across space. Functions to facilitate analyses are available in the ICvectorfields software package. These methods and functions are likely to produce novel insights in theoretical and landscape ecology because they facilitate visualization and comparison of theoretical and observed data in complex and heterogeneous environments.
在ICvectorfields包中使用一种新的数字图像相关实现来量化人口运动
图像的运动吸引着人的眼睛和想象力。他们也对研究时空动力学的各种科学学科感兴趣。常用的图像运动量化方法包括粒子图像测速和数字图像相关。这两种方法在工程和材料科学中应用广泛,但在其他学科中应用较少。本文描述了一个基本的数字图像相关算法在R开源软件中的实现,以及一个用于量化三个或更多图像序列中持续运动速度的扩展。算法被应用于景观生态学的新领域,以量化人口的移动,并产生向量场,以便轻松地可视化跨空间的复杂移动模式。ICvectorfields软件包中提供了方便分析的功能。这些方法和功能可能在理论和景观生态学中产生新的见解,因为它们促进了复杂和异质环境中理论和观测数据的可视化和比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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