DrpToolkit: An automated workflow for aligning and analysing vegetation and ground surface time‐series imagery

Abstract

Abstract Analysis of ecological data from digital repeat photography requires consistent file and metadata management, image alignment and data extraction across the time series of data collection. Current open‐source methods facilitate one or a few of these tasks, but in many cases require manual intervention by the user, as well as associating independent processes into an overall workflow. We introduce ‘drpToolkit’, an open‐source Python package that automates the workflow of data management, image alignment and data extraction from time‐series image sets. The toolkit operates on a folder of images and generates an aligned image time series using a user‐defined keyframe, and extracts derived greenness and snow indices from user‐defined regions of interest. Imagery alignment improves the spatial consistency among images in a set and allows repeated measures within the set. Particularly among small regions of interest, data extracted from aligned imagery reflects observed changes in greenness compared to unaligned imagery. This software simplifies the process of converting raw imagery stored on an SD card to useful ecological data. It automatically refiles imagery using a standardized format used in other applications, increasing the opportunity for cross‐study comparisons of phenology, and collaboration among researchers and agencies to improve understanding of fine‐scale ecological response to climate change.