Rapid ecological data collection from 360‐degree imagery using visualisation and immersive sampling in the R pannotator package

Abstract With rapid environmental change occurring globally there is an urgent need for new field survey methods that enable fast and efficient collection of ecological information from impacted systems. Advances in 360‐degree (panospheric) camera technology now allow the collection of geospatially explicit visual records of ecological properties far more cheaply, quickly and efficiently than traditional field survey methods. However, generalisable workflows and software platforms to manage, extract, process and use ecological information from large panospheric image datasets are still lacking. Here we develop a flexible, integrative workflow for rapid acquisition, preparation and extraction of macroscopic data from 360‐degree imagery. We introduce the open‐source R package Panospheric Image Annotator in R (pannotator), which allows the user to visualise and annotate images and extract cropped geocoded sub‐images in a repeatable, systematic way using customisable drop‐down menus and help files. We demonstrate the workflow and pannotator package using panoramic images collected from a study area at Uluṟu‐Kata Tjuṯa National Park in central Australia, which was affected by severe drought and fire in 2018–2020. In this study, 180 images were captured using GoPro Max cameras and imported into the pannotator package for data annotation. We extracted data for three key ecological attributes (plant species distribution, understorey cover and tree health) and show how these data can be used to spatially reconstruct species richness and community structure, plant size class, mortality and burn history. Modern 360‐degree cameras and immersive sampling using the pannotator package now offer a transformative solution for efficiently capturing and extracting macroscopic ecological and biogeographical data from field surveys. More generally, the package may be used to visualise and extract data from any georeferenced panospheric imagery and generate cropped images with embedded geolocation data for use in downstream artificial intelligence/machine learning–based applications.