Enhancing animal movement analyses: spatiotemporal matching of animal positions with remotely sensed data using Google Earth Engine and R

Crego, R. D., Masolele, M. M. , Connette, G. and Stabach, J. A. (2021) Enhancing animal movement analyses: spatiotemporal matching of animal positions with remotely sensed data using Google Earth Engine and R. Remote Sensing, 13(20), 4154. (doi: 10.3390/rs13204154)

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Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary to incorporate the vast spatiotemporal aspects of datasets available, especially in countries with less economic resources, limiting the scope of ecological inquiry. We developed an R coding workflow that bridges the gap between R and the multi-petabyte catalogue of remotely sensed data available in Google Earth Engine (GEE) to efficiently extract raster pixel values that best match the spatiotemporal aspects (i.e., spatial location and time) of each animal’s GPS position. We tested our approach using movement data freely available on Movebank (movebank.org). In a first case study, we extracted Normalized Difference Vegetation Index information from the MOD13Q1 data product for 12,344 GPS animal locations by matching the closest MODIS image in the time series to each GPS fix. Data extractions were completed in approximately 3 min. In a second case study, we extracted hourly air temperature from the ERA5-Land dataset for 33,074 GPS fixes from 12 different wildebeest (Connochaetes taurinus) in approximately 34 min. We then investigated the relationship between step length (i.e., the net distance between sequential GPS locations) and temperature and found that animals move less as temperature increases. These case studies illustrate the potential to explore novel questions in animal movement research using high-temporal-resolution, remotely sensed data products. The workflow we present is efficient and customizable, with data extractions occurring over relatively short time periods. While computing times to extract remotely sensed data from GEE will vary depending on internet speed, the approach described has the potential to facilitate access to computationally demanding processes for a greater variety of researchers and may lead to increased use of remotely sensed data in the field of movement ecology. We present a step-by-step tutorial on how to use the code and adapt it to other data products that are available in GEE.

Item Type:Articles
Additional Information:This research was funded by the Smithsonian Conservation Commons Working Land & Seascapes Amplification and Innovation Award (grant no. WLS-2020-11) and the Smithsonian’s Movement of Life Initiative.
Keywords:Movement ecology, MODIS, NDVI, remote sensing, telemetry, tracking devices,
Glasgow Author(s) Enlighten ID:Masolele, Majaliwa
Creator Roles:
Masolele, M. M.Methodology, Writing – review and editing
Authors: Crego, R. D., Masolele, M. M., Connette, G., and Stabach, J. A.
College/School:College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Remote Sensing
ISSN (Online):2072-4292
Published Online:16 October 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Remote Sensing 13(20): 4154
Publisher Policy:Reproduced under a Creative Commons License

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