Abstract
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.
Highlights
The code to extract remote sensing data that best match the time stamp of telemetry data is designed in a specific workflow that users can modify for their own interests (Figure 1)
Once the telemetry data are loaded into R, the time stamp of the telemetry data needs to be converted into a factor with the format “YYYY-MM-DDTHH:MM:SS” to be converted into milliseconds since midnight on 1 January 1970, a format used in Google Earth Engine (GEE)
The severity of dissimilarity differs by species and region, with both overestimation and underestimation on the NDVI occurring depending on the time of year (Figure 2)
Summary
There are at least 58 different packages developed for use in the R programming language (https://www.r-project.org; accessed date: 10 October 2021) [6,7], one of the most popular open-source programs for data analysis among ecologists [8]. With such an abundance in data and new methods, the limitation to address relevant scientific questions of interest frequently lies in the required computing power and the technological expertise to do so
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