Enhancing Animal Movement Analyses: Spatiotemporal Matching of Animal Positions with Remotely Sensed Data Using Google Earth Engine and R

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