Thermal Images in R

Post provided by REBECCA SENIOR (@REBECCAASENIOR)

Why use Thermal Images?

Temperature is important in ecology. Rising global temperatures have pushed ecologists and conservationists to better understand how temperature influences species’ risk of extinction under climate change. There’s been an increasing drive to measure temperature at the scale that individual organisms actually experience it. This is made possible by advances in technology.

Enter: the thermal camera. Unlike the tiny dataloggers that revolutionised thermal ecology in the past decade or so, thermal images capture surface temperature, not atmospheric temperature. Surface temperature may be as (if not more) relevant for organisms that are very small or flat, or thermoregulate via direct contact with the surface. Invertebrates and herps are two great examples of these types of organisms – and together make up a huge proportion of terrestrial biodiversity. Also, while dataloggers can achieve impressive temporal extent and resolution, they can’t easily capture temperature variation in space.

Like dataloggers, thermal cameras are becoming increasingly affordable and practical. The FLIR One smartphone attachment, for example, weighs in at 34.5 g and costs around ~US$300. For that, you get 4,800 spatially explicit temperature measurements at the click of a button. But without guidelines and tools, the eager thermal photographer runs the risk of accumulating thousands of images with no idea of what to do with them. So we created the R package ThermStats. This package simplifies the processing of data from FLIR thermal images and facilitates analyses of other gridded temperature data too. Continue reading