Satellites can be useful for tracking weather and even geology. Now, though, scientists have used NASA satellite data to actually track deer.

Mule deer mothers are often in sync with their environment. In other words, when they reproduce corresponds with plant growth patterns. This means that by tracking plants from space, researchers can predict when mother deer give birth to fawns.

"We have never tracked the deer population this way, and we had never been able to predict it with such precision," said David Stoner of Utah State University, lead author of the new study. "We can estimate the start and peak of the season using satellite imagery, and then we can map and predict when the deer are giving birth in any given region."

Scientists closely monitor mule deer populations in order to set the proper number of hunting permits. This allows for sustainable hunting while allowing the population to thrive. But being able to see how many deer are in an area specifically can be difficult. That's why researchers are looking at alternative methods.

In this latest study, the researchers used remote sensing with a tool, called the Normalized Difference Vegetation Index (NDVI), to track when vegetation greens up and how productive it is compared to drought or wet years. The NDVI looked at the "greenness" of the landscape by measuring how plants absorb and reflect light.

In order to better calculate the correlation between vegetation greenness and fawns, the researchers measured the NDVI for each day of the calendar year. This revealed that vegetation peaks earlier in northern latitudes, which means that mule deer mothers had vegetation when they needed it most. In the south, the mothers were more dependent on rain from summer monsoons.

"This kind of applied research is very important for making remote sensing data relevant to wildlife management efforts," said Jyoteshwar Nagol, one of the researchers of the new study.

The findings reveal a bit more about how to use remote sensing in order to track these animals. This could help wildlife managers better establish proper hunting permits. More specifically, it also shows how researchers can use this new technology to better understand the environment.

The findings are published in the March 2016 journal PLOS One.