COS 260-1 - Measuring associations between understory vegetation structure and mammal diversity using a novel mixed-reality device: the Microsoft Hololens

Background/Question/Methods

Most ecological studies of vegetation structure have relied on manual field measurements that are labor intensive and time-consuming. Many current alternatives to classical measurements are expensive or difficult to transport to field settings. Here we evaluated a new method for measuring understory vegetation with a novel mixed-reality, remote sensing device, the Microsoft HoloLens. We developed a vegetation sensing application called VegSense that allows the HoloLens user to control the device’s environmental scanners to measure understory vegetation. Using VegSense, we tested the ability of the Microsoft HoloLens relative to classical field measurements to 1) detect trees and saplings, 2) measure diameter at breast height (DBH), 3) detect individual understory vegetation structures, and 4) estimate understory vegetation complexity as the amount of vegetation detected in the sampling area. We also use the HoloLens to test the relative importance of understory vegetation structure in determining mammal species richness, functional diversity, and functional richness at the microhabitat level in a protected tropical forest.

Results/Conclusions

We found that VegSense performed well in detecting and measuring trees with a DBH of 17 cm or more and estimating vegetation complexity and performed moderately at detecting understory vegetation. In addition, we found a positive relationship between understory vegetation surface area and multiple measurements of mammal diversity in a protected tropical forest. The effect of surface area was stronger than the effects of vegetation volume and elevation. Our results indicate that the HoloLens is a suitable alternative for a number of classical field measurements of understory vegetation. This method costs tens of thousands of dollars less than typical terrestrial LiDAR systems, and can facilitate efficient, high quality environmental data collection. In addition, we found that understory vegetation complexity, as measured by surface area, is a strong predictor of mammal diversity in this tropical forest, and may relate to associations between vegetation surface area and species diversity more widely.