Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

Abstract

In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized difference vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well with height (r = 0.70, p r = 0.63, p r = 0.45, p r = −0.60, p r = −0.77, p r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well.

Keywords

• climate change
• evergreen plants
• extreme events
• flavonol and chlorophyll sensor (Dualex)
• greenness indices
• mosses