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 < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = −0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = −0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well.

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

“…In the seventh step (7), new models were set up using CGDD (cumulative growing degree days) and DFS (days from sowing) values to minimize the impact of environmental factors. RMSE values were also calculated for these new models.…”

“…Over the last two decades, a significant boom in using remote-sensing tools for agricultural purposes has occurred. Data collection can be carried out by various means, Drones 2024, 8, 88 2 of 20 including different satellites [5][6][7][8], uncrewed aerial vehicles (UAVs) [9][10][11][12][13], or ground-based platforms [14][15][16][17]. These tools provide opportunities to monitor changes in plant and soil conditions, to predict in-season yields [18] and nutrient requirements [19], and to detect various diseases [20].…”

Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

“…The integration of satellite RS data in GIS systems for vegetation monitoring is used in three papers: one paper dealing with winter stress on arctic understory vegetation [5], one on the application of Copernicus (CMEMS GlobColour-Merged CHL-OC5 Satellite Observations) satellite-derived data concerning the aquatic environment [6] and one on the application of MODIS NDVI data and GIS to assess the effect of wildlife upon tropical savannah vegetation [7].…”