Predicting Leaf Phenology in Forest Tree Species Using UAVs and Satellite Images: A Case Study for European Beech (Fagus sylvatica L.)

Abstract

Understanding forest tree phenology is essential for assessing forest ecosystem responses to environmental changes. Observations of phenology using remote sensing devices, such as satellite imagery and Unmanned Aerial Vehicles (UAVs), along with machine learning, are promising techniques. They offer fast, accurate, and unbiased results linked to ground data to enable us to understand ecosystem processes. Here, we focused on European beech, one of Europe’s most common forest tree species, along an altitudinal transect in the Carpathian Mountains. We performed ground observations of leaf phenology and collected aerial images using UAVs and satellite-based biophysical vegetation parameters. We studied the time series correlations between ground data and remote sensing observations (GLI r = 0.86 and FCover r = 0.91) and identified the most suitable vegetation indices (VIs). We trained linear and non-linear (random forest) models to predict the leaf phenology as a percentage of leaf cover on test datasets; the models had reasonable accuracy, RMSE percentages of 8% for individual trees, using UAV, and 12% as an average site value, using the Copernicus biophysical parameters. Our results suggest that the UAVs and satellite images can provide reliable data regarding leaf phenology in the European beech.