Unpacking satellite pixels: UAVs reveal fine-scale drivers of land surface phenology in a winter rainfall shrubland

Abstract

Land surface phenology (LSP) can reveal important connections between vegetation dynamics and climate but remains poorly understood in evergreen winter-rainfall shrublands globally. Field-based studies have indicated diverse plant functional strategies in shrublands, but further work is required to link LSP to vegetation functional composition in these regions. We analysed time-series of the normalised difference vegetation index (NDVI) in fynbos shrublands of South Africa using multi-spectral imagery from satellites and unmanned aerial vehicles (UAVs). We investigated the climate drivers of seasonal vegetative phenology and long-term NDVI trends at multiple spatial scales ranging from the landscape to individual species. At coarse spatial resolutions, NDVI time-series indicated rainfall-driven vegetation dynamics in fynbos, both at inter and intra-annual time scales. However, high-resolution time-series from UAVs exposed an underlying divergence in vegetative phenology and long-term NDVI trends between shallow and deep-rooted growth forms. Phenophases and NDVI trends of isolated, deep-rooted, overstory shrubs were decoupled from rainfall relative to dense overstory patches and shallow-rooted understory growth forms. Variations in growth form phenology were not detected at coarse spatial scales due to scaling and competitive effects based on the functional composition of the vegetation.