Abstract

Climate change influences climate variability, increasing the frequency and severity of droughts. These events may trigger vegetation browning, a key indicator of drought propagation and shifts in resilience. While long-term trends often measure browning, rapid vegetation declines require alternative approaches. This study examines drought-induced vegetation browning, resilience, and propagation in central Chile using Moderate Resolution Imaging Spectroradiometer (MODIS) time series of normalised difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP). The Continuous Change Detection and Classification (CCDC) algorithm identified negative vegetation changes, filtering out non-browning events to reduce uncertainties. Spatial variations in browning were analysed across latitudinal gradients, topographies, and vegetation types, while shifts in temporal autocorrelation served as a proxy for resilience. Results indicated declines in NDVI across 19% of the study area, GPP in 12%, and LAI in 8%. NDVI responded to drought within six months, with productivity losses lagging by 8.7 months. Recovery was slow, averaging 3.6 years, and only 20%-25% of the affected areas recovered. Variations in browning timing and magnitude were driven by topography, vegetation, and latitude. A decline in vegetation resilience highlights the need for strategies to enhance adaptability to climate change.