Using Satellite-Based NDVI to Monitor Subtle Changes in Native Grassland Condition Across Multiple Years

Detecting changes in vegetation condition is crucial for monitoring heterogeneous systems like natural grasslands. However, a background of high spatial and temporal variability in environmental variables and plant responses challenges field surveys and remote sensing. Monitoring fine-scale heterogeneity and transitions influenced by invasive species remains challenging. To address this gap, this study developed an approach to map vegetation condition across multiple years using condensed seasonal NDVI patterns derived from Sentinel-2 time series. The approach was evaluated in the temperate grasslands of South Australia (Mediterranean-type climate), dominated by iron-grass (Lomandra effusa) and impacted by invasive annuals. A beta regression model was trained using an NDVI time series and field-based iron-grass cover from a single year (2022), achieving a pseudo-R2 of 0.63 (RMSE = 9.48 ± 3.43%). Extrapolating the model across 2019–2025 yielded similar spatial patterns in cover, revealing good agreement between field-based data and predictions (pseudo-R2 = 0.53 to 0.69) and between predictions for each year (pseudo-R2 = 0.84 to 0.9). Despite rainfall and NDVI variability, the approach enabled the detection of subtle changes and the identification of trends. This approach holds great potential for mapping continuous attributes of vegetation condition over time, contributing to the conservation and monitoring of grasslands.