Themeda-Based Spatiotemporal Deep Learning for Predicting Vegetation Dynamics and Fire-Induced Land Cover Change in Northern Australia Using ConvLSTM and Temporal U-Net Models

The tropical savannas of Northern Australia, dominated by the perennial C4 grass Themeda triandra (kangaroo grass), are among the most fire-prone and ecologically significant biomes in the Southern Hemisphere. These savannas cover approximately 1.9 million km² and experience annual fire frequencies affecting 20–40% of the total area. Understanding and predicting vegetation dynamics and fire-induced land cover change in these landscapes presents substantial challenges for land managers, conservation practitioners, and carbon accounting frameworks. This study presented a novel spatiotemporal deep learning framework integrating Convolutional Long Short-Term Memory (ConvLSTM) and Temporal U-Net architectures to simulate and map fire-driven land cover change across Northern Australian tropical savannas. The framework incorporated multi-source satellite time series spanning 2019–2022 MODIS NDVI composites (250 m), Landsat 8/9 OLI surface reflectance (30 m), North Australia Fire Information (NAFI) fire scar records, Bureau of Meteorology rainfall grids, and Sentinel-2 MSI data. A novel Themeda Vegetation Index (TVI) was developed from the spectral properties of curing C4 grass tissue and validated against 84 spatially independent field plots (Pearson r = 0.87, p