Land-cover mapping was performed in Google Earth Engine using ee.Classifier.smileRandomForest (500 trees) was trained on manually digitised polygons sampling four classes (vegetated, non-vegetated, water, sand) from Sentinel-2 L2A Surface Reflectance Harmonized median composites of June-August each target year. Cloud and shadow masking used the Scene Classification Layer with a 1-2 pixel buffer. Classification used bands B2, B3, B4, B8 and B11 with a 70/30 train-test split, validated using overall accuracy, Cohen's Kappa, Producer's Accuracy and User's Accuracy (Congalton, 1991). A majority filter was applied to reduce salt-and-pepper noise. Chainage-level metrics were generated in ArcGIS Pro using the Tabulate Area tool, with 10 km chainage polygons as zone features and the classified rasters / canopy-height raster as class rasters. The CHIRPS June-August 2024 cumulative rainfall raster was aggregated using Zonal Statistics (mean) over the same chainage polygons.