Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems

Remote sensing is an essential tool for monitoring the extent and biophysical attributes of vegetation. Multi-sensor approaches, that can reduce the costs of developing high-quality datasets and improve predictive performance, are increasingly common. Despite this trend, the advantages of these data-fusion techniques are rarely reported beyond statistical performance. We use airborne lidar-derived metrics to develop models of canopy cover (CC, %) and woody vegetation (WV, presence/absence) using dry-season imagery from the Sentinel-1 (S1 C-band, 5.5 cm wavelength, Synthetic Aperture Radar) and Sentinel-2 (S2, multispectral optical) satellite constellations across natural and modified agricultural ecosystems in Tasmania, southeast Australia. Validation statistics at 18,876 sample locations demonstrated strong performance for both CC (R² = 0.83, RMSE = 0.13) and WV (OA = 0.94, Kappa = 0.87) when using both S1 and S2 variables for prediction. The small improvement in statistical performance provided by SAR variables (typically 1–2 % for CC and WV) understated the benefits of S1 for discriminating woody vegetation and quantifying canopy cover in non-woody ecosystems (e.g., alpine vegetation, heathlands, wetlands, coastal scrub), demonstrating the complementary benefits of multi-sensor prediction. The emergence and growth of natural capital accounting and frameworks such as the Nature Positive Initiative, mean that high-quality, cost-effective spatial datasets will continue to be in demand. Our study demonstrates the potential of non-commercial, publicly accessible remote sensing imagery to improve fine-scale analyses that may otherwise be cost-prohibitive to apply at scale.