A cost-effective method for mapping land cover at national scale

Timely and accurate national-scale land-cover mapping is essential for resource management. However, achieving this with limited resources is a challenge, particularly in mountainous and ecologically diverse regions with frequent cloud cover like New Zealand. We present a cost-effective, scalable methodology for land-cover classification that integrates Sentinel-2 imagery, spectral decision rules, temporal NDVI analysis, and deep learning (U-Net) within a unified, reproducible workflow. Our approach generates land-cover maps at a spatial resolution of 10 m. National classification was generated in less than 12 h of computing time. Validation against 4,500 samples stratified by map class yielded an overall classification accuracy of 96%, outperforming leading global products. This method balances automation with expert-informed logic, enabling accurate differentiation of challenging classes such as exotic forest, indigenous forest, and croplands. Although developed for New Zealand, the workflow should be adaptable to other countries seeking low-cost, high-frequency land-cover mapping. These land-cover maps can support a range of environmental applications, including carbon accounting, biodiversity assessment, erosion modelling, and detection of land-use change. • Accurate land-cover maps can be produced cost-effectively using satellite imagery. • Method combines spectral rules with deep learning and temporal analysis. • Limitations of global land-cover products are addressed. • Land-cover information is suitable for a wide range of environmental applications.