A typology of global relief classes derived from digital elevation models at 1 arcsec resolution

Abstract

Understanding land surface morphology and its relief components, which record the dynamics of the planet’s evolution and interaction of multiple environmental factors, constitutes a critical aspect of Earth system science. Advances in Earth observation technologies have enabled access to higher-resolution data, e.g. remote sensing imagery and digital elevation models (DEMs). However, classified relief and landform data with a resolution of approximately 1 arcsec (approximately 30 m) are lacking at the global scale, which limits the progress of related studies at finer scales. Here, we propose a novel framework for global relief classification and release a unique dataset called global relief classification (GRC), which incorporates a comprehensive set of objects that constitute the range of terrains and landforms on Earth. Constructed from multiple 1 arcsec DEMs, GRC covers the global land and ranks among the highest-resolution global geomorphic datasets to date. Its development integrates land surface ontologies, with cores, transitions and boundaries, and key derivatives to strike a balance between mitigating local noise and preserving valuable landform details. GRC categorizes Earth’s land relief into two levels, yielding raster files and discrete vector units that record relief type and distribution. Comparative analyses with previous datasets reveal that GRC better captures details of surface morphology, enabling a more precise depiction of geomorphological boundaries. This refinement facilitates the identification of finer and more precise spatial disparities in landform patterns than before, exemplified by marked contrasts between Asia and other continents, and highlights the distinct prominence of Peru and China in terms of relief diversity. Given that the data resolution of GRC accords well with accessible remote sensing imagery and other Earth science datasets, it is readily incorporated into analytical workflows, exploring the relationship between land morphology, surface runoff, climate, and land cover. The full dataset is available on the Deep-time Digital Earth Geomorphology platform and from Zenodo (https://doi.org/10.5281/zenodo.15641257, Yang et al., 2024).