25 Sep 2025

Expansion of a Landform Reference Dataset in the Chihuahuan Desert for Dust Source Characterization Applications

Abstract: This report details the development of an extensive landform reference dataset for the Chihuahuan Desert region to support validation of a machine-learning-based landform classification model. Building upon previous work by Cook et al. (2022), we expanded both the quantity and spatial coverage of reference points to better represent the study domain’s geomorphic diversity. Analysts integrated information from published literature, government databases, and satellite imagery interpretation to create a dataset of 236,582 points across 12 landform classes, aligned to a 500 m resolution grid. The bedrock/pediment/plateau class was the dominant class (58%), followed by alluvial fans (21%), aeolian sands (11%), and aeolian dunes (5%). Approximately 85% of the reference points received high analyst confidence ratings, and ratings were especially high for classes with distinctive signatures, such as bedrock features, fine-grained lake deposits, urban/developed areas, water, and agricultural lands. Classification challenges consistently emerged in transitional zones between land-forms, areas with anthropogenic modifications, and complex landform assemblages where mapping resolution proved insufficient. The resulting dataset is a valuable resource for model validation and offers insights into arid region geomorphology. Additionally, it has the potential to support multiple applications, including dust hazard forecasting, terrain mobility assessment, soil property inference, and rangeland management.