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Title: Landform identification in the Chihuahuan Desert for dust source characterization applications : developing a landform reference data set
Authors: Cook, Samantha N.
Bigl, Matthew F.
LeGrand, Sandra L.
Webb, Nicholas P.
Treminio, Ronald
Tyree, Gayle
Keywords: Chihuahuan Desert
Dust emission
Geospatial data
Machine learning
Remote sensing
Terrain characterization
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC TR-22-20
Abstract: ERDC-Geo is a surface erodibility parameterization developed to improve dust predictions in weather forecasting models. Geomorphic landform maps used in ERDC-Geo link surface dust emission potential to landform type. Using a previously generated southwest United States landform map as training data, a classification model based on machine learning (ML) was established to generate ERDC-Geo input data. To evaluate the ability of the ML model to accurately classify landforms, an independent reference landform data set was created for areas in the Chihuahuan Desert. The reference landform data set was generated using two separate map-ping methodologies: one based on in situ observations, and another based on the interpretation of satellite imagery. Existing geospatial data layers and recommendations from local rangeland experts guided site selections for both in situ and remote landform identification. A total of 18 landform types were mapped across 128 sites in New Mexico, Texas, and Mexico using the in situ (31 sites) and remote (97 sites) techniques. The final data set is critical for evaluating the ML-classification model and, ultimately, for improving dust forecasting models.
Description: Technical Report
Gov't Doc #: ERDC TR-22-20
Rights: Approved for Public Release; Distribution is Unlimited
Appears in Collections:Technical Report

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