Combining Unsupervised and Supervised Machine Learning for Quantifying Watershed Organization and Functions Based on High-resolution Airborne Remote Sensing Data

Haruko M Wainwright1, Nicola Falco2, Jashvina Devadoss3, Baptiste Dafflon2, Sebastian Uhlemann4, Michelle E Newcomer3, Maya Franklin5, Charuleka Varadharajan6, Roelof Versteeg7, Eoin Brodie2, Nicholas Bouskill6, James Bentley Brown8, Karl Bernard Schaettle9, Burke J Minsley10, Jeffery Deems11, Brian Joseph Enquist12, Kenneth Hurst Williams13 and Susan Hubbard2, (1)Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences, Berkeley, CA, United States, (3)University of California Berkeley, Berkeley, CA, United States, (4)British Geological Survey Keyworth, Nottinghamshire, United Kingdom, (5)Lawrence Berkeley National Laboratory, Berkeley, United States, (6)Lawrence Berkeley National Lab, Berkeley, CA, United States, (7)Subsurface Insights, Hanover, NH, United States, (8)Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology, Berkeley, CA, United States, (9)University of California Berkeley, Departments of Chemistry, Bioengineering, Chemical and Biomolecular Engineering, Berkeley, CA, United States, (10)USGS Geology, Geophysics, and Geochemistry Science Center, Denver, CO, United States, (11)National Snow and Ice Data Center, Boulder, United States, (12)University of Arizona, Ecology and Evolutionary Biology, Tucson, AZ, United States, (13)Colorado School of Mines, Golden, CO, United States