Resolving Spatiotemporal Distribution of Suspended Sediment Concentration over the Columbia and Snake River Using Remote Sensing
This research effort will leverage advances in the field of remote sensing and cloud computing to: (1) Develop a robust remote sensing-based machine learning model to accurately infer SSC levels using Landsat and Sentinel satellite imagery, (2) Apply this model to derive a comprehensive picture of hotspots of sediment sources and sinks along the CS River between 1984-present, and (3) Provide a user friendly, freely available Google Earth Engine (GEE) APP that can readily estimate SSC levels along the CS River for collaborative land management planning and post disturbance rehabilitation. This project would provide a bridge between management and science as a GEE-based APP is user friendly and allows land managers or non-scientists to not be encumbered by difficultly in navigating complex models.
Need and Benefit
The goal of this collaborative project is to provide actionable science and user-friendly tools to natural resource and water managers to evaluate the distribution of Suspended Sediment Concentration (SSC) along the Columbia and Snake River (CS River) in face of a changing climate, increased wildfires and evolving land management strategies.
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