Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

Journal Article Resource
January 2022

Abstract

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to testing data, indicating outstanding model performance.

 

Citation: 

Gerlach, M.E.; Rains, K.C.; Guerrón-Orejuela, E.J.; Kleindl, W.J.; Downs, J.; Landry, S.M.; Rains, M.C. Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams. Remote Sens. 2022, 14, 63. https://doi.org/10.3390/rs14010063. Available at: https://www.mdpi.com/2072-4292/14/1/63/htm