Wet channel network extraction by integrating LiDAR intensity and elevation data

Abstract

The temporal dynamics of stream networks are vitally important for understanding hydrologic processes including surface water and groundwater interaction and hydrograph recession. However, observations of wet channel networks are limited, especially in headwater catchments. Near-infrared LiDAR data provide an opportunity to map wet channel networks owing to the fine spatial resolution and strong absorption of light energy by water surfaces. A systematic method is developed to map wet channel networks by integrating elevation and signal intensity of ground returns. The signal intensity thresholds for identifying wet pixels are extracted from frequency distributions of intensity return within the convergent topography extent using a Gaussian mixture model. Moreover, the concept of edge in digital image processing, defined based on the intensity gradient, is utilized to enhance detection of small wet channels. The developed method is applied to the Lake Tahoe area based on eight LiDAR snapshots during recession periods in five watersheds. A power law relationship between streamflow and wetted channel length during recession periods is derived, and the scaling exponent (L ∝ Q0.44) is within the range of reported values from fieldwork in other regions.