Environmental Science Division (EVS) a Division of Argonne National Laboratory
Program Highlights Index

Mapping Surface Hydrologic Features in Desert Landscapes

EVS developed an algorithm for mapping ephemeral streams that could facilitate the development of cost-effective monitoring strategies for water resource management in arid environments.

Water is the limiting factor in many ecologic systems, and changes in water regimes affect various natural resources. One critical aspect of water resources monitoring is the study of surface hydrologic processes involving ephemeral streams — their flow conveyance, sediment transport, and groundwater recharge. In arid environments, knowledge about ephemeral streams is vital for understanding the hydrologic cycle, local ecosystems, and water availability for human use. However, quantifying surface hydrologic processes is extremely challenging, because runoff events in arid landscapes are episodic, and established methods for accurately mapping ephemeral stream networks and characterizing their functionality have been lacking.

Remote sensing technologies permit spatially contiguous data collection over large areas, automated processing, and streamlined data analysis. Remote sensing has been applied to identify stream channels across various landscapes. However, previous methods were inadequate for reliably mapping ephemeral stream channels and their properties, because of the complexity of channel networks, the absence of flow, and the small topographic gradient of desert drainage systems.

Very high resolution imagery of the Palo Verde Mesa, California (148 km<sup>2</sup>). The  15-cm-resolution images consist of visible and near-infrared bands. Close-up views show various ephemeral channels in the landscape.
Very high resolution imagery of the Palo Verde Mesa, California (148 km2). The 15-cm-resolution images consist of visible and near-infrared bands. Close-up views show various ephemeral channels in the landscape. [Source: Argonne National Laboratory]

By applying knowledge about desert landscapes and multispectral imagery at very high resolution, EVS scientists have developed a new algorithm for mapping ephemeral channel networks and their properties. The Argonne algorithm combines a series of spectral transformations and spatial statistical operations with expert knowledge to generate spatially explicit, spatially contiguous data that represent desert vegetation and the ground surface. From the patterns of vegetation occurrence and density, as well as surface brightness and its spatial heterogeneity, the algorithm detects stream channels, extracts channel centerlines, and calculates channel length and width.

Overview of the knowledge-based channel extraction algorithm. Vegetation and surface (or soil) brightness of the desert landscape are characterized in a series of operations.
Overview of the knowledge-based channel extraction algorithm. Vegetation and surface (or soil) brightness of the desert landscape are characterized in a series of operations. [Source: Argonne National Laboratory]

The Argonne knowledge-based algorithm extracts well-defined single channels, complex braided streams, and small tributaries across a large heterogeneous desert landscape and generates a highly detailed map of dry stream networks and their geometry. The algorithm could contribute significantly to advancing hydrologic modeling and could facilitate the development of cost-effective monitoring strategies for water resource management in desert regions.

The Argonne algorithm can extract 900 times more ephemeral stream channels in length than those recorded in the National Hydrography Dataset.
The Argonne algorithm can extract 900 times more ephemeral stream channels in length than those recorded in the National Hydrography Dataset. [Source: Argonne National Laboratory]

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photo of Yuki Hamada
Assistant Remote Sensing Scientist
Capabilities: Applications of optical remote sensing and geospatial modeling approaches for analyzing and monitoring terrestrial ecosystem functions and processes; application of plant spectroscopy to hyperspectral image analysis for terrestrial ecosystem research; development of novel image processing algorithms to extract and characterize land surface features and properties; use of geospatial information technologies in development of a framework for data interpolation, extrapolation, and scaling from fine-resolution local scale to coarse regional scale.