Environmental Science Division (EVS)a Division of Argonne National Laboratory
Predictive environmental understanding
 

Mapping Surface Hydrologic Features in Desert Landscapes

EVS developed an algorithm for mapping detailed 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. Changes in water regimes affect various natural resources and ecosystem services. One critical aspect of water resources monitoring in arid environments is the study of surface hydrologic processes involving ephemeral streams — their flow conveyance, sediment transport, and groundwater recharge. Knowledge of ephemeral stream distributions, attributes, and their changes over time is vital for understanding the hydrologic cycle, local ecosystems, and water availability for human use and wildlife in the region. However, quantifying surface hydrologic processes is extremely challenging because presence of surface flow or runoff events in arid landscapes are episodic, and established methods for accurately mapping ephemeral stream networks and characterizing their functionality have been lacking.

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]

Remote sensing technologies permit spatially comprehensive data collection over large areas, automated processing, and streamlined data analysis. 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.

Image transformations from the input image to ephemeral stream detection.
Image transformations from the input image to ephemeral stream detection. [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. These data greatly complement other publicly available hydrologic data such as USGS' National Hydrologic Dataset by providing much needed details of surface hydrologic features on drylands. 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 extracts significant details of ephemeral stream channel networks that could complement the National Hydrography Dataset (NHD)
The Argonne algorithm extracts significant details of ephemeral stream channel networks that could complement the National Hydrography Dataset (NHD) [Source: Argonne National Laboratory]
Accuracy of Ephemeral Stream Channel Maps Derived From Remotely Sensed Imagery
Pooled West East
Classification Accuracy
Overall accuracy (%) 77.0 91.1 79.8
Producer's accuracy (%) 85.8 91.8 88.2
User's accuracy (%) 52.2 48.5 51.4
Kappa coefficient 0.55 0.61 0.54
Centerline Extraction Accuracy
Total channel length (km) 146.0
[154.0]
50.9
[66.3]
95.1
[87.7]
Accurate channel area (km2) 2.0
[2.7]
0.8
[1.3]
1.2
[1.5]
Accurate channel length (km) 92.3
[153.7]
37.7
[66.0]
54.6
[87.7]
Channel density (km/km2) 8.5
[13.3]
6.4
[11.2]
9.7
[15.3]
Average channel width (m) 33.7
[32.0]
19.7
[18.1]
47.6
[45.9]
Accurate delineation 70% (%) 56.3 53.0 59.5
Accurate delineation 50% (%) 66.5 65.5 67.5
Recognized (%) 89.0 86.0 92.0
The Argonne algorithm can reliably measure ephemeral stream channel attributes, particularly total length and width, which indicates great promise for monitoring ephemeral stream channels over time and provide vital information for understanding the hydrologic cycle, local ecosystems, and water availability for human use and wildlife in the landscape. The values in brackets indicate reference data.

 Paper: Hamada, Y.; O'Connor, B.; Orr, A.; Wuthrich, K. Mapping ephemeral stream networks in desert environments using very-high-resolution multispectral remote sensing. Journal of Arid Environments. 130: 40-48.

 Poster: Hamada, Y.; O'Connor, B.; Orr, A.; Wuthrick, K. Mapping Ephemeral Streams Using Aerial Very High Resolution Remote Sensing & Knowledge-Based Feature Extraction Algorithm (346 KB)

Related Research Areas

See the Research Highlights Index for a complete list of EVS Research Areas.

photo of Yuki Hamada
Biophysical Remote Sensing Scientist
Capabilities: Applications of optical and infrared 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 and aquatic 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-resolution regional scale.