DOEs Argonne National Laboratory (Argonne) has developed a new image-processing algorithm for mapping dry-land ephemeral stream channels — features with an important role in water cycling in desert environments. To facilitate speedy, sustainable utility-scale solar energy development, algorithm development was initiated with funding from DOEs SunShot Initiative.
Anticipating increased utility-scale solar energy development over the next several decades, federal agencies and other organizations have identified a need to develop comprehensive long-term monitoring programs specific to solar energy development. Environmental groups indicate that they are less likely to oppose individual solar projects that include a firm commitment to long-term monitoring.
Argonnes image-processing algorithm for remotely sensed data is intended to lower barriers to development by providing a means of monitoring changes in key surface hydrologic features in landscapes and facilitating timely responses to undesirable effects.
Federal agencies like the U.S. Department of the Interior, Bureau of Land Management (BLM), are obligated to protect the full array of resources and values on managed lands. Increasingly, stakeholders request that the agencies develop rigorous, comprehensive long-term monitoring programs. The BLM has committed to such monitoring as part of its new Solar Energy Program. Argonne assists in the development of effective monitoring programs to protect land resources while preventing unnecessary or ineffective restrictions to utility-scale solar energy development.
Changes in water regimes affect various natural resources in arid environments. Knowledge about ephemeral streams is vital for understanding the hydrologic cycle, local ecosystems, and water availability for human use. Remote sensing methods based on digital elevation models (DEMs) have been applied widely for mapping drainages. Existing methods, however, are inadequate for dry ephemeral streams, with their complex networks, lack of flow, and small topographic gradient in the landscape. Thus, a new method is needed for reliably mapping detailed channel networks and studying the dynamics of arid land hydrology.
The Argonne algorithm couples desert landscape features and structures associated with surface hydrology to extract ephemeral channel networks in arid environments. The algorithm was developed for Palo Verde Mesa in Riverside County, California, an area in the BLMs Riverside East Solar Energy Zone. Argonne is coordinating its research on applications of remote sensing methods with the BLMs efforts to develop a long-term monitoring and adaptive management strategy for this zone.
In November 2012, Argonne collected 1,527 frames of multispectral aerial images at 15-cm resolution from 1,350 m above ground level and created a seamless image mosaic. Information about vegetation occurrence and density, as well as surface brightness and its spatial heterogeneity, was gathered from expert knowledge and field observations. The information was incorporated into the feature extraction algorithm by using a series of spectral transformations and spatial statistical operations.
Argonnes knowledge-based feature extraction algorithm identifies well-defined single channels, complex braided streams, and small tributaries across desert landscape and creates a map of ephemeral stream networks considerably more detailed than the U.S. Geological Surveys National Hydrography Dataset. The transferable algorithm is applicable for mapping dry stream channels in other physiognomically comparable desert regions. Thus, the algorithm could help to advance hydrologic modeling and facilitate the development of cost-effective monitoring strategies for water resource management in desert regions.
Argonne is currently using a new dataset collected in January 2014 to test the effectiveness of the algorithm for detecting and quantifying changes in ephemeral stream networks. Future work will focus on prioritizing hydrologic features that require protection on the basis of functionality, then developing an integrative remote sensing methodology that considers multiple land resources for cost-effective environmental monitoring.
The Argonne team is currently seeking new partnerships with developers who can facilitate speedy, smart utility-scale solar energy development by strategically integrating cost-effective monitoring into real-world energy projects.