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Quantifying Structural Physical Habitat Attributes Using Lidar and Hyperspectal Imagery
Robert K. Hall 1, Russell Watkins 2, Daniel T. Heggem 3, K. Bruce Jones 3, and Phil Kaufmann 4
1 USEPA Region IX, WTR2, 75 Hawthorne St., San Francisco, CA 94105
2 BAE Systems Advanced Technologies Inc., 3907 SW 5th Place, Gainesville, FL 32607
3 USEPA ORD Environmental Science Division, Landscape Ecology Branch, Las Vegas, NV 89119
4 USEPA ORD Western Ecology Division, NHEERL, Corvallis, OR. 97333
Structural physical habitat attributes include indices of stream size, channel gradient, substrate size, habitat complexity and cover, riparian vegetation cover and structure, anthropogenic disturbances and channel-riparian interaction. These habitat attributes will vary dependent on ecological setting and in the presence of anthropogenic disturbances. Lidar is an airborne scanning laser system that provides information on topography, as well as height and structure of vegetation and other ground features. Lidar-derived DEMs, at 1 meter horizontal and 0.3 meter vertical resolution, allow for the measuring of approximate channel dimensions (width, depth, volume), slope, channel complexity (residual pools, morphometric complexity, hydraulic roughness), riparian vegetation (height), dimensions of riparian zone, anthropogenic alterations and disturbances, and channel and riparian interaction. Hyperspectral imagery is comprised of narrow spectral bandwidths (10nm) with a continuous spectrum in the visual to near infrared portion of the electromagnetic spectrum. Hyperspectral imagery offers the advantages of high spectral and spatial resolution allowing for the detection and identification of riparian vegetation and natural and anthropogenic features not possible with satellite imagery. When combined, or fused, these technologies comprise a powerful geospatial dataset for assessing and monitoring environmental characteristics and condition, and in delineating and quantifying structural physical habitat attributes at different spatial scales (reach, sub-basin, watershed). Examples taken from Nevada and Oregon pilot projects illustrate the utility and capability of high resolution remote sensing in detecting a variety of features (e.g., vegetation type, sedimentation, water column constituents, potential sources of non-point source pollution), channel attributes, and in identifying ecological condition.
Keywords: physical habitat attributes, lidar, hyperspectral, vegetation, channel dimensions