High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams

• Published in: Geomorphology (Amsterdam, Netherlands) Vol. 55; no. 1; pp. 363 - 380
• Main Authors: Marcus, W.Andrew, Legleiter, Carl J, Aspinall, Richard J, Boardman, Joseph W, Crabtree, Robert L
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30.09.2003
• Subjects: Depth; Freshwater; In-stream habitat; Morphology; Mountain stream; Remote sensing; Woody debris; In-stream habitat; Woody debris; Morphology; Remote sensing; Depth; Mountain stream
• ISSN: 0169-555X; 1872-695X
• DOI: 10.1016/S0169-555X(03)00150-8

This article evaluates the potential of 1-m resolution, 128-band hyperspectral imagery for mapping in-stream habitats, depths, and woody debris in third- to fifth-order streams in the northern Yellowstone region. Maximum likelihood supervised classification using principal component images provided overall classification accuracies for in-stream habitats (glides, riffles, pools, and eddy drop zones) ranging from 69% for third-order streams to 86% for fifth-order streams. This scale dependency of classification accuracy was probably driven by the greater proportion of transitional boundary areas in the smaller streams. Multiple regressions of measured depths ( y) versus principal component scores ( x 1, x 2,…, x n ) generated R 2 values ranging from 67% for high-gradient riffles to 99% for glides in a fifth-order reach. R 2 values were lower in third-order reaches, ranging from 28% for runs and glides to 94% for pools. The less accurate depth estimates obtained for smaller streams probably resulted from the relative increase in the number of mixed pixels, where a wide range of depths and surface turbulence occurred within a single pixel. Matched filter (MF) mapping of woody debris generated overall accuracies of 83% in the fifth-order Lamar River. Accuracy figures for the in-stream habitat and wood mapping may have been misleadingly low because the fine-resolution imagery captured fine-scale variations not mapped by field teams, which in turn generated false “misclassifications” when the image and field maps were compared. The use of high spatial resolution hyperspectral (HSRH) imagery for stream mapping is limited by the need for clear water to measure depth, by any tree cover obscuring the stream, and by the limited availability of airborne hyperspectral sensors. Nonetheless, the high accuracies achieved in northern Yellowstone streams indicate that HSRH imagery can be a powerful tool for watershed-wide mapping, monitoring, and modeling of streams.