Abstract
Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images from a deep, clear-flowing channel to develop techniques for inferring the maximum detectable depth, d m a x , directly from an image and identifying optically deep areas that exceed d m a x . Optimal Band Ratio Analysis (OBRA) of progressively truncated subsets of the calibration data provided an estimate of d m a x by indicating when depth retrieval performance began to deteriorate due to the presence of depths greater than the sensor could detect. We then partitioned the calibration data into shallow and optically deep ( d > d m a x ) classes and fit a logistic regression model to estimate the probability of optically deep water, P r ( O D ) . Applying a P r ( O D ) threshold value allowed us to delineate optically deep areas and thus only attempt depth retrieval in relatively shallow locations. For the Kootenai River, d m a x reached as high as 9.5 m at one site, with accurate depth retrieval ( R 2 = 0.94 ) in areas with d < d m a x . As a first step toward scaling up from short reaches to long river segments, we evaluated the portability of depth-reflectance relations calibrated at one site to other sites along the river. This analysis highlighted the importance of calibration data spanning a broad range of depths. Due to the inherent limitations of passive optical depth retrieval in large rivers, a hybrid field- and remote sensing-based approach would be required to obtain complete bathymetric coverage.
Highlights
The scope of fluvial remote sensing has expanded in recent years, both in terms of the number of studies incorporating remotely sensed data and the diversity of applications [1,2], one of the earliest approaches to characterizing river systems via remote sensing continues to be among the most important: estimating water depth from passive optical image data (e.g., [3,4,5,6])
Whereas OPTID of a Compact Airborne Spectrographic Imager (CASI) image of the Snake suggested that the sensor was capable of detecting the full range of depths present in that river [24], the Kootenai featured pools up to 16.69 m deep and provided a better opportunity to probe the limits of depth retrieval, even in a clear-water environment
Remote sensing techniques are increasingly called upon to aid in characterizing river systems, but, as the use of these methods continues to expand, so must awareness of the inherent limitations of this approach
Summary
The scope of fluvial remote sensing has expanded in recent years, both in terms of the number of studies incorporating remotely sensed data and the diversity of applications [1,2], one of the earliest approaches to characterizing river systems via remote sensing continues to be among the most important: estimating water depth from passive optical image data (e.g., [3,4,5,6]). Because field-based methods of measuring depth, velocity, and other channel characteristics are relatively inefficient, remote sensing techniques are increasingly used to obtain data on channel morphology and hydraulics. Measuring river discharge from remotely sensed data has emerged as a key research objective in hydrology. More recently developed water-penetrating, green wavelength lidar systems are designed for measuring bathymetry but are subject to similar environmental limitations [12,13,14,15,16,17]
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