.The unsupervised Iterative Self-Organizing Clustering System (ISOCLS) and the supervised Earth Resources Interactive Processing System (ERIPS) were used to detect, delineate and classify near-surface turbidity patterns in the Galveston and Trinity Bays, Texas and adjacent coastal waters. Data used in the analysis was ERTS-l Multispectral Scanner (MSS) digital data in the visible spectral bands from 0.5 to 1.1 micrometers, and related in situ water measurements.

Theoretical considerations suggest that because solar radiation attenuates with water depth and water constituents as a function of wavelength, classification of turbidity levels based on spectral characteristics is a classification based on spectral signatures from varying water depths; that is, a classification of spatially different points. In classification of turbidity therefore, combinations of spectral radiance in several visible and near infrared bands should yield varying geographic patterns.

An experiment was designed to 1) study turbidity classification utilizing ERTS-l multispectral scanner data, and 2) to calibrate spectral reflectance with turbidity levels. Preliminary results indicate theoretical and empirical compatibility in classification using a single channel of information and the potential for ground calibration of the ERTS-l multispectral scanner data measurement of turbidity. Additionally it was found that turbidity induces linearity in 2 channels for the distribution of water as a class and that the unsupervised IS0CL5 classification procedure handled the non Gaussian distribution better than the ERIP5 supervised technique of classification.

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