Anthony Vodacek


This article may also be accessed from the publisher's website at Remote sensing is routinely used for understanding many aspects of the earth environment that are important to sustainability. Remote sensing is used in weather forecasting and global climate studies, natural hazard analysis, crop condition and yield prediction, and forestry applications, for example. The techniques and hardware used to obtain the remotely sensed data for these applications are as widely varying as the applications themselves. Remote imaging systems may collect spectral data of reflected sunlight, emitted thermal or microwave radiation, or reflected radar signals to provide the desired information on the current status of the environment. These data can be collected from the air or from space and may be useful in the form of numerical data or in the form of an image. The goal of this paper is to examine the current state of the art in transforming remotely sensed image data into more useful information by integration with predictive models.

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Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus