A GIS Software Toolkit for Monitoring Areal Snow Cover and Producing Daily Hydrologic Forecasts using NASA Satellite Imagery

Award Information
Agency:
National Aeronautics and Space Administration
Amount:
$84,530.00
Program:
SBIR
Contract:
NNX08CB99P
Solitcitation Year:
2007
Solicitation Number:
N/A
Branch:
N/A
Award Year:
2008
Phase:
Phase I
Agency Tracking Number:
075410
Solicitation Topic Code:
S6.04
Small Business Information
Aniuk Consulting, LLC
7015 61st Avenue, Kenosha, WI, 53142-1429
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
806918343
Principal Investigator
 Brian Harshburger
 Principal Investigator
 (262) 997-2070
 brian.harshburger@gmail.com
Business Contact
 Brian Harshburger
Title: Business Official
Phone: (262) 997-2070
Email: brian.harshburger@gmail.com
Research Institution
N/A
Abstract
Aniuk Consulting, LLC, proposes to create a GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts. This toolkit will be packaged as multiple extensions for ArcGIS 9.2 and provides users with a means for easily ingesting NASA EOS satellite images (for snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include: a technique for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model inputs and results. The toolkit will be an expert system designed for operational users that need to generate numerous streamflow forecasts accurately and in a timely-fashion without having to hassle with the science and physics behind the hydrologic model. This proposal targets the "Data Analyzing and Processing" research initiative announced by the Goddard Space Flight Center by exploiting spatial tools in order to increase the utility of scientific research data, models, and visualizations. The beauty of the proposed toolkit is the ability to incorporate hydrologically relevant spatial datasets at the applied, operational level to produce high quality functional products.

* information listed above is at the time of submission.

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