NASA SBIR 2005 Solicitation


SUBTOPIC TITLE:Geospatial Data Analysis Processing and Visualization Technologies
PROPOSAL TITLE:Automated Extraction of Crop Area Statistics from Medium-Resolution Imagery

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
GDA Corp.
200 Innovation Blvd., Suite 234
State College, PA 16803-6602
(814) 237-4060

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dmitry L. Varlyguin
200 Innovation Blvd., Suite 234
State College, PA  16803-6602
(814) 237-4060

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
This project is focusing on the strategic, routine incorporation of medium-resolution satellite imagery into operational agricultural assessments for the global crop market. Automated algorithms for rapid extraction of field-level crop area statistics from Landsat and Landsat-class imagery (including Landsat 5 TM, Landsat 7 ETM+, AWiFS, ASTER, SPOT, LDCM, etc.) are under development. For prototype development, the project is collaborating with the Production Estimates and Crop Assessment Division of the USDA Foreign Agricultural Service. The Phase I prototype algorithms, based on Bayesian Probability Theory, incorporate multiple lines of evidence in the form of prior and conditional probabilities and implement an innovative approach to supervised image classification allowing for automated class delineation. The knowledge-based expert classifiers prototyped during Phase I were tested and validated at selected pilot sites across the globe. The results of the Phase I work have clearly demonstrated the technical feasibility of the GDA approach to automated crop area assessment with medium resolution imagery. Development undertaken during Phase I resulted in a robust, fully functional set of modules that are capable of processing large volumes of data and allow for accurate crop detection, area estimation, and crop acreage change assessment with minimal user intervention. The prototype algorithms were tested on a range of test sites, sensors, crop types, and crop conditions. A non-rigorous validation study proved the reliability and accuracy of the prototype algorithms. The overall results of the project will enhance global agricultural production estimates by improving the timeliness and accuracy of field-level crop area estimates.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The effort will contribute to NASA's Applied Science Program by addressing the goals of the Agricultural Efficiency and Disaster Management applications of national priority. The project addresses the NASA SBIR topic by developing unique, rapid analyses of NASA medium-resolution imagery through the development of fusion software for the efficient production and near-real time delivery of data products derived from NASA satellite data that will directly contribute to both the nation's economic security and environmental stewardship. The developed technologies will support both the scientific and commercial applications of NASA Earth Science data and will be benchmarked for practical use against an international model for agricultural production estimates. Furthermore, it will also help to ensure continued use of, and reliance on, NASA data by USDA and other federal agencies in their transition from Landsat 5 TM and 7 ETM+ to the new medium resolution LDCM mission.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
Results of the proposed effort can be used to enhance economic opportunities for agricultural producers and commodities. The results of the project will aid in the provision of accurate and timely information on global crop production at a country or regional level, thereby helping producers make better marketing decisions. The results will allow for, among other things, the provision of early warning of unusual crop conditions or changes in the production outlook of a country or region, which, in turn, can assist the private marketplace in price determination and adjustment. The largest potential customers may include the commodity exchanges, U.S. Agribusiness, and U.S. Government agencies, particularly USDA. Other commercial applications of proposed algorithms may include land cover re-mapping activities, forest and agricultural monitoring and inventory, and assessments of agricultural/farmer compliance with environmental standards.

NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.

Autonomous Reasoning/Artificial Intelligence
Expert Systems

Form Printed on 07-25-06 17:04