Use of Remotely Sensed Image Data to Automatically Map Wind Erosion Vulnerability (Eolian Mapping) and Rainfall Mapping |
The main objective of this project is to use remotely sensed satellite images to research and develop a model that can be used to automatically map the level of vulnerability of surfaces to wind erosion (Eolian Mapping) in arid and semi-arid environments. A direct result of this research is the development of tools and capabilities to characterize surface features and detect surface changes, including the critical capability to do radiometric calibration and correction (both relative and absolute) of remotely sensed satellite image data. Also, models and procedures to analyze, evaluate, and monitor the vulnerability of land and vegetation to environmental and climate-induced changes on a regional and global scale are being developed. Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data are being used to automatically map the vulnerability to wind erosion and generate images representing an Eolian Mapping Index (EMI), along with percent of vegetation cover, spatial variability (roughness images), and reflectance images. Also, because of the importance of rainfall to vegetation cover, eolian erosion, and global change in general, generation of digital 'rainfall image maps' which can be merged and correlated with the image map products derived from the remotely sensed multispectral and multitemporal data are being developed.
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Project Team: |
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Pat S. Chavez, Jr. | Remote Sensing Scientist/ Team Leader |
Dave MacKinnon | Field Geologist |
Miguel G. Velasco | Lead Image Processor on this project |
Stuart C. Sides | Computer Scientist and Primary Web Page Design |
Jeff Anderson | Computer Scientist |
Rosendo R. Gonzalez | Programmer |
Deborah L. Soltesz | Web Page Updates and Design |
For more information about this project, contact:
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Pat S. Chavez, Jr.Email: pchavez@usgs.govU.S. Geological Survey 2255 N. Gemini Dr. Flagstaff, AZ 86001 Tel: (520) 556-7221 FAX: (520) 556-7169 |
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