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 | Use of Remotely Sensed Image Data to Automatically Map Wind Erosion Vulnerability (Eolian Mapping) and Rainfall Mapping |
Description:
Monitoring regional and global ecosystems requires a capability to map surface features and to detect surface change. Vegetation cover, changes in vegetation cover and density, as well as surface soil types, are especially important parameters for evaluating ecosystems for a variety of hazards and conditions, including eolian erosion vulnerability. Surface sampling and instrumentation can provide very detailed data records at a particular ground location. These data can be collected at a very high temporal frequency which is important for certain types of applications and analyses. However, ground based data can have the disadvantage of poor spatial resolution/representation when dealing with regional and global ecosystem scales. Remotely sensed satellite images can be used to monitor surface features and their changes over large areas, such as the southwestern United States deserts. The images can show how different parts of an ecosystem are reacting to climate and other environmental changes at a resolution and scale not possible with ground-based instrumentation. 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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Accomplishments:
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 |
References:
- Chavez, P. S., Jr., 1992, A change detection technique to identify differences in multitemporal remotely sensed image data: Example detecting dust storms and vegetation changes in southwestern U.S., American Society of Photogrammetry and Remote Sensing Annual Conference, Albuquerque, New Mexico, March, 1992.
- Chavez, P. S., Jr., and MacKinnon, D., 1992, Change detection of dust storms and vegetation changes in the Southwestern United States, EOSAT seminar on GIS and Remote Sensing for Natural Resource Management, Universal City, California, September, 1992.
- Chavez, P. S., Jr., and MacKinnon, D. J., 1994, Automatic detection of vegetation changes in the southwestern United States using remotely sensed images, in special issue of Journal of Photogrammetric Engineering and Remote Sensing, Vol. 60, No. 5, pp. 571 - 583.
- MacKinnon, D., and Chavez, P. S., Jr., 1991, Qualitative evaluation of multispectral NOAA-11 AVHRR images for suspended dust occurrences during the joint U.S./U.S.S.R. Tadzhikistan S.S.R. dust storm experiment, AGU 1991 Fall Meeting (abstracts published as a supplement to EOS, October 19, 1991), p. 106.
- MacKinnon, D., and Chavez, P. S., Jr., 1993, Dust Storms, May 1993, Earth Magazine (invited article), pp. 60 - 64
Related Publications and Resources:
- Chavez, P.S., Jr., and Kwarteng, A.W., 1989. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis, Photogrammetric Engineering and Remote Sensing, 55 (3): pp. 339-348.
- Chavez, P.S., Jr., 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images, Photogrammetric Engineering and Remote Sensing, 55 (9): pp. 1285-1294.
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USGSMIPS Home page: http://TerraWeb.wr.usgs.gov/software/mips/
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CLIMVIS: http://www.ncdc.noaa.gov/onlineprod/drought/xmgr.html -- NOAA National Climatic Data Center; excellent resource for online access to drought, precipitation, and temperature data, plus daily, global, and historical data and summaries.
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National Weather Service
For more information about this project, contact:
