Wind Erosion Vulnerability and Rainfall Mapping in the Southwestern United States
Pat S. Chavez, Jr., Dave MacKinnon, Miguel G. Velasco, Stuart C. Sides, and Deborah L. Soltesz
U.S. Geological Survey, Flagstaff, AZ and Menlo Park, CA
Monitoring regional and global ecosystems requires a capability to map
and detect changes in surficial features. Density and type of vegetative
cover are particularly indicative of the health of the ecosystem that it
supports. In the southwestern United States, vegetative cover responds
dramatically to rainfall variability and, as vegetation wanes during drought,
underlying soils become vulnerable to irreversible and potentially catastrophic
wind erosion. The removal of fine sediments by the wind, lifted as dust or
displaced as sand, destroys the nutrient base for the vegetation and ultimately
affects the ecosystem itself. In arid and semi-arid environments,
remotely sensed satellite images can be useful in mapping vegetative
cover and identifying soils vulnerable to eolian erosion should vegetative cover
be altered either by climate or human impact. By comparing images acquired over
time, they can show how different parts of an ecosystem area respond to climate
and other environmental changes at a resolution and scale that uniquely
complements ground-based studies.
One objective of our work is to generate image maps of two parameters
which either are affected by, or affect, the vegetative cover in an ecosystem:
wind erosion vulnerability and rainfall. Currently, Landsat Multispectral Scanner
(MSS) and Thematic Mapper (TM) satellite images have been used to automatically
map the vulnerability of the
surface to wind erosion and to express the results in images representing an
Eolian Mapping Index (EMI). In addition, the percent of vegetative cover, spatial
brightness variability (roughness images), and surface reflectance of soils can
also be mapped. Landsat MSS data have also been used to generate digital change
images. Also, to show the effects of rainfall on desert vegetation
rainfall image maps were derived using rainfall data recorded at surface
stations throughout the southwestern United Sates. The rainfall image maps can be
merged and correlated with image map products, such as the EMI and digital change
images derived from the remotely sensed multispectral and multitemporal data.
Image results generated using the satellite and rainfall data are presented in the pages linked below.
Note that many of the images on these pages are large and can be viewed best if
your browser window is maximized.
|Pat S. Chavez, Jr.
||Remote Sensing Scientist/Team Leader
||Physical Scientist, Surficial Processes
|Miguel G. Velasco
|Stuart C. Sides
||Computer Scientist and Primary Web Page Design
|Deborah L. Soltesz
||Web Page Updates and Design
- 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.
- Chavez, P.S., Jr., and MacKinnon, D.J., 1994. Automatic detection
of vegetation changes in the southwestern United States using
remotely sensed images, Photogrammetric Engineering and Remote
Sensing, 60 (5): pp. 571-583.
- MacKinnon, D.J., and Chavez, P.S., Jr., 1993. Dust Storms, May
1993, Earth Magazine, pp. 60-64.
- USGS MIPS Home page: http://TerraWeb.wr.usgs.gov/TRS/software/mips/