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![]() | USGS Earth Surface Processes Team: Southwest Climate Impacts
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Purpose | ![]() |
WHAT'S NEW! |
![]() ![]() Landsat TM Satellite Image Mosaic of the Southwestern U.S. & Capturing Dust Storms with Digital Cameras! |
At this web site we display some of our evolving work related to the use of remotely sensed satellite images to detect and study climate-induced and man-caused dust storms and vegetation change in the southwestern United States. We are currently using satellite-image data to detect and map active dust sources and sites of vegetation change. Ultimately we will focus our effort to provide a regional-scale database into which measurements of:
Because of the ongoing nature of this research, this webpage will be updated with developements on a regular basis, and contains provisional data and information. Please read the disclaimer for further information concerning the review status of this document. Visit this link to be added to our general e-mail notification list when this page is updated. For more information about this project contact either Pat Chavez or Dave MacKinnon at the addresses shown below.
Project Objective | ![]() |
The initial phase of this project is to investigate the use of satellite image data and their potential to detect active dust storms and/or detect and map areas vulnerable to eolian erosion. The satellite image data being investigated include those from GOES, Landsat MSS, Landsat TM, WiFS, and SeaWiFS. Below (under the Image Data heading) is a table showing the spatial, spectral, and temporal resolutions of the various data sets and clickable thumbnail images linked to examples of some of our image results generated using data from that particular sensor.
Many parameters influence the amount of wind erosion that occurs, however, several are critical in the eolian process. Two of these critical parameters are the percent of vegetation cover and the type of surface soils. Using satellite digital multispectral data a simple model has been developed that allows an image to be generated that emphasizes areas with low vegetation density and high reflectance soils. Generally, this automatically highlights two of the important eolian erosion parameters (i.e., amount of vegetation cover/density and general surface soil type). Using this algorithm, an image can be generated that shows areas where these two conditions occur simultaneously. The red and near-infrared spectral bands, along with their ratio, are used in the algorithm. In this first-order eolian erosion vulnerability image map, various shades of yellow indicate different levels of low vegetation density and high reflectance soils, and serve as a guide to the relative level of erosion potential/ vulnerability to wind, and can be used to generate an Eolian Mapping Index (EMI) value at each pixel. In general, both the Landsat TM and WiFS image products shown on this webpage yellows have a higher eolian erosion potential, with the non-yellows having little or no wind erosion potential. The WiFS image data saturated in the brightest parts of many dry lake beds, so the resulting vulnerability image maps have anomalously bright yellow areas. These areas should be yellow, but not as bright as shown in the images. Part of the project in the future will include the radiometric calibration of the WiFS data to the Landsat TM so that resulting eolian erosion vulnerability image maps are comparable.
Eolian Erosion Vulnerability Image Mapping Examples |
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![]() WiFS, 200 m resolution 800 x 746, 210 k | ![]() Landsat TM, 30 m resolution 750 x 453, 426 k |
Spatial Resolution Comparison Example |
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![]() Landsat TM vs. WiFS vs. GOES 750 x 700, 319 k |
Climate Information | ![]() |
Precipitation Graph: 1990-1998, Southeastern California | Modified Palmer Drought Severity Index Graph 1990-1998, Southeastern California |
Image Data | ![]() |
Field-based Image Data
![]() 2/21/2001 | ![]() |
Satellite | Spatial Resolution | Spectral Resolution | Temporal Resolution | Comments |
![]() GOES Updated 27 May 1999 | 1 km | 1 visible | 15 - 30 minutes |
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![]() Landsat MSS Updated 22 June 1998 | 75 m | 2 visible 2 NIR | 2 weeks |
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![]() Landsat TM ![]() Updated 21 February 2001 | 30 m | 3 visible 1 NIR 2 mid-IR | 2 weeks |
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![]() WiFS Updated 12 August 1998 | 200 m | 1 visible 1 NIR | 5 days |
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SeaWiFS | 1 km | 1 UV 2 blue 4 green/red 1 NIR | 1 day |
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Project Team | ![]() |
Pat S. Chavez, Jr. | Remote Sensing Scientist |
Dave MacKinnon | Physical Scientist |
Miguel G. Velasco | Image Processor |
Stuart C. Sides | Computer Scientist |
Deborah L. Soltesz | Web Design |
References & Credits | ![]() |
Related Publications and Resources | ![]() |
Contact Information | ![]() |
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Pat S. Chavez, Jr.
Email: pchavez@usgs.gov
U.S. Geological Survey 2255 N. Gemini Dr. Flagstaff, AZ 86001 Tel: (520) 556-7221 FAX: (520) 556-7169 |
Dave MacKinnon
Email: dmackinn@usgs.gov
U.S. Geological Survey 2255 N. Gemini Dr. Flagstaff, AZ 86001 Tel: (520) 556-7162 FAX: (520) 556-7169 |
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DISCLAIMER:Because of the ongoing nature of this research, information presented on this webpage is preliminary in nature. This information is provided with the understanding that it is not guaranteed to be complete, and conclusions drawn from such information are the responsibility of the user. Content has been reviewed by all members of the project team for technical correctness, layout, and hypertext navigability. If you would like to be alerted when new image data and information for this project is made publically available, please subscribe to the TerraWeb Updates mailing list. TOP |
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