USGS Earth Surface Processes Team: Southwest Climate Impacts Using Remote Sensing to Detect Active Dust Storms and Map Areas Vulnerable to Eolian Erosion

Purpose

WHAT'S NEW!

2/21/2001 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:

1. meteorlogical parameters,
2. sand- and dust-emission, and
3. vegetation change.

1. more accurate predictions of the consequences and magnitudes of expected climate change in the southwestern United States, and
2. possible transfer of the algorithms and procedures to other arid areas of the world.

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.

• Mapping Eolian Erosion Vulnerability

  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
  WiFS, 200 m resolution 800 x 746, 210 k	Landsat TM, 30 m resolution 750 x 453, 426 k

  
  

  Spatial Resolution Comparison Example

  Landsat TM vs. WiFS vs. GOES 750 x 700, 319 k

Climate Information

Image Data

Field-based Image Data 2/21/2001

Digital Camera Station Information and Examples

Satellite	Spatial Resolution	Spectral Resolution	Temporal Resolution	Comments
GOES Updated 27 May 1999	1 km	1 visible	15 - 30 minutes	Poor spatial and spectral resolution. Excellent temporal resolution.
Landsat MSS Updated 22 June 1998	75 m	2 visible 2 NIR	2 weeks	Very good spatial resolution. Fair spectral resolution. Poor temporal resolution.
Landsat TM Updated 21 February 2001	30 m	3 visible 1 NIR 2 mid-IR	2 weeks	Excellent spatial resolution. Good spectral resolution. Poor temporal resolution.
WiFS Updated 12 August 1998	200 m	1 visible 1 NIR	5 days	Fair spatial resolution. Fair spectral resolution. Fair temporal resolution.
SeaWiFS	1 km	1 UV 2 blue 4 green/red 1 NIR	1 day	Poor spatial resolution. Excellent spectral resolution. Good temporal resolution.

Project Team

References & Credits

• 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.

• 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., 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.

• MacKinnon, D. J., Chavez, P. S., Jr., Fraser, R. S., Niemeyer, T. C., and Gillette, D. A., 1996, Calibration of GOES-VISSR, visible-band satellite data and its application to the analysis of a dust storm at Owens Lake, California, Geomorphology 17 (1996), pp. 229 - 250

• MacKinnon, D., and Chavez, P. S., Jr., 1993, Dust Storms, May 1993, Earth Magazine (invited article), pp. 60 - 64

• 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.

• Tigges, R.K., Sides, S.C., Ohms, M. (2001). ARDIS: Automated Remote Digital Image System.
• Photography: USGS banner background is a photograph of a plume rising 4000' above the ground near Bakersfield, CA; photograph taken by Sam Chase

Related Publications and Resources

• USGS Earth Surface Processes: Impacts of Climate Change and Land Use on the Southwestern U.S., http://climchange.cr.usgs.gov/info/sw/
  
  
• USGS TerraWeb: Use of Remotely Sensed Images for Eolian and Rainfall Mapping, http://TerraWeb.wr.usgs.gov/projects/eolian/
  
  
• 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., 1996. Image-based atmospheric corrections - revisited and improved, Photogrammetric Engineering and Remote Sensing, Vol. 62, No. 9, September 1996, pp. 1025 - 1036.
  
  
• USGS MIPS Home page: http://TerraWeb.wr.usgs.gov/software/mips/
  
  
• NOAA National Climatic Data Center
  • CLIMVIS: http://www.ncdc.noaa.gov/onlineprod/drought/xmgr.html -- excellent resource for online access to drought, precipitation, and temperature data, plus daily, global, and historical data and summaries.
    
    
• USGS -- Where Desert Meets City: Recoverability and Vulnerability of the Mojave Desert Ecosystem, http://geology.wr.usgs.gov/MojaveEco/

Contact Information

For more information about this project, contact:
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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