Impacts of Climate Change and Land Use  on the Southwestern United States

Impacts of Climate Change on the Land Surface

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.

Project Team:

Pat S. Chavez, Jr. Remote Sensing Scientist/Team Leader
Dave MacKinnon Physical Scientist, Surficial Processes
Miguel G. Velasco Image Processor
Stuart C. Sides Computer Scientist and Primary Web Page Design
Deborah L. Soltesz Web Page Updates and Design

Selected publications:

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