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Title: Mapping total vegetation cover across western rangelands with moderate-resolution imaging spectroradiometer data

Author
item HAGEN, S. - Applied Geosolutions, Llc
item Heilman, Philip - Phil
item MARSETT, R. - Retired ARS Employee
item TORBICK, N. - Applied Geosolutions, Llc
item SALAS, W. - Applied Geosolutions, Llc
item VAN RAVENSWAY, J. - Michigan State University
item QI, J. - Michigan State University

Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/8/2012
Publication Date: 10/22/2012
Citation: Hagen, S., Heilman, P., Marsett, R., Torbick, N., Salas, W., Van Ravensway, J., Qi, J. 2012. Mapping total vegetation cover across western rangelands with moderate-resolution imaging spectroradiometer data. Rangeland Ecology and Management. 65(5):456-467. https://doi.org/10.2111/REM-D-11-00188.1.
DOI: https://doi.org/10.2111/REM-D-11-00188.1

Interpretive Summary: Budgetary pressures will increasingly limit the time available for field monitoring of publicly owned rangelands in the West. Remote sensing has long had the potential to complement field monitoring. In practice, the use of remote sensing has been limited, in part because the variables provided by remote sensing did not correspond to variables monitored in the field, in part because of the cost of image processing, and also because remote sensing has not been integrated into the workflow of public land management agencies. This paper describes progress on the first two of those issues. A method to scale green and senescent (total) vegetation cover field measurements to Landsat imagery (30 m) and then to the MODIS scale (500 m) is described. Canopy cover is one of the variables that public rangeland managers collect in the field, but remotely sensed vegetation products are often limited to green cover. With the method described in this paper, as composite MODIS imagery is available every 8 days, it is now possible to inexpensively create a cover time series across a region or state. Landsat imagery could be processed once or twice a year to complement the frequent, but spatially coarse, MODIS imagery. Both sets of imagery would need to be interpreted with some amount of field monitoring data. Additional time and work is still required to integrate the total vegetation imagery products into agency policies and workflow.

Technical Abstract: Remotely sensed observations of rangelands provide a synoptic view of vegetation condition unavailable from other means. Multiple satellite platforms in operation today (e.g. Landsat, moderate-resolution imaging spectroradiometer [MODIS]) offer opportunities for regional monitoring of rangelands. However, the spatial and temporal variability of rangelands pose challenges to consistent and accurate mapping of vegetation condition. For instance, soil properties can have a large impact on the reflectance registered at the satellite sensor. Additionally, senescent vegetation, which is often abundant on rangeland, is dynamic and its physical and photochemical properties can change rapidly along with moisture availability. Remote sensing has been successfully used to map local rangeland conditions. However, regional and frequently updated maps of vegetation cover in rangelands are not currently available. In this research, we compare ground measurements of total vegetation cover, including both green and senescent cover, to reflectance observed by the satellite and develop a robust method for estimating total vegetation canopy cover over diverse regions of the western United States. We test the effects of scaling from ground observations up to the Landsat 30-m scale, then to the MODIS 500-m scale, and quantify sources of noise. The soil-adjusted total vegetation index (SATVI) captures 55% of the variability in ground measured total vegetation cover from diverse sites in New Mexico, Arizona, Wyoming, and Nevada. Scaling from the Landsat to MODIS scale introduces noise and loss of spatial detail, but offers inexpensive and frequent observations and the ability to track trends in cover over large regions.