|BROWN, JOEL - Natural Resources Conservation Service (NRCS, USDA)
Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/10/2014
Publication Date: 1/31/2015
Citation: Levi, M.R., Bestelmeyer, B.T., Brown, J. 2015. Digital soil mapping as a tool for quantifying state-and-transition models [abstract]. 68th Annual Meeting of Society for Range Management January 31, 2015 - February 6, 2015. Sacramento, CA.
Technical Abstract: Ecological sites and associated state-and-transition models (STMs) are rapidly becoming important land management tools in rangeland systems in the US and around the world. Descriptions of states and transitions are largely developed from expert knowledge and generally accepted species and community level responses to external drivers. A major limitation of current STMs is the lack of quantitative data on vegetation dynamics to describe state transitions. Detailed soil property characterization is also needed to better understand soil-landscape controls of state transitions resulting from land use and climate drivers. We used spatiotemporal patterns of normalized difference vegetation index (NDVI) response to precipitation and soil properties in southeastern Arizona to determine how hydraulic soil properties mediate attributes of vegetation dynamics at a landscape level. Pixels in a time series of NDVI derived from Landsat reflectance data were clustered according to types of vegetation dynamics for each pixel-time series combination. Clustered time series data were linked to a high-resolution digital soil map of physical soil properties related to soil moisture dynamics to help explain vegetation response patterns. Monthly PRISM climate data was used to interpret vegetation dynamics. Linking high-resolution soil property maps with vegetation dynamics at broad scales can be used to quantify the interactions between soil properties and land use/climate drivers on vegetation. This process can also be used for forecasting climate change effects on vegetation at relatively fine spatial scales.