The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness

GIScience and Remote Sensing
By: , and 



The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of "historical patterns" (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation's response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.
Publication type Article
Publication Subtype Journal Article
Title The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness
Series title GIScience and Remote Sensing
DOI 10.2747/1548-1603.47.1.25
Volume 47
Issue 1
Year Published 2010
Language English
Publisher Bellwether Publishing, Ltd.
Publisher location Columbia, MD
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 28 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title GIScience and Remote Sensing
First page 25
Last page 52
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