A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Tsegaye Tadesse, Jesslyn F. Brown, Michael J. Hayes

Research output: Contribution to journalArticlepeer-review

82 Scopus citations

Abstract

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

Original languageEnglish (US)
Pages (from-to)244-253
Number of pages10
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume59
Issue number4
DOIs
StatePublished - Jun 2005
Externally publishedYes

Keywords

  • Data mining
  • Drought
  • NDVI
  • Prediction
  • Satellite metrics
  • Vegetation stress

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences

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