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Monitoring and Predicting General Vegetation Condition Using Climate, Satellite, Oceanic, and Biophysical Data Tsegaye Tadesse 1, Brian D. Wardlow 1, and.

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Presentation on theme: "Monitoring and Predicting General Vegetation Condition Using Climate, Satellite, Oceanic, and Biophysical Data Tsegaye Tadesse 1, Brian D. Wardlow 1, and."— Presentation transcript:

1 Monitoring and Predicting General Vegetation Condition Using Climate, Satellite, Oceanic, and Biophysical Data Tsegaye Tadesse 1, Brian D. Wardlow 1, and Jae H. Ryu 1 1 National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln, NE What is VegOut? The Vegetation Outlook (VegOut) is a new experimental tool that provides future outlooks of vegetation conditions (seasonal greenness) based on an analysis of: 1. climate-based drought index data (PDSI & SPI); 2. satellite-based vegetation condition information (standardized seasonal greenness from NDVI); 3. biophysical characteristics (e.g., land cover type, ecoregion type, irrigation status, and soil available water capacity); and 4. oceanic indicators (e.g., Multivariate El Niño/Southern Oscillation index, MEI). Summary The VegOut is a new drought monitoring tool that provides outlooks of general vegetation conditions. VegOut integrates climate information and satellite-based observations of current vegetation conditions with oceanic index data and other biophysical information about the environment to produce 1-km resolution maps of projected general vegetation conditions. Acknowledgements This work was funded by the United States Department of Agriculture Risk Management Agencys Partnership Agreement 05-IE The complexity of drought characteristics and the diverse temporal and spatial climate-vegetation interactions make monitoring drought impacts on vegetation very challenging. Improved meteorological observations and new analytical methods coupled with recent advances in satellite-based remote sensing offer great potential to improve our ability to monitor the impact of drought on vegetation. Such information can be utilized in drought early warning systems. In addition, recent studies have found significant improvements in seasonal climate predictions when ocean- atmosphere relationships are considered, and such teleconnection information should be integrated into drought-related vegetation condition monitoring and prediction. Traditional climate or satellite-based vegetation index (VI) data has formed the basis for most drought monitoring tools for vegetation. However, new methods that integrate both types of data to leverage the strengths of both approaches have the capability to provide more accurate and reliable information regarding drought-related vegetation conditions. Recent studies have shown that data mining techniques are effective for integrating diverse, large, and often complex data sets and identifying hidden patterns within these data to investigate complex relationships among many variables related to phenomena such as drought. Data mining techniques can be used to analyze climate, satellite, and biophysical data in an effort to assess the current drought stress on vegetation and also to predict future conditions based on historical patterns in these data. In this study, a new approach for identifying and predicting the spatio- temporal patterns of drought and its impact on vegetation is presented. A regression tree modeling technique was applied to a 17-year time-series record of climate and satellite-based VI data and other biophysical information (e.g., soil characteristics and land cover type) to identify historical relationships and patterns among these variables that are similar to currently observed conditions, which are then used to predict the general vegetation conditions at several time steps into the future (i.e., 2-, 4-, and 6- weeks in advance). This new drought monitoring tool is called the Vegetation Outlook (VegOut). VegOut maps are produced using rule-based regression tree models that were generated to identify similar historical relationships (patterns) in space and time between satellite-derived vegetation conditions, climate-based drought indices, oceanic indices, and biophysical data. The data used to produce the VegOut maps include Standardized Seasonally integrated satellite vegetation Greenness (SSG); climate drought indices such as the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), oceanic indices that include the Southern Oscillation Index (SOI), Multivariate ENSO index (MEI), Pacific Decadal Oscillation (PDO), and Atlantic Multi-decadal Oscillation (AMO); and biophysical parameters such as land cover type, available soil water capacity, percent irrigated farm land, and ecoregion. Because the models can be applied iteratively with input data from previous time periods, the method can be used to predict vegetation conditions later in the growing season based on information about prior conditions in the year. An overview of the VegOut methodology and examples of the regional-scale VegOut maps are presented and future work tasks are highlighted. For further information contact: Dr. Tsegaye Tadesse Dr. Brian Wardlow National Drought Mitigation Center National Drought Mitigation Center University of Nebraska-Lincoln University of Nebraska-Lincoln Telephone: (402) Telephone: (402) Climate Prediction Applications Science Workshop (CPASW) March 4 - 7, 2008 Chapel Hill, North Carolina, USA. Methodological Approach Figure 2. Two-week Vegetation Outlooks (VegOut), which predict SSG values, are presented for: (a) spring (period 11: May 21 – June 3), (b) mid-summer (period 16: July 30 – August 12), and (c) fall (period 18: August 8 – September 9) phases of the 2006 growing season. Observed SSG values and patterns for periods 10 (early growing season: May 7 – 20), 11, 16, and 18 are presented in (e) through (g), respectively. Evaluation: Figure 3: (a) Two-week Vegetation Outlook (VegOut) map that predicted SSG values for the bi-weekly period ending on September 4, 2006; (b) bi- weekly SSG observed for the period ending September 4, 2006; (c) a difference map comparing the predicted vs. observed SSG values (i.e., VegOut minus the observed SSG). Note: If the difference is +1. Current and Future Works At present, the VegOut uses rule-based regression tree models that are generated by identifying relationships between satellite-derived vegetation conditions, climatic drought indices, oceanic indices, and other biophysical data. Alternative modeling techniques including association rules and neural networks are being investigated to compare with the current VegOut models. Ensemble techniques that base predictions on the results from multiple data mining techniques are also under consideration. In addition, new inputs into the current VegOut models are also being investigated in an effort to provide more accurate predictions of future vegetation conditions. The current VegOut research is focusing on the development of 2-, 4-, and 6-week vegetation outlooks in the U.S. Great Plains, but expansion of VegOut to other areas of the U.S. is planned in the near future. Researchers are selecting the best predictive variables, using higher correlation and integrating the best climate and/or oceanic variables that correlate with vegetation condition to produce an improved drought monitoring tool (VegOut). VegOut information will be provided to enhance the U.S. Drought Monitor. Spatio-temporal drought monitoring and predictive information will be provided through a web-based client-server delivery system to agricultural producers and decision makers, and a fully operational, web-based drought decision support system is being developed. Semi-operational maps are planned for the 2008 growing season (a) 2-week outlook(a) Observed SSG Abstract Predicted vs. Observed SSG Figure 1 Figure 2 Figure 3 (c) Difference map


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