Transcript Tadesse_poster - Southeast Regional Climate Center

Monitoring and Predicting General
Vegetation Condition Using Climate,
Satellite, Oceanic, and Biophysical Data
Tsegaye Tadesse1, Brian D. Wardlow1, and Jae H. Ryu1
1National
Drought Mitigation Center, School of Natural
Resources, University of Nebraska-Lincoln, NE 68583-0988
What is VegOut?
Evaluation:
Predicted vs. Observed SSG
Figure 3
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).
Vegetation Condition
Extreme stress
Severe stress
Moderate stress
Fair (near normal)
Good vegetation
Very good vegetation
Out of season
Methodological Approach
Abstract
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 oceanatmosphere relationships are considered, and such teleconnection
information should be integrated into drought-related vegetation condition
monitoring and prediction.
(a) Observed SSG
Figure 3: (a) Two-week Vegetation
Outlook (VegOut) map that predicted
SSG values for the bi-weekly period
ending on September 4, 2006; (b) biweekly 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).
Figure 1
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.
Legend
Under predict
Similar
Over predict
(c) Difference map
Note: If the difference is < 1 standard deviation (SD), it is
classified as similar; otherwise it was labeled as “under
predict” if the SD was < -1 or “over predict” if the SD was >
+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 this study, a new approach for identifying and predicting the spatiotemporal 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 6weeks 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.
(a) 2-week outlook
• 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.
Figure 2
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.
• 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
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 Agency’s Partnership Agreement 05-IE-0831-0228.
For further information contact:
Climate Prediction Applications Science Workshop (CPASW)
March 4 - 7, 2008 Chapel Hill, North Carolina, USA.
Dr. Tsegaye Tadesse
National Drought Mitigation Center
University of Nebraska-Lincoln
Telephone: (402) 472-3383
Email: [email protected]
Dr. Brian Wardlow
National Drought Mitigation Center
University of Nebraska-Lincoln
Telephone: (402) 472-6729
Email: [email protected]