Short-Term Prediction of Spring Foliage Coloration from VIIRS Data
Near Real Time VIIRS Daily Vegetation Index
Updated to reflect images through mid June
Foliage Phase Prediction Derived from VIIRS Daily Vegetation Index
12 June 2015 -
Xiaoyang Zhang (formerly at STAR and currently at South Dakota State
University) and Bob Yu (STAR/SMCD/EMB) have developed a new method to
monitor and predict short-term spring green foliage development using
the VIIRS daily vegetation index. Developed with the support of the JPSS
Proving Ground and Risk Reduction Program, the new system currently
monitors foliage development across the United States every 3 days and
further makes prediction to 10 days ahead.
Spring foliage development indicates the occurrence of spring events
across the vegetated land surface. Although easily observed in field, it
is very challenging to detect in near real time from satellite data.
This preliminary product is the first time to measure and predict green
foliage progress from a satellite data time series.
Monitoring and predicting the development of vegetation phenology from
satellite data are particularly important for:
- Assisting farmers to predict the optimum timing for cultivation practices and for monitoring drought occurrences and crop germination;
- Helping foresters detecting disturbances related to hurricane destruction, forest pests, disease outbreaks, and species invasion;
- Informing the work of environmental and weather modelers regarding the accurate modeling of seasonal carbon sequestration and land-surface physical properties; and,
- Assisting tourists and tourism businesses to plan activities around viewing spring wildflowers and fall foliage colors.
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