[Grad-postdoc-assn] seminar announcement: Satellite derived vegetation dynamics: a phenological approach to monitoring changes in the Southwestern US
Stefanie Herrmann
stefanie at ucar.edu
Mon Oct 15 13:16:26 MDT 2007
**
Hi all,
Dr. Wim van Leeuwen, a collaborator from the Office of Arid Land Studies
at the University of Arizona, is going to be here for two days this week.
He is going to give a seminar on satellite derived vegetation dynamics
on Friday, October 19 at 10 am in room FL2-1001.
Everybody interested in the topic is welcome to attend.
Stefanie
***Satellite Derived Vegetation Dynamics: a Phenological Approach to
Monitoring Changes in the **Southwestern U.S.***
*Dr. Wim J.D. van Leeuwen*
*University** of **Arizona***
*Office of Arid Lands Studies & *
*Geography and Regional Development*
*Tucson***
*leeuw at ag.arizona.edu*
Current climate is changing the timing of vegetation life cycle events
(vegetation phenology). Phenology has significant ecological and
socio-economic consequences on, for example, forest and agricultural
productivity and planning, occurrence of diseases and pests, and
tourism. Understanding inter-annual and intra-annual vegetation
phenological metrics (Pheno-metrics: Beginning, peak, and duration of
growing season, magnitude, seasonality) derived from MODIS NDVI
(Moderate Resolution Imaging Spectroradiometer - Normalized Difference
Vegetation Index) time series data will allow for the detection and
prediction of climate and environmental changes or trends. Several steps
are used to analyze the available time series satellite data: a) Use
MODIS product-pixel based cloud and snow quality assurance flags to
remove noise, b) Apply a smoothing filter to the NDVI time series to
reduce residual noise, c) Evaluate the benefit of smoothed data to
derive pheno-metrics, and d) fit a function to MODIS NDVI time series
data. The NDVI derived pheno-metrics were related to climate data and
evaluated for a variety of land use and wildfire events in the
Southwestern U.S. Results illustrate how reducing the noise-effects of
clouds and snow on NDVI series data can significantly improve the
derivation and analysis of pheno-metrics. Timing and effects of drought
and wildfire significantly affected vegetation dynamics and the
resulting pheno-metrics. The satellite based vegetation phenological
parameters, in combination with temporal vegetation greenness data, can
be used to monitor post-wildfire vegetation recovery and disturbace due
to droughts. More work needs to be done to create an integrated
phenology monitoring network that includes phenology observations in
concert with meteorological, environmental and remotely sensed data.
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