[Grad-postdoc-assn] seminar announcement: Satellite derived vegetation dynamics: a phenological approach to monitoring changes in the Southwestern US

[Stefanie Herrmann]

**

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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