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Time Series Analysis of Remote Sensing Data for
Assessing Response to Community Based
Rangeland Management
Jay Angerer
Texas A&M University
MOR2 Annual Meeting
June, 2013
Research Questions
During the last 30 years, has change occurred in vegetation characteristics such as green up, peak biomass and vegetation condition across Mongolia that can be detected with remote sensing?
Are there differences in vegetation response (using remote sensing data as a proxy) in soums (or households) managed by CBRM versus those that are not?
Can we detect changes in rainfall variability that could influence the status of grazinglands as equilibrium vs. non-equilibrium systems?
Historical Time Series
Analysis Time series analysis to examine
trends in satellite greenness (NDVI) for historical record (nationwide)
Patterns of green-up and senescence
Patterns in integrated NDVI (proxy for biomass accumulation)
Trends in vegetation condition index
Time Series Analysis
TIMESAT software will be used for the developing the
time series data Calculates yearly beginning of season, end of season, amplitude, integrated
NDVI values
Available from: http://www.nateko.lu.se/TIMESAT/timesat.asp?cat=0
Green-up End of
Season
Integrated
NDVI
Time Series Variables
a. Start of Season - time of
year for the start of
vegetation green-up
b. End of Season - time for
which the vegetation
greenness and biomass
accumulation is declining
Time Series Variables
f. Seasonal amplitude - difference between the maximum greenness value and the base level.
g. Length of the season - time from the start to the end of the
season.
h. Small Seasonal Integral -
integral of the difference between the function describing the season and the base level from season start to season end.
Time Series Variables
i. Large Seasonal Integral -
integral of the function
describing the season from
the season start to the
season end.
j. Base Value - the average of
the left and right minimum
values – represents average
of the lowest levels of NDVI
for a year
Paired Soum Analysis
Time series analysis for paired soums Do differences exist in green-up (start of
season), end of season, integrated NDVI/EVI, for paired CBRM and non-CBRM soums?
Pre and Post CBRM analysis
Rainfall as a covariate
Livestock numbers
Data Sources Advanced Very High Resolution Radiometer (AVHRR) –
Normalized Difference Vegetation Index (NDVI) data Data processed by NASA-Global Inventory Modeling and
Mapping Studies
1981 to 2010
8 km resolution
Widely used
Available at http://www.glcf.umd.edu/data/gimms/
Pre and Post CBREM Analysis for each study soum 1981 to 1998 as Pre CBREM
1999 to 2010 as Post-CBREM
Data Sources
Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and Enhanced Vegetation Index (EVI) data 2000 to present
Resolution of 250m
Available for Asia region
Download from: https://lpdaac.usgs.gov/get_data/data_pool
Data Processing for Paired
Soum Analysis
Desert Steppe Vegetation
Example Time Series
Steppe Vegetation
Time Series Example
Statistical Design
Main Factor = CBRM Status
Repeated Measure = Year
Covariate – Annual Rainfall
Stratified by Ecological Zone
Analysis of Covariance with Repeated
Measures
Rainfall as a Covariate
Preliminary Results
For the majority of the time series
variables, the effect of CBRM was not
statistically significant
Analysis reflects Enhanced Vegetation Index
response at the soum level
Large aggregate area may be masking
response at the scale of herd management
area
May require further stratification of area within
soums
Large Integral Response
Small Integral Response
Base EVI Response
Start of Season
End of Season
Season Length
Season Amplitude
Next Steps
Examine AVHRR NDVI (1981 to 2009)
see if similar patterns exist where datasets
overlap
Pre and Post CBRM
Examine additional stratifications
Herd management boundaries
Ecological Sampling Points
Changes in Livestock Numbers over the
time series