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