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 Integrated NDVI Green-up End of Season Available from: http://www.nateko.lu.se/timesat/timesat.asp?cat=0
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