Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 1/10/2015
Publication Date: 1/31/2015
Citation: Maynard, J.J., Karl, J.W., Browning, D.M. 2015. Detecting long-term vegetation change in an arid rangeland ecosystem: Investigating effects of spatial image support within satellite time-series [abstract]. 68th Annual Meeting of the Society for Range Management. January 31-February 2, 2015. Sacramento, CA.
Technical Abstract: Arid rangelands within the southwestern United States have been severely degraded over the past century due to intensive land-use practices (e.g., livestock overgrazing, recreation) and the increasing effects of drought and climate change. Consequently, there is a critical need to develop monitoring approaches that can detect significant changes in vegetation health and distribution across vast spatial extents. Multi-temporal remote sensing techniques are ideally suited to address these challenges; however, considerable uncertainty exists regarding the effects of changing image resolution on the ability to detect ecologically meaningful change from satellite time-series. The main objective of this study was to explicitly test the effects of changing image resolution on the ability of NDVI time-series to detect observed long-term changes in plant biomass. Satellite time series of NDVI were compiled for the period between 2000 and 2013 from the Landsat (30-meter, ~16-day resolution) and MODIS (250-meter, 16-day resolution) sensors at the Jornada Experimental Range (JER) in southwestern New Mexico. Each time-series image was coarsened using mean resampling to yield a range of spatial resolutions (i.e., Landsat: 30, 90, 240, 990 meters; MODIS: 250, 500, 1000, 3000 meters). Long-term plant biomass data from 15 sites at the JER, representing both grassland and shrubland ecosystems, were used to examine these scaling effects. Each time-series was decomposed into seasonal and long-term trend components using the Breaks For Additive Seasonal and Trend (BFAST) model and significant deviations from modeled trends were identified. Plant biomass data was then regressed against the decomposed time-series, with model results used to identify optimal spatial scales. Preliminary results indicate that finer resolution imagery produces stronger correlations with temporal shifts in plant biomass. However, when considering the use of finer resolution imagery, any potential improvement in identifying shifts in vegetation dynamics must be weighed against the higher computational requirements involved with its use.