Effect of spatial image support in detecting long-term vegetation change from satellite time-series

Arid rangelands have been severely degraded over the past century. Multi-temporal remote sensing techniques are ideally suited to detect significant changes in ecosystem state; however, considerable uncertainty exists regarding the effects of changing image resolution on their ability to detect ecologically meaningful change from satellite time-series. (1) Assess the effects of image resolution in detecting landscape spatial heterogeneity. (2) Compare and evaluate the efficacy of coarse (MODIS) and moderate (Landsat) resolution satellite time-series for detecting ecosystem change. Using long-term (similar to 12 year) vegetation monitoring data from grassland and shrubland sites in southern New Mexico, USA, we evaluated the effects of changing image support using MODIS (250-m) and Landsat (30-m) time-series in modeling and detecting significant changes in vegetation using time-series decomposition techniques. Within our study ecosystem, landscape-scale (> 20-m) spatial heterogeneity was low, resulting in a similar ability to detect vegetation changes across both satellite sensors and levels of spatial image support. While both Landsat and MODIS imagery were effective in modeling temporal dynamics in vegetation structure and composition, MODIS was more strongly correlated to biomass due to its cleaner (i.e., fewer artifacts/data gaps) 16-day temporal signal. The optimization of spatial/temporal scale is critical in ensuring adequate detection of change. While the results presented in this study are likely specific to arid shrub-grassland ecosystems, the approach presented here is generally applicable. Future analysis is needed in other ecosystems to assess how scaling relationships will change under different vegetation communities that range in their degree of landscape heterogeneity.