Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset

Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset

Environmental Monitoring and Assessment

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001–2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.