Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011

Abstract

Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, USGS global land cover characteristics data and HWSD soil type data with a modified dimidiate pixel model, which considered vegetation and soil types and mixed pixels decomposition. The evaluation of the robustness and accuracy of the GIMMS FVC with MODIS FVC and Validation of Land European Remote sensing Instruments (VALERI) FVC show high reliability. Trends of the annual FVCmax and FVCmean datasets in the last 30 years were reported by the Mann–Kendall method and Sen’s slope estimator. The results indicated that global FVC change was 0.20 and 0.60 in a year with obvious seasonal variability. All of the continents in the world experience a change in the annual FVCmax and FVCmean, which represents biomass production, except for Oceania, which exhibited a significant increase based on a significance level of p = 0.001 with the Student’s t-test. Global annual maximum and mean FVC growth rates are 0.14%/y and 0.12%/y, respectively. The trends of the annual FVCmax and FVCmean based on pixels also illustrated that the global vegetation had turned green in the last 30 years. A significant trend on the p = 0.05 level was found for 15.36% of the GIMMS FVCmax pixels on a global scale (excluding permanent snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVCmean similarly produced a total of 16.64% significant pixels with 2.28% with a negative trend and 14.36% with a positive trend. The North Frigid Zone represented the highest annual FVCmax significant increase (p = 0.05) of 25.17%, which may be caused mainly by global warming, Arctic sea-ice loss and an advance in growing seasons. Better FVC predictions at large regional scales, with high temporal resolution (month) and long time series, would advance our ability to understand the characteristics of the global FVC changes in the last 30 years and predict the response of vegetation to global climate change.