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 FVC max and FVC mean 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 FVC max and FVC mean , 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 FVC max and FVC mean 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 FVC max pixels on a global scale (excluding permanent OPEN ACCESSRemote Sens. 2014, 6 4218 snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVC mean 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 FVC max 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.

Dynamics of Fractional Vegetation Coverage and Its Relationship with Climate and Human Activities in Inner Mongolia, China

“…Before the trend analysis, we computed the serial correlation coefficient according to the Section 3.2.3 of reference [27] and found that only 7.2% of the total area has shown significance at the 5% level and is mainly distributed in the western desert area of study area. Therefore, we think the autocorrelation is negligible.…”

“…A trend analysis was used to simulate the annual change trend of FVC for each pixel; the algorithm is shown in reference [26,27].…”

Human Activity Influences on Vegetation Cover Changes in Beijing, China, from 2000 to 2015

“…Numerous researchers have used FVC to reveal laws of spatial variation of the surface, to discuss driving factors of change, and to analyze regional ecological transitions [11,[16][17][18]. For example, Wu et al [19] used FVC to estimate the change trend from 1982 to 2011 by GIMMS NDVI data on a global scale. Zhang et al [20] analyzed and appraised the changes in vegetation patterns and revealed a pattern of spatial change in the vegetation coverage of a typical oasis region in the Tarim River Watershed, by producing a map of FVC levels.…”

Stochastic Spatio-Temporal Models for Analysing NDVI Distribution of GIMMS NDVI3g Images

“…These studies are mainly based on including temporal correlation of individual pixels at different resolutions but ignoring spatial dependence among them. Perhaps the most broadly used method for analysing NDVI temporal changes is the non-parametric Mann-Kendall test (see, for example, [11][12][13][14]). When plotting significant changes, a discrete pixel by pixel map of the NDVI trend changes is obtained.…”