The Mongolian Steppe is one of the largest remaining grassland ecosystems. Recent studies have reported widespread decline of vegetation across the steppe and about 70% of this ecosystem is now considered degraded. Among the scientific community there has been an active debate about whether the observed degradation is related to climate, or over-grazing, or both. Here, we employ a new atmospheric correction and cloud screening algorithm (MAIAC) to investigate trends in satellite observed vegetation phenology. We relate these trends to changes in climate and domestic animal populations. A series of harmonic functions is fitted to Moderate Resolution Imaging Spectroradiometer (MODIS) observed phenological curves to quantify seasonal and inter-annual changes in vegetation. Our results show a widespread decline (of about 12% on average) in MODIS observed normalized difference vegetation index (NDVI) across the country but particularly in the transition zone between grassland and the Gobi desert, where recent decline was as much as 40% below the 2002 mean NDVI. While we found considerable regional differences in the causes of landscape degradation, about 80% of the decline in NDVI could be attributed to increase in livestock. Changes in precipitation were able to explain about 30% of degradation across the country as a whole but up to 50% in areas with denser vegetation cover (P < 0.05). Temperature changes, while significant, played only a minor role (r(2)  = 0.10, P < 0.05). Our results suggest that the cumulative effect of overgrazing is a primary contributor to the degradation of the Mongolian steppe and is at least partially responsible for desertification reported in previous studies.

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Our limited understanding of the climate controls on tropical forest seasonality is one of the biggest sources of uncertainty in modeling climate change impacts on terrestrial ecosystems. Combining leaf production, litterfall and climate observations from satellite and ground data in the Amazon forest, we show that seasonal variation in leaf production is largely triggered by climate signals, specifically, insolation increase (70.4% of the total area) and precipitation increase (29.6%). Increase of insolation drives leaf growth in the absence of water limitation. For these non-water-limited forests, the simultaneous leaf flush occurs in a sufficient proportion of the trees to be observed from space. While tropical cycles are generally defined in terms of dry or wet season, we show that for a large part of Amazonia the increase in insolation triggers the visible progress of leaf growth, just like during spring in temperate forests. The dependence of leaf growth initiation on climate seasonality may result in a higher sensitivity of these ecosystems to changes in climate than previously thought.

[1] The analysis of a global data set of monthly leaf area index (LAI), derived from satellite observations of normalized difference vegetation index (NDVI) for the period July 1981 to September 1994, is discussed in this paper. Validation of this retroactive, coarse resolution (8 km) global multiyear data set is a challenging task because repetitive ground measurements from all representative vegetation types are not available. Therefore the magnitudes and interannual variations in the derived LAI fields were assessed as follows. First, the use of a NDVI-based algorithm, as opposed to a more physically based approach, is estimated to result in relative errors in LAI of about 10-20%, which is comparable to the mean uncertainty of AVHRR NDVI data. Second, the satellite LAI values compared reasonably well to ground measurements from three field campaigns. Third, comparison with an existing multiyear LAI data set showed qualitative agreement with regards to interannual variability, although the LAI values of the earlier data were consistently larger than those derived here. Fourth, interannual variations in LAI were evaluated through correlations with climate data sets, e.g., sea surface temperatures and precipitation in tropical semiarid regions known for ENSO impacts, temperature dependence of vegetation growth, and therefore LAI, in the northern latitudes. The general consistency between these independent data sets imbues confidence in the LAI data set, at least for use in large-scale modeling studies. Finally, improvements in near-surface climate simulation are documented in a companion article when satellite LAI values were used in a global climate model. The data set is available to the community via our Web server (http://cybele.bu.edu).