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Tree phenology mediates land-atmosphere mass and energy exchange and is a determinant of ecosystem structure and function. In the dry tropics, including African savannas, many trees grow new leaves during the dry season - weeks or months before the rains typically start. This syndrome of pre-rain green-up has long been recognized at small scales, but the high spatial and interspecific variability in leaf phenology has precluded regional generalizations., We used remote sensing data to show that this precocious phenology is ubiquitous across the woodlands and savannas of southern tropical Africa., In 70% of the study area, green-up preceded rain onset by > 20 d (42% > 40 d). All the main vegetation formations exhibited pre-rain green-up, by as much as 53 ± 18 d (in the wet miombo). Green-up showed low interannual variability (SD between years = 11 d), and high spatial variability (> 100 d)., These results are consistent with a high degree of local phenological adaptation, and an insolation trigger of green-up. Tree-tree competition and niche separation may explain the ubiquity of this precocious phenology. The ubiquity of pre-rain green-up described here challenges existing model representations and suggests resistance (but not necessarily resilience) to the delay in rain onset predicted under climate change. [ABSTRACT FROM AUTHOR]

Carbon emissions from tropical land-use change are a major uncertainty in the global carbon cycle. In African woodlands, small-scale farming and the need for fuel are thought to be reducing vegetation carbon stocks, but quantification of these processes is hindered by the limitations of optical remote sensing and a lack of ground data. Here, we present a method for mapping vegetation carbon stocks and their changes over a 3-year period in a > 1000 km2 region in central Mozambique at 0.06 ha resolution. L-band synthetic aperture radar imagery and an inventory of 96 plots are combined using regression and bootstrapping to generate biomass maps with known uncertainties. The resultant maps have sufficient accuracy to be capable of detecting changes in forest carbon stocks of as little as 12 MgC ha−1 over 3 years with 95% confidence. This allows characterization of biomass loss from deforestation and forest degradation at a new level of detail. Total aboveground biomass in the study area was reduced by 6.9 ± 4.6% over 3 years: from 2.13 ± 0.12 TgC in 2007 to 1.98 ± 0.11 TgC in 2010, a loss of 0.15 ± 0.10 TgC. Degradation probably contributed 67% (96.9 ± 91.0 GgC) of the net loss of biomass, but is associated with high uncertainty. The detailed mapping of carbon stock changes quantifies the nature of small-scale farming. New clearances were on average small (median 0.2 ha) and were often additions to already cleared land. Deforestation events reduced biomass from 33.5 to 11.9 MgC ha−1 on average. Contrary to expectations, we did not find evidence that clearances were targeted towards areas of high biomass. Our method is scalable and suitable for monitoring land cover change and vegetation carbon stocks in woodland ecosystems, and can support policy approaches towards reducing emissions from deforestation and degradation ( REDD). [ABSTRACT FROM AUTHOR]