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We combined two existing datasets of vegetation aboveground biomass ( AGB) ( Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets. [ABSTRACT FROM AUTHOR]

Human conflicts may sometimes benefit wildlife by depopulating wilderness areas but there is evidence from Africa that the impacts tend to be negative. The forested states of West Africa have experienced much recent human conflict but there have been no assessments of impacts on the wildlife. We conducted surveys of mammals in the 710-km2 Gola Forest reserves to assess the impact of the 1991–2001 civil war in Sierra Leone. Gola is the most important remaining tract of lowland forest in the country and a key site for the conservation of the highly threatened forests of the Upper Guinea region. We found that Gola has survived well despite being in the heart of the area occupied by the rebels. We recorded 44 species of larger mammal, including 18 threatened, near-threatened and endemic species, accounting for all species recorded in pre-war surveys and adding several more (African buffalo Syncerus caffer nanus and water chevrotain Hyemoschus aquaticus). Populations of primates were healthy with little evidence of decline. Duiker detection rates were low and further work is required to confirm their numbers as they include five species endemic (or near endemic) to the Upper Guinea region, three of which are threatened. However, the population of African forest elephants Loxodonta cyclotis has collapsed, with only a few individuals remaining from c. 110 in the mid 1980s. We conclude that peacetime pressures from the bushmeat trade, clearance for agriculture, logging and mining are likely to be far greater for Gola than the pressures from the civil war. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (>10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location ( R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR. [ABSTRACT FROM AUTHOR]