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International audience; Northern Eurasia is the largest terrestrial reservoir of carbon, and its dynamics and interactions with climate are globally significant. We present five independent estimates of the contemporary carbon balance of central Siberia using three different methodologies: a landscape-ecosystem approach (LEA) that amalgamates comprehensive vegetation, soil, hydrological and morphological information into a Geographical Information System, linked to regression-based estimates of carbon flux; two Dynamic Global Vegetation Models (DGVMs); and two atmospheric inversions. Apart from one of the DGVMs, all methods produce estimates of the net biome productivity (NBP) that are consistent both amongst themselves and with a range of other estimates. They indicate the region to be a carbon sink with a NBP of 27.5 +/- 7.2 g C m-2 yr-1, which is equivalent to 352 +/- 92 Mt C yr-1 if considered representative for boreal Asia. This is comparable with fossil fuel emissions for the Russian Federation, currently estimated as 427 MtC yr-1, and implies that boreal Asia does not play the major role in the northern hemisphere land sink, typically estimated to be of magnitude 1.5-2.9 Gt C yr-1. The LEA and DGVM approaches produce very different partitioning of NBP into its component fluxes. The DGVMs find net primary production (NPP) to be nearly balanced by heterotrophic respiration, disturbance being a relatively small term pushing the system closer to equilibrium. In the LEA, heterotrophic respiration is significantly less than NPP, and disturbance plays a much larger role in the overall carbon balance. The use in the LEA of observationally based estimates of heterotrophic respiration and fire disturbance, along with a more complete description of disturbance fluxes, suggests that the partitioning derived by the LEA is more likely, and that improved process descriptions and constraints by data are needed in the DGVMs.

The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change., Surveys in Geophysics, 40 (4), ISSN:0169-3298, ISSN:1573-0956