Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others.

Biomass structure is an important feature of terrestrial vegetation. The parameters of forest biomass structure are important for forest monitoring, biomass modelling and the optimal utilization and management of forests. In this paper, we used the most comprehensive database of sample plots available to build a set of multi-dimensional regression models that describe the proportion of different live biomass fractions (i.e., the stem, branches, foliage, roots) of forest stands as a function of average stand age, density (relative stocking) and site quality for forests of the major tree species of northern Eurasia. Bootstrapping was used to determine the accuracy of the estimates and also provides the associated uncertainties in these estimates. The species-specific mean percentage errors were then calculated between the sample plot data and the model estimates, resulting in overall relative errors in the regression model of −0.6%, −1.0% and 11.6% for biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF), and root-to-shoot ratio respectively. The equations were then applied to data obtained from the Russian State Forest Register (SFR) and a map of forest cover to produce spatially distributed estimators of biomass conversion and expansion factors and root-to-shoot ratios for Russian forests. The equations and the resulting maps can be used to convert growing stock volume to the components of both above-ground and below-ground live biomass. The new live biomass conversion factors can be used in different applications, in particular to substitute those that are currently used by Russia in national reporting to the UNFCCC (United Nations Framework Convention on Climate Change) and the FAO FRA (Food and Agriculture Organization's Forest Resource Assessment), among others.

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.

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