Russian forest sequesters substantially more carbon than previously reported.

Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%). Yet remote sensing products indicate increased vegetation productivity, tree cover and above-ground biomass. Here, we challenge these statistics with a combination of recent National Forest Inventory and remote sensing data to provide an alternative estimate of the growing stock of Russian forests and to assess the relative changes in post-Soviet Russia. Our estimate for the year 2014 is 111 ± 1.3 × 109 m3, or 39% higher than the value in the State Forest Register. Using the last Soviet Union report as a reference, Russian forests have accumulated 1163 × 106 m3 yr-1 of growing stock between 1988-2014, which balances the net forest stock losses in tropical countries. Our estimate of the growing stock of managed forests is 94.2 × 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988-2014, or 47% higher than reported in the National Greenhouse Gases Inventory.

Spatial distribution of arable and abandoned land across former Soviet Union countries.

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.