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Urbanization is responsible for large environmental changes worldwide. Although traditionally urbanization was related to negative environmental impacts, recent research also highlights positive impacts like the potential of urban areas to store soil organic carbon. The net effect of urbanization on soil organic carbon is poorly understood. Negative influences of construction and soil sealing may be compensated by the establishment of green areas. Possible net effects of future urbanization on soil organic carbon stocks were explored for the Moscow Region, based on the soil survey and land conversion model. The regional urbanization was modelled as a function of environmental, socio-economic and neighbourhood factors. This yielded three alternative scenarios for urbanization: i) including neighbourhood factors; ii) excluding neighbourhood factors and focusing on environmental drivers; and iii) considering the New Moscow Project that includes the establishment of 1500 km2 of new urbanized area following governmental regulation. The three scenarios showed substantial expansion of urban areas on 30, 10 and 80%. The model, considering neighbourhood effect was the most accurate with 91% of correct predictions. Urbanization in the region mainly converted forests, fallow and arable lands. The negative effect of urbanization (i.e soil sealing and excavation of topsoil for building construction) was compensated by positive effect (e.i. urban greenery and "cultural layers"). In result an increase of soil organic carbon stocks of 4.2 ± 1.7 to 11.0 ± 2.6 Tg C was shown for all three scenarios. The highest increases in soil organic carbon stocks occurred on the less fertile Orthic Podzols and Eutric Podzoluvisols, whereas SOC stocks in Orthic Luvisols, Luvic Chernozems, Dystric Histosols and Eutric Fluvisols increased less. Subsoil C-stocks were much more affected with an extra 4 ± 1.6 to 10 ± 2.4 Tg soil organic carbon than those in the topsoils. The highest increase of both topsoil and subsoil soil organic carbon stocks occurred in the New Moscow scenario with the highest urbanization. Even when the relatively high uncertainties of the absolute C-stocks are considered, the outcomes highlight the potential of cities to enhance soil organic carbon storage. This will progressively become more important in the future following the increasing world-wide urbanization. © 2017 Elsevier Ltd

Identifying historical forest disturbances is difficult, especially in selectively logged areas. LiDAR is able to measure fine-scale variations in forest structure over multiple kilometers. We use LiDAR data from ca. 16 km2 of forest in Sierra Leone, West Africa, to discriminate areas of old-growth from areas recovering from selective logging for 23 years. We examined canopy height variation and gap size distributions. We found that though recovering blocks of forest differed little in height from old-growth forest (up to 3 m) they had a greater area of canopy gaps (average 10.2% gap fraction in logged areas, compared to 5.6% in unlogged area); and greater numbers of gaps penetrating to the forest floor (162 gaps at 2 m height in logged blocks, and 101 in an unlogged block). Comparison of LiDAR measurements with field data demonstrated that LiDAR delivered accurate results. We found that gap size distributions deviated from power-laws reported previously, with substantially fewer large gaps than predicted by power-law functions. Our analyses demonstrate that LiDAR is a useful tool for distinguishing structural differences between old-growth and old-secondary forests. That makes LiDAR a powerful tool for REDD+ (Reduction of Emissions from Deforestation and Forest Degradation) programs implementation and conservation planning. ; This research was funded by the European Union under the EuropeAid Programme, as a part of the Across the River Transboundary Peace Park Project DCI/ENV/2008/151-577; by a Cambridge Conservation Initiative Collaborative Fund grant "Applications of airborne remote sensing to the conservation management of a West African National Park"; and by the ERC grant Africa GHG #247349. We would also like to thank the British Technion Society for the generous funding of the post-doctoral Coleman-Cohen fellowship of R. Kent. ; This is the final version of the article. It first appeared from MDPI via http://dx.doi.org/10.3390/rs70708348

This proceedings book focuses on advanced technologies to monitor and model urban soils, vegetation and climate, including internet of things, remote sensing, express and non-destructive techniques. The Smart and Sustainable Cities (SSC) conference is a regular event, organized each second year in RUDN University (Russia) and providing a multidisciplinary platform for scientists and practitioners in urban environmental monitoring, modeling, planning and management.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions. © 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.