336 publications from this institution
Russia and Ukraine are countries with large untapped agricultural potentials, both in terms of abandoned agricultural land and low yields. In this study, we apply a global economic agricultural sector model to provide a comprehensive analysis of different scenarios, simulating the utilization of different crop production potentials in Russia and Ukraine and their impacts on a regional and global scale. Our results show that substantial potentials in crop production do exist and that large parts of the additional production would be exported to world markets. Production potentials due to intensification are higher than potentials due to re-cultivation of abandoned land.
No abstract is provided for this article.
, and oth-ers) caused by vegetation fires in Russia between 2004-2008. Major goals of the assessment were: (1) to providespatial and temporal quantification of the emissions on a monthly basis; and (2) to minimize the uncertainties ofthe assessment, taking into account the fuzzy character of the problem. A hybrid land cover (LC) developed as abaseline dataset of all relevant information sources (different maps, multi-sensor remote sensing data, results ofdifferent land and forest inventories, measurements in situ) was used as an information background for the assess-ment. The multilayer hierarchical classification of land classes allowed detailed parameterization of vegetation andsurface soil layers with respect to indicators used in the calculation of fire emissions. The approach resulted ina comprehensive numerical description of stock and structure of vegetation combustibles (e.g., for forests: stemwood, branches, foliage, understory, green forest floor, coarse woody debris – snags, logs, dry branches of livetrees, on-ground litter, organic matter of the upper soil layer) for each 1 km pixel. Burnt areas were estimated ona monthly basis by remote sensing data (mostly based on thermal channels of AVHRR and MODIS, data obtainedby Sukachev Institute of Forest, Krasnoyarsk, Russia) and superimposed with the LC. The modeling frameworkincluded regional regularities of (1) long period seasonal distribution of burnt areas by type of fire (five typeshave been used for forests: crown, superficial ground, stable ground, peat (soil), underground fires); (2) averageintensity of burning (amount of consumed combustibles) dependently on time of fire season, type of fire, and veg-etation class; (3) partition of consumed carbon (gas composition, particles); (4) content of nitrogen in major typesof combustibles; (5) expected amount of post fire dieback in perennial vegetation; and others. Intensity of firewas corrected based on average monthly weather conditions. Amount of emitted nitrogen was estimated based onits proportion to carbon by types of combustibles. Gas composition was estimated based on published results ofmeasurements. The results obtained indicate high variability of emissions by land classes, type of fire, time of fireseason, specifics of weather with overall consumption of combustibles over the country in the range 140 to 330 TgC year
No abstract is provided for this article.
A number of global and regional maps of forest extent are available, but when compared spatially, there are large areas of disagreement. Moreover, there was no global forest map that is consistent with forest statistics from FAO (Food and Agriculture Organization of the United Nations). By combining these diverse data sources into a single forest cover product, it is possible to produce a global forest map that is more accurate than the individual input layers and to produce a map that is consistent with FAO statistics. In this paper we applied geographically weighted regression (GWR) to integrate eight different forest products into three global hybrid forest cover maps at a 1 km resolution for the reference year 2000. Input products included global land cover and forest maps at varying resolutions from 30 m to 1 km, mosaics of regional land use/land cover products where available, and the MODIS Vegetation Continuous Fields product. The GWR was trained using crowdsourced data collected via the Geo-Wiki platform and the hybrid maps were then validated using an independent dataset collected via the same system. Three different hybrid maps were produced: two consistent with FAO statistics, one at the country and one at the regional level, and a “best guess” forest cover map that is independent of FAO. Independent validation showed that the “best guess” hybrid product had the best overall accuracy of 93% when compared with the individual input datasets. The global hybrid forest cover maps are available at http://biomass.geo-wiki.org. More details can be found in the paper: Schepaschenko D., See L., Lesiv M., McCallum I., Fritz S., et al. (2015). Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. <em>Remote Sensing of Environment </em>162 208-220. https://doi.org/10.1016/j.rse.2015.02.011. The data set consists of following files: 1. for2000_bg.zip - Global forest mask "best guess" - percentage forest cover at a 1 km spatial resolution for the year 2000;<br> 2. for2000_ca_cou.zip - Global forest mask calibrated to the FAO FRA statistics at national scale;<br> 3. for2000_ca_reg.zip - Global forest mask calibrated to the FAO FRA statistics at continental scale;<br> 4. training_pc.csv - training data, which contains visual interpretation of very high resolution imagery at 20159 locations;<br> 5. validation.csv - validation data, which contains visual interpretation of very high resolution imagery at 1816 locations.
Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products.
Short-rotation woody plantations (SRWPs) play a major role in climate change mitigation and adaptation plans, because of their high yields of woody biomass and fast carbon storage. However, their benefits, trade-offs and growing-success are heavily location-dependent. Therefore, spatial data on the distribution of SRWPs are indispensable for assessing current distribution, trade-offs with other uses and potential contributions to climate mitigation. As current global datasets lack reliable information on SRWPs and full global mapping is difficult, we provide a consistent and systematic approach to estimate the spatial distribution of SRWPs in (sub-)tropical biomes under current and future climate. We combined three advanced methods (maximum entropy, random forest and multinomial regression) to evaluate spatially explicit probabilities of SRWPs. As inputs served a large empirical dataset on SRWP observations and 17 predictor variables, covering biophysical and socio-economic conditions. SRWP probabilities varied strongly between regions, and might not be feasible in major parts of (sub-)tropical biomes, challenging the feasibility of global mitigation plans that over-rely on tree plantations. Due to future climatic changes, SRWP probabilities decreased in many areas, particularly pronounced in higher emission scenarios. This indicates a negative feedback with higher emissions resulting in less mitigation potential. Less suitable land for SRWPs in the future could also result in fewer wood resources from these plantations, enhancing pressure on natural forests and hampering sustainability initiatives that use wood-based alternatives. Our results can help adding a more nuanced treatment of mitigation options and forest management in research on biodiversity and land use change.
The plantation forestry sector in Indonesia has seen a change in species from Acacia mangium to Eucalyptus pellita. This change, forced by diseases spreads, has affected more than 90% of the plantation area in Indonesia and it is unprecedented in its scale in the history of plantation forestry (Nambiar et al., 2018). It is also a change of tree species with very different eco-physiological patterns: Acacias – in contrast to eucalypts - are leguminous trees and therefore self-sufficient in nitrogen supply and capable of building significant stocks of nitrogen and carbon in the soil, two main determinants of plantation productivity. The large-scale species shift therefore raises questions about the sustainability of the pulp and paper sector in Indonesia in the coming decades as well as the role of past land use and site quality in Indonesia’s forest restoration pledge. The proposed contribution therefore aims at analyzing the sustainability in terms of productivity of the Indonesian plantation forestry sector under the new eucalypts regime. To that end, we will deploy the BioGeoChemistry Management Model (BGCMAN; Pietsch, 2014) which is capable of representing the carbon, water and nitrogen cycles in great detail. Expected results will include a reconstruction of the rise and fall of A. mangium and shift to E. pellita as well as forecasts of plantation productivity and soil fertility over the next decades; it will notably answer the question for how long soil N-stock accumulated by acacias will be able to feed eucalypts.
No abstract is provided for this article.
No abstract is provided for this article.
ВВЕДЕНИЕНа протяжении последних десятилетий мировое сообщество рассматривает парадигму устойчивого управления лесами (УУЛ) как философскую и методологическую основу коэволюции человека и леса, а также как исходный постулат стратегии национальных лесных политик и международных инициатив.Реализация и прогресс УУЛ оцениваются при помощи си-стем критериев и индикаторов (КиИ), которые с теми или иными особенностями группируются вокруг семи основных тем: 1) поддержание и улучшение лесных ресурсов и их вклада в углеродный цикл; 2) здоровье и жизненность леса; 3) ресурсные (древесные и недревесные) функции; 4) биоразнообразие; 5) защитные функции лесов, особенно охрана и защита вод и почв; 6) усиление социально-экономических функций леса и 7) развитие соответствующих по-СИБИРСКИЙ ЛЕСНОЙ ЖУРНАЛ.2017.№ 6. С.
The data products consist of two layers that include estimates of<br> 1. growing stock volume (GSV, unit: m3/ha) for the year 2014 (raster data set)<br> Definition: volume of all living trees more than 6 cm in diameter at breast height measured over bark from ground to a top stem diameter of 0 cm. Excludes: branches, twigs, foliage, flowers, seeds, stump and roots.<br> 2. per-pixel of growing stock volume uncertainty expressed as standard error in m3/ha (raster data set) The maps have a spatial resolution of 3.2 arc sec (ca 0.5 ha for Russia). The GSV estimates were obtained by calibration of two remote sensing-based maps with ca 8000 ground plots from the National Forest Inventory:<br> • The global GlobBiomass map of GSV (Santoro, 2018; Santoro et al., 2020) is based on synthetic aperture radar (SAR) observations acquired around the year 2010 and has a spatial resolution of 100m, units m³ ha<sup>-1</sup>.<br> • The global Climate Change Initiative (CCI) Biomass map of AGB (Santoro and Cartus, 2019) is also based on SAR data, acquired in 2017 and has a spatial resolution of 100m, units t ha<sup>-1</sup>. The methodology, input data and software are published here: Schepaschenko, D., Moltchanova, E., Fedorov, S. <em>et al.</em> Russian forest sequesters substantially more carbon than previously reported. <em>Sci Rep</em> <strong>11, </strong>12825 (2021). https://doi.org/10.1038/s41598-021-92152-9
