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The article deals with the features of soil-agrochemical inspection of the nursery area with the use of geoinformation technologies. The purpose of this work was to conduct a soil survey of arable sod-podzolic medium - or light-loamy soils of the territory of the forest nursery, as well as estimation of production area contamination with weeds. The peculiarity of this study was the fact that the process of collection, systematization and processing of all compiled information was focused on the use of modern geographic information systems. Survey points were chosen in coordination with the administration of the nursery and were precised according to the actual space images. Geographical reference of testing points was carried out using global positioning technologies (GPS, GLONASS) based on the NextGIS mobile application (Android OS). Photofixation of nursery fields was performed by means of geotagging technologies, which allow implementation of the obtained graphic information directly into the geographic information system (GIS). The soil samples collected in the field were analyzed in the soil laboratory in order to determine the main indicators of soil fertility. On the basis of the received data the cartograms were developed by means of GIS, which provide information on the main nutrients content in the soil of nursery fields as well as on the most important indicators characterizing the soil absorbing complex (soil acidity, degree of base saturation, etc.). The study of weed infestation allowed developing the thematic map representing the weeds propagation on the nursery fields. All collected information was combined into a comprehensive geographic information system developed on the basis of Quantum GIS shell. As a result, the developed GIS will contribute to operational monitoring of soil fertility and ensure informational support for agricultural machinery applied in the nursery for growing planting material.

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.

Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported (FAO, 2014) almost no changes in growing stock (+1.8%) and biomass (+0.6%). Yet remote sensing products indicate increased vegetation productivity (Guay et al., 2014), tree cover (Song et al., 2018) and above-ground biomass (Liu et al., 2015). Here, we challenge the official national statistics with a combination of recent National Forest Inventory and remote sensing data products to provide an alternative estimate of the growing stock of Russian forests and assess the relative changes in the post-Soviet era. Our estimate for the year 2014 is 118.29±1.3 109 m3, which is 48% higher than the official value reported for the same year in the State Forest Register. The difference is explained by increased biomass density in forested areas (+39%) and larger forest area estimates (+9%). Using the last Soviet Union report (1988) as a reference, Russian forests have accumulated 1163×106 m3 yr-1 of growing stock between 1988–2014, which compensates for forest growing stock losses in tropical countries (FAO FRA, 2015). 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 (National Inventory Report, 2020).Acknowledgement: The research plots data collection was performed within the framework of the state assignment of the Center for Forest Ecology and Productivity of the Russian Academy of Sciences (no. АААА-А18-118052590019-7), and the ground data pre-processing were financially supported by the Russian Science Foundation (project no. 19-77-30015).

We have studied the effect of soil conditions on the productivity (site index) of pine forests in the zone of coniferous–broad-leaved forests. The research object is natural pine forests of different productivities related to the effect of soil and ground factors. The research is performed in northeastern Moscow oblast at the Sverdlovsk branch of the Moscow educational–experimental forestry. It is revealed that it is the main soil properties that determine the site index of the studied pine forests, include groundwater level, the thickness of the ungleyed mineral part of soil profile, and humus reserves in the mineral part of soil. Gleying rate, the depth of the gley horizon, the thickness of the organic horizon, and the occurrence of ortsteins and iron-manganese concretions are the most important soil indicators of the water regime, which is closely related to forest productivity (site index). The cluster analysis has enabled us to assign the soil varieties to five groups with respect to their productivity and taxonomic position. These informative soil properties should be included into forest GIS and taken into account when planning forestry measures. The diagnostic model may be used in assessing the potential productivity of forest soils and elaborating measures for the rational use of forest lands.

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., Measurement(s)above-ground biomass • organic materialTechnology Type(s)tree species census • measurement methodFactor Type(s)geographic location • tree speciesSample Characteristic - Environmentforest biomeSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9850571

With this article, we provide an overview of the methods, instruments and initiatives for forest biomass observation at a global scale. We focus on the freely available information provided by both remote and in-situ observations. The advantages and limitation of various space borne methods, including optical, radar (C, L and P band) and LiDAR, as well as respective instruments available on the orbit (MODIS, Proba-V, Landsat, Sentinel-1, Sentinel-2 , ALOS PALSAR, Envisat ASAR) or expecting (BIOMASS, GEDI, NISAR, SAOCOM-CS) are discussed. We emphasize the role of in-situ methods in the development of a biomass models, providing calibration and validation of remote sensing data. We focus on freely available forest biomass maps, databases and empirical models. We describe the functionality of Biomass.Geo-Wiki.org portal, which provides access to a collection of global and regional biomass maps in full resolution with unified legend and units overplayed with high-resolution imagery. The Forest-Observation-System.net is announced as an international cooperation to establish a global in-situ forest biomass database to support earth observation and to encourage investment in relevant field-based observations and science. Prospects of unmanned aerial vehicles in the forest inventory are briefly discussed. The work was partly supported by ESA IFBN project (contract 4000114425/15/NL/FF/gp).

We review up-to-date, open access remote sensing (RS) products related to forest. We created a hybrid forest/non-forest map using geographically weighted regression (GWR) based on a number of recent RS products and crowdsourcing. The hybrid map has spatial resolution of 230 m and shows the extent of forest in Russia in 2010. We estimate area of Russian forest as 711 million ha (in accordance with Russian national forest definition). Compared to official data of the State Forest Register (SFR), RS estimates the area of forest to be considerably larger in European part (+12.2 million ha or +8%) and smaller in Asian (-39.8 million ha or -7%) part of Russia. We report the changing forest area in 2001-2010 and discuss main drivers: wildfire and encroachment of abandoned arable land. The methodology used here can be applied for monitoring of forest cover and enhancing the forest accounting system in Russia.

A number of global and regional maps of forest extent are available, but when compared spatially, there are large areas of disagreement. Moreover, there is currently 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 .