Publications

Stock of standing dead trees in boreal forests of Central Siberia

A significant part of carbon assimilated by forest is deposited in tree trunks. Growth and development of tree stands is accompanied by accumulation of standing dead trees (snags) due to natural tree mortality and as a result of the impact of exogenous factors. Carbon accumulated in these dead trunks is excluded from the fast turnover due to low rate of wood decomposition, so that snags can be considered as a pool of organic carbon with a slow rate of its return to the atmosphere. We estimated stock of snags on 54 sample plots, which represent the main types of forest ecosystems in the northern and middle taiga of Central Siberia. In the middle taiga, stock of snags varied from up to 7 m 3 ha -1 in Siberian spruce forests to 20-42 m 3 ha -1 in Scots pine forests. Larch forests in the northern taiga had the similar stock of snags as larch forests in the middle taiga despite significantly higher growing stock in the later. Snags contributed from 4 to 19% to the total stock of woody biomass in studied forests. This study indicated the significance of snags and can be used to estimate carbon budget of forest ecosystems of the region.

New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI

As more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. Although machine learning is being used in some citizen science projects, advances in generative artificial intelligence (AI) are yet to be fully exploited. This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI.

Negative emissions—interactions with other mitigation options: a case study for South East Asia

A SYSTEM FOR HETEROTROPHIC SOIL RESPIRATION ASSESSMENT OF RUSSIAN LAND

A model cluster for soil respiration assessment was developed. It is based on 3592 in-situ measurements and considered climatic parameters, soil and vegetation types, land use, vegetation productivity and disturbances. Heterotrophic efflux from Russian soil was assessed as 3.47 Pg C year -1 or 215 g C m -2 year -1 .

Aboveground live biomass of trees of larix cajanderi mayr at north-east of Yakutia

Extreme droughts, heat waves, frosts, precipitation, wind storms and other climate extremes may impact the structure, composition, and functioning of terrestrial ecosystems, and thus carbon cycling and its feedbacks to the climate system. Yet, the interconnected avenues through which climate extremes drive ecological and physiological processes and alter the carbon balance are poorly understood. Here we review literature on carbon-cycle relevant responses of ecosystems to extreme climatic events. Given that impacts of climate extremes are considered disturbances, we assume the respective general disturbance-induced mechanisms and processes to also operate in an extreme context. The paucity of well-defined studies currently renders a quantitative meta-analysis impossible, but permits us to develop a deductive framework for identifying the main mechanisms (and coupling thereof) through which climate extremes may act on the carbon cycle. We find that ecosystem responses can exceed the duration of the climate impacts via lagged effects on the carbon cycle. The expected regional impacts of future climate extremes will depend on changes in the probability and severity of their occurrence, on the compound effects and timing of different climate extremes, and on the vulnerability of each land-cove type modulated by management. Though processes and sensitivities differ among biomes, based on expert opinion we expect forests to exhibit the largest net effect of extremes due to their large carbon pools and fluxes, potentially large indirect and lagged impacts, and long recovery time to re-gain previous stocks. At the global scale, we presume that droughts have the strongest and most widespread effects on terrestrial carbon cycling. Comparing impacts of climate extremes identified via remote sensing vs. ground-based observational case studies reveals that many regions in the (sub-)tropics are understudied. Hence, regional investigations are needed to allow a global upscaling of the impacts of climate extremes on global carbon-climate feedbacks.

Global forest map: exploring the synergy of remote sensing, crowd sourcing and statistics

No abstract is provided for this article.

90Sr Content in the Stemwood of Forests within Ukrainian Polissya

The consequences of the Chernobyl disaster continue to threaten humans and ecosystems across fallout gradient in Northern Ukraine and nearby. Forest ecosystems contain substantial stocks of long-lived radionuclide 90Sr which was leached from the fuel matrix during the disaster. Nowadays, there is a lack of information about current transfer factors (TF) of this radionuclide from soil to the stemwood of native tree species. We have estimated 90Sr content in the forest stemwood of three tree species utilizing models of their growth and yield and collected woody samplings. TFs provided here vary greatly across studied tree species (18.0 × 2.1±1, 8.7 × 2.8±1, and 10.4 × 6.0±1 n × 10−3 m2·kg−1 (geometrical mean (GM) ± geometrical standard deviation, GSD) for the above species, respectively) and together with indicators of soil contamination allow us to reliably assess local stocks in the stemwood. Silver birch stands are estimated to deposit the highest 90Sr stocks. Herewith, at 25 years old Black alder stands could accumulate higher stocks (up to 35 MBq·ha−1) under rich growth conditions. TFs obtained in this study substantially exceed values provided by the International Atomic Energy Agency for studied tree species and thus could entail respective restrictions on use of firewood across large areas in Ukrainian Polissya. Data provided here may be harnessed to support decisions of respective stakeholders to provide credibly safe management of the contaminated forest ecosystems.

Wetland maps including open water extent dynamics based on ENVISAT ASAR WS for Siberia, 2007 and 2008, links to GeoTIFFs

Mapping growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m3 ha−1 and for live biomass of about 2 t ha−1 over the study area.

Estimating the global distribution of field size using crowdsourcing

There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

Ground Data are Essential for Biomass Remote Sensing Missions

Several remote sensing missions will soon produce detailed carbon maps over all terrestrial ecosystems. These missions are dependent on accurate a

Respiration of Russian soils: Climatic drivers and response to climate change

Soil respiration is one of the major ecosystem carbon fluxes and has a strong relationship with climate. We quantified this dependence for the Russian territory based on coupling climate data and in-situ soil respiration (Rs) measurements compiled into a database from the literature using regression and random forest models. The analysis showed that soil properties are a strong factor that mediates the climate effect on Rs. The vegetation class determines the contribution of the autotrophic respiration to the total Rs flux. The heterotrophic soil respiration efflux of Russia was estimated to be 3.2 Pg C yr−1 or 190 g C m−2 yr−1, which is 9–20% higher than most previously reported estimates. According to our modeling, heterotrophic soil respiration is expected to rise by 12% on average by 2050 according to the RCP2.6 climate scenario and at 10% based on RCP6. The total for Russia may reach 3.5 Pg C yr−1 by 2050. By the end of the century heterotrophic respiration may reach 3.6 Pg C yr−1 (+13%) and 4.3 Pg C yr−1 (+34%) based on RCP2.6 and RCP6, respectively. In order to understand to what extent the lack of information on disturbances impact contributes to uncertainty of our model, we analyzed a few available publications and expert estimates. Taking into account the specifics of Russian forest management and regional disturbance regimes, we have found that for the entire territory of Russia, the disturbances are responsible for an increase in heterotrophic soil respiration by less than 2%.

Can the uncertainty of full carbon accounting of forest ecosystems be made acceptable to policymakers?

In accordance with the concept that only full accounting of major greenhouse gases corresponds to the goals of the United Nations Framework Convention on Climate Change and its Kyoto Protocol, this paper considers uncertainties of regional (national) terrestrial...

Design and performance of the Climate Change Initiative Biomass global retrieval algorithm

The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band Synthetic Aperture Radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (<50 Mg ha−1) and under-predictions in the high AGB range (>300 Mg ha−1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

Forest map and its uncertainty as an important input for carbon sink estimation for Poland and Ukraine

Improving knowledge on the land cover and forest ecosystems is of a high importance for carrying out spatial inventories of emissions and removals in forestry as the best way to achieve reliable results of forest carbon account. The region of the study is the territory of Poland and Ukraine, covering a substantial part of European diversity of natural landscapes. In addition, Ukraine and Poland have a high potential to sequester carbon through afforestation. The accuracy of available forest maps varies considerably over space. We have applied the method of geographically weighted regression to generate a hybrid forest map for Poland and Ukraine. This method predicts land cover types based on crowdsourced data obtained from the Geo- Wiki project, and land cover/forest cover products derived from remote sensing. The hybrid forest cover was found to be more accurate than the individual forest maps extracted from global remote sensing land cover products.

Global crowdsourced data on land cover, land use and human impact (2011-2012)

Carbon and nitrogen emissions due to vegetation fire in Russia in 2004-2008 (Abstract)

No abstract is provided for this article.

Supporting a forest observation system for Siberia: Earth observation for monitoring, assessing and providing forest resource information

More than 50 percent of the Russian forest inventory was updated more than 25 years ago. The consequence is that most of the existing forest inventory is outdated. Human and environmental forest disturbances continuously affect changes of forest cover and biomass stocks. The magnitude and extent of ongoing environmental pressures (e.g., forest fragmentation and the impact of global climate change) and the loss rates of particular habitat types is unknown. The aim of the paper is to provide an integrated concept for the monitoring and assessment of forest resources, as demonstrated for Central Siberia in the framework of the ZAPAS project and the Siberian Earth System Science Cluster (SIB-ESS-C). Regional and local scale in-situ and multi-agency satellite data were analyzed to generate biomass and forest cover change maps at local scales which were validated with up-to-date forest inventory data. At regional scales a synergy map of land cover and biomass information was developed. Annual PALSAR backscatter data proved to be applicable for local scale forest biomass monitoring and reforestation assessment. The forest resource maps were integrated in a data middleware system to provide Web-based analyses interoperability, data access of multi-scale forest resource maps to local stakeholders, and decision support for regional and local forest management in Central Siberia.

Improving the method of mapping organic carbon in the forest litter in Central Federal District

No abstract is provided for this article.