Publications

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

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.

. Very high resolution (VHR) satellite imagery from Google Earth and Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, this imagery is used to create detailed time-sensitive maps, e.g. for emergency response purposes, or to validate coarser resolution products such as global land cover maps. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global snapshot of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885767.

Boreal forests of Northern Eurasia are experiencing ongoing changes in climate, strong impacts by humans including transformation of previously untouched landscapes, and dramatically accelerating disturbance regimes. Current global and regional climatic models predict for this region the most dramatic climatic change over the globe. Unregulated and often destructive anthropogenic impacts on the environment and natural landscapes may substantially accelerate the negative consequences of climatic change. Complexities of the situation are evident: need to take decisions for underspecified dynamics systems under uncertainties; relevancy to consider dual strategy that integrates mitigative and adaptive measures, particularly under no-regret and win-win considerations; necessity to derive minimum mitigation standards from the limits of adaptation; inevitability of non-linear responses and feedbacks and probability to meet surprises in the biosphere's behavior; etc. It defines a need for development of new philosophy of cognition and policy making by using open, iterative, distributed-modular systems based on shared pools of models, tools libraries, and data sets. Such a situation defines an obligatory need to comprehensively use methodologies of applied system analysis and integrated modeling. Following the basic steps of applied systems analysis (fixation of the problem; diagnostics; (list of) stakeholders; problem mess; setting the goal; criteria; experimental research; analysis of input information; selection of the strategy, development and improvement of the model; depiction of alternatives; and decision making implementation) put the modeling on a solid scientific basis. Integrated models that include components of different nature (ecological, economic, social) are considered as a major tool of perception of future trajectories of forests in a changing world in spite of the fact that application of integrated modeling generates many cognitive problems, trade-offs and challenges. A central point of adaptation and mitigation strategies is development of robust policies. Robust policies should (1) ensure long-term stability of systems. behavior against multiple events (scenarios); (2) account for extreme events that require specific models; (3) consider uncertainties in a possible comprehensive and explicit form; (4) allow for flexibility to form a diversity of decisions dependent on associated risks and costs, performance indicators of stakeholders, (5) consider in an explicit spatio-temporal way ecological, economic and social dimensions, (6) collective risk; (7) include safety criteria, constraints, and performance indicators of involved agents. Adaptation and mitigation measures in the forest sector could be effective if they are part of a wide strategy which would involve all relevant sectors of national economy, particularly energy, industry, agriculture, tourism etc. combined in common political and institutional frameworks. Adaptation and anticipatory strategy should be an inherent part of transition to sustainable forest management. However, background philosophy of classical forestry becomes less and less reliable in a continuously changing world. Thus, modeling becomes a working tool for practical adaptive forest management. Adaptive forest management is defined as a management approach that acknowledges the lack of unequivocal and definite knowledge about the ways in which forest ecosystems work, and the uncertainty that dominates interactions with them. We consider major requirements to and specifics of adaptation and mitigation strategies in boreal forests which include inter alia: (1) a concept of sustainable development and sustainable forest management of regions of high latitudes; (2) integrated land observing systems; (3) a new system of specially protected territories; (4) new strategy and institutional background of forest fire protection; (5) legislative and normative base of adaptation and mitigation as a background of adaptive forest management; (6) system of adaptation of structure of boreal landscapes to climate change; and (7) considering management of major biogeochemical cycles, primarily carbon cycle, as a crucial issue of future strategies. We illustrate some practical results obtained by IIASA-ESM Integrated Modeling Cluster and other approaches. These examples show that recommended strategies could result in higher stability and productivity of forest ecosystems, increased abundance of favored species and reduced fragmentation of forests. However, the biggest problem deals with difficulties to reduce losses from worsening the environment and disturbances. No single strategy appears able to achieve all possible forest management objectives, and adaptation and mitigation strategies should be connected to regional climatic, ecological and social peculiarities.

Background Climate change is expected to increase fire activity across the circumboreal zone, including central Siberia. However, few studies have quantitatively assessed potential changes in fire regime characteristics, or considered possible spatial variation in the magnitude of change. Moreover, while simulations indicate that changes in climate are likely to drive major shifts in Siberian vegetation, knowledge of future forest dynamics under the joint influence of changes in climate and fire regimes remains largely theoretical. We used the forest landscape model, LANDIS-II, with PnET-Succession and the BFOLDS fire extension to simulate changes in vegetation and fire regime characteristics under four alternative climate scenarios in three 10,000-km 2 study landscapes distributed across a large latitudinal gradient in lowland central Siberia. We evaluated vegetation change using the fire life history strategies adopted by forest tree species: fire resisters, fire avoiders, and fire endurers. Results Annual burned area, the number of fires per year, fire size, and fire intensity all increased under climate change. The relative increase in fire activity was greatest in the northernmost study landscape, leading to a reduction in the difference in fire rotation period between study landscapes. Although the number of fires per year increased progressively with the magnitude of climate change, mean fire size peaked under mild or moderate climate warming in each of our study landscapes, suggesting that fuel limitations and past fire perimeters will feed back to reduce individual fire extent under extreme warming, relative to less extreme warming scenarios. In the Southern and Mid-taiga landscapes, we observed a major shift from fire resister-dominated forests to forests dominated by broadleaved deciduous fire endurers ( Betula and Populus genera) under moderate and extreme climate warming scenarios, likely associated with the substantial increase in fire activity. These changes were accompanied by a major decrease in average cohort age and total vegetation biomass across the simulation landscapes. Conclusions Our results imply that climate change will greatly increase fire activity and reduce spatial heterogeneity in fire regime characteristics across central Siberia. Potential ecological consequences include a widespread shift toward forests dominated by broadleaved deciduous species that employ a fire endurer strategy to persist in an increasingly fire-prone environment.

Information about land cover and land use is needed for a wide range of applications such as nature protection and biodiversity, forest and water management, urban and transport planning, natural hazard prevention and mitigation, monitoring of agricultural policies and economic land use modelling. A number of different remotely-sensed global land cover products are available but studies have shown that there are large spatial discrepancies between these different products when compared. To address this issue of land cover uncertainty, a tool called Geo-Wiki was developed, which integrates online and mobile applications, high resolution satellite imagery available from Google Earth, and data collection through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on land cover and land use. Through its growing network of volunteers and a number of successful data collection campaigns, almost 5 million samples of land cover and land use have been collected at many locations around the globe. This paper provides an overview of the main features of Geo-Wiki, and then using a series of examples, illustrates how the crowdsourced data collected through Geo-Wiki have been used to improve information on land cover and land use.

Spruce (Picea abies (L.) H. Karst.) is one of the major coniferous species represented in Moscow region. It grows on loam or clay loam soils and it is sensitive to soil moisture. The drought of 2010 adversely affected spruce forest. Weak spruce is further highly influenced by various pathogens and pests, among which the most aggressive is bark beetle (Ips Typographus L.). Many researchers have investigated the role of climatic fluctuations in respect of the development of pathogens; however, soil and hydrological factors are often out of the focus. The aim of our work was to study the soil factors affecting the state and resistance of spruce stands. The object of the study was spruce stands of the Moscow educational-experimental forest unit. Forest inventory data in combination with satellite imagery in a GIS environment allowed us to discover a plot with alive resistant spruce surrounded by dead spruce stands of the same age. During field surveys, we investigated soil in both alive and died spruce plots. The study showed that alive spruce forest has developed on soil with specific alternation of horizons of different texture (contains sand layer of varying thickness, underlain by loam horizons). The surrounding soils with dead spruce do not have such a sandy layer. The combination of layers with different texture support accumulation of soil moisture. This moisture, which is trapped by capillary forces in the small pore layer of the soil. Most probably, the ability of soil to store more water has allowed spruce to survive during the drought. We plan to continue our research and investigate more plots with alive spruce stands, to prove our hypothesis.