Publications

The empirical relationship between satellite-derived tropospheric NO2 and fire radiative power and possible implications for fire emission rates of NOx

The effects of climate change and abatement policies on the value of natural resources in Northern Europe and in the Arctic Sea area

The impact of the climate on the Arctic plays a crucial role for Finland's, as well as other Nordic countries', current and future climatic conditions. Far-reaching and multi-faceted changes are taking place in the Arctic, which have profound consequences for the region's economic and political significance in international relations. The review analyses the effects of climate change and likely climate abatement policies on the accessibility and value of natural resources in Northern Europe in the Arctic Sea area and on the logistical position of Northern Europe with a special emphasis on Finland.

Map of growing stock volume of Russian forests for the year 2014

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^-1.<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^-1. 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

Improving high-resolution spatial information on agricultural land use management in Europe for economic land use modelling and the assessment of policy impacts

There is currently a lack of high-resolution pan-European information on land use management, especially in terms of how intensively and extensively cropland and grassland are managed. This is partly due to the lack of ground-based information, which is needed to downscale these types of management practices (some of which are captured in different types of agricultural censuses and surveys) as well as the inability of remote sensing to capture different kinds of land use. This type of information is needed for economic land use modelling and for assessing policy impacts, such as the latest reforms from the Common Agricultural Policy (CAP) and other European Union (EU) Green Deal targets. These types of analyses are undertaken using economic land use models such as GLOBIOM and CAPRI, which is one of the main aims of the Horizon Europe funded LAMASUS project (https://www.lamasus.eu/). &amp;#160; This presentation will provide an overview of the ongoing developments in creating high-resolution spatially explicit layers on agricultural and grassland management for Europe to support the LAMASUS project. The proposed cropland and grassland management classes will be outlined along with the methodology for how they have been implemented using existing data layers from remote sensing, statistical data from Eurostat, the Joint Research Centre of the EU, agricultural ministries, and other sources. One of the key challenges is ensuring that the high-resolution data matches official statistics at the national (and NUTS2 level where available) so that they can be used by the economic land use models in LAMASUS. A method will be presented for how this is achieved using priors in the form of integrated layers of cropland and grassland probability created from existing high-resolution remotely sensed input layers. &amp;#160;

Assessment of carbon budget of terrestrial ecosystems of Russia and comparison of bottom-up and top-down estimates

Global patterns of tree wood density

Wood density is an emergent property resultant of tree growth strategies modulated by local edapho-climatic and stand development conditions. It is associated with the biomechanical support of trees and hydraulic conductivity or safety, directly and indirectly influencing a range of ecological processes, including, among others, tree growth, tree resistance to disturbances, and mortality. Tree wood density is also crucial for assessing vegetation carbon stocks by supporting the link between a volumetric retrieval and a mass estimate. Earlier studies based on tree-level wood density measurements have reported significant relationships between wood density, environmental conditions, and tree growth strategies. However, these were either regionally focused or suffering from data availability, lacking a representative large-scale and spatially explicit representation of factors influencing tree wood density. This study collects and collates information from several sources to construct a global database of 28,822 tree-level wood density measurements alongside with a wide set of climate, soils, topography, and Earth observation covariates to support the development of statistical models for wood density. The dataset, consisting of more than 3,000 global covariates, is used for training different machine learning models, including random forest model (RF), light gradient boosting model (LGBM), extreme gradient boosting model (XGBoost), and bagged trees models. The experimental design considers six cross-validation approaches: either random 5-fold; according to two sets of climate classifications, land cover types, ecozones, or latitudinal ranges. Model performances are assessed with the coefficient of determination (R2) and Root-mean-square errors (RMSE) when predicting an independent test subset of wood density. The top ten models show a prominent performance (R2 &gt; 0.67 and RMSE &lt; 0.09), and their ensemble mean, and standard deviation are considered the best estimation and uncertainty in wood density predictions, respectively. Systematic underestimation biases are observed around the low northern latitudes (0&amp;#186;-20&amp;#186;N), primarily due to the lack of wood density measurements. Further analysis of sources of uncertainties and their quantification support the generation of a global quantitative and spatially explicit representation of wood density. The ecological interpretation and quantitative assessment of global wood density, and associated uncertainties aim to contribute to improving predictions of vegetation biomass and inferring ecosystem resistance under current and future climate scenarios.

Integrating Remote Sensing and Geospatial Big Data for Land Cover and Land Use Mapping and Monitoring

The last few decades have seen an explosion in the availability of remotely sensed and geospatial big data, which are defined by the 3 Vs: a large volume of data; a variety of different forms of data; and the rapid velocity of data arrival [...]

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground forest biomass (dry mass, AGB) with a spatial resolution of 1 ha. Using an extensive database of 110,897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high carbon stock forests with AGB &gt; 250 Mg ha−1 where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country’s national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps, and identify major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the sub-tropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon and socio-economic modelling schemes, and provides a crucial baseline in future carbon stock changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018).

Supplementary material to &amp;quot;The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations&amp;quot;

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations A.1 Auxiliary datasetsThe European Space Agency (ESA) Climate Change Initiative Land Cover (CCI-LC) dataset consists of annual (1992-2018) maps classifying the world's land cover into 22 classes (Table S6).The overall accuracy of the 2010 land cover dataset was 76% (Defourny et al., 2014), with the most relevant commission and omission errors in mixed classes or in regions of strongly heterogeneous land cover.The land cover maps were provided in equiangular projection with a pixel size of 0.00278888° in both latitude and longitude.In this study, we used the land cover map of 2010, version 2.07.The dataset was reprojected to the map geometry of our AGB dataset.The Global Ecological Zones (GEZ) dataset produced by the Food and Agriculture Organization (FAO, 2001) divides the land surface into 20 zones (Figure S1, Table S2) with "broad yet relatively homogeneous natural vegetation formations, similar (but not necessarily identical) in physiognomy" (FAO, 2001).In this study, the dataset has been rasterized to the geometry of the images requiring stratification by ecological zones.Spatially explicit datasets of GSV representative of dense forests were obtained to support the model calibration described in Sections A.2 and A.3.This dataset was first compiled by assigning a value to the centre of each tile in a regular 2°×2° grid.Where available, in situ measurements from field plots or spatially explicit datasets of GSV were used.The GSV of dense forests was then defined as the 90 th percentile of the histogram within the 2°×2° area (Santoro et al., 2011).Elsewhere, it was estimated with an empirical piece-wise linear function (Santoro et al., 2015a) starting from values of the average biomass reported at provincial or national level.For tiles including several provinces or nations, the average biomass representative for the tile was obtained by weighting the individual averages by the area of each within the tile.In regions where values based on inventory measurements were unavailable but we could gather more than one map of AGB (preferably based on laser scanning observations), we

Fifty years dynamics of Russian forests: Impacts on the earth system

No abstract is provided for this article.

Hybrid cover in 2009 for Russia: A methodology for integrating statistics, remote sensing and in-situ information

Creation of forest map for Ukraine using global land cover maps

The method for building forest digital map for Ukraine is presented based on the information of forest land in the global digital maps. The latest global maps covering the territory of Ukraine: Glob Cover 2009, MODIS v.5 and GLC-2000 were analyzed. Based on the developed method the digital map of Ukrainian forests was created with the information on forest land with high probability (all global products agreed) and land with low and medium probability of growing forest. The achieved results were compared with the national data of forest land account. The forest land was better defined in the Forest natural area, the Carpathians, and the Crimea; small forest was really badly identified in the steppe.

Productivity of Northern Eurasian forests: Analysis of uncertainties

Indicators of biological productivity of forests (live and dead biomass, net primary production, net and grossgrowth) are crucial for both assessment of the impacts of terrestrial ecosystems on major biogeochemical cyclesand practice of sustainable forest management. However, different information and the diversity of methods usedin the assessments of forests productivity cause substantial variation in reported estimates. The paper containsa systems analysis of the existing methods, their uncertainties, and a description of available information. Withrespect to Northern Eurasian forests, the major reasons for uncertainties could be categorized as following: (1)significant biases that are inherent in a number of important sources of available information (e.g., forest inventorydata, results of measurements of some indicators in situ); (2) inadequacy and oversimplification of models ofdifferent types (empirical aggregations, process-based models); (3) lack of data for some regions; and (4) upscalingprocedure of “point” observations.Based on as comprehensive as possible adherence to the principles of systems analysis, we made an attemptto provide a reanalysis of indicators of forests productivity of Russia aiming at obtaining the results for whichuncertainties could be estimated in a reliable and transparent way. Within a landscape-ecosystem approach it hasrequired (1) development of an expert system for refinement of initial data including elimination of recognizedbiases; (2) delineation of ecological regions based on gradients of major indicators of productivity; (3) transitionto multidimensional models (e.g., for calculation of spatially distributed biomass expansion factors); (4) use ofprocess-based elements in empirical models; and (5) development of some approaches which presumably do nothave recognized biases. However, taking into account the fuzzy character of the problem, the above approach (aswell as any other individually used method) is not able to recognize structural uncertainties. In order to assess those,a special statistical procedure for harmonizing the multiple constraints of the estimates obtained by independentmethods (landscape-ecosystem approach; flux measurements; process-based vegetation models; inverse modeling)was used to estimate uncertainty of the final results. Application of the above methodology resulted in a reliableassessment of major indicators of productivity. For instance, live biomass at the country’s level is estimated withuncertainty of 4-6%, net primary production – 7-10% (confidential interval 0.9). It was recognized the tendencyof increasing productivity of Russian forests during the last four decades at level of 0.5 0.2% year

Integrated Land Information System - a relevant step for development of information background for PEEX?

No abstract is provided for this article.

Verified terrestrial ecosystems full carbon account for Russia: A reanalysis (Abstract)

Siberian forest carbon sink: Estimates by the CO2 inverse modeling and forest inventory methods

No abstract is provided for this article.

Forest mask of Russia based on fusion remote sensing, statistics and crowdsourcing

No abstract is provided for this article.

Increasing crop production in Russia and Ukraine—regional and global impacts from intensification and recultivation

Russia and Ukraine are countries with relatively large untapped agricultural potentials, both in terms of abandoned agricultural land and substantial yield gaps. Here we present a comprehensive assessment of Russian and Ukrainian crop production potentials and we analyze possible impacts of their future utilization, on a regional as well as global scale. To this end, the total amount of available abandoned land and potential yields in Russia and Ukraine are estimated and explicitly implemented in an economic agricultural sector model. We find that cereal (barley, corn, and wheat) production in Russia and Ukraine could increase by up to 64% in 2030 to 267 million tons, compared to a baseline scenario. Oilseeds (rapeseed, soybean, and sunflower) production could increase by 84% to 50 million tons, respectively. In comparison to the baseline, common net exports of Ukraine and Russia could increase by up to 86.3 million tons of cereals and 18.9 million tons of oilseeds in 2030, representing 4% and 3.6% of the global production of these crops, respectively. Furthermore, we find that production potentials due to intensification are ten times larger than potentials due to recultivation of abandoned land. Consequently, we also find stronger impacts from intensification at the global scale. A utilization of crop production potentials in Russia and Ukraine could globally save up to 21 million hectares of cropland and reduce average global crop prices by more than 3%.