Publications

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

No abstract is provided for this article.

Independent Monitoring: Building trust and consensus around GHG data for increased accountability of mitigation in the land use sector

The Paris Agreement stresses the importance of the land use sector and many countries have included land use sector targets in their nationally determined contributions (NDCs). They will need to account for emissions and removals from the sector in a manner that promotes transparency, accuracy, completeness, comparability and consistency. Stakeholders involved have therefore called for independent monitoring” (IM) approaches, i.e. authoritative, unbiased sources of information that they could rely on for their specific needs. More and more datasets and portals that serve these needs have recently emerged (e.g. Global Forest Watch, OpenForis). A stakeholder survey was carried out to identify the level of satisfaction with existing datasets and portals. These were also assessed in a SWORG analysis. We identified common misunderstandings and challenges, such as technical data issues, difficulties regarding data use and interpretation and issues of access and capacities. There is also a lack of awareness and capacities to use IM approaches. In four case studies we illustrate possible pathways to overcome these challenges. We present key elements that are considered to be essential for effective use of IM approaches for land use sector MRV. Bringing the assessment of user needs, opportunities for existing approaches and identification of gaps together, we formulate concrete recommendations for specific stakeholder groups, including data providers and users.

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

No abstract is provided for this article.

The global forest age dataset (GFADv1.0)

The global forest age dataset (GFAD) describes the age distributions of plant functional types (PFT) on a 0.5-degree grid. Each grid cell contains information on the fraction of each PFT within an age class. The four PFTs, needleaf evergreen (NEEV), needleleaf deciduous (NEDE), broadleaf evergreen (BREV) and broadleaf deciduous (BRDC) are mapped from the MODIS Collection 5.1 land cover dataset, crosswalking land cover types to PFT fractions. The source of data for the age distributions is from country-level forest inventory for temperate and high-latitude countries, and from biomass for tropical countries. The inventory and biomass data are related to fifteen age classes defined in ten-year intervals, from 1-10 up to a class greater than 150 years old. The GFAD dataset represents the 2000-2010 era.

Validation data set

Crowd-driven tools for the calibration and validation of Earth Observation products

In recent years there has been a rapid diffusion in open access Earth Observation (EO) data available at global scales to help scientists address planetary challenges including climate change, food security and disaster management. For example, since 2016 the European Space Agency (ESA), via its Sentinel-2 satellites, has been providing frequent (5 day repeat cycle) and fine-grained (10 meter resolution) optical imagery for open and public use. As such, the EO community is faced with the need to design methods for transforming this abundance of EO data into well-validated environmental monitoring products. To help facilitate the training and validation of these products (i.e. land cover, land use), several crowd-driven tools that engage stakeholders (within and outside the scientific community) in various tasks, including satellite image interpretation, and online interactive mapping, have been developed. This paper will highlight the new results and potential of a series of such tools developed at the International Institute for Applied Systems Analysis (IIASA), namely the Geo-Wiki engagement platform, the LACO-Wiki validation tool, and Picture Pile, a mobile application for rapid image assessment and change detection. Through various thematic data collection campaigns, these tools have helped to collect citizen-observed information to improve global maps of cropland and agricultural field size, to validate various land cover products and to create post natural disaster damage assessment maps. Furthermore, Picture Pile is designed as a generic and flexible tool that is customizable to many different domains and research avenues that require interpreted satellite images as a data resource. Such tools, in combination with the recent emergence of Citizen Observatories (i.e. LandSense, GROW, GroundTruth 2.0, SCENT funded by Horizon2020), present clear opportunities to integrate citizen-driven observations with established authoritative data sources to further extend GEOSS and Copernicus capacities, and support comprehensive environmental monitoring systems. In addition, these applications have considerable potential in lowering expenditure costs on in-situ data collection and current calibration/validation approaches within the processing chain of environmental monitoring activities both within and beyond Europe.

Carbon budget of Russian forests

Net Ecosystem Carbon Balance (NECB) of Russian forest for 2007-2009 is presented based on consistent application of applied systems analysis and modern information technologies. Use of landscape-ecosystem approach resulted in the NECB at 546+/-120 Tg C year ^1, or 66+/-15 g C ^-2 year ^-1. There is a substantial difference between the NECB of European and Asian parts, as well as the clear zonal gradients within these geographical regions. While the total carbon sink is high, large forest areas, particularly on permafrost, serve as a carbon source. The ratio between net primary production and soil heterotrophic respiration, together with natural and human-induced disturbances are major drivers of the magnitude and spatial distribution of the NECB of forest ecosystems. Using the Bayesian approach, mutual constraints of results that are obtained by independent methods enable to decrease uncertainties of the final result.

An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods

. We determine the net land to atmosphere flux of carbon in Russia, including Ukraine, Belarus and Kazakhstan, using inventory-based, eddy covariance, and inversion methods. Our high boundary estimate is −342 Tg C yr−1 from the eddy covariance method, and this is close to the upper bounds of the inventory-based Land Ecosystem Assessment and inverse models estimates. A lower boundary estimate is provided at −1350 Tg C yr−1 from the inversion models. The average of the three methods is −613.5 Tg C yr−1. The methane emission is estimated separately at 41.4 Tg C yr−1. These three methods agree well within their respective error bounds. There is thus good consistency between bottom-up and top-down methods. The forests of Russia primarily cause the net atmosphere to land flux (−692 Tg C yr−1 from the LEA. It remains however remarkable that the three methods provide such close estimates (−615, −662, −554 Tg C yr–1) for net biome production (NBP), given the inherent uncertainties in all of the approaches. The lack of recent forest inventories, the few eddy covariance sites and associated uncertainty with upscaling and undersampling of concentrations for the inversions are among the prime causes of the uncertainty. The dynamic global vegetation models (DGVMs) suggest a much lower uptake at −91 Tg C yr−1, and we argue that this is caused by a high estimate of heterotrophic respiration compared to other methods.

A hybrid Land Cover Dataset for Russia: a new methodology for merging statistics, remote sensing and in-situ information

There is a critical need for accurate land cover information for resource assessment, biophysical modeling, greenhouse gas studies, and for estimating possible terrestrial responses and feedbacks to climate change. However, practically all existing land cover datasets have quite a high level of uncertainty and suffer from a lack of important details that does not allow for relevant parameterization, e.g., data derived from different forest inventories.

Russian forest sequesters substantially more carbon than previously reported

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 × 10 9 m 3 , 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 × 10 6 m 3 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 × 10 9 m 3 , 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.

Impact of Disturbances on the Carbon Cycle of Forest Ecosystems in Ukrainian Polissya

Climate change continues to threaten forests and their ecosystem services while substantially altering natural disturbance regimes. Land cover changes and consequent management entail discrepancies in carbon sequestration provided by forest ecosystems and its accounting. Currently there is a lack of sufficient and harmonized data for Ukraine that can be used for the robust and spatially explicit assessment of forest provisioning and regulation of ecosystem services. In the frame of this research, we established an experimental polygon (area 45 km2) in Northern Ukraine aiming at estimating main forest carbon stocks and fluxes and determining the impact caused by natural disturbances and harvest for the study period of 2010–2015. Coupled field inventory and remote sensing data (RapidEye image for 2010 and SPOT 6 image for 2015) were used. Land cover classification and estimation of biomass and carbon pools were carried out using Random Forest and k-Nearest Neighbors (k-NN) method, respectively. Remote sensing data indicates a ca. 16% increase of carbon stock, while ground-based computations have shown only a ca. 1% increase. Net carbon fluxes for the study period are relatively even: 5.4 Gg C·year−1 and 5.6 Gg C C·year−1 for field and remote sensing data, respectively. Stand-replacing wildfires, as well as insect outbreaks and wind damage followed by salvage logging, and timber harvest have caused 21% of carbon emissions among all C sources within the experimental polygon during the study period. Hence, remote sensing data and non-parametric methods coupled with field data can serve as reliable tools for the precise estimation of forest carbon cycles on a regional spatial scale. However, featured land cover changes lead to unexpected biases in consistent assessment of forest biophysical parameters, while current management practices neglect natural forest dynamics and amplify negative impact of disturbances on ecosystem services.

Supplementary material to "Improving the dynamics of northern vegetation in the ORCHIDEE ecosystem model"

No abstract is provided for this article.

Global hybrid forest mask for the year 2000

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.

Full verified carbon account of forest ecosystems as a fuzzy system: An attempt to assess uncertainty

Carbon cycling of terrestrial ecosystems is a fuzzy (underspecified) system that imposes substantial constrains on possibility to get unbiased estimates of basic intermediate components (e.g., Net Primary Production, Heterotrophic Respiration) and final results (e.g., Net Ecosystem Carbon Budget) of the account within strictly defined confidential intervals based on any individually used carbon cycling method or model. We present a methodology attempting at minimizing possible biases and restricting the multivariate uncertainty.s space. The methodology follows the principles of applied systems analysis and is based on integration of major independent methods of carbon cycling study (landscape-ecosystem approach, process-based models, eddy covariance and inverse modelling) with following harmonizing and mutual constraints of the results. Based on a case study for Russia's forests, we discuss strengths and limitations of the outlined methodology.

Global change and landscape structure in Ukraine: Ecological and socio-economic implications

Crowd-driven tools for the calibration and validation of Earth Observation products

In recent years there has been a rapid diffusion in open access Earth Observation (EO) data available at global scales to help scientists address planetary challenges including climate change, food security and disaster management. For example, since 2016 the European Space Agency (ESA), via its Sentinel-2 satellites, has been providing frequent (5 day repeat cycle) and fine-grained (10 meter resolution) optical imagery for open and public use. As such, the EO community is faced with the need to design methods for transforming this abundance of EO data into well-validated environmental monitoring products. To help facilitate the training and validation of these products (i.e. land cover, land use), several crowd-driven tools that engage stakeholders (within and outside the scientific community) in various tasks, including satellite image interpretation, and online interactive mapping, have been developed. This paper will highlight the new results and potential of a series of such tools developed at the International Institute for Applied Systems Analysis (IIASA), namely the Geo-Wiki engagement platform, the LACO-Wiki validation tool, and Picture Pile, a mobile application for rapid image assessment and change detection. Through various thematic data collection campaigns, these tools have helped to collect citizen-observed information to improve global maps of cropland and agricultural field size, to validate various land cover products and to create post natural disaster damage assessment maps. Furthermore, Picture Pile is designed as a generic and flexible tool that is customizable to many different domains and research avenues that require interpreted satellite images as a data resource. Such tools, in combination with the recent emergence of Citizen Observatories (i.e. LandSense, GROW, GroundTruth 2.0, SCENT funded by Horizon2020), present clear opportunities to integrate citizen-driven observations with established authoritative data sources to further extend GEOSS and Copernicus capacities, and support comprehensive environmental monitoring systems. In addition, these applications have considerable potential in lowering expenditure costs on in-situ data collection and current calibration/validation approaches within the processing chain of environmental monitoring activities both within and beyond Europe.

Metadata record for: The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

This dataset contains key characteristics about the data described in the Data Descriptor The Forest Observation System, building a global reference dataset for remote sensing of forest biomass. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br><br>Versioning Note:A revised version was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. 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 using visual interpretation.