Publications

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.

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.

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.

Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25 m from ALOS-PALSAR and 1 km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands.

We are undertaking analysis and case studies to develop proof of concept for publicly available, comprehensive, global, and independent spatial information systems on land cover and land use, their dynamics and the associated carbon stocks and flows (emissions). Current global monitoring systems provide an opportunity for comparative analysis. But there is also an increasing demand for more efficient and effective systems addressing different user needs and allowing for performance assessment of climate and development policies and their outcomes. Such assessments are increasingly important in the post-Paris world and with regard to the SDGs and other coordinated development efforts. For performance assessments, the baselines and underlying assumptions must be clear and transparent. While some authors ask for uniformed, standardized approaches, we contend that the increasing portfolio of aspects that need to be measured and assessed will have the consequence that different users will have different needs with regard to data type, time and scale resolution, and national circumstances. Getting performance assessment right is important for many aspects of global governance that will be discussed at this conference: Policy transformation will ultimately depend on possibilities to assess the outcomes; justice, fairness and equity will depend on clear and transparent data and rules of the game across multiple levels of government and policy action. A better analysis of discourse can be achieved in the light of unambiguous data. Here we will provide an update on the efforts, discuss them in view of current developments and the Paris climate agreements, and identify what needs to be done over the next 5 years (post-2015) and beyond (post-2020).

No abstract is provided for this article.

There is 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 but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping 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 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) 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 no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others. The dataset (global field sizes.zip) contains:<br> - map of dominant field sizes (dominant_field_size_categories.tif) and description of legend items (legend_items.txt)<br> - table with all submissions by the participant (those who completed more than 10 classifications) and table description<br> - table with quality score of all the participants and table description<br> - table with estimated dominant field sizes at each location and table description

. Gross primary production (GPP) is the process by which carbon enters ecosystems. Models based on the theory of light use efficiency (LUE) have emerged as an efficient method to estimate ecosystem GPP. However, problems have been noted when applying global parameterizations to biome-level applications. In particular, model–data comparisons of GPP have shown that models (including LUE models) have difficulty matching estimated GPP. This is significant as errors in simulated GPP may propagate through models (e.g. Earth system models). Clearly, unique biome-level characteristics must be accounted for if model accuracy is to be improved. We hypothesize that in boreal regions (which are strongly temperature controlled), accounting for temperature acclimation and non-linear light response of daily GPP will improve model performance. To test this hypothesis, we have chosen four diagnostic models for comparison, namely an LUE model (linear in its light response) both with and without temperature acclimation and an LUE model and a big leaf model both with temperature acclimation and non-linear in their light response. All models include environmental modifiers for temperature and vapour pressure deficit (VPD). Initially, all models were calibrated against five eddy covariance (EC) sites within Russia for the years 2002–2005, for a total of 17 site years. Model evaluation was performed via 10-out cross-validation. Cross-validation clearly demonstrates the improvement in model performance that temperature acclimation makes in modelling GPP at strongly temperature-controlled sites in Russia. These results would indicate that inclusion of temperature acclimation in models on sites experiencing cold temperatures is imperative. Additionally, the inclusion of a non-linear light response function is shown to further improve performance, particularly in less temperature-controlled sites.

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.