The Arctic Plant Aboveground Biomass Synthesis Dataset

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m-2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa

Invasive plant species pose a direct threat to biodiversity and ecosystem services. Among these, Rosa rugosa has had a severe impact on Baltic coastal ecosystems in recent decades. Accurate mapping and monitoring tools are essential to quantify the location and spatial extent of invasive plant species to support eradication programs. In this paper we combined RGB images obtained using an Unoccupied Aerial Vehicle, with multispectral PlanetScope images to map the extent of R. rugosa at seven locations along the Estonian coastline. We used RGB-based vegetation indices and 3D canopy metrics in combination with a random forest algorithm to map R. rugosa thickets, obtaining high mapping accuracies (Sensitivity = 0.92, specificity = 0.96). We then used the R. rugosa presence/absence maps as a training dataset to predict the fractional cover based on multispectral vegetation indices derived from the PlanetScope constellation and an Extreme Gradient Boosting algorithm (XGBoost). The XGBoost algorithm yielded high fractional cover prediction accuracies (RMSE = 0.11, R² = 0.70). An in-depth accuracy assessment based on site-specific validations revealed notable differences in accuracy between study sites (highest R² = 0.74, lowest R² = 0.03). We attribute these differences to the various stages of R. rugosa invasion and the density of thickets. In conclusion, the combination of RGB UAV images and multispectral PlanetScope images is a cost-effective method to map R. rugosa in highly heterogeneous coastal ecosystems. We propose this approach as a valuable tool to extend the highly local geographical scope of UAV assessments into wider areas and regional evaluations.

Integrating ecosystem services into decision support for management of agroecosystems: Viva Grass tool

The area covered by low-input agroecosystems (e.g. semi-natural and permanent grasslands) in Europe has considerably decreased throughout the last century. To support more sustainable management practices and to promote biodiversity and ecosystem service values of such agroecosystems, a decision support tool was developed. The tool aims to enhance the implementation of ecosystem services and address the challenge of their integration into spatial planning.

The Viva Grass tool aims to enhance the maintenance of ecosystem services delivered by low-input agroecosystems. It does so by providing spatially-explicit decision support for land-use planning and sustainable management of agroecosystems. The Viva Grass tool is a multi-criteria decision analysis tool for integrated planning. It is designed for farmers, spatial planners and policy-makers to support decisions for management of agroecosystems. The tool has been tested to assess spatial planning in eight case studies across the Baltic States.

Mapping and assessing ecosystem services in the EU - Lessons learned from the ESMERALDA approach of integration

The European Union (EU) Horizon 2020 Coordination and Support Action ESMERALDA aimed at developing guidance and a flexible methodology for Mapping and Assessment of Ecosystems and their Services (MAES) to support the EU member states in the implementation of the EU Biodiversity Strategy’s Target 2 Action 5. ESMERALDA’s key tasks included network creation, stakeholder engagement, enhancing ecosystem services mapping and assessment methods across various spatial scales and value domains, work in case studies and support of EU member states in MAES implementation. Thus ESMERALDA aimed at integrating various project outcomes around four major strands: i) Networking, ii) Policy, iii) Research and iv) Application. The objective was to provide guidance for integrated ecosystem service mapping and assessment that can be used for sustainable decision-making in policy, business, society, practice and science at EU, national and regional levels. This article presents the overall ESMERALDA approach of integrating the above-mentioned project components and outcomes and provides an overview of how the enhanced methods were applied and how they can be used to support MAES implementation in the EU member states. Experiences with implementing such a large pan-European Coordination and Support Action in the context of EU policy are discussed and recommendations for future actions are given.

A multitiered approach for grassland ecosystem services mapping and assessment: The Viva Grass tool

Throughout the second half of the 20th Century, the area of semi-natural grasslands in the Baltic States decreased substantially, due to agricultural abandonment in some areas and intensification in more productive soil types. In order to halt the loss of biodiversity and ecosystem services provided by grasslands, the LIFE+ programme funded project, LIFE Viva Grass, aims at developing an integrated planning tool that will support ecosystem-based planning and sustainable grassland management. LIFE Viva Grass integrated planning tool is spatially explicit and allows the user to assess the provision and trade-offs of grassland ecosystem services within eight project case study areas in Estonia, Latvia and Lithuania.

In order to ensure methodological adaptability, the structure of the LIFE Viva Grass integrated planning tool follows the framework of the tiered approach. In a multi-tier system, each consecutive tier entails an increase in data requirements, methodological complexity or both. The present paper outlines the adaptation of the tiered approach for mapping and assessing ecosystem services provided by grasslands in the Baltic States. The first tier corresponds to a deliberative decision process: The matrix approach is used to assess the potential supply of grassland ecosystem services based on expert estimations. Expert values are subsequently transferred to grassland units and therefore made spatially explicit. The data collected in the first tier was further enhanced through a Principal Components Analysis (PCA) in order to explore ES bundles in tier 2. In the third tier, Multi-Criteria Decision Analysis is used to target specific policy questions.