Green Infrastructures and Essential Variables Workflows towards SDG 15

The Sustainable Development Goals (SDGs) established to be achieved by 2030 are an ensemble of 17 goals to address global environmental and social economic concerns [1]. SDG 15 concerns the protection of terrestrial ecosystems to halt biodiversity loss. Target 15.9 states that by 2020, ecosystem and biodiversity values should be integrated into national and local planning, and is related to Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020, which also involves integrating biodiversity values into national accounting and reporting systems [2]. The importance of maintaining ecosystem integrity is becoming widely recognized, not only to halt biodiversity loss, but also to preserve Nature’s benefits to human well-being, and has been included in many other targets such as the EU 2020 Biodiversity Strategy’s target 2, which requires the restoration of at least 15% of degraded ecosystems as well as the establishment of green infrastructures to enhance ecosystem services (ES) [3]. The Green Infrastructures (GI) framework is used as a policy tool and promotes the multi-functional use of landscapes to improve biodiversity conservation and benefits to society. It is formulated as a “strategically planned network of natural and semi-natural areas” [4] and is based on three main pillars: key habitats for target species, connectivity and ES [5]. As part of ERA-PLANET’s GEOEssential project (Essential Variables workflows for resource efficiency and environmental management), our study aims at demonstrating how the GI framework can be implemented at any geographical area or time-period through reproducible modeling workflows from field data to Essential Variables (EV) data products and policy relevant indicators to monitor and inform advances towards environmental targets. A proof of concept workflow was already set in place for computing the indicator 15.1.2: Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem, while other workflows will follow. The execution platform is the GEOEssential Virtual Laboratory, a cloud-based virtual platform which enables access to, and execution of workflows for the ecosystem science community of practice and even more. REFERENCES: 1. UNSD, 2016. Sustainable Development Goals Report. https://unstats.un.org/sdgs/report/2016/ (accessed 18 May 2018). 2. CBD Secretariat, 2010. The Strategic Plan for Biodiversity 2011-2020, and the Aichi Biodiversity Targets. Secretariat of the Convention on Biological Diversity, Nagoya. 3. European Commission, 2011. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions - Our life insurance, our natural capital: an EU biodiversity strategy to 2020, Brussels. 4. European Commission, 2013. Green infrastructure (GI) - Enhancing Europe’s Natural Capital, Brussels. 5. Liquete, C., Kleeschulte, S., Dige, G., Maes, J., Grizzetti, B., Olah, B., & Zulian, G., 2015. Mapping green infrastructure based on ecosystem services and ecological networks: A Pan-European case study. Environmental Science & Policy, 54, 268–280

Implementing Green Infrastructure for the Spatial Planning of Peri-Urban Areas in Geneva, Switzerland

The concept of green infrastructure (GI) seeks to identify and prioritize areas of high ecological value for wildlife and people, to improve the integration of natural values in landscape planning decisions. In 2018, the canton of Geneva, Switzerland, established a roadmap for biodiversity conservation, which includes the operationalization of GI covering 30% of the territory by 2030. In this paper, we demonstrate a GI mapping framework in the canton of Geneva. Our approach is based on the combined assessment of three 'pillars', namely species' distribution, landscape structure and connectivity, and ecosystem services, to optimize the allocation of conservation actions using the spatial prioritization software, Zonation. The identified priority conservation areas closely overlap existing natural reserves. Including the three pillars in the landscape prioritization should also improve adhesion to the GI idea, without undermining the protection of threatened species. With regards to land use planning, public and private land parcels with high values for GI may require specific incentives to maintain their desirable characteristics, as they are more likely to be degraded than areas with more building restrictions. Visualizing priority conservation areas in a spatially explicit manner will support decision-makers in Geneva to optimally allocate limited resources for ecosystem preservation.Peer reviewe

Implementing Green Infrastructure for the Spatial Planning of Peri-Urban Areas in Geneva, Switzerland

The concept of green infrastructure (GI) seeks to identify and prioritize areas of high ecological value for wildlife and people, to improve the integration of natural values in landscape planning decisions. In 2018, the canton of Geneva, Switzerland, established a roadmap for biodiversity conservation, which includes the operationalization of GI covering 30% of the territory by 2030. In this paper, we demonstrate a GI mapping framework in the canton of Geneva. Our approach is based on the combined assessment of three ‘pillars’, namely species’ distribution, landscape structure and connectivity, and ecosystem services, to optimize the allocation of conservation actions using the spatial prioritization software, Zonation. The identified priority conservation areas closely overlap existing natural reserves. Including the three pillars in the landscape prioritization should also improve adhesion to the GI idea, without undermining the protection of threatened species. With regards to land use planning, public and private land parcels with high values for GI may require specific incentives to maintain their desirable characteristics, as they are more likely to be degraded than areas with more building restrictions. Visualizing priority conservation areas in a spatially explicit manner will support decision-makers in Geneva to optimally allocate limited resources for ecosystem preservation

Implementing Green Infrastructure: integrating biodiversity, connectivity, and ecosystem services into landscape planning decisions in the Geneva region

Nature forms interdependent networks in a landscape, which is key to the survival of species and maintenance of genetic diversity. Nature also provides crucial socio-economic benefits to people, but are typically undervalued in political decisions. This has led to the concept of Green Infrastructure (GI), which defines an interconnected network of (semi-)natural areas designed and managed to preserve a wide range of ecological, social, and economic benefits. GI is increasingly being recognized as a policy instrument to better integrate nature's values into landscape planning decisions, but there is no consensus in the scientific literature on how to map and implement GI, and its operationalization in spatial planning has never been done in Switzerland. Consequently, this thesis aims to examine how the concept of GI can be effectively implemented to support the integration of natural capital values into landscape planning decisions, with a focus on the canton of Geneva, Switzerland

Towards Sentinel-2 Analysis Ready Data: a Swiss Data Cube Perspective

Earth Observations Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with Earth Observations (EO) data. They can provide access to large spatio-temporal data in analysis ready format. Systematic and regular provision of Analysis Ready Data (ARD) can significantly reduce the burden on EO data users by minimizing the time and scientific knowledge required to access and prepare remotely-sensed data having consistent and spatially aligned calibrated surface reflectance observations. Currently, Sentinel-2 ARD are not commonly generated by the Copernicus program and consequently getting uniform and consistent Sentinel-2 ARD remains a challenging task. This paper presents an approach to generate Sentinel-2 ARD using an automated processing services chain. The approach has been tested and validated to complete the Swiss Data Cube with Sentinel-2 data

Methods for identifying green infrastructure

Nature forms interdependent networks in a landscape, which is key to the survival of species and the maintenance of genetic diversity. Nature provides crucial socio-economic benefits to people, but they are typically undervalued in politi- cal decisions. This has led to the concept of Green Infrastructure (GI), which defines an interlinked network of (semi-) natural areas with high ecological values for wildlife and people, to be conserved and managed in priority to preserve biodiversity and ecosystem services. This relatively new concept has been used in different contexts, but with widely diverging interpretations. There is no apparent consensus in the scientific literature on the methodology to map and implement GI. This paper serves as an informed primer for researchers that are new to GI mapping understand the key principles and terminology for the needs of their own case-study, and as a framework for more advance researchers will- ing to contribute to the formalization of the concept. Through a literature review of articles on creating GI networks, we summarized and evaluated commonly used methods to identify and map GI. We provided key insights for the assessment of diversity, ecosystem services and landscape connectivity, the three ‘pillars’ on which GI identification is based accord- ing to its definition. Based on this literature review, we propose 5 theoretical levels toward a more complex, reliable and integrative approach to identify GI networks. We then discuss the applications and limits of such method and point out future challenges for GI identification and implementation

From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations across Switzerland

Forests represent important habitats for species and provide multiple ecosystem services for human well-being. Preserving forests and other terrestrial ecosystems has become crucial to halt desertification, land degradation, and biodiversity loss worldwide, and is also one of the Sustainable Development Goals (SDGs) to be achieved by 2030. Remote sensing could greatly contribute to measuring progress toward SDGs by providing consistent and repetitive coverage of large areas, as well as information in various wavelengths, which facilitates the monitoring of environmental trends at various scales. This paper focuses on SDG indicator 15.1.1—“Forest area as a percentage of total land area„ to demonstrate the potential of Earth Observation Data Cubes for SDGs. The approach presented here uses Landsat Analysis Ready Data (ARD) stored in the Swiss Data Cube, and offers a complementary method to ground-based approaches to monitor Switzerland’s forest extent based on the Normalized Difference Vegetation Index (NDVI). The proposed method performs time-series analyses to extract a forest/non-forest map and a graph representing the trend of SDG 15.1.1 indicator over time. Preliminary results suggest that this approach can identify similar forest extent and growth patterns to observed trends, and can therefore help monitor progress toward the selected SDG indicator more effectively