Search

Abstract To achieve the goals of the 2030 Global Biodiversity Framework, the European Biodiversity Strategy, and the EU Green Deal, biodiversity monitoring is critical. Monitoring efforts in Europe, however, suffer from gaps and biases in taxonomy, spatial coverage, and temporal resolution, resulting in fragmented and disconnected data. To assess user and policy needs in biodiversity monitoring, we employed a four‐step user‐centered stakeholder engagement process with over 300 stakeholders including a public stakeholder workshop, online survey, interviews, and a meeting with experts from 18 EU member states, the European Commission, and the European Environment Agency. The stakeholders identified policy needs, current challenges, and potential solutions. Based on the policy and stakeholder assessment, we recommend establishing a European Biodiversity Observation Coordinating Centre to optimize existing observation efforts, harmonize data, and enhance our ability to predict and respond to key challenges related to biodiversity loss in Europe.

The European Commission developed an amendment to Regulation 691/2011 on European environmental economic accounts to include reporting on ecosystem accounts compliant to the United Nations Statistical Commission System of Environmental-Economic Accounts – Ecosystem Accounts (SEEA-EA) standard. To support Member States implementing this regulation, an open source tool, known as INCA-tool, to generate ecosystem service accounts has been developed, based on the Knowledge Innovation Project on Integrated Systems of Naural Capital and Ecosystem Services Accounting (KIP-INCA) methodologies. The INCA-tool was developed by taking into account the FAIR principle for software and data, as well as existing interoperability standards by the SEEA community. Three types of users were identified with their specific needs, interactions and skills. To meet their needs, the INCA-tool was split into two parts, a python package to perform the calculations and an acessible and easy-to-use user interface in QGIS to integrate national information. With a first version of the toolkit in place, improvements to the existing calculation methods and alignment with the upcoming EU regulation can be achieved. Further, feedback from Member States beta-tests and their experiences is currently collected and implemented and the full public roll-out is planned for the end of 2022. The software packages in the toolkit were already used to extend the existing nine INCA European wall-to-wall account series with the year 2018.

Measurement(s) forest management type Technology Type(s) Geo-Wiki toolbox

Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.

In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation.

The Copernicus Global Land Service (CGLS) provides global time series of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and fraction of vegetation cover (fCOVER) data at a resolution of 300 m and a frequency of 10 days. We performed a quality assessment and validation of Version 1 Collection 300 m products that were consistent with the guidelines of the Land Product Validation (LPV) subgroup of the Committee on Earth Observation System (CEOS) Working Group on Calibration and Validation (WGCV). The spatiotemporal patterns of Collection 300 m V1 LAI, fAPAR and fCOVER products are consistent with CGLS Collection 1 km V1, Collection 1 km V2 and Moderate Resolution Imagery Spectroradiometer Collection 6 (MODIS C6) products. The Collection 300 m V1 products have good precision and smooth temporal profiles, and the interannual variations are consistent with similar satellite products. The accuracy assessment using ground measurements mainly over crops shows an overall root mean square deviation of 1.01 (44.3%) for LAI, 0.12 (22.2%) for fAPAR and 0.21 (42.6%) for fCOVER, with positive mean biases of 0.36 (15.5%), 0.05 (10.3%) and 0.16 (32.2%), respectively. The products meet the CGLS user accuracy requirements in 69.1%, 62.5% and 29.7% of the cases for LAI, fAPAR and fCOVER, respectively. The CGLS will continue the production of Collection 300 m V1 LAI, fAPAR and fCOVER beyond the end of the PROBA-V mission by using Sentinel-3 OLCI as input data.

Water body detection worldwide using spaceborne remote sensing is a challenging task. A global scale multi-temporal and multi-spectral image analysis method for water body detection was developed. The PROBA-V microsatellite has been fully operational since December 2013 and delivers daily near-global synthesis with a spatial resolution of 1 km and 333 m. The Red, Near-InfRared (NIR) and Short Wave InfRared (SWIR) bands of the atmospherically corrected 10-day synthesis images are first Hue, Saturation and Value (HSV) color transformed and subsequently used in a decision tree classification for water body detection. To minimize commission errors four additional data layers are used: the Normalized Difference Vegetation Index (NDVI), Water Body Potential Mask (WBPM), Permanent Glacier Mask (PGM) and Volcanic Soil Mask (VSM). Threshold values on the hue and value bands, expressed by a parabolic function, are used to detect the water bodies. Beside the water bodies layer, a quality layer, based on the water bodies occurrences, is available in the output product. The performance of the Water Bodies Detection Algorithm (WBDA) was assessed using Landsat 8 scenes over 15 regions selected worldwide. A mean Commission Error (CE) of 1.5% was obtained while a mean Omission Error (OE) of 15.4% was obtained for minimum Water Surface Ratio (WSR) = 0.5 and drops to 9.8% for minimum WSR = 0.6. Here, WSR is defined as the fraction of the PROBA-V pixel covered by water as derived from high spatial resolution images, e.g., Landsat 8. Both the CE = 1.5% and OE = 9.8% (WSR = 0.6) fall within the user requirements of 15%. The WBDA is fully operational in the Copernicus Global Land Service and products are freely available.