Mapping the Potential of Natural Pest Control Services in Pan-European Landscapes (2018 Update)

This collection contains high-resolution geospatial data, methodologies, and scripts developed for mapping the Natural Pest Control Index (NPCi) across European agricultural landscapes. The data integrates Copernicus High-Resolution Layers, including Tree Cover Density, Grasslands, and Woody Vegetation Mask for the year 2018, combined with field survey results and morphological spatial pattern analysis.JRC.D.5 - Food Securit

State of play and future steps for the EU Biodiversity Strategy (EU BDS) dashboard

→ The EU BDS dashboard is publicly accessible through the website of the European Commission’s Knowledge Centre for Biodiversity (KCBD) under “Tools”. → The EU BDS dashboard currently contains 10 indicators to monitor progress on 5 out of the 16 targets. → The three additional indicators selected following last EUBP meeting are planned to be published by the end of 2023, resulting in a dashboard with 13 indicators to monitor progress on 8 out of the 16 targets. → Next steps to fill the gaps are presented in this note, including the proposal of two indicators to be added in 2024, the presentation of another one for initial feedback and an update on placeholders. → EUBP is invited to give its feedback on the addition of the two proposed indicators in the EU BDS dashboard and the datasets and disaggregations which should be used for the presented representativeness indicator. → This note also includes an update on the indicator to track progress on the EU BDS commitment to unlock at least € 20 billion/year for biodiversity.JRC.D.6 - Nature Conservation and Observation

Artificial Intelligence at the JRC

This document presents the contributions presented at the first internal workshop on Artificial Intelligence (AI), organized by the Joint Research Centre (JRC) of the European Commission. This workshop was held on 23rd May at the premises of the JRC in Ispra (Italy), with video-conference to all JRC's sites. The workshop aimed to gather JRC specialists on AI to share their experience, to identify opportunities for meeting the EC demands on AI, and explore synergies among different JRC's working groups on AI. The full-day session workshop was organized around three main topical strands entitled Policy support, New Initiatives and Technology Development. Contributions covered a wide range of areas, including applications of AI to Cybersecurity, Transport, Environment, Health and other specific issues. This report is structured according to those main topics of study. According to the JRC Director General Vladimír Šucha: "The workshop was very stimulating and interesting presenting a broad spectrum of activities and competencies across JRC. It gave a great opportunity to build a strong and hopefully useful position in the field of AI/ML". While the first part of the workshop was mainly informative, in the second part we collectively discussed about JRC priorities and the set-up of a Community of Practice (now available at https://webgate.ec.europa.eu/connected/groups/community-of-practice-ai-and-big-data) dealing with AI and Big Data. Finally, the preliminary results of the online survey were presented. All colleagues were excellent in communicating their scientific activity in a flash and efficient way.JRC.B.6 - Digital Econom

Using a Vegetation Index as a Proxy for Reliability in Surface Reflectance Time Series Reconstruction (RTSR)

Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions. Reconstruction methods have been proposed, most of which focus on time series of a single vegetation index. Under the assumption that relatively high vegetation index values can be considered as trustworthy, a successful approach is to adjust the smoothed value to the upper envelope of the time series. However, this assumption does not hold for surface reflectance in general. Clouds and cloud shadows result in, respectively, high and low values in the visible and near infrared part of the electromagnetic spectrum. A novel spectral Reflectance Time Series Reconstruction (RTSR) method is proposed. Smoothed values of surface reflectance values are adjusted to approach the trustworthy observations, using a vegetation index as a proxy for reliability. The Savitzky–Golay filter was used as the smoothing algorithm here, but different filters can be used as well. The RTSR was evaluated on 100 sites in Europe, with a focus on agriculture fields. Its potential was shown using different criteria, including smoothness and the ability to retain trustworthy observations in the original time series with RMSE values in the order of 0.01 to 0.03 in terms of surface reflectance.JRC.T.4 - Data Governance and Service

Monitoring African Surface Water Dynamic Using Medium Resolution Daily Data Allows Anomalies Detection in Nearly Real Time

This paper proposes to use a water detection methodology based on a colorimetric approach to develop a near real time system allowing to monitor and to detect anomalies at a fine time resolution and in a systematic way The algorithm was calibrated over Africa using daily reflectance MODIS data from 2003 to 2011. The proposed approach has 3 major outputs updatable in near real time: (1) a permanent water mask (2) a every 10-days surface water map consolidated with time series and (3) an anomalies detection using 10 years of detection reanalysis. Three validation approaches are developed to deal with the large coverage and the high temporal resolution. The methodology is generic and could be applied to other extent and sensors.JRC.H.3-Global environment monitorin

Exploring the capacity to grasp multi-annual seasonal variability of winter wheat in continental climates with MODIS

This paper presents some exploratory results of the FP-7 MOCCCASIN project that aims to MOnitor Crops in Continental Climates through ASsimilation of Satellite Information. MOCCCASIN is a collaborative project which focuses on improving the monitoring of winter-wheat and forecasting of winter-wheat yield in Russia by combining modelling techniques with satellite data assimilation. In continental climate, winter wheat is particularly affected by low temperatures during the winter which determine whether rapid regrowth is possible in spring. A pre-requisite to use satellite earth observation to characterize the effect of winter kill on wheat is to determine if the multi-annual seasonal variability over the entire growing season can be grasped by remote sensing indicators. The results over an exploratory study site in Tula region for 5 years (2005- 2009) demonstrate that it was possible to retrieve crop status indicators using an approach combining radiative transfer modeling and neural networks which could inform on where winter kill has stricken.JRC.H.4-Monitoring Agricultural Resource