Outline. The European Commission s science and knowledge service. Introduction Rationale

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1 The European Commission s science and knowledge service Joint Research Centre D5 Tools for data and information exchange Wim Devos, Roman Franielczyk, Paolo Isoardi, Giovanni Di Matteo, Dominique Fasbender Lisbon, 25/11/2016 Outline Introduction Rationale State of data operations What the logs show Did you know? 2016 new functionality A source of information What this tells us LPIS management CwRS operations A basis for knowledge Guidance requests Conclusion and outlook 1

2 Rationale of these MS/EC tools Reg 2013/1306 Article 4: The EAGF shall be implemented in shared management between the Member States and the Union Article 21: The Commission shall (agree and) supply (and recover) those satellite images free of charge to the control bodies or to suppliers of services Article 48.1: Member States shall make available to the Commission all information necessary for the smooth operation of the Funds and shall take all appropriate measures to facilitate the checks which the Commission deems appropriate Reg 2014/640 Article 6.2: Member States shall perform the assessment referred to in the paragraph 1 on the basis of a sample of reference parcels to be selected and provided by the Commission Implementation 2

3 data Operations Notification mails G 4 CAP LPIS QA WikiCAP (2015) (2016) 730 (2015) >1147 (2016) Ca 20? Downloads 135/month 44(100+)/yr 717/month Q1 Active users (103) ? Yearly volume 71,5Gb 62Gb in files only n/a Requests (QL) n/a (articles) Updates / 545 xml * 680 Bidirectional data exchange Unidirectional content exchange Image delivery/return by image contractor *: testing runtime: 50 hrs, 19 min, 04 sec 3

4 Four seasons in a year monthly logs soften daily peaks before the deadlines! G 4 CAP Myths and Legends on technical guidance 4

5 G 4 CAP and image campaign Post-IRs and Campaign Results processing automatized, integrated with the other CwRS context information cross-check to minimize the risk of errors. S2Alert system seamless integration of ESA APIs inform on S2 imagery for all CwRS zones Lower threshold complement HR imagery provision. LPIS QA sampling Population statistics Asynchronous comparison with previous years Based on screening From 2010 on Help detection of scoping issues 5

6 LPIS QA MTS (model test suite) 1. TG IXIT (implementation extra information for testing) structured approach to lineage to control inspection workflow based on Reg concepts 2. data model test Correct mapping of your DB to ETS documentation for scoping the RP population performing ETS observations/tests compiling data exchange packages 3. metadata Small selection of INSPIRE meta datarecord, covering the LPIS and sources (ortho/dem) Purpose: MTS package delivery Deadlines: MTS package is synchronous with ETS reporting package Data exchange: For 2016 Jan 2017) 1 st complete delivery: 2 options Either 3 xsd s package upload (benefit: immediate validation by portal) Or 1 xls followed by manual entry in LPIS QA portal (disadvantage: 2 edits) After an upgrade of LPIS: package re-upload After update of dynamic data values for GSAA uptake temporalextent of SUT image specification/lineage/contractor third party metadata Update of affected values only via LPIS QA portal application 6

7 The key to success of tools cumulating 29 PY of domain experience knowledge understanding empathy anticipation in pace with IT evolution Similar MS teams: community Risk: 5 years on a job is long EC HR/contract information 7

8 AL Info on spatial distribution of agricultural area 2016 Texture triangle PG PC Note: DK identified a delivery issue by itself Info on LPIS update activities Average yearly activity over 6 years Patterns of activity? Appropriate intensity? Stability? Noise in the data? 8

9 Info on impact of implementation choice agricultural area >< RP relationship About half of the available MS only have homogeneous RPs 9 MS have at least 10% of mixed RPs (up to 80% for 1 MS) knowledge 9

10 Benchmarking of RP type PB systems have the largest shares of mixed RPs and missing land cover class as well! Complexity? Unexpected high percentage for one AP system Improving representativeness of LPIS samples Population statistics: Min, max, mean, var, range, intervals of 95%, Sample statistics: Min, max, mean, var, range, intervals of 95%, With 1, 2, 3, images until acceptable match Applied at parcel level 10

11 Investigating efficiency of OTSC zones Image Request by MS Control zones Planned controls (number, ha) Costs of imagery Verification by GTCAP/JRC Density of eligible land Density of RPs from 1/5000 databases ha of imagery ha of eligible land covered Do we capture everything? NO! Establishing imagery needs through scenarios Control requirements: BPS/SAPS, greening, 2 nd pillar, Zone coverage: BPS/SAPS, greening, 2 nd pillar, 10x 10 km² 11x 11 km² 12x 12 km² 1 image images images Now applied at holding level!!! 11

12 Efficient zone dimension depends on dimension of the holding (i.e. spread of RP per holding) and target coverage of each holding For 9 control campaigns out of 10, there is enough imagery If 80% criterium 5% of holdings requires ±15% of territory If 100% criterium 5% of holdings requires ±60% of territory Criteria of RP holding coverage for OTSC Disclaimer: graphs specific for each MS!!! Planning OTS methods CwRS >< classical OTS Same tool, two applications for the MS: Assessment of exact needs for imagery (Total area, zone dimension) for themselves Planning of controls CwRS><classical OTS for the EC: Allocation of CwRS budget (Total area, zone dimension) among MS Disclaimer: graphs specific for each MS!!! 12

13 Wisdom? Conclusion These tools are essential and rapidly developing into a wider instrument for shared management standalone processes: self-evident started to produce management information missing spatial data component: holding = (RP h ) brings the EC better understanding of individual MS implementations meaningful EU dimension In a win-win setup 13

14 Outlook JRC s tools: Short term: further integration of the processes Long term: information and knowledge sharing Methodologies like those mentioned Agricultural and cover catalogue (grasslands!) CAPI and mapping rules Microwave processing algorithms (complementing CAPI) Environmental performance monitoring MS impact: help share the burden: deliver content these developments will be in pace with and accompanied by MSs IACS developments 14