lessons learned from connectingeo: connectingeo gap

Lessons learned from ConnectinGEO:

ConnectinGEO gap analysis methodology and

results in the GEOSS in-situ datasets.

Joan Masó

May 10th, 2017

1:30 - 3:00

Onyx Room ISRSE37

This project received funding from the European Union’s Horizon 2020 research and

innovation programme under grant agreement No 641538

Lessons learned from Connecting GEO: Coordinating an Observation Network of Networks

EnCompassing saTellite and IN-situ to fill the Gaps in European Observations

ConnectinGEO in brief

• H2020 funded project that lasted 2 years

(641538) in the Societal Challenge 5

“Climate action, environment, resource

efficiency and raw materials”

• A contribution of the EU to EO and

GEOSS

• Coordinate and Support Action (CSA)

• Consortium of15 partners

• Finalized in February 2017



• Conduct a gap analysis in GEOSS with focus in

• Europe

• In-situ

• Start an European Network of Earth Observation Networks

• Active

• Useful

• Help the EC in creating community around GEOSS

Project aims



• Analyze the role of EO in the SDG in

• monitoring

• implementation

• Participate in the EVs development

• Spread the idea of the EVs to other domains

• An Observation Inventory and a Knowledge base

Project bonus



ConnectinGEO TeamCore TeamConceptual

Thematic

Industry

Technical



GAP ANALISYS. A MIXTURE

OF TOP-DOWN AND BOTTOM-

UP APPROACHES.



BONUS 1:

SDG. TOP-DOWN APPROACH



Sustainable Development Goals

There is a need to monitor the achievement of the

SDG. Some targets for each SDG and indicators for

each target has been defined.

This need to be done in a quantifiable manner

• Socieoconomical data

• Earth observation data



Sustainable Development Goals with EO• Goal 2: End hunger, achieve food security and

improved nutrition and promote sustainable agriculture

• Goal 3: Ensure healthy lives and promote wellbeing for all at all ages

• Goal 6: Ensure availability and sustainable management of water and sanitation for all

• Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all,

• Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable

• Goal 13: Take urgent action to combat climate change and its impacts

• Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development

• Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems,sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss



SDGs assessment with EVs



BONUS 2:

EV: TOP- DOWN APPROACH



EV’s. The Climate people got it first

http://ceos.org/wp-content/uploads/2015/10/GCOS_poster_FINAL.jpg



ECV: Essential Climatic Variables but not only



ConnectinGEO and GEO Workshop in Bari



• The domains with a more mature development of EV lists

are Climate, Ocean and Biodiversity.

• The Water domain is also maturing a set of EVs in

GEOSS.

• There are also domains that are working with a common

set of variables that can be considered essential for them.

• Spotting overlaps

Status



• Agricultural monitoring is conducted both by the USA and EU in a similar way; Crop Area, Crop Type, Crop Condition, etc., are obvious candidates for Agriculture EV’s.

• Ecosystems is a cross-domain area that can make use of existing sets of EVs (such as ECVs, EOVs and EBVs) complemented by socioeconomic variables

• Renewable energy can also make use of the ECVs but there is a need for additional variables. For example, solar surface irradiance and wind at different levels next to the ground are good candidates to explore.

• The Disaster SBA is one of the most heterogeneous areas dealing with disasters caused by a wide range of natural and anthropogenic hazards. • Different sets of EVs are required fro the different hazards, the vulnerability

of exposed assets, and the impacts of the hazards on communities.

• In particular, socioeconomic EVs are required to characterize vulnerability and resilience (e.g., demographics, availability of public services, productive infrastructures, etc.) and to measure the extent of the hazard impacts on human societies leading to disasters.

Other domains are close



BONUS 3:

OBSERVATION INVENTORY

BOTTOM-UP APPROACH



• The concept of enrichers to add automatic metadata fields

for easy query about

EVs and detect gaps

Observation inventory database schema

and queryable fields



AIM 1:

GAP ANALISYS



Overall approach for gaps

Observation

needs

Observation

inventory

EV and

gap

analysis

Priorities

SDG

indicators

S&T

stakeholders

network.

(Models and

forecast)Industry

stakeholders

& Industry

Challenges

Sustainable

development

goals

Consultation process

Challenge

outcomes

Consultation process

UN report

Previous

inventories &

analysis

Observation

networks and

CoPs

SEE IN

Knowl.

Base

National

and

regional

actions and

funding



Example: Gap 18• Gap type: No measured (7.1)

Status: Detected (1)Theme: Energy (EN)Other Themes: EV: EVOther EV: Gap description: In mineral resources there is the lack dedicated EO system or program and currently use EO systems and programs from other SBAs.Thread: Research programs targets (2)RS/In-Situ: BothEditor: Guillem ClosaAmbassador: Traceability: CA-06. GEO 2016 WORK PROGRAMMEPurpose: Develop global coverage by high-spectral resolution sensorsDate: Review: Remedy: Feasibility: Low (1)Feasibility rational: the technology and communication standards are availableImpact: Very high (4)Impact rational: in-situ data is crucial in early warningCost: Low (1)Cost rational: not much added cost regarding exixting technologies; it's more a matter of real application in practiceTimeframe: Short term (1)Time rational: Priority: High (3)Priority rational: Recommendation:

http://tinyurl.com/EOgaps



Geo

grap

hic

alex

ten

t(1

.1)

Ver

tica

l ext

ent

(1.2

)

Tem

po

ral e

xten

t (1

.3)

Spat

ial r

eso

luti

on

(2

.1)

Ver

tica

l res

olu

tio

n(2

.2)

Tem

po

ral

reso

luti

on

(2.3

)

Un

cert

ain

ty (

3.1

)

Geo

grap

hic

alin

con

sist

ency

(4.1

)

Tem

po

ral

inco

nsi

sten

cy (

4.2

) B

ou

nd

ary

con

dit

ion

s is

sue

(4.3

)

No

cat

alo

gue

(5.1

)

Cat

alo

gue

satu

rati

on

(5

.2)

Can

no

t b

e vi

ewed

(5

.3)

No

eas

yac

cess

(5.4

)

No

n w

ell k

no

wn

fo

rmat

(5

.5)

No

pro

cess

able

(5.6

)

Un

kno

wn

sem

anti

cs(5

.7)

No

fas

t ac

cess

to

b

ig d

ata

(5.8

)

No

acc

ess

(6.1

)

No

op

en a

cces

s (6

.2)

No

qu

alit

y (6

.3)

No

pro

ven

ance

(6

.4)

Bad

met

adat

a (6

.5)

No

met

adat

a (6

.6)

No

mea

sure

d (

7.1

)

No

par

amet

er (

7.2

)

No

inte

rdis

cip

linar

y co

ord

. (8

.1)

No

co

ord

inat

ion

of

ob

s. s

ites

(8

.2)

AgV 6 1 7B_CCT 1 1B_EFDR 1 1B_GCA 1 1B_GCB 1 1B_GCC 1 1B_GCP 1 1B_SPA 1 1B_SPD 1 1B_SPS 1 1C_AGB 1 1C_C 1 1 1 1 4C_CD 1 1 1 1 4C_CLD 1 1 2 3 1 8C_CO2 5 10 1 2 2 2 1 1 1 25C_FIRE 1 1C_GHG 9 10 1 2 2 1 1 1 1 28C_GLA 6 2 8C_ICE 2 2C_LAI 1 1C_LAK 1 1 2C_LCV 1 1 1 1 1 5C_O3A 2 13 2 6 16 1 2 2 2 1 1 2 1 51C_OAS 1 1 1 1 4C_OC 2 1 1 1 1 1 1 8C_OOD 1 1 1 1 4C_PAS 4 1 1 1 7C_PFR 3 3C_PLK 1 1C_PRE 4 1 1 6C_RAS 1 1 1 3C_RIV 1 1 2C_SALD 2 1 1 1 5C_SICE 2 4 6C_SL 1 1 2 1 1 1 7C_SNC 1 1C_SS 2 1 2 1 1 1 8C_SSS 3 3 1 1 1 1 1 11C_SST 5 1 2 1 1 1 11C_TAS 1 1 2C_TD 2 1 1 1 1 1 7C_TU 1 2 18 1 2 2 1 1 3 1 32C_WAS 1 1 1 3C_WNU 1 1C_WTS 1 1C_WVAS 1 1 2C_WVU 2 7 1 17 2 2 2 1 1 4 1 40E_LULC 1 1 2E-BAT 1 1EBV 1 2 1 1 1 1 7E-CUR 1 1ECVA 1 1 1 3E-ELEV 1 1E-OFL 1 1EOV 1 1EREV 1 1E-SSI 1 1E-TDL 1 1EV 1 1 2 2 1 7N_ACO 1 1 2N_APOL 4 1 5N_NOI 1 1O_CAR 1 1O_NUT 1 1O_TRTR 1 1W_EVA 1 1W_Q 1 1

48 5 54 7 3 35 91 8 1 1 3 7 10 11 10 2 4 0 3 3 19 1 6 1 26 6 2 4 371

• 91 gaps address a to high uncertainty information. Lack of quality

measures is also the concern in 18 gaps.

• 102 gaps address the spatial and temporal extent are other data

characteristics that score many gaps. In 35 cases, the gaps address

temporal resolution of the data.

• There are still some concerns about data access issues and these are

• reflected in 31 gaps.

• 26 gaps reveals concerns about EVs that are not measured, mainly in

the biodiversity theme and to a lesser amount in the ocean and

atmospheric composition themes.



Criteria for gap priorities

Observation

needs

Observation

inventory

EV and

gap

analysis

Prioritiy

(p)

Feasibility (f)

Costs (c)

Impact (i)

Feasibility

Impact

Costs

1st target

2nd target

Time frame (t)



Priorities• Quantitative:

• Lack of continuity and uniform temporal sampling in time series (CGT-174; p = 16). • Remedy: Implement data fusion techniques to generated regular interpolated samples.

• Lack of tidal, ocean currents and water elevation prediction services (CGT-175; p = 12)• Remedy: Implement a forecast system based on recent data.

• Lack of tools for Big Data analysis: merge time series, proper map and statistics visual representation, (CGT-176; p = 12),

• Remedy: develop the tools for big data analysis and visualization.

• Insufficient accounting for environmental variables in SDG indicators (SDG-GP-2; p = 12)• Remedy: integration of socio-economic and environmental data.

• Skills required for matching providers and policy makers (SDG-GP-6; p = 12), • Remedy : develop educational programs that focus on inter-sectorial skills.

• Expert assessment • Lack of LIDAR global dataset (CGT-023).

• Remedy: Design and execute a satellite mission

• Lack of sufficient spatial coverage for many climatic applications, specially in the Southern hemisphere (CGT-092)

• Remedy: extend spatially the number of mooring sites with current meters at least for key dynamic areas (e.g., main energetic currents, Aghulas retroflection, Malvinas confluence) particularly in the Southern ocean.

• No European in-situ cross-domain coordination initiative (CGT-219)• Remedy: Implemented through the European Network of Earth Observation Networks (ENEON).



AIM2:

ENEON

European Network of Earth Observation Networks

for in-situ coordination in Europe

Joan Masó

May 11th, 2017

3:30 - 5:00

Crystal Room ISRSE37

This project received funding from the European Union’s Horizon 2020 research and

innovation programme under grant agreement No 641538



AIM3: COMMUNITY

CREATION IN GEOSS



Workshops

WS1: GEOSS Science and

Technology Stakeholder

Network (GSTSN). March 23-

25, 2015 in Norfolk, VA, USA

WS2: Towards a sustainability

process for GEOSS Essential

Variables (EVs). June 11-12,

2015 in Bari, Italy

WS3: ENEON first workshop.

September 21-22, 2015 in

Paris, France

Project time line

WS5: ARMINES/MINES

ParisTech organized a energy

stakeholder workshop. January

27-29, 2016 in Sophia-Antipolis,

France.

WS6: ENEON first workshop.

October10-13, 2016 in

Laxenburg, Austria

WS4: ECSA conference

Session 13 Citizen science

May 19-21, 2016 in Berlin,

Germany



Summary of Outcomes• METHOD:

• Gaps methodology

• TOOL: • Observation inventory (back into the DAB).

• Socio-Economic and Environmental Information Knowledge Base

• Renewable energy SWE webservice

• ENEON commons

• EO in-situ networks graph

• DATA: • Gap Analysis Table/database

• Vocabulary of EVs

• NETWORK: • ENEON

• OUTREACH: • Product of the year award Thank you!

[email protected]

lessons learned from connectingeo: connectingeo gap

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