Design of an Integrated Monitoring System

Module of Remote Sensing and GIS Integration Course 2015

Lammert Kooistra, Ron van Lammeren, Valerio Avitabile

RGIC general set-up

Academic Consultancy Training (ACT) project (20 d) Phase 1 Phase 2 Phase 3

Project proposal Analysis Report & Presentation

+ excursions: 2 days

Management skills

Project management

Communication (2 d)

Geo-skills GSNS LBS

Field Spectroscopy

(4 d)

Design of Integrated Monitoring System (DIMS: 9 d)

Learning objectives for DIMS

Understand the role of geo-information science in integrated

monitoring of the system Earth;

Explain which concepts and techniques for integrated

monitoring systems are currently applied and for which earth

system processes;

Prepare a design for integrated monitoring system for a

selected beneficial area taking into account state-of-the-art

developments in the field of geo-information science;

Develop and implement a validation and fieldwork plan to

assess the quality of the main data sources of this monitoring

system;

Demonstrate the use of proper visualization techniques for

effective communication of the information in the monitoring

system.

Program kick-off meeting DIMS module

Time Title Presenter

8:30 – 9:15 Integrated Monitoring Systems: concepts and

approaches

Lammert Kooistra

9:15 – 9:30 Break

9:30 – 10:15 Developing an integrated monitoring system for

forest biomass

Valerio Avitabile

10:15 – 10:30 Break

10:30 – 11:00 Introduction to the Assignment Design of an

Integrated Monitoring System

Lammert Kooistra

11:30 – 15:30 Brainstorm on selection of benefit area and

monitoring system requirements

Project teams

15:30 – 16:30 Feedback session: introduction pitch (3 minutes)

per team on selected integrated monitoring

system

Project teams

Integrated Monitoring Systems:

Concepts and Approaches

Lammert Kooistra and Martin Herold

Laboratory of Geo-Information Science and Remote Sensing

Wageningen University

DIMS Module of Remote Sensing and GIS Integration Course 2013

Environmental Resource Management in the Anthropocene Era

Climate change

Land use change

Invasive species

disasters: flooding, hurricanes, …

Need for spatial-temporal monitoring

Not only WHAT and WHERE but also WHEN

& real-time !?

Some definitions

Measurement: direct observation of a phenomenon (measuring length in m)

Monitoring: to be aware of the state of a system, repeated measurements to analyze changes

Need to measure and monitor a phenomenon to properly manage it

Often not a direct relationship between what can be efficiently and cost-effectively measured and monitored and the phenomenon and its societal benefits (e.g., biodiversity)

Monitoring for a reason

Monitoring happens for a reason -> societal benefit

Clearly defined objectives, users and uses are essential for efficient monitoring

“the perfect is the enemy of the good”

User requirements for earth system monitoring Tim

e

hour –

day –

week –

month –

quarter –

year –

decade –

Space

–

–

–

–

–

– local regional state/country continental global

Up-/Down- Scaling

Agricultural parcel: precision agriculture (farmer) Nature reserve: nature management (nature service)

River catchments: flood protection (river & water board) Nature reserves: fire protection (forest service)

Climate change: carbon accounting (national government) Invasive species

Drought monitoring Biodiversity

Climate change Land use

Structural monitoring service

Event monitoring service

Developments in Remote Sensing

Long term time-series

Range of sensor types

Range of products and services

Global organization and cooperation

Improved (web-based) accessibility

Also for non-experts

Standards and quality control

Near-real time availability

Development of early warning systems

Developments in Geo-Sensor Networks

Traditional:

● Broad range of sensor networks

● Stable and well organized

● Often not real-time (manual or data-logger)

Trends:

● Miniaturization of microelectronics

● Wireless communication

● Developments of new materials & sensors

Consequences:

● Embedding devices into almost any man-made and some natural devices, and

● connecting the device to an infinite network of other devices, to perform tasks, without human intervention.

● Information technology becomes omnipresent.

Crossbow Mica Mote

Source: Nittel, Sensors, 2009

Developments in Mobile Sensing

All kind of platforms combined with different sensor types

● Difficult accessible areas

● Disaster monitoring (flexibility)

● Mobile processes: traffic jams or animal tracking

Increasing autonomy of sensor network

Adaptive learning

Developments in human sensing:

● Use of mobile phones

● Citizen observatories

● Crowd sourcing

Opportunities for Integrated Sensing Tim

e

hour –

day –

week –

month –

quarter –

year –

decade –

Space

–

–

–

–

–

– local regional state/country continental global

Up-/Down- Scaling

Agricultural parcel: precision agriculture (farmer) Nature reserve: nature management (nature service)

River catchments: flood protection (river & water board) Nature reserves: fire protection (forest service)

Climate change: carbon accounting (national government) Invasive species

Drought monitoring Biodiversity

Climate change Land use

Structural monitoring service

Event monitoring service

Opportunities for Integrated Sensing Tim

e

hour –

day –

week –

month –

quarter –

year –

decade –

Space

–

–

–

–

–

– local regional state/country continental global

Up-/Down- Scaling

Agricultural parcel: precision agriculture (farmer) Nature reserve: nature management (nature service)

River catchments: flood protection (river & water board) Nature reserves: fire protection (forest service)

Climate change: carbon accounting (national government) Invasive species

Drought monitoring Biodiversity

Climate change Land use

Structural monitoring service

Event monitoring service

Concepts for spatio-temporal data organisation

Digital Earth (Craglia et al., 2012):

● ‘a multi-resolution, three-dimensional representation of the planet that

would make it possible to find, visualise and make sense of vast amounts

of geo-referenced information on physical and social environments’

Sensor Webs (Teillet, 2010):

● ‘a system of autonomous, wireless, intra-communicating, spatially-

distributed sensor pods that can be deployed to monitor and explore new

environments, a smart macro instrument for coordinated sensing’

Global Earth Observation System of Systems (Lautenbacher, 2006):

● ‘The focus of GEOSS is to produce societal benefits through more

coordinated observations, better data management, increased data

sharing, and application to societal needs.’

Global Earth Observation System of Systems: GEOSS

A Global, Coordinated, Comprehensive and Sustained System of Earth Observing Systems

GEO is a voluntary partnership of 72 governments and the European Commission, 52 intergovernmental organizations (Feb 2008)

source: www.earthobservations.org

User driven approach Interoperability

arrangements Web portal and

clearing house Support new

observation methods Dissemination

knowledge

Time, Stakeholder group dominating effort

Pro

min

ence

of

the

issu

es

(Su

m o

f p

eop

le, c

lou

t, a

nd

co

nce

rn d

edic

ated

to

th

is is

sue)

Pioneer watchdogs

Action groups

Policy Makers

Mitigation groups

Monitoring groups

Science

Policy

Who will have to pay?

How much and where? Is it a problem?

Is there a problem?

What causes the problem?

Who did it?

Will everybody tackle the problem?

What does it cost?

What are the mitigation options?

Is the problem properly address?

What are cheap, replicable

indicators?

Who is not complying to the mitigation?

Issue life cycle

Societal benefits from land monitoring

Monitoring and indicator frameworks

Essential Climate Variables (ECVs)

Essential Biodiversity Variables (EBVs)

Plant traits (System Ecology)

Ecosystem Services (Biodiversity)

MRV REDD (Forestry)

The New Production of Knowledge

Mode 1 Mode 2

• Academic context • Disciplinary • Homogeneity • Autonomy • Traditional quality control

(peer review)

• Context of application • Trans disciplinary • Heterogeneity • Reflexity/social

accountability • Novel quality control

Source: Hessels et.al. 2008 after Gibbons 1994

Position of DIMS and ACT

Pure basic research

Use-inspired research

Pure applied research

Quest

for

fundam

enta

l unders

tandin

g

Considerations of use

Yes

Yes

No

No

Source: Stokes 1997

DIMS

ACT

PhD/MSc thesis

Triple helix

University – Government – Industry

Complex relationship

● Confusion of roles

● Simultaneously Competing and

Cooperating

Silicon Valley, Food Valley, Brainport

Eindhoven, Mechatronic Valley, Health

Valley

Science became less autonomous

More customer-contractor relations

“Market based” system

Application: Precision agriculture

Problem: Derive operational indicators for crop status/health over the growing season

Research challenges:

● Indicators: VIs -> temporal signal -> Fs -> crop models

● Sensor integration: near-remote sensing, platforms (both vegetation and soil)

● Backward integration: user friendly services

Partners: BLGG AgroXpertus, TTW, Terrasphere, vd Borne Aardappelen, WUR-PRI/PPO, MTT Finland

11 June 23 June 5 July 14 juli 19 aug 6 sept

Project Interreg IV A SMART INSPECTORS

31 May

Integrated sensor approach for PA

close sensing

+ flexible acquisition

+ active system

– no processing standards

– point observations

spatial-temporal- spectral

scaling model

remote sensing whole parcel +

preprocessing + clouds –

not all days –

high-frequency time-series

UAVs to support environmental management

detection

diagnosis & decision rules

management activity

Source: report van der Voet, 2012

Summary

Integrated monitoring includes:

●New concepts for organization

●State-of-the-art observation or sensing techniques

●Explicitly taking users and beneficiaries into account (societal context)

●Combining different societal benefit areas

References

Craglia et al., 2012. Digital Earth 2020: towards the vision for the next decade. International Journal of Digital Earth 5: 4-21.

Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., van de Velde, M., Kraxner, F., Obersteiner, M. (2012). Geo-Wiki: An online platform for improving global land cover. Environmental Modelling and Software 31: 110-123.

Lautenbacher, C.E. (2006). The Global Earth Observation System of Systems: Science Serving Society. Space Policy 22: 8-11.

Kooistra, L.; Thessler, S.; Bregt, A.K. (2009). User requirements and future expectations for geosensor networks – an assessment. In: GeoSensor Networks 2009 / Trigoni, N., Markham, A., Nawaz, S., . - Berlin-Heidelberg : Springer, (Lecture Notes in Computer Science 5659) .

Ligtenberg, A.; Kooistra, L. (2009). Sensing a changing world. Sensors 9: 6819 - 6822.

Nittel, S. (2009). A Survey of Geosensor Networks: Advances in Dynamic Environmental Monitoring. Sensors 9: 5664-5678.

Teillet, P. (2010). Sensor Webs: A geostrategic technology for integrated earth sensing. IEEE JSTARS 3: 47-80.