Workshop on Remote Sensing and Environment Change in the Horn of Africa 12-13 June 2007, Nairobi, Kenya Open Standards and Spatial Data Infrastructures:

Workshop on Remote Sensing and Environment Change in the Horn of Africa12-13 June 2007, Nairobi, Kenya

Open Standards and Spatial Data Infrastructures:

A Cure for Digital Constipation?

Mick Wilson, United Nations Environment Programme, Division of Early Warning and Assessment (UNEP/DEWA),

on behalf of the UN Geo-Information Working Group (UNGIWG) and the UN Spatial Data Infrastructure (UNSDI)


Remote sensingEnvironment changeHorn of Africa

Time for Lunch?


The Problem?

Zillions and Zillions of Mega-bytes

42


The Usual Approach

The Historical Stack

single uselocalizedinflexibleinextensibleexpensiveubiquitous

People

Person 1

A home

Intellectual capacity

$$$$ Cost $$$$

Data, Images

ProcessingHardware

The Traditional Stack

ProcessingSoftware

Am

ount of, Availability


Seeking

re-use Hardware and software Algorithms and methodologies Experiences and contacts

extensibility and adaptability emerging requirements and technologies

"life" beyond the lifetime of any one project

Infrastructure


Example 1 – Fire in South Africa

• Frequent, intense grassfires• ESKOM: over 250,000 km of

HT and distribution lines• $50m damage in 2001

• Problem is not just burnt infrastructure

• Gases, smoke cause arcing between HT lines -> damage to industrial and domestic equipment hundreds of km away


AFIS – Advanced Fire Information System• Goals: Rapid detection, characterization (size, location)• Effective response: less disruption, less damage

CSIR-SAC

Meteosat SEVERIGEO, 5km res. 15 min revisit

Terra/ Aqua MODIS LEO, 1km res. 6 hr revisit

0

10 20 40 5030

10

20

Thickness

Fre

que

ncy

Rectification, Feature extraction, Hotspot ID

Hotspot Archive Spatial Database

Infra-redsignature

Context analysis, “Any hotspots with 5km?”Fire Alerts Generator

WFS

SOS

GSM SMS

Contextual SpatialDatabase e.g. HT lines,

depot locations

WF

S,

WC

S

SMTP

!


Consider the Transformations

Image Data

“42”NASA, Eumetsat CSIR ESKOM

Field Ops

ActionFeatures

ESKOMCSIR/ Meraka

100,000,000s 1,000,000s

SignificantFeatures

10,000s

Decision,Instruction

100s

HemisphericObservationGeosync Orbit40,000 km

Just down the Road10 km

NationalSystem1000 km

Threatswithin5km

Which parts of this sequence is Africa best equipped to manage?


Consider the Possible Extensions

0

10 20 40 5030

10

20

Thickness

Fre

que

ncy

Rectification, Feature extraction, Hotspot ID

Hotspot Archive Spatial Database

WFS

SOS

GSM SMS

Contextual SpatialDatabase e.g HT lines,

depot locations

WFS, W

CS

SMTP

!

!WFS

Protected areas/ biodiversity Spatial Database

WFS

Context analysis:“Which hotspots are inside national Parks and

are more than 5km from a usable track?”Intelligent Fire Alerts Generator

GSM SMS

SMTP

SOS

National Roads Spatial Database WFS

Context analysis:“Which hotspots are within 5km and have strong winds from the

wrong direction?”Intelligent Fire Alerts Generator

SO

S

National weather servicewind stress

WF

S


Salient points

Outwardly simple Extensible – new components can be added Adaptable – Existing components can have

new uses Incremental – don’t need all the pieces

before anything works Migratable – develop components overseas,

bring them home as capacity improves Requires agreement, governance Undermines control?


Example 2 – Glacial Lake Outburst Floods

• Increasing risk as temps rise• Over 9000 glacier lakes in

Himalaya/ Hindu Kush• Estimate ~100 may pose threat

• Sudden, catastrophic, destructive floods

• Difficult to monitor lakes– altitude, cloud cover, severe terrain


Concept: GLOF risk assessment by Radar

• Pros:– All images cloud-free– Works day or night– Spatial resolution good– Flat surfaces readily

detected (no return signal)– Altimetry comes for free ->

good change detection

• Cons:– No free data– Very large data volumes, difficult to move and process in HHK region– Complex processing, specialized algorithms– SAR looks cross-track so seeing into steep valleys can be tricky


Concept: GLOF risk assessment by Radar

• Constraints: we the UN cannot– Redefine EU data policies– Build new receivers, new internet capacity in Nepal– Change the physics of radar signal– Fix the geometric limitations of synthetic apertures

• Advantages: we the UN can:– Negotiate access to special capabilities (algorithms,

procedures, high-velocity networks, MIPs)

So, try to use our influence broker arrangements where our partners:Get better access to better data productsDeal less with data and more with information


GLOF Monitoring/Alert System Concept

Envisat

ESA Frascati, Italy

Initial cleaning

Change detection (level)Feature analysis (extent)Threat ModelingEtc etc

Provenalgorithms

Hi-capacityparallel

processing

Hi-speednetworks

Governancestructure

EuropeanDataGrid

Internet2Intern

et2Inte

rnet

2

Internet2

Data Repository, location TBD (JRC? UCL? UNOSAT/CERN?)

Analyze, contextualizeadvise Member States

Internet

ICIMOD, Nepal


Salient points

“Outsources” data supply, processing and governance Explores new technology combinations – open GIS and

“grid” processing Extensible – new components can be added Adaptable – Existing components can have new uses Incremental – don’t need all the pieces before anything

works Migratable –bring components home as their utility and

value are demonstrated


And what about SDI’s

Bring together technologies, methods and governance

Use standard building blocks with well-know interfaces using open standards

Encourage component-based, incremental development

Distribute R&D/development costs Reusability

Small economies can develop needed components rather than whole systems

New components can be developed and “plugged in” based on known interfaces

Focus on services rather than products


Key messages

Distributed systems built on open standards:

overcome access bottlenecks

offer extensibility, re-use and adaptability

mean you focus on the important bits of the problem because the infrastructure’s in place

are in use today

can be relevant to assessment and decision-support tasks in the Horn of Africa today


Finale

UN Geo-Information Working Group (UNGIWG) UN Spatial data Infrastructure (UNSDI) concept SDI’s a key capacity to the UN’s future ability to serve

Member States

Consultation on East African requirements for UNSDI

late July 2007, UN Complex, Nairobiin conjunction with KNSDI?

mick.wilson @ unep.orgsdi-ea @ als.unep.org

phone +254 (20) 7623436

Documents

Workshop on Remote Sensing and Environment Change in the Horn of Africa 12-13 June 2007, Nairobi, Kenya Open Standards and Spatial Data Infrastructures: