The Coastal Urban DEM Project aims to provide high resolution elevation data to support coastal risk assessment and adaptation planning in Australia. Over 60,000 square kilometers of LiDAR data has been acquired for 8 major urban areas, exceeding the original target of 20,000 square kilometers. This data and associated tools like the Visualising sea level rise tool are helping local governments and other decision makers better understand and plan for risks of coastal inundation from sea level rise and storms. However, more national analysis is still needed to understand risks from combined hazards and to assess adaptation options. Ongoing funding and data access models remain challenges.
Coastal Urban DEM project - Mapping the vulnerability of Australia's Coast
1. The Coastal Urban DEM Project
Mapping the Vulnerability of
Australia’s Coasts
Phil Tickle and Nathan Quadros
Cooperative Research Centre for Spatial information
Better data for improved decision making
2. Background, national scale:
• Since 1990, observed sea level rise of >3mm per year
corresponds to upper limit of IPCC projections
• Science indicates greater than predicted sea level rise
of potentially >1m by 2100
• Australia has 25,700km of coastline
• Increased frequency and intensity of storms
• 80% of population lives in the coastal zone; 85% within
50km of coast; 25% within 3km
3. Context: Commonwealth Department of
Climate Change
Addressing capacity gaps to support risk
assessment & adaptation
─ High resolution elevation data acquisition
and distribution
─ Coastal Landform database – mapping
coastal stability
─ National Storm surge modelling and
event frequency
─ Mapping shoreline recession
─ Integrated coincident event modelling
4. Context: National Coastal Risk Assessment
$226 B in commercial, industrial, road and rail, and residential assets are
potentially exposed to inundation and erosion hazards at 1.1 m SLR (high
end scenario for 2100).
Assets at risk from impact of inundation and shoreline recession:
– 5,800-8,600 commercial buildings @$58-81 B (2008 values)
– 3,700-6,200 light industrial buildings @$4.2-6.7 B (2008 values)
–27,000-35,000 km of roads and rail, @ $51-67 B (2008 values)
5. Local Level Implications
• Decision support for planning rules, building restrictions, infrastructure &
insurance rely upon vulnerability maps, which in turn rely upon DEMs
• Decisions about future development, particularly in areas highly exposed
to the impacts of climate change, should not increase risk.
• Poor decisions as a consequence of DEMs of insufficient resolution can
have significant economic and social impacts
6. Local Level Implications
• Imperative issue in coastal environments is determining
adaptation response to predicted sea-level rise
• Associated vulnerability and risks need to be quantified
and communicated to potentially affected populations
• Topography is a key parameter and availability of
DEMs becomes central to preparation of vulnerability
maps & risk analysis
• Too date, there’s been excessive reliance upon
inundation scenarios built upon DEM data of insufficient
resolution to support fine-scale differentiation of
processes driving coastal change
7. Local Level Implications
• Increased demand for infrastructure and service
provision (more with less)
• Changing demographic trends –pressures on coastal
property values and coastal property development
• Difficult to resource and retain coastal adaptation
planning and management skills
• Uncertain future liability
8. Background to the NEDF
The potential impacts of rising
sea levels was identified as a
national priority by COAG in 2007
COAG also noted the need for a
fit-for-purpose coastal DEM to
assess the impacts of potential
sea-level-rise.
ANZLIC, DCCEE (then AGO), GA
and the CRCSI engaged in a
partnership to drive the
development of the National
Elevation Data Framework
The Urban DEM Project aimed to
deliver the initial phase of the
NEDF
9. NEDF Strategies (2009-2011)
Governance structures
Mechanisms for funding which
promote cost sharing,
Technical standards which
maximise the utility and
interoperability of data
Access, distribution and use
arrangements
Industry development and capacity
building
10. The Urban Digital Elevation
Modelling (UDEM) Project
Played a major role in implementing
the NEDF
$8.1 million 2009-12 investment to
underpin nationally consistent coastal
risk assessments
A focus on the issues of standards,
licensing and governance outlined in
the NEDF Strategic Plan
11. The Urban Digital Elevation
Modelling (UDEM) Project
Motivation: Policy makers need improved toolsets to
quantitatively assess risks to infrastructure, communities
and natural systems from coastal inundation & other
impacts of climate change
Scope: Begin implementation of the National Elevation
Data Framework (NEDF)
─ generation of high resolution elevation data for key
urban areas –
High-resolution, 15cm accurate datasets
Initial coverage of 8 major urban areas covering
20,000 km2 & >15 million population
─ Develop prototype interactive, web-based
visualisation tools for sea level rise
─ Develop an online web portal for elevation data
─ Undertake necessary applied research
12. The Need for High Resolution Elevation Data
Why use High resolution elevation data?
Provides valuable information and visualisation
capability to modellers and coastal decision makers
Enables more accurate coastal analysis
Enables the forecasting of inundation levels,
visualisation of coastal change, establishment of
shorelines
When combined with other datasets, provides for
more comprehensive scenarios (shoreline movement,
storm surge, high astronomical tide levels, erosion,
geomorphological analysis, asset risk etc )
The exposure of coastal assets is already widespread
and will increase into the future. Exposure will also
increase as the population grows.
13. The NEDF Portal
National Elevation Data Framework Portal
http://nedf.ga.gov.au
15. 2. National LiDAR Standards
http://www.icsm.gov.au/icsm/elevation/index.html
16. 2. National LiDAR Standards
New classification levels for LiDAR data
LiDAR doesn’t always “see” the ground
17. Classification Levels
Level Description
C0 Unclassified Point Cloud.
C1 Automated Classification.
Ground Anomaly Removal.
C2
These are major errors only, i.e. quickly and easily identifiable.
C3 Manual Ground Correction
C4 Full Classification
18. 3. Applied Research
Project 1 – Performance of DEM
generation technologies in coastal
environments
Project 2 – Integration of multi-
resolution DEMs
Project 3 – Vertical datum
harmonisation across the littoral
zone
Project 4 - User requirements for
bathymetric data collection
Project 5 – The need for
hydrological conditioning of DEMs
19. 3. Acquisition of over 60,000sqkm of LiDAR
Around 60,000sqkm of
LiDAR has been
acquired for coastal
vulnerability modelling
─ Original objective of
20,000sqkm
─ Partnerships made it
possible
Seamless LiDAR from
Cooktown to Adelaide
Seamless Coastal DEM
being developed
20. 4. Communication Products
Communication products have increased awareness
across government, community and the private sector
Maps highlight the potential impacts of different sea level
rise scenarios
─ Three sea level rise scenarios relevant to a 2100 time period with a very high tide (eg king
tide):
low (50 cm sea level rise + HAT)
medium (80 cm SLR + HAT)
high (1.1m SLR + HAT)
21. 4. Communication Products
available online via the OzCoasts
website (www.ozcoasts.org.au)
Publicly available information
important –facilitates private sector
and community engagement
Three sea level rise scenarios relevant
to a 2100 time period with a very high
tide (eg king tide):
low (50 cm sea level rise + HAT)
medium (80 cm SLR + HAT)
high (1.1m SLR + HAT)
300,000 downloads of maps
500,000 page views in the first month
and 10,000-40,000 visits per month
22.
23.
24. Visualising sea level rise tool
(VisTool)
Available to governments (public good
use)
Being accessed by 150 local governments
Based on high resolution elevation data
(vertical accuracy +/- 10-15cm)
Hydrologically conditioned – to better show
how water will flow such as streams,
stormwater drains
Bucket fill modelling approach only -
assumes calm ocean surface
What information do decision makers need
and how do they need to access it?
Interactive tool that can be used to
consider various futures of sea level rise
Potential to add in other data layers eg
national storm tide model data, landform
stability data etc
25.
26.
27. Visualising sea level rise tool limitations
does not provide guidance about flood risk from an extreme event, eg storm
surge, influence of wind and waves etc
does not consider landform structure (geomorphological factors), eg
potential erosion
does not model potential change to tidal flows
does not take account of some existing sea walls and other protective
structures, eg some flood mitigation structures
does not take account of the effects of coincident catchment flooding from
extreme rainfall events
does not indicate depth of flooding
31. Observations & Conclusions
National level work to build capacity to understand risks (DEMs,
storm tide models, shoreline erosion, sediments, wave modelling)
Significant national analysis yet to occur re SLR and coincident
storm events combined with hazard predictions (erosion, inundation
extent)
Understanding of ,and communication of, modelling outcomes has a
way to go
Differing adaptation options with differing costs = tools to assess
costs and benefits options required
32. Observations & Conclusions
On-ground experience in acquiring,
processing and delivering high resolution
elevation data has provided many insights
relevant to key national information
infrastructure projects (hardest first!)
UDEM being used as a case study for ANZLIC
Framework datasets
In the space of 4 years the technology, how
its processed, and the market has changed
dramatically
A coordinated and informed purchasing has had a
significant impact on price and quality
Coordination and Collaboration across all
levels of government is the key to success
33. Observations & Conclusions
On-going funding models for fundamental
data are still problematic
Public-private partnerships are yet to be fully
explored
USGS study has demonstrated a 4.3-4.9
benefit:cost ratio when a coordinated national
approach is taken
Despite “open-government” policies,
existing cost-recovery models are limiting
access to private and research sectors
Appropriate access is critical to community
awareness, changing risk and insurance
Findings are largely consistent with accuracy expectations Accuracy gap between LIDAR & IFSAR or ADS40 DEMs only a factor of 3 to 4 according to specs., but difference is accentuated in automated classification & filtering , espec. of vegetationLIDAR has significant advantages, not matched in vegetated areas by radar & photogrammetry, except through skill-intensive and expensive manual editing Systematic filtering errors can compromise integrity of bare-earth DEMs in low-lying vegetated & urbanised coastal areas; majority of the populated coastal regions of Australia fit this description Due to classification/filtering issues, and to a lesser extent differing vertical resolution, can conclude that LIDAR is preferred option for DEM generation in coastal regions vulnerable to sea level rise
Findings are largely consistent with accuracy expectations Accuracy gap between LIDAR & IFSAR or ADS40 DEMs only a factor of 3 to 4 according to specs., but difference is accentuated in automated classification & filtering , espec. of vegetationLIDAR has significant advantages, not matched in vegetated areas by radar & photogrammetry, except through skill-intensive and expensive manual editing Systematic filtering errors can compromise integrity of bare-earth DEMs in low-lying vegetated & urbanised coastal areas; majority of the populated coastal regions of Australia fit this description Due to classification/filtering issues, and to a lesser extent differing vertical resolution, can conclude that LIDAR is preferred option for DEM generation in coastal regions vulnerable to sea level rise
Nathan.Can you drop in a couple of slides from your presentation
By the the end of 2012, the majority of Australia’s developed coastline will have seamless Airborne LiDAR data suitable for systematically modelling the impacts of coastal inundation and flooding