Australian Fuel Classification€¦ · and fire dynamics Probable approach Indirect, gradient model Direct, indirect gradient model Field reconnaissance Mapping/Classification entities

Australian Fuel Classification: Overview Jim Gould1,2, Miguel Cruz1 Jen Hollis1 & Tom Jovanovic1 1 CSIRO Ecosystem Sciences, Canberra ACT 2 CSIRO Digital Productive Flagship- Digital Technology & Services for Disaster Management, Canberra, ACT

ECOSYSTEM SCIENCES/DIGITAL PRODUCTIVITY FLAGSHIP

Australian Fuel Classification (AFC): The project

1. Project initiated and coordinated by:

Australasian Fire and Emergency Services Authorities Council (AFAC)

Forest Fire Management Group (FFMG)

Attorney General Department- National Emergency Management program (particle funding)

In-kind support from rural and land management agencies

2. Background A National Bushfire Fuel Classification System to provide the

following opportunities: • Avoided duplication of effort in designing, reviewing and

implementation of classifications and fuel data systems;

• Increased authority for systems by drawing on a greater pool of expertise;

• Increased interoperability across borders for response and prevention activities; and

• Increased data quality for national reporting initiatives.

CSIRO: Australian Bushfire Classification: Fire Weather & Risk Workshop

Australian Fuel Classification (AFC): Multi-stage project

1. Stage I (completed 2011) • Reviews- national and international

• Scope and framework

2. Stage II (completed 2012) • Glossary of terms

• Fuel sampling

• Architecture

3. Stage III (work in progress) • Implementation – pilot project ACT Parks and Conservation Service

• End user workshop (October, 2013?) & Science publication

4. Future work (concepts) • Implementation to other agencies (?)

• Web site (?)

• Links with other bushfire DSS (?)


Fuel Classification Current situation

Broad fuel types linked to fire behaviour models: • Strong focus on forest fuels and adoption of OFHG

• Fuel load from fuel accumulation curves- some science based, others expert opinion

No standard for assessment of fuel complex characteristics;

No standard for assessment of fuel hazard;

Invalidated linkages between visual fuel hazard and biomass (Gould et al 2011; Watson et al 2012);

Wealth of fuel inventory studies – not used;

Seasonal and temporal fuel dynamics not being considered

(with exceptions).

Stage I

Behave

Plus

FOFEM

NFDR

United States • National Fire Danger Rating (20 fuel models)

• Fire Behaviour models (11 original models, expanded to 40 fuel models) • Based on laboratory sites and limited field validation

• Inconsistency between NFDR and FB fuel models

• Fuel Characteristic Classification System • 6 fuel layer stratum

• Limited field validation

• Web base/geo-spatial/software applications- e.g. LANDFIRE

Canada (Canadian Forest Fire Danger Rating System) • Organises fuels into 5 major groupings, with 16

discrete fuel types

• Fuel types are used to describe fire behaviour characteristics that would be expected under various burning conditions

• Fuel types are described qualitatively

New Zealand (NZ Forest Fire Danger Rating System)

• Adopt the Canadian FWI for pine plantations

• Expanded to 17 fuel models and 7 spread models

Fuel Classification International review

Stage I

Fuel are complex in structure and diverse in their physical attributes and biological origin of their components

Comprehensive system of fuel classification requires fuel models that capture this diversity

Primary focus on fire behaviour for fuel classification:

• Fire danger rating

• Fuel hazard and risk

• Rating potential rate of spread or rate of perimeter growth

• Suppression difficulty/resistance to control

Future application

• Fire effects

• Carbon/Smoke/GHG/etc

AUSTRALIAN FUEL CLASSIFICATION Objectives and scope

Stage I

Description for fuel classification Across different scales

FUEL CLASSIFICATION LARGE SCALE MEDIUM SCALE FINE SCALE

Primary application Fire danger Fire risk and hazard Fire behaviour

Possible scales >1000 m 250 – 1000 m 30 – 250 m

Fire uses Plan and allocate resources Locate and prioritize treatment areas

Fire prediction, fire effects,

Simulate fire behaviour

Other possible uses Global carbon budget Biodiversity conservation assessments

Simulate ecosystems and fire dynamics

Probable approach Indirect, gradient model Direct, indirect gradient model

Field reconnaissance

Mapping/Classification entities

Land use type Fuel models Fuel models, field data

Stage I

Fuel Classification framework

Carnahan, J.A., 1977. Vegetation. In: Jeans, D.N. (Ed.), Australia: A Geography. University of Sydney Press, pp. 175–195.

Specht, R.L., 1970. Vegetation. In: Leeper, G.W. (Ed.), The Australian Environment. CSIRO & Melbourne University Press,

Melbourne, VIC, pp. 44–67.

Stage I



• Scope and frame work


• Fuel sampling

• Architecture




• Web site (?)



1. Glossary has been assembled to provide and maintain consistency of definition and clarity for fuel terms used in the AFC.

2. Terms identified under three key categories: 1) General fire terms

2) Fuel

3) Sampling and statistics

3. Fuel terminology is constantly evolving

4. WikiFuel - to accommodate updates, amendments and new terms a wiki model is sought (concept)

Australian Fuel Classification Glossary of terms

Stage II

– Factor influencing sampling design

• Information required and its desired precision

• Composition of the fuel/vegetation type and its variability

• Topography and access

• Availability of personnel and level of skill

• Time and money available for the work

– Sampling techniques:

• Easily taught to field crew

• Quickly implemented

• Scalable so that any sampling unit can be measured

• Accurate enough so estimate can be used as inputs

• Repeatable so that estimates can be measured required precision

Fuel assessment Review and applications

Stage II

Suitability of sampling techniques Accuracy and cost

Fuel parameter Sampling method Accuracy Cost

Load Destructive H H

Line transect M-H M

Photo guide M L-M

Fuel dynamic models M L-M

Visual L L

Hazard rating Visual H L

Curing Line transect M-H M

Photo guide L L

Satellite M L-M

Cover Line transect H M

H= High, M= Moderate, L= Low

Stage II

Suitability of sampling techniques Accuracy and cost

Fuel parameter Sampling method Accuracy Cost

Cover Line transect H M

Aerial photos H L-M

Satellite M L-M

LiDAR H L-M

Visual L L

Vegetation mapping Aerial photos H L-M

Satellite M-H L-M

LiDAR H L-M

H= High, M= Moderate, L= Low

Stage II

Application through Australia

Accommodate a wide range of users at different scales, with various levels of detail and quality and quantity of data

Data and methods are scientifically creditable

Capture the fuelbed variability

Flexible with potential expansion

Standardised output

User interface that easily understood

PROPOSED ARCHITECTURE Concepts

Stage II

Application of Specht (1970) structural forms to fuel classification Road map

Photo : NSW Rural Fire Service

Structural forms to fuel classification Forest type

Stage II





• Fuel sampling

• Architecture




• Web site (?)



PILOT PROJECT FOR FUEL CLASSIFICATION ACT Parks and Conservation Service

Vegetation description

Fire history maps

Ground

truthing

Input fuel parameters:

• Fire behaviour

• Fire danger

•Prescribed burn planning

• Fuel hazard assessment

•Fire effects

• etc

Photo/Video

Documentation

Fuel Models

Fuel attributes:

• Fuel hazard rating

• Load

• Height

• Curing

•Particle size

• etc

Fuel type

matching

Stage III

Silvertop Rd.mov

Wildland-urban interface- fuel classification

CSIRO/ACT (subproject)- application of LiDAR (CLW, CMAR)

Stage IiI





• Fuel sampling

• Architecture




• Web site (?)



AUSTRALIAN FUEL CLASSIFICATION Future concept

Stage IV?

LINKAGE National Fire Behaviour Knowledge Base (work in progress, CMIS)

CSIRO - National Fire Behaviour Knowledge Base

Australian Fuel Classification

Stage IV?

AFC will: – Catalogue fuel data

• Fuel inputs for fire behaviour models and various DSS

– Provide accurate information for bushfire management planning

– Have different users in mind

• Fire agencies

• Local government

• Land owners

– Account both spatial and temporal scales

– Module for new knowledge

Australian Fuel Classification

AFC will not:

– Predict fire behaviour, risk, etc

– Replace existing practices


Australian Fuel Classification Constraints

Institutional barriers—a major shift in integrating each agency’s current fuel classification into a national network

Documentation and training material availability—documentation and training material is required to implement the fuel classification program

Current knowledge gaps and fuel sampling needs—e.g. fuel data is lacking for a number of fuel types throughout Australia

Custodian role—the need for an organisation to assume a custodian role to update and maintain a national fuel classification.


Thank you Jim Gould

Principal Research Scientist

Ecosystem Sciences- Bushfire Dynamics and Application t +61 2 6246 4220 e [email protected] w www.csiro.au

Miguel Cruz

Senior Research Scientist

Ecosystem Sciences- Bushfire Dynamics and Application e Miguel. [email protected]

ECOSYSTEM SCIENCES/CLIMATE ADAPTATION FLAGSHIP

Tom Jovanovic

GIS Scientist

CSRIO Ecosystem Sciences- Forest Systems

e [email protected]

Jen Hollis

Research Scientist

Documents

Australian Fuel Classification€¦ · and fire dynamics Probable approach Indirect, gradient model Direct, indirect gradient model Field reconnaissance Mapping/Classification entities