Managing Patch Edge Fuel Managing Patch Edge Fuel Effects Fire Spread in a Effects Fire Spread in a Fragmented LandscapeFragmented Landscape

By: Jacob J. LaCroix, Soung-By: Jacob J. LaCroix, Soung-Ryoul Ryu, Qinglin Li, Daolan Ryoul Ryu, Qinglin Li, Daolan

Zheng, and Jiquan ChenZheng, and Jiquan Chen

IntroductionIntroductionForests are unique in overall structure Forests are unique in overall structure – No two forests can be classified in the same way based on No two forests can be classified in the same way based on

remote sensingremote sensing

The author decides on how to best represent a forest The author decides on how to best represent a forest in a GIS model from the many natural and man made in a GIS model from the many natural and man made features features – Usually depends on the variable one is interested in Usually depends on the variable one is interested in

studying to determining which ones are okay to leave in or studying to determining which ones are okay to leave in or not includenot include

It would be nice to be able to identify the influence of It would be nice to be able to identify the influence of a feature on the dependent variablea feature on the dependent variable

Introduction to CNFIntroduction to CNFOne of our primary study sites in Northern Wisconsin, One of our primary study sites in Northern Wisconsin, Chequamegon National ForestChequamegon National Forest– We have done both real field data collection and theoretic We have done both real field data collection and theoretic

modeling with this site modeling with this site (Zheng and Chen 2000) and (Watkins et. al. (Zheng and Chen 2000) and (Watkins et. al. 2002)2002)

Some things that we know about it are: Some things that we know about it are: – It is highly fragmented, heavily harvested and intensely used It is highly fragmented, heavily harvested and intensely used

for recreationfor recreation– Numerous small patches and edges dominate the structure Numerous small patches and edges dominate the structure

of the forest of the forest

Therefore with a fire spread study, edge fuel dynamics Therefore with a fire spread study, edge fuel dynamics should influence fire fuel loadingshould influence fire fuel loading– Logical to try and consider its effects Logical to try and consider its effects

Fire with EdgesFire with Edges

However, edges are not easy to quantify or modelHowever, edges are not easy to quantify or model– Patch edge dynamics are essentially unique in space Patch edge dynamics are essentially unique in space

and time and we cannot measure every patch. and time and we cannot measure every patch.

Harper Harper et al. (2005)et al. (2005) synthesized edge literature, synthesized edge literature, defined terms and encourage us to look at forest defined terms and encourage us to look at forest edge dynamics from a landscape perspective and edge dynamics from a landscape perspective and with ecological processes that may be influenced with ecological processes that may be influenced by edgesby edgesExamine fire spread in this way, in a WI Examine fire spread in this way, in a WI landscape where the edge feature is stronglandscape where the edge feature is strong

Landscape ApproachLandscape Approach

Assigning all edge fuels together, given a Assigning all edge fuels together, given a range of low, medium and high fuel loading range of low, medium and high fuel loading scenarios relative to the dominate scenarios relative to the dominate classification of the current forest classification of the current forest

Allows us to keep the high connectivity of the Allows us to keep the high connectivity of the feature in relation to other fuels on the feature in relation to other fuels on the landscape, which is important for fire spread landscape, which is important for fire spread and other processes such as plant and and other processes such as plant and animal edge dynamicsanimal edge dynamics

Chequamegon National Forest

BrushRed Pine

Hardwood

SlashEdge

Water

ObjectivesObjectivesTo determine what impact fuels in edges will have on To determine what impact fuels in edges will have on burned area and rate of spread burned area and rate of spread To determine the level of landscape loading of the To determine the level of landscape loading of the current classification without edgescurrent classification without edges– Impact of the feature Impact of the feature

To make patch level inferences about other landscape To make patch level inferences about other landscape features with high connectivityfeatures with high connectivity– Examples: Man made: roadsides, power line corridors, Examples: Man made: roadsides, power line corridors,

railroads, and hiking trails or Natural: timberline, meadows, and railroads, and hiking trails or Natural: timberline, meadows, and riparian zones.riparian zones.

– If a majority of patches from those features were assigned to If a majority of patches from those features were assigned to fuel loadings at a high, medium or low level based on the range fuel loadings at a high, medium or low level based on the range of the current classification, what would be the result of the current classification, what would be the result

Edge Assumptions Edge Assumptions

Best based on newly created i.e. clear Best based on newly created i.e. clear cut forest edges dynamicscut forest edges dynamics

75% of our landscape is forested fuel 75% of our landscape is forested fuel assignmentsassignments– Helps infer what if a majority of patch Helps infer what if a majority of patch

assignments were low, medium or high in assignments were low, medium or high in this edge structure.this edge structure.

Assumptions/SimplificationsAssumptions/SimplificationsFreezes edge dynamics at one point in timeFreezes edge dynamics at one point in timeDistance of Fuel Influence (DFI) set at 30m is likely Distance of Fuel Influence (DFI) set at 30m is likely smaller than the probable entire Distance of Edge smaller than the probable entire Distance of Edge Influence (DEI)Influence (DEI)– 30m is subjective but is the smallest distance resolution of the 30m is subjective but is the smallest distance resolution of the

model; okay fit from literaturemodel; okay fit from literature

Equal on both sides of the edgeEqual on both sides of the edgeForms a uniform 60m belt or corridor on the landscapeForms a uniform 60m belt or corridor on the landscapeNo gradient along the DFI No gradient along the DFI We only manipulate fuel in the edge and not moisture or We only manipulate fuel in the edge and not moisture or wind in the edgewind in the edge– One regional set of weather inputs applies to all One regional set of weather inputs applies to all

Justifications for Fuel Loading Justifications for Fuel Loading ScenariosScenarios

At new forest edge creation: At new forest edge creation: (Harper et al 2005)(Harper et al 2005)

– Snags and logs from tree mortality/damage: increase fuelSnags and logs from tree mortality/damage: increase fuel– Primary process responsesPrimary process responses

Productivity increases: raising fuelsProductivity increases: raising fuelsDecomposition increases: lowering potential fuelsDecomposition increases: lowering potential fuels

– Primary structural responsesPrimary structural responsesCanopy cover, tree density and biomass decrease: decreases fuelsCanopy cover, tree density and biomass decrease: decreases fuelsDowned wood increases: increasing fuelsDowned wood increases: increasing fuels

– Secondary process responseSecondary process responseRecruitment, growth and mortality increase: increases fuelsRecruitment, growth and mortality increase: increases fuels

– Secondary structural responseSecondary structural responseSapling density, under story cover increase: increasing fuelSapling density, under story cover increase: increasing fuel

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MethodsMethodsFARSITE Model of fire spread based on CNFFARSITE Model of fire spread based on CNF– 5 layer GIS map, manipulated the fuel layer5 layer GIS map, manipulated the fuel layer– Based on the current 4 fuel categoriesBased on the current 4 fuel categories– Created 4 landscapesCreated 4 landscapes

1 - No edges assigned - 4 fuels1 - No edges assigned - 4 fuels3 – Where same edge buffer was defined: 30m to 3 – Where same edge buffer was defined: 30m to either side of either side of the edgethe edge

– Absorbed all patches less than 60m in the 4 landscapes Absorbed all patches less than 60m in the 4 landscapes – 3 custom fuels, with a range of loadings for forests, were placed into this 3 custom fuels, with a range of loadings for forests, were placed into this

edge area, one at a timeedge area, one at a time– Giving each edge landscape 5 fuels Giving each edge landscape 5 fuels

– April 2004 Weather-no rain for a 7 day durationApril 2004 Weather-no rain for a 7 day durationEliminates the rain effectEliminates the rain effect

– 16 systematic fire locations to represent the entire landscape 16 systematic fire locations to represent the entire landscape

Visual Landscape Visual Landscape Comparison Comparison

Edge LandscapeNo Edge Landscape

Landscape ComparisonsLandscape Comparisons

 

% Area in each classification

Landscapes scenarios Anderson’s fuel model #’s Custom fuel model #’s

Brush Red Pine Hardwood Slash Low Medium High

5 8 10 11 20 21 22

Control No Edge Fuel 24.5 14.5 52.5 8.5 0 0 0 Edge Low Fuel Loading 15.2 8.9 42.3 4.4 29.2 0 0 Edge Medium Fuel Loading 15.2 8.9 42.3 4.4 0 29.2

0  

 

 

Edge High Fuel Loading 15.2 8.9 42.3 4.4 0 0 29.2

Fuel Assignment ComparisonFuel Assignment Comparison     

Model Fuel Loading Rate of Spread Flame Length # tons/ac ft/min ft

1hr 10hr 100hr

5 Brush 1.00 0.50 0.00 14.0 3.5 20 Low 0.75 0.50 1.25 1.0 0.6 8 Red Pine 1.50 1.00 2.50 2.2 1.1 21 Medium 1.46 0.20 0.07 4.9 1.6 11 Slash 1.50 4.51 5.51 6.7 3.5 10 Hardwood 3.01 2.00 5.01 8.2 4.8 22 High 4.51 3.00 7.51 12.4 7.0

 

 

ANOVA Results ANOVA Results

Source DF Sum of Squares Mean Square F-Value P-value Model 18 17,880,234 993,346 34.34 < 0.0001Error 45 1,301,820 28,929Corrected Total 63 19,182,054 R-Square = 0.932 Source DF ANOVA SS Mean Square F-value P-Value Landscape 3 14,238,072 4,746,024 164.06 < 0.0001Location 15 3,642,162 242,810 8.39 < 0.0001

Results Results

0

500

1000

1500

2000

Are

a (

ha)

Low Edge Med Edge No Edge High Edge

Mean Area Burned by Landscape

Rate of Spread of all LandscapesRate of Spread of all Landscapes

Area Burned / Day by Landscape

0

500

1000

1500

2000

2500

1 2 3 4 5 6 7

Are

a (h

a)

Low Edge Medium Edge No Edge High Edge

Results Results

0200400600800

1000120014001600

Are

a (

ha

)

1 3 5 7 9 11 13 15

Locations

Mean Area Burned by Location

ConclusionsConclusionsPatch edge fuels influence the rate of fire spread and Patch edge fuels influence the rate of fire spread and improve our classification accuracy improve our classification accuracy current classification is equivalent to a landscape loading current classification is equivalent to a landscape loading that produces rates of fire spread and flame lengths that produces rates of fire spread and flame lengths between medium and high-level edge loading scenarios between medium and high-level edge loading scenarios This data allows us to predict what may happen if similar This data allows us to predict what may happen if similar or more landscape features with high connectivity are or more landscape features with high connectivity are included in fuel classification, when fire encounters it included in fuel classification, when fire encounters it Managers can use this information to control fires by Managers can use this information to control fires by altering fuel in edges during normal harvesting altering fuel in edges during normal harvesting operations operations

AcknowledgmentsAcknowledgments

Funding provided by the Joint Fire Science Funding provided by the Joint Fire Science Project Project

LEES LabLEES Lab

QuestionsQuestions