An overview of 20 years of research at UNB (Fredericton, Canada) on fuel moisture estimation using remote

sensing in boreal forests in Alberta, the Northwest Territories, and Alaska

Brigitte Leblon, Ph.D. ,

University of New Brunswick

Canadian Forest Fire Danger Rating System (CFFDRS)

Risk (Lightning &

Human-caused)

WEATHER TOPOGRAPHY FUEL

FIREOCCURRENCEPREDICTION

SYSTEM

FIREBEHAVIOR

PREDICTIONSYSTEM

FIREWEATHER

INDEXSYSTEM

CFFDRS

ACCESSORYFUEL

MOISTURESYSTEM

Fire Weather Index Sub-System

After Van Wagner 1987

Organic Layer Fuel Moisture and FWI codes

BWEM BWEM -- Process StructureProcess Structure

Lower Duff

Upper Duff

Live Moss

Dead Moss

Mineral Soil

1.2 cm FFMC

7.0 cm DMC

18 cm DC

After Bourgeau-Chavez 2013)

NOAA-AVHRR NDVI and Ts images over Northern Alberta and Southern

Northwest Territories

• Students: S. Oldford, G. Strickland, P.A. Fernandez-Garcia, S. White, L. Gallant

• Collaborators: M. Flannigan, M. Alexander, D. MacLean

Spatial Resolution

FWI NDVI Ts

3 days before the fire:increasing of Ts

(a) (b)

(c) (d)

235145

217

216

215216

216 195197197197

198

197197

197

198196199198199

200

211211

190

15

20

25

30

35

40

Ts(o

C)

0246810DBF

0.600.45

0.47

0.43

0.540.47

0.46 0.510.550.550.52

0.46

0.550.55

0.48

0.540.500.520.450.54

0.59

0.670.67

0.54

15

20

25

30

35

40

Ts(o

C)

0246810DBF

HF

F

H

FH

H FFHH

F

FH

H

FHFFF

H

HH

F

15

20

25

30

35

40

Ts(o

C)

0246810DBF

6.65.9

25.3

17.4

20.523.2

22.0 16.314.715.016.6

19.6

13.613.4

17.5

16.019.211.313.211.9

11.0

4.330.9

22.6

15

20

25

30

35

40

Ts(o

C)

0246810DBF

Non-July FiresJuly Fires

Relationship with FWI

211

217211

145

235

199

200

199198

197

197

195

197

197

195

199

198198

197

195

197

200

200

197

197

197

197196

211

198

200

207

215

216

190

216

195190217

215

15

20

25

30

35

40

Ts(o

C)

0 5 10 15 20 25 30 35FWI

(a) Burned Areas

211 211

145235

200

199199198197

197

197197

198

195197216197

196

198

215

216

190

216

217

15

20

25

30

35

40

Ts(o

C)

0 5 10 15 20 25 30 35FWI

(b) Unburned Areas

OutliersData used in Regression

y=4.53 + 22.41log(x)

R2 =0.5474 (P<0.0001)

RMSE=3.16 ( o C)N=37

y=0.50 + 25.80log(x)

R2 =0.6532 (P<0.0001)

RMSE=5.08 ( o C)N=22

1:1(a) Closed Coniferous

Adj R2 = 0.65

p<0.0001

(b) Closed Mixedwood

Adj R2 = 0.71

p<0.0001

1:1

(c) Open Coniferous

Adj R2 = 0.34

p<0.0001

(d) Open Mixedwood

Adj R2 = 0.75

p<0.0001

1:1 1:1

Observed DC

0 100 200 300

0

100

200

300

0 100 200 300

0

100

200

300

0 100 200 300

0

100

200

300

0 100 200 300

Pre

dic

ted

DC

0

100

200

300

RMSE = 41.00N = 38

RMSE = 42.26N = 65

RMSE = 56.89N = 136

RMSE = 38.32N = 44

Predictedversus Observed DC

NOAA-AVHRR

NDVI Ts

WeatherDatazh

LAIzo

QnQG

Ta

AET

QH

ra

rc

Via a more deterministic approach

➢Surface temperature as water stress index

➢NDVI for computing QG and ra

➢Model based on theenergetic budget equation:

AET = Qn - QG -(Cp*(Ts-Ta)/(ra+rc))

Awards

• S. Oldford (2005)• Fraser Inc. Prize for Excellence in Forestry

• Canadian Remote Sensing Society award for the best graduate thesis in remote sensing across Canada

• Best M.Sc. Thesis of the UNB Faculty of Forestry and Environmental Management

• G. Strickland (2001):• Wildfire Award given by Wildfire Fire Equipment Inc. to the best senior thesis

in Forest Fire Management across Canada

• Best senior thesis of the UNB Faculty of Forestry and Environmental Management

RADARSAT-1 & ERS-1/2 over Northwest

Territories and Alaska

Keith Abbott, Marty Alexander, David MacLean, Eric Kaschishke, Gordon Staples (with data from Laura Bourgeau- Chavez)

a) Burned boreal forest b) Mature boreal forest

Drought Code

ERS C-VV & 4 black spruce burns in Alaska(Bourgeau-Chavez et al. 1999)RADARSAT-1 C-HH & jack pine burn in NWT (Abbott et al. 2007)

ERS C-VV & 4 jack pine forest in NWT(Leblon et al. 2002)RADARSAT-1 C-HH & jack pine forest in NWT (Abbott et al. 2007)

RADARSAT-2 & Alos-Palsar polarimetric SAR

over Alaska

• Student: Laura Bourgeau-Chavez,

• Collaborators: Joseph Buckley, François Charbonneau

Low Burn Severity Moderate Burn Severity 1 Moderate Burn Severity 2

1999 Burn 10.9g/m2 woody biomassOrganic Soil depth 11 cm

1999 Burn 12.4g/m2 woody biomassOrganic Soil depth 3.4 cm

1999 Burn 22.1g/m2 woody biomassOrganic Soil depth 1.6 cm

Recently Burned Herbaceous Dominant Sites

Sparse Spruce ForestShrubby Regrowth Dense Spruce Forest

1987 Burn 163.9 g/m2 woody biomassOrganic Soil depth 8.2 cm

~1900 Burn 2880 g/m2 woody biomassOrganic Soil depth 11 cm

~1900 Burn 5050 g/m2 woody biomassOrganic Soil depth NM

Unburned Woody Dominant Sites

Two Types of Analyses

• Wet versus Dry dates comparison of data at both C-and L-bands

• Empirical algorithm development for multiple dates of C-band data to retrieve organic layer fuel moisture information

𝑵𝒐𝒓𝒎𝒂𝒍𝒊𝒛𝒆𝒅 𝑫𝒊𝒇𝒇𝒆𝒓𝒆𝒏𝒄𝒆 (%) =𝑾𝒆𝒕 𝒅𝒂𝒕𝒆 − 𝑫𝒓𝒚 𝑫𝒂𝒕𝒆

𝑾𝒆𝒕 𝑫𝒂𝒕𝒆𝒙 𝟏𝟎𝟎

Sparse Spruce

Forest

Dense Spruce Forest

Low Burn Severity

Site

Shrubby

Regrowth Site

Moderate Burn

Severity Sites

Fort Greely

Weather

Station

09 August 2008

23 August 201010 July 2010

17 May 2007

©J

AX

A 2

00

7©

JA

XA

20

10

Ma

cD

on

ald

, D

ett

wile

ra

nd

Asso

cia

tes L

td.

(20

08

) -A

ll R

igh

ts R

ese

rve

dM

ac

Do

na

ld, D

ett

wile

ra

nd

Asso

cia

tes L

td.

(20

10

) -A

ll R

igh

ts R

ese

rve

d

PALSAR L-band ~24 cm Radarsat-2 C-band ~5.6 cm

HH

HV

VV

Wet vs. Dry

DC 73 VMC ~52% DC 93 VMC 52%

DC 573 VMC 17%DC 373 VMC ~25%

Change in Backscatter Wet to Dry Conditions

-

-

-

C-Band L-Band

C-Band L-Band

Change in Decomposition Parameters

Wet to Dry Conditions

Entropy is greater on the dry date

ANOVAParameter RADARSAT-2

C-band

ALOS-PALSAR

L-band

HH 0.000 0.00

HV 0.031 0.74

VV 0.000 0.02

RR 0.000 0.84

LR 0.005 0.11

LL 0.020 0.00

Cloude-Pottier Alpha 0.698 0.31

Cloude-Pottier Anisotropy 0.577 0.29

Cloude-Pottier Entropy 0.609 0.25

Freeman Durden Double Bounce 0.052 0.01

Freeman Durden Odd Bounce 0.005 0.01

Freeman Durden Volume Scatter 0.020 0.84

van Zyl Double Bounce 0.003 0.01

van Zyl Odd Bounce 0.001 0.00

van Zyl Volume Scatter 0.020 0.86

Influence of Structural Complexity

0.0

0.2

0.4

0.6

0.8

1.0

CP

-A

Dry PALSAR Image Date WetPALSAR Image Date

0.0

0.2

0.4

0.6

0.8

1.0

CP

-H

Wet-Dry Analysis Summary

• Cloude Pottier decomposition parameters relatively unaffected by changes in moisture, but related to variations in structural complexity of the sites

• L-band: best suited to the woody – dominated sites and those with higher structural complexity

• C-band: best suited to the low biomass, recently burned sites, but still shows change from wet to dry even for the Dense Spruce site (limitation is likely around 3kg/m2)

• Strongest changes at both L- and C-band for the HH polarizations and the van Zyl surface scatter (double bounce for the Dense Spruce site)

Development of Organic Layer Fuel Moisture Retrieval Algorithms

3 types of models:

1. recent burn sites (herbaceous dominated);

2. unburned sites (shrubby and forested <3 kg/m2);

3. all sites combined (recently burned and unburned)

Model Independent

Variables (X1- X4)

SE (%) R2 p-value Lilliefors Multicoll-

inearity

1 C-HH, Intercept* 9.2 0.57 <0.005 0.452 C-HH, C-HV*, Intercept* 9.1 0.59 <0.005 0.293 C-HH, C-HV*, C-VH*,C-VV*,

Intercept*8.8 0.66 <0.005 0.35 Yes

4 Dmax, C-RR, C-VH, intercept 6.7 0.79 <0.005 0.18 Yes5 Dmax, C-VH, intercept 7.5 0.72 <0.005 0.766 Dmax, Unpolmax, C-VH, intercept 7.0 0.77 <0.005 0.23

All Sites Calibration

*coefficient not significant at 5%

All Sites Validation

Algorithm

RMSE (%Moisture) 07/11/2009

(med-wet date)

RMSE2 (%Moisture) 07/11/2009

+ 8/23/2010 (Dry Date)

C-HH 10.0 10.2

C-HH & C-HV 9.1 9.9

CHH & CHV & CVH & CVV 8.2 9.7

dmax & C-VH 8.6 9.3

dmax & Unpolmax & C-VH 7.4 6.7

dmax & C-RR & C-VH 6.4 8.8

Predicted vs. Actual Moisture Content

Pre

dic

ted

12

-15

cm

% V

olu

met

ric

Soil

Mo

istu

re

Actual 12-15 cm % Volumetric Soil Moisture

All Sites Burned Sites Unburned Sites

Moisture Across the Burned Sites

% Volumetric Moisture

Future work

➢Grassland (and tundra) fuel moisture•Canadian Prairies•Kruger National Park (South Africa)

➢DC estimation using empirical relationships→more deterministic approach

➢RADARSAT-3 compact polarimetric mode

➢Fire scar mapping

Acknowledgments

• Funding:

• Field data:

• Images: