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University of Calgary

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Schulich School of Engineering Schulich School of Engineering Research & Publications

2018

Environmental Modelling

Hassan, Quazi K.

http://hdl.handle.net/1880/106247

lecture

https://creativecommons.org/licenses/by-nc-sa/4.0

©2018 Quazi K. Hassan, under a Creative Commons Attribution-NonCommercial-ShareAlike

license: https://creativecommons.org/licenses/by-nc-sa/4.0/

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Suggested citation: Hassan, Q.K. 2018. Lecture note on: Foresting of forest fire danger conditions, In Environmental Modelling, Calgary, AB, Canada.

Except otherwise noted, ©2018 Quazi K. Hassan, under a Creative Commons Attribution-NonCommercial-ShareAlike license: https://creativecommons.org/licenses/by-nc-sa/4.0/ 1

Dept. of Geomatics Engineering; and Centre for Environmental Engineering Research and Education

Schulich School of EngineeringUniversity of Calgary

Lecture Noteon:

Forecasting of Forest Fire Danger Conditions

Environmental Modelling(ENGO 583/ENEN 635)

Review of Last Topics



Topics of Discussion

o Introduction to forest fire

o Factors involved in the fire occurrences

o Current forest fire forecasting system in Canada

o A case study

“Use of remote sensing-derived variables in developing a forest fire danger forecasting system”

Introduction (1)

o Canada posses ~10% of the global forest

o Forest occupies ~45% of Canada’s landmass

o ~77% of the forested area is known as boreal forest

Source:http://atlas.nrcan.gc.ca

Source: Natural Resources Canada that allows non-commercial reproduction. Such reproduction is a copy of an official work that is published by the Government of Canada and that the reproduction has not been produced in affiliation with, or with the endorsement of the Government of Canada; Retrieved from http://www.nrcan.gc.ca/forests/measuring-reporting/classification/13179 on 21 February 2018.



Introduction (2)

According to Natural Resources Canada (http://www.nrcan.gc.ca/forests/fire-insects-disturbances/fire/13143 as retrieved on 16 Feb. 2018):

o Canada has observed about 7,466 forest fire occurrences on an average every year over the last 25 years. Though, the total area burned varies widely from year to year, but averages about 2.5 million hectares annually.

o Only 3% of all wildland fires that start each year in Canada grow to more than 200 hectares in area. However, these fires account for 97% of the total area burned across the country.

o Fire suppression costs over the last decade in Canada have ranged from about $500 million to $1 billion a year.

Introduction (3)

Note: The reproduction is a copy of an official work that is published by the Government of Canada and that the reproduction has not been produced in affiliation with, or with the endorsement of the Government of Canada. Retrieved from http://cwfis.cfs.nrcan.gc.ca/ha/nfdb on 21 February 2018.



Introduction (4)

Akther and Hassan (2011) summarizes both the consequences and benefits in relation to forest fires.

Consequences

o Economic loss due to burning of the tress and properties nearby,

o Emitting CO2 into the atmosphere,

o Health hazard due to smoke, and

o Loss of human life in fighting large fires, etc.

Benefits

o Controls the diseases and insects, and stand species and communities,

o Influences wildlife habitat patterns and populations,

o Exposes the mineral soils to promote the regeneration of the trees,

o More exposure to the incident solar radiation,

o Release of seeds for certain tree-species,

o Small fires are very useful in reducing the excessive loading of the dead vegetation, that helps to control the large fires, etc.

o Fire occurrence § It is defined as the number of fires started in a given area over a

particular time period, as a function of the conditions of weather and fuel, and source of ignition (Akther and Hassan, 2011).

o Fire danger § It may be defined as the probability of fire occurrences.

o Fire risk § It represents the fire starting probability or chance due to the

potential number of ignition sources in a given area (Wotton, 2009).

Introduction (5)



Factors Involved in the Fire Occurrences

o There are three major factors in the fire occurrences:

§ Weather conditions: ‒ temperature, rainfall amounts and duration, relative humidity, cloud

clover, and wind speed and direction, etc.

§ Fuel conditions: ‒ the type of fuel, quantity, fuel moisture condition, etc.

§ Source of ignition‒ Lightning‒ Man-made

Current Forest Fire Forecasting System in Canada (1)

(Adapted from van Wagner, 1987)



Current Forest Fire Forecasting System in Canada (2)

o In Canada, the forest fire danger conditions are calculated on daily basis using a component of Canadian Forest Fire Danger Rating System (CFFDRS), known as Fire Weather Index (FWI) (van Wagner, 1987).

o The FWI requires point-based measurements of several weather/climatic variables (i.e., noontime air temperature, relative humidity, and wind speed; and 24-h accumulated rainfall).

o Consequently, the spatial dynamics of the fire danger is calculated using geographic information system (GIS)-based interpolation techniques. However, the implementation of different interpolation techniques (e.g., inverse distance weighting, spline, kriging, etc.) may produce different map outputs using the same input datasets (Chile`s and Delfiner, 2012).

o In order to eliminate these uncertainties, remote sensing-based data have greater advantage over the point-based data as it acquires the spatial variability and able to capture information over remote areas (Leblon, 2005; Wang et al., 2013).

(Adopted from ©Chowdhury and Hassan, 2013 licensed under CC BY)

“Use of remote sensing-derived variables in developing a forest fire danger forecasting system”




Objectives

o The overall objective was to develop a forest fire danger forecasting system (FFDFS) by combining MODIS-derived indices and its implementation over the boreal forested region of Alberta during the 2011 fire season. The indices of interest were the 8-day composites of:

§ TS (surface temperature);§ NMDI (a measure of water content in canopy); and § NDVI (a measure of vegetation greenness).

o There were two specific objectives, such as:§ The implementation of a data gap-filling technique in replacing the null values

in the primary input variables (TS, NMDI, and NDVI) due to cloud contamination and other reasons; and

§ Quantitative evaluation between the outcome of the fire danger conditions and actual fire occurrences.


Study Area

o Panel (a) Location of Alberta province in Canada; andpanel (b) extent of study area within a MODIS-derived land cover map during 2008.

o Evergreen and deciduous broadleaf and needleleaf forest cover 75% of the study area.




Data Requirements

o MODIS-based 8-day composites of TS (i.e., MOD11A2 v.005) images at 1 km spatial resolution from April to September, 2011.

o Surface reflectance data (i.e., MOD09A1 v.005) at seven spectral bands at 500 m resolution.

§ Bands centered at 0.645 µm (i.e., red), 0.86 µm (i.e., near infrared, NIR), 1.64 µm (i.e., shortwave infrared1, SWIR1), 2.13 µm (i.e., SWIR2) were used.

o MODIS based fire spots images (i.e., MOD12A2 v.005) at 1 km spatial resolution were used to validate the outcomes of the FFDFS.


The NDVI is the most widely used index in the history of remote sensing. 8-day composite values of MODIS at 500 m spatial resolution were computed using the expression first described in (Rouse et al., 1973) as follows:

where, ρ is the surface reflectance-values of the near infrared (NIR)(centered at 0.86 µm) and red (centered at 0.645 µm) spectral bands.

Data Pre-processing (1)


𝑵𝑫𝑽𝑰 =𝝆𝟎.𝟖𝟔 − 𝝆𝟎.𝟔𝟒𝟓𝝆𝟎.𝟖𝟔 + 𝝆𝟎.𝟔𝟒𝟓



The NMDI is a relatively new index first described by Wang and Qu (2007). Its 8-day composite values at 500-m resolution were computed using the following expression:

where, ρ is the surface reflectance values of NIR (centered at 0.86 µm) and SWIR (centered at 1.64 and 2.13 µm) spectral bands.



𝑵𝑴𝑫𝑰 =𝝆𝟎.𝟖𝟔 − 𝝆𝟏.𝟔𝟒 − 𝝆𝟐.𝟏𝟑𝝆𝟎.𝟖𝟔 + 𝝆𝟏.𝟔𝟒 − 𝝆𝟐.𝟏𝟑

Data gaps in MODIS-derived input variables

Amount of data gaps in TS, NMDI, and NDVI images were calculated where pixel values were not produced due to cloud effects and other reasons by considering:

§ Quality control (QC) image of the respective TS images; and

§ Quality Assurance (QA) image considering both of the MODLAND QA bits and band-specific quality bits of the respective surface reflectance images for calculating both NMDI and NDVI variables.





{ }m x mm x m 1)(iX(i)XAbs1)X(iX(i) --±-=

Methodology (1)

The gap-filling algorithm:

where, X(i) and X(i-1) are the filled and non-gap values for the variables(i.e., TS/NMDI/NDVI) of interest during i and i-1 period respectively;

are the mean values of the variables (i.e., TS/NMDI/NDVI)of interest for the window size of m x m during i and i-1 period respectively.

mxmmxm 1iXandiX )( )( -


X(i-1)

XiX(i-1)

Xi

if X(i) m x m ≥ X(i-1) m x m thenX(i-1) + Absolute difference

m x m window

TS, NDVI and NMDI gap-filling window

5 x 5 window

3 x 3 window

Period

if X(i) m x m < X(i-1) m x m thenX(i-1) – Absolute difference

{ }m x mm x m 1)(iX(i)XAbs1)X(iX(i) --±-=

(Adapted from ©Chowdhury and Hassan, 2013 licensed under CC BY)

Methodology (1 contd.)



T S(i)

/NM

DI(i

)/ND

VI(i)

i i+1

TS(i) or NMDI(i) or NDVI (i)

1 6 11 16 21 26(a) Period

Input variables

Danger (i+1) High Low

TS(i) ≥ TS(i) < TS(i)

NMDI(i) ≤ NMDI(i) > NMDI(i)

NDVI(i) ≤ NDVI(i) > NDVI(i)

Comments

Low moisture in vegetation may support fire

Low greenness may support to initiate fire as it relates with a set of biophysical variables

High temperature favor fire

(b)


𝑫𝒂𝒏𝒈𝒆𝒓 = 𝑓 𝑻𝒔 𝒊 /𝑵𝑴𝑫𝑰 𝒊 /𝑵𝑫𝑽𝑰 𝒊

Methodology (2)

If all variables represent high

At least two variables represents high

At least one variable represent high

None of the variables represent high

Very high

High

Moderate

Low


Input variables

Danger (i+1) High Low

TS(i) ≥ TS(i) < TS(i)

NMDI(i) ≤ NMDI(i) > NMDI(i)

NDVI(i) ≤ NDVI(i) > NDVI(i)

Comments

Low moisture in vegetation may support fire

Low greenness may support to initiate fire as it relates with a set of biophysical variables

High temperature favor fire

Methodology (2 contd.)




Data gaps were found approximately 11.41%, 0.86%, and 0.08% in the TS, NMDI, and NDVI images respectively during the entire study period

Results and Discussion (1)


Window

size

DOYAverage

97 137 177 217 249

r2 RMSE r2 RMSE r2 RMSE r2 RMSE r2 RMSE r2 RMSE

3×3 0.87 0.819 0.90 0.869 0.89 1.116 0.90 0.804 0.94 0.808 0.90 0.883

5×5 0.81 1.009 0.85 1.085 0.83 1.413 0.86 0.994 0.91 0.981 0.85 1.096

7×7 0.77 1.112 0.81 1.215 0.79 1.583 0.82 1.106 0.89 1.085 0.81 1.220

9×9 0.74 1.178 0.79 1.305 0.76 1.693 0.80 1.181 0.87 1.150 0.79 1.301

11×11 0.72 1.227 0.76 1.371 0.74 1.768 0.78 1.234 0.86 1.210 0.77 1.362

13×13 0.70 1.264 0.75 1.424 0.73 1.824 0.77 1.274 0.85 1.235 0.76 1.404

15×15 0.69 1.295 0.73 1.468 0.71 1.868 0.76 1.306 0.85 1.285 0.75 1.444

Study

area0.20 2.116 0.42 2.328 0.49 2.626 0.33 2.233 0.37 2.597 0.36 2.380

Coefficient of determination (r2) and root mean square error (RMSE) between observed and predicted TS variable using different window size.




(a) 97 (b) 137 (c)177 (d) 217 (e) 249 day of year

Res

ults

and

Dis

cuss

ion

(3)

Valid

atio

n of

TS

data

gap

-filli

ng


(a) 113 (b) 153 (c)193(d) 233 (e) 265 day of year


Res

ults

and

Dis

cuss

ion

(4)

Valid

atio

n of

NM

DI d

ata

gap-

fillin

g




Res

ults

and

Dis

cuss

ion

(5)

Valid

atio

n of

ND

VI d

ata

gap-

fillin

g

(a) 105 (b) 145 (c)185 (d) 225 (e) 257 day of year


o Temporal dynamics of averaged values for TS, NMDI, and NDVI during 2011 shows identical to the generalized pattern.

o The quadratic fits to the variable of interest as a function of DOY, we found strong relations which were similar to previous studies (Akther and Hassan, 2011).

Mea

n va

lues

of T

S

0.0

0.2

0.4

0.6

0.8

260

270

280

290

300

89 137 185 233 281

Mea

n va

lues

of N

MD

I/ND

VI

NDVI

TS

NMDI

DOY

NDVI: y = -4E-05x2+0.0185x-1.13, r²=0.97TS: y = -0.0018x2+0.6898x+226.74, r²=0.82

NMDI: y = -2E-05x2+0.0083x-0.33, r²=0.91





No. of input variables satisfying the fire danger conditions

Fire danger classes % of data Cumulative % of

data

All Very high 33.58 33.58

At least 2 High 47.62 81.21

At least 1 Moderate 15.29 96.50

None Low 3.50 100.00

Percentage of data under each fire danger categories using combined input variables of TS, NMDI, and NDVI in comparison to the fire spot and fire

occurrence data during the period 2011.



Fire danger classVery high

Moderate High

Low Fire mask

Fire danger map for the period 9–16 May 2011 generated by combining the TS, NMDI, and NDVI variables acquired during the prior 8-day period (i.e., 1–8 May 2011).

Fire Danger Class

Slave Lake

Fort McMurray

% o

f pix

els

in th

e fir

e m

ask

0255075

1000

25

50

75

100

17.4

34.1 35.6

12.9

0

20

40

60

Rel

ativ

e Fr

eque

ncy

(%)




Concluding Remarks

§ Here we evaluated the potential of three MODIS-based indices/variables (i.e., TS, NMDI, and NDVI) for forecasting the forest fire danger conditions in the boreal forested region of Alberta.

§ Our quantitative analysis revealed that the combined variables could better forecast the forest fire danger condition.

§ Thus, it will potentially be incorporated in the framework of forest fire management in the Province of Alberta.


The following issues need to be considered:

§ Other important weather variables, in particular to precipitation and relative humidity.

§ The incorporation of lightning data during i+1 period with our fire danger map from the i period could be considered.

§ Vegetation phenological stages (i.e., the influence of climatic variables on the plant developmental phases).

§ The current temporal scale (i.e., 8-day) remains a bit challenging from operational point of view. Thus, it requires to develop methods to forecast at daily temporal scales.

§ These developments should be tested prior to implementing in other the ecosystems rather than boreal ones.

Further Considerations




o Akther, M.S. and Hassan, Q.K. 2011. Remote sensing-based assessment of fire danger conditions over boreal forest. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 4, 992-999.

o Chile`s, J.-P. and Delfiner, P. 2012. Geostatistical modeling spatial uncertainty, 2nd ed., Wiley, Hoboken 699p.

o Chowdhury E.H., and Hassan Q.K. 2013. Use of remote sensing-derived variables in developing a forest fire danger forecasting system. Natural Hazards, 67, 321-334.

o Leblon, B. 2005. Using remote sensing for fire danger monitoring. Natural Hazards, 35, 343-359.o Rouse, J.W., Hass, R.H., Schell, J.A., and Deering, D.W. 1973. Monitoring vegetation systems in the

Great Plains with ERTS. NASA SP-351, Washington, DC NASA, Third ERTS-1 symposium, 309-317.o van Wagner, C.E. 1987. Development and structure of the Canadian forest fire weather index.

Canadian Forestry Service.o Wang, L., and Qu, J.J. 2007. NMDI: a normalized multi-band drought index for monitoring soil and

vegetation moisture with satellite remote sensing. Geophysical Research Letters, 34, L20405.o Wang, L., Zhou, Y., Zhou, W., and Wang, S. 2013. Fire danger assessment with remote sensing: a

case study in Northern China. Natural Hazards, 65, 819-834.o Wotton, B.M. 2009. Interpreting and using outputs from the Canadian Forest Fire Danger Rating

System in research applications. Environmental and Ecological Statistics, 16, 107-131.

Reference

Sample Questions?

o Explain the disadvantages and advantages of forest fire.

o Explain the factors involved in forest fire occurrences.

o Explain the current forest fire forecasting system in Canada.

o Describe some of the remote sensing-based methods of monitoring forest fire danger conditions.

o Explain a remote sensing-based forest fire forecasting method.

o Define the following: § Fire occurrence§ Fire danger§ Fire risk§ Normalized multi-band drought index§ Normalized difference vegetation index

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