Pre and Post fire vegetation behavioral trends from satellite

MODIS/NDVI time series in semi-natural areas

Tiziana Montesano, Antonio Lanorte, Fortunato De Santis, Rosa Lasaponara

Institute of Methodologies for Environmental Analysis, National Research Council, Tito, Italy

Outline

•Fire disturbance

•Scaling properties

•DFA (Detrended Fluctuation Analysis)

Previous studies and focus of the current areas

•Study cases

•Results

•conclusion

• Among the environmental phenomena, vegetation can be viewed as very

complex

WHY ?

Changes in vegetation cover play active role

WHY ?

surface energy, water balance (Betts et al., 1996)

carbon cycle (Betts, 2001)

Variation in composition and distribution

response to natural hazards (drougth, wind, floods, rain erosion)

response to anthropic stresses (industry, land abandonment, fires,..)

•Traditionally, vegetation monitoring by remotely sensed data has been carried out by using vegetation indices, which are quantitative measures, based on vegetation spectral properties, that attempt to measure biomass or vegetative vigour

•Vegetation indices are mainly derived from reflectance data from the red (R) and near-infrared (NIR) bands. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red against the high reflectance of leaf mesophyll in the near infrared

.4 .6 .8 1.0 1.2

Green and healthy Veg

Dry and stressed Veg

Red NIR

Plant Reflectance

NDVI =NDVI =NIR - REDNIR - RED

NIR + REDNIR + RED

Spectral bands

FIRES

significant influence on dynamics of ecosystems and permanent changes in the composition of vegetation community

-decrease in forests, loss of biodiversity, change in ecosystems functioning-soil degradation, alteration of landscape patterns and ecosystem functioning thus speeding desertification processes up

Vegetation stress can be defined as any disturbance that adversely influences plants

July 3, 1998

Pre-fire NDVI map

Post-fire NDVI map

FractalFractal ScalingScaling

Power-law statisticsPower-law statistics

f=f(x) f(ax)=g(a)f(x) f(x)=bg(x)

g(x)=xc

FRACTAL: EXPECTED MEASURE OF THE SAMPLE PATH INCLUDED WITHIN SOME RADIUS SCALES WITH THE SIZE OF

THE RADIUS

TEMPORAL FLUCTUATIONSCORRELATION STRUCTURES

MEMORY PHENOMENA

Purely random

Antipersistent

Persistent

1E-3 0.01 0.110-11

10-10

10-9

10-8

10-7

10-6

10-5

10-4

Frequency (1/hour)

S(f

)

=0

10-4 10-3 10-2 10-1 100

10-5

10-4

10-3

10-2

10-1

100

101

102

103

104

S(f

)

f (1/hour)

>0

1E-3 0.01 0.110-10

10-9

10-8

10-7

10-6

10-5

10-4

Frequency (Hz)

Pow

er

<0

)()( fSnI I

ffS I

1)(

Lasaponara et al., Chaos Solitons & Fractals, 24, 139-149, 2005

Telesca et al., Ecol. Model., 185, 531-544, 2005

Telesca and Lasaponara, Physica A, 359, 747-751, 2006

Telesca et al., Chaos Solitons & Fractals, 2006

Telesca et al., Comm. Nonlin. Sci. Numeric. Simul. , 2006

ARE FIRES A FRACTAL PROCESS?

101 102 103 104

1

10

48 h

24 h

AF

=1.11+0.05

AF

()

(min)

2000

0 10 20 30 40 50

0.5

1.0

AF

Ath 0.0 5.0x105 1.0x106 1.5x106 2.0x106 2.5x106 3.0x106 3.5x106 4.0x106

200

400

600

800

c) Siz

e

t (min)

0.00

0.25

0.50

0.75

1.00

b)

1.01.11.21.31.41.5

a)

DC

dclusterizeT

Poissonian1

)(2

))()(()(

21

TN

TNTNTAF

k

kkAllan Factor(Thurner et al., Fractals, 5, 1997)

Data: Fire cataloguesData: Fire catalogues

Telesca and Lasaponara, Rem. Sens. Environ. , 101, 95-103, 2005

Telesca et al., Physica A, 361, 699-706, 2006

Lasaponara and Telesca, Int. J. Remot. Sens., 2006

IS VEGETATION A FRACTAL SYSTEM?

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

-2.2

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

a)

d=0.87

DFA=0.74

log 10

(F(n

))

log10

(n) (decade)

forest cover

F()

<0.5 antipersistence =0.5 white noise > 0.5 persistence

Detrended Fluctuation Analysis(Peng et al., CHAOS, 1995)

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

-2.2

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

b)

d=0.82

DFA=0.64

shrub-land cover

log 10

(F(n

))

log10

(n) (decade)0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

-2.4

-2.2

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

c)

d=1.05

DFA=1.1

mixed cover

log 10

(F(n

))

log10

(n) (decade)

Data: SPOT-VGT decadal NDVI time series

Data: SPOT-VGT decadal NDVI time series

Could the fractal dynamics of vegetation covers be significantly influenced by fires? Is it possible to identify and quantify such effect?

PROBLEM STATEMENT

Telesca and Lasaponara,Geophys. Res. Lett., 32, L21401, 2005

Telesca et al., Fluctuation Noise Lett., 5, L479-L487, 2005

Telesca et al., Int. J. Nonlin. Sci. Numeric. Simul., 7, 279-284, 2006

Telesca and Lasaponara, Int. J. Remote Sensing,, 2007

Telesca and Lasaponara, Physica A, 2007

burned unburned0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

ave,

+ burned: 23 sites

+ unburned: 10 sites

Telesca et al., submitted, 2006

Telesca et al., submitted, 2006

Data: SPOT-VGT decadal NDVI time series

Data: SPOT-VGT decadal NDVI time series

Focus of the current areas

Investigating the scaling properties of managed areas versus “natural”areas

• 2001-2009 series of MODIS satellite data

• Sites: (i) Andriace and (ii) Crotone

• Fire Time Occurence: • (i) July 19, 2003, 234 ha• (ii) September 10, 2004• 210 ha• Data available at NASA website Crotone

Andriace

Crotone site Land use from Corine

Andriace Land use from Corine

NDVId = [NDVI - <NDVI>]/

In order to eliminate the phenological fluctuations, for each decadal composition of each pixel, we focused on the departure

from the decadal mean <NDVI>. The decadal mean <NDVI> is calculated for each decade, e.g. 1st decade of January, by averaging over all years in the record. Investigations were conducte on the NDVId departure series computed using formula

Elimination of phenological fluctuations

Time variation of pixel_1 of the Crotone site of NDVI

Time variation of NDVI d for pixel_1 of the Crotone site

Time variation of NDVI for pixel_1 of the Andriace site

Time variation of NDVId for pixel_1 of the Andriace site

Scaling exponents relating to NDVId for the Crotone (indicated as KR) and Andriace (indicated as AN) test sites, average and

standard deviations for each set of scaling exponents.

CONCLUSIONS

1. Burned and unburned vegetation covers are characterized by fractal behaviour

2. The scaling exponents of both types (burned and unburned) suggest a persistent behaviour of the vegetation dynamics.

5. . Results from this paper clearly pointed out the diverse vegetation behavior observed for natural and managed areas before and after fire occurrence.

3. For managed areas (cultivated) Post-fire vegetation dynamics are less persistent than pre-fire vegetation dynamics

4. The difference between the two groups(burned/unburned or pre-fire/post-fire vegetation covers) is significant

THANKS!!