On the Use of Satellite Remote Sensing Data to

Characterize and Map Fuel Types

Antonio Lanorte & Rosa Lasaponara

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Outline:

Fuel modelling and fuel mapping

Spatial scale and fuel purposes

Use of satellite data

Study cases

Conclusion

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Pollino National Park – Fuel Characterization

Prometheus system adapted for the study area

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Pollino National Park – Fuel Characterization

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Mappa delle tipologie vegetazionali (Basilicata)

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Mappa dei tipi di combustibile Prometheus

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Mappa dei modelli di combustibile (sistema NFFL)

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nullomolto bassobassomedioaltomolto alto

Mappa del potenziale pirologico dei combustibili

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Fuel maps are essential to fire management at many spatial and temporal scales (Keane et al. (2001).

Coarse scale fuel maps (500 m–5 km)

Mid-scale fuel maps  (30–500 m)

Fine fuel maps (5–30 m)

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Coarse scale fuel maps

Coarse scale fuel maps are at global, national down to “regional” fire danger assessment to :

more effectively plan, allocate, and mobilize suppression resources at weekly, monthly and yearly evaluation intervals.

inputs for simulating regional carbon dynamics, smoke scenarios, and biogeochemical cycles

A Number of studies have been performed see for example : Deeming et al., 1972 [6], 1977 [7]; Werth et al. 1985 [8]; Chuvieco and Martin 1994 [9]; Simard 1996[10]; Burgan et al. 1998 [11]; Klaver et al. 1998 [12]; de Vasconcelos et al. 1998 [13]; Pausas and Vallejo, 1999 [14])

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Mid-scale fuel map

Mid-scale or regional-level digital fuel maps are important in

(1) rating ecosystem health;

(2) locating and rating fuel treatments;

(3) evaluating fire hazard and risk for land management planning;

(4) aiding in environmental assessments and fire danger programs

Several studies see for example: Pala and Taylor 1989 [18]; Ottmar et al. 1994 [19]; Salas and Chuvieco 1994 [20]; Wilson et al. 1994 [21]; Hawkes et al. 1995 [22]; Cohen et al. 1996 [23] ; Sapsis et al. 1996 [24]; Chuvieco et al. 1997 [25]).

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Fine scale fuel map

Fine scale or landscape-level fuel maps are essential for:

local fire management because they also describe:

– fire potential for planning and

– prioritizing specific burn projects

inputs to spatially explicit fire growth models to simulate planned and unplanned fires to more effectively manage or fight them

A number of studies see for example (Chuvieco and Congalton 1989 [26]; Pala et al. 1990 [27]; Maselli et al. 1996 [28) (Stow et al. 1993 [29]; Hardwick et al. 1996 [30]; Gouma and Chronopoulou-Sereli 1998 [31]; Grupe 1998 [32]; Keane et al. 1998a [33]).

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Satellite time series available free of charge

Satellite data Resolutions availability Multispectral

NOAA/AVHRR

Spatial resolutions5 channels

1 km 1980th

630-690 nm (red)

760-900 nm (near IR)

2 Thermal channels

3700 nm

Landsat /TM

Spatial resolutions7 channels

30 m 1970th

450-520 nm (blue)

520-600 nm (green)

630-690 nm (red)

760-900 nm (near IR)

SPOT/VEGETATION

Spatial resolutions 4 channels

1 km 1998

450-520 nm (blue)

625-695 nm (red)

760-900 nm (near IR)

nm (near IR)

ATSR Spatial resolutions 1990th4 channels

-

1 km Red, NIR and thermal

MODIS

Spatial resolutions 2001 36 channels

1 km, 500m, 250m

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VHR Satellite

Satellite data Resolutions Panchromatic Multispectral

IKONOS (1999)

Spatial resolutions 1 mt 4 mt

Spectral range 450-900 nm

445-516 nm (blue)

506-595 nm (green)

632-698 nm (red)

757-853 nm (near IR)

QuickBird (2001)

Spatial resolutions 0,61 mt 2,44 mt

Spectral range 450-900 nm

450-520 nm (blue)

520-600 nm (green)

630-690 nm (red)

760-900 nm (near IR)

GeoEye (2008)

Spatial resolutions 0,41 mt 1,65 mt

Spectral range 450-900 nm

450-520 nm (blue)

520-600 nm (green)

625-695 nm (red)

760-900 nm (near IR)

WorldView1 (2007)Spatial resolutions 0,50 mt -

Spectral range 450-900 nm -

WolrldView-2 (2009)

Spatial resolutions 0,46 mt 1,84 mt

Spectral range 450-780 nm

400 - 450 nm (coastal)

450-520 nm (blue)

520-585 nm (green)

585 - 625 nm (yellow)

630-690 nm (red)

705 - 745 nm (red edge)

760-900 nm (near IR1)

860 - 1040 nm (near IR1)

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Pollino National Park – Fuel Characterization

MIVIS RGB picture

from 13-7-1 spectral channels MIVIS ML classification

Characteristic of the MIVIS spectral bands

Confusion Matrix

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Pollino National Park – Fuel Characterization

ASTER RGB picture from 4-2-1 spectral channels

ASTER ML classification

Characteristic of the ASTER spectral bands

Confusion Matrix

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Pollino National Park – Fuel Characterization

ASTER plot

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Pollino National Park – Fuel Characterization

Landsat TM picture from 3-2-1 spectral channels

Landsat TM ML classification

Spectral characteristic of the Landsat TM

Confusion Matrix

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Pollino National Park – Fuel Characterization

Landsat TM subpixel classification using MTMF

(Mixture Tuned Matching Filtering

Confusion MatrixTM and MIVIS ML classification TM MTMF classification

Spectral signatures LANDSAT-TM

0

100

200

300

400

500

600

700

800

0,485 0,56 0,6 0,83 1,65 2,215

TM bands (microns)

refl

ecta

nce v

alu

e fuel type 1

fuel type 2

fuel type 3

fuel type 4

fuel type 5

fuel type 6

fuel type 7

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San Giovanni in Fiore – Fuel Characterization

Aster RGB (3,2,1) Aster maximum likelihood classification

Confusion Matrix

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San Giovanni in Fiore – Fuel Characterization

QuickBird RGB (3,2,1) QuickBird maximum likelihood

classification

Confusion Matrix

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San Giovanni in Fiore – Fuel Characterization

Aster neural net classification

Confusion Matrix

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San Giovanni in Fiore – Fuel Characterization

Aster K-Means

Aster Mahalanobis Distance

Aster Minimum distance

Aster Spectral Angle Mapper

Aster Maximum Likelihhod

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San Giovanni in Fiore – Fuel Characterization

Confusion Matrices

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Comune di Latronico

5

4

3

2

1

arato

Fire susceptibility maps zoom

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Fuel maps as input to improve Fire severity map

.

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LiDAR –BASED FUEL TYPE MAP

LiDAR based classification

• Classify low point ; • Classify ground; • Classify points below surface; • Classify points by class; • Classify points by height from ground for different heights • Classify isolated points • Shape identification and load computation

Shape identification: examples of customized models based on TerraScan software

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ConclusionSatellite multispectral images provide valuable data to overcome the long and expensive field reconnaissance campaigns, which in the past were the only possible approach for fuel type mapping.

New technologies, such LiDAR data offer the possibility to characterize the single tree

Obviously, field surveys are still indispensable for fuel mapping to obtain (i) the basic source of data, (ii) to assess products generated at a lower level of detail, and (iii) to parameterise each fuel type.

Field surveys are also recommended to create field reference datasets (i.e. ground-truth) and to validate maps created from remotely sensed data products.

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GRAZIE