O E C D S U M M A R Y R E P O R T

N E W H Y P E R - S P E C T R A L A N A L Y S I S

M E T H O D S F O R W I L D F I R E

I N V E S T I G A T I O N A N D

C H A R A C T E R I S A T I O N

Dr. Stefania Amici

1. Subject of the Research

Wildfire is amongst the most significant natural disturbance agents, impacting a wide range of

ecosystems at small and large scales in almost all forested ecosystems of the planet. The subject of

this research is wildfire in the frame of preservation of forests as a natural resource, and specifically

the detection, characterisation and study of wildfires using new remote sensing methods that are

capable of being applied to the next generation of small, light and affordable imaging

spectroradiometers that can be mounted on civil protection aircraft, unmanned aerial vehicles or

Earth-orbiting micro-satellites.

Host supervisor: Prof. Martin Wooster, Geography Department, Kings College

of London

Dates: April 6th 2010-September 21st 2010

2. Relevance

The most significant outcome of the proposed work will be a full and critical evaluation of

the use of low-cost, new technology hyperspectral sensors for the detection of small-to-large

actively burning fires (from airborne vantage points, and via-simulation also from low Earth

orbit) and the determination of the ability of such methods to separate areas of smouldering

and flaming vegetation. During the active-fire phase, real time spectrally-based information

from airborne surveys provides the prompt operational benefit of localizing the flaming

area, thus aiding in their mapping and suppression. Further, quantitatively distinguishing

smoldering from flaming vegetation provides information useful in refining quantitative

estimates of the range of gas species released in these two distinct combustion phases. If

we broaden our consideration to the civil use of unmanned aircraft (UAS—Unmanned Aerial

Systems) as applied to fire study and mitigation, such quantitative spectral analysis

algorithms may yield real-time maps of the fire during fires, and of remaining hotspots

during the post-burn phase. Further, for pre- and post- fire phases, information derived from

VIS-SWIR sensor data mounted on a UAS may give information about erosional

vulnerability, state of the burned soil, burned area mapping, evaluation of infrastructure

damage, and so on.

3 Objectives of the fellowship

This research effort will exploit new state-of-the-art hyperspectral remote sensing data for

forest fire detection and characterisation, focusing on information that can be in future used

by forest protection services.

Traditional remote sensing studies of actively burning wildfires focus on the detection and

study of the fires various physical characteristics, and are usually based on broadband

measurements in the middle infrared (MIR; 3-5 µm), thermal infrared (TIR; 8-14 µm) and

shortwave infrared (SWIR; 1.0 -2.5 µm) spectral regions. However, Vodacek et al. (2002)

demonstrated that a remotely detectable signature, specific to flaming combustion, is

produced by the excitation of the potassium contained within the fuel (vegetation) as it is

heated within the flames (so-called K-emission lines).

The main objective of the proposed project is to further develop K-emission remote sensing

for forest fire detection and characterisation, for eventual use of the methods within fire

information systems used by forest protection and civil protection services. We have

concentrated on developing analysis methods for the spectral signature fires acquired by

new, lighter and lower cost imaging spectrometers. These instruments are much more

adaptable and deployable than previous such instrumentation, and have none of the

technical difficulties found when using cooled thermal imaging devices. We have

investigated new methods to detect actively burning fires and characterise their intensity,

based on the K-emission signatures in combination with SWIR emission data, and provide

an assessment of the capability that such instruments and analyses will add to forest fire

information services – for example in identifying the most intensely burning regions for

application of fire control measures or for post-fire recuperative strategies.

4. Major Achievements

In the following section we describe the methods and the results obtained in the frame of

the study. We first describe tha airborne data analysis concerning: data pre-processing are

section 4.1, K emission analysis (section 4.2) , comparison between K emission (VIS) and

Fire R Power (FRP, see section 4.3). Section 4.3 is dedicated to the laboratory scale data

and results analysis.

4.1 Hyper Data

The used data were collected during a series of Mediterranean wildfire aerial surveys using

a HYPER/SIM.GA spectrometer designed and built by Selex Galileo.

HYPER is the VIR-SWIR component of an aircraft modular system actually consisting of

two Electro-Optical Heads (EOH) integrated in a single box. The Visible Near Infrared

(VNIR) head covers the spectral range 400 - 1000 nm with a spectral resolution of 1.2 nm,

whilst the SWIR head covers 1000 - 2500 nm with a spectral resolution of 5.4 nm. Raw

data are acquired with a 12 bit ADC for Visible Optical Head and a 12 bit ADC for the SWIR

Optical Head. The final result is a three-dimensional data set (named Data Cube) that

correlates each ground pixel with its corresponding electromagnetic spectrum. The

―modular‖ philosophy at the base of HYPER allows a flexible arrangement of instrument

accommodation and therefore the possibility to place the instrument on small platforms by

changing the mechanical interface.

The study area was located in Latium region of Italy during August 2006, normally the month

of peak fire occurrence in the area. The strategy for the operational activity was the

following: the aircraft was held ready to fly by receiving an event communication in three

different ways: 1) The Civil Protection National Unified Command for operations of airborne

fire fighting (COAU), coordinating all the aircraft (Canadair CL 205 and CL 405) operating in

Italy provided by mobile the locations of events occurring in the area of interest; 2) Several

helicopter bases, operated by private companies under Latium Region contract, were in

touch with the base to communicate a fire event as soon as identified; 3) The aircraft, in

particular the ultra light, patrolled the northern Latium area in search on of fires during early

afternoon (1:00 -3:00pm local time) when most fires were expected to occur. This strategy

led to the discovery of some fire events, though mainly of a rather limited size.

The HYPER system was standing ready to operate for the whole month of August 2006.

When a fire event occurred, a real time flight was performed to attempt to image the wildfire

and record its spectral emissions. The HYPER system was operated by the pilot who could

change certain instrument settings and parameters ( e.g. calibration procedure selection;

integration time selection, etc.). Though the summer of 2006 was unusually wet, with wildfire

numbers half those of the previous year, imaging over flights of ten active wildfires were

successfully obtained. Figure 1 shows the location of fires occurred on August 2006 in

Latium area.

Figure1 geo-located position of wildfire occurred during August 2006 in Latium area.

We concentrate here on the analysis on four of these fires (table1) that are characterised by

different kinds of vegetation fuels (according to the CORINE LAND COVER database of

ARPA - Italy characterizing the canopy typology of Italy). The altitude of the over flights

ranged between 800m and 1500m, providing a VIS channel pixel size of 0.7 to 1.5m. The

date were stored on board in HDF format.

Table 1 fires analysed considering different kinds of vegetation fuels (according to the CORINE LAND COVER database of ARPA (Italy) that characterizes the canopy typology of Italy).

A quick look of the data was realized to evaluate the data quality and to realize a preliminary

classification according to the acquisition date, the target, integration time, line and fire

position. These data were subsequently transformed into spectral radiance units (W m2 sr-1

µm-1) based on information on the flat field, dark current, integration time and instrument

transfer functions provided by the instrument manufacturer. These calibration procedures

are described in Fiorani et al. (2007) and reportedly provide spectral radiances accurate to

within 6%. Unfortunately, most of the data acquired over the actively burning fires, proved

to be saturated in the SWIR bands due to the intense thermal emissions resulting from

flaming action. The exception was the Fire 1 acquired on August 4 2006 for which the

instrument integration time was set short enough such that saturation was disallowed in

most of the active fire pixels.

4.2 Potassium Emission analysis

The first results of data analysis has pointed out the good performance of HIPER to resolve

the K douplet. This represent the first time that K emission doublet has been resolved by

airborne data. Figure 2 shows HYPER data from Fire 3, whose characteristics are listed in

Table 1. The true colour composite (Figure 2a) indicates substantial smoke generation and

a clearly recognizable flaming area, with the final made apparent by the substantial visible

radiation emanating from the. Spectra of an apparently smoke-free pixel located in this

flaming area indicates the K doublet to be strong and well resolved (Figure 2b; spectra A),

while spectra from a completely smoke-covered pixel (Figure 2b; spectra B) shows a much

weaker but still clearly evident K-line signature. At the site of the smoke-free flaming pixel

the sodium (Na) emission line signature can also be seen, with a peak centred at 592 nm.

Fire No.

Date (2006)

Location Coordinates (lat/long)

Data Collection Local Time (GMT+2hrs)

Vegetation canopy type

1 4 Aug Magliano/ Campagnano

N 42° 08‘ 41.641‖ E 12° 25‘ 50.237

13:42hrs Bushes

2 14 Aug Magliano / Campagnano

N 42° 08‘40.729‖ E 12°27‘ 20.009‖

16:32 - 17:16 hrs

Mixed vegetation

3 14 Aug Manziana / Oriolo

N 42° 11‘ 28.329‖ E 12° 08‘ 47.061‖

17:42 – 19:47hrs

Broad-leaved woodland

4 19 Aug Cerveteri N42° 00‘ 13.125‖ E12° 05‘ 12.53‖,

15:18-15:30hrs orchard and cropped fields

Figure 2 Wildfire imaged at 17:36 GMT on 14 August 2006 in Manziana/Oriolo, Italy ( 42° 11’, 12° 08’) by the HYPER sensor. At left (a) is the R=621 nm, G =569 nm, B=511 nm colour composite that highlights the flaming fire and smoke-covered fire location, whilst at right (b) is the spectral profile of location A (flaming) and B (smoke covered). The location of the K doublet and the O2 and Na absorption is indicated.

A second step consisted in testing new metrics by using the two Potassium peaks. Until now

detection of fire by K emission has been limited by low spectral resolution at 50-70%

detection. Firstly the band ratio between the two emission peaks has been tested. Secondly

an ‗advanced‘ K Band Difference (AKBD) was derived, consisting of the signal difference

between the maximum spectral radiance recorded in the spectral window corresponding to

the K-band doublet range, and that recorded just outside of this window (i.e. at 779 nm):

Advanced K Band Difference = Max|Band Ki| -Bkg (1)

where, Max|Band Ki| is the maximum spectral radiance recorded in the 764 to 772 nm

wavelength range, and Bkg is that recorded at 779 nm. All are expressed in standard units

of spectral radiance (e.g. W.m-2.sr-1.µm-1). The advantage of a algorithm based on band

differences, rather than band ratios, is that it quantifies the magnitude of the K-line emission

over and above the level of the background 'Planckian' emission curve. A metric based on

band ratios would vary with the level of the background Planckian signal, and not only with

the magnitude of the K-band emission line. Figure 3, 4 , 5, 6 shows the respectively the

visible image and the corresponding AKBD.

a) b)

Figure 3 August 14 2006 in Manziana/Oriolo, Italy17:45 a) The Hyper RGB image (R = 621 nm,G =569 nm, B = 511 nm) is compared to the AKBD, b).

Figure 4 August 14 2006–Magliano/Campagnano 16:32. a) The Hyper RGB image (R = 621 nm,G =569 nm, B = 511 nm) is compared to the AKBD ( b).

Figure 5 August 4 200 –Campagnano wildfire (a); The AKBD (b) image point out two different areas interested by fire: one (1) stronger that suggest flaming source and a second (2) weaker, that suggests a decreasing flaming process. Note: images are not geo-located.

Figure 6 August 19 2006-Cerveteri wildfire , extended flaming area are detected by AKBD respect to VIS

image.

1

2

These data have shown an improvement in the detection capability of flaming areas. the

AKBD metric allows the detection of small fires that may be important as precursors to

larger burns and as predictors of fire spread when incorporated into operational fire models.

4.3 SWIR analysis

As regards SWIR measurements, unfortunately much of the SWIR data from HYPER

were saturated due to the intense thermal emissions resulting from flaming fires. The

Fire 1 (Table 1), was an exception since the pixel integration time was set short

enough to avoid saturation of most of pixels at wavelengths shorter than ~ 2000 nm.

The variation of the thermal endmember approach described in details by Wooster

et al. (2005) was applied. the Visible data were resampled at the same SWIR spatial.

An customised version of the Wooster et al. (2005) FRP retrieval method was

applied. A series of solar reflected spectral endmembers following the method

described by Dennisson et al. (2006), were collected from non-burning pixels in the

scene, and the modelled spectrum of the FRP retrieval method selected as the

combined solar reflected and thermally emitted signal whose sum best matched that

of the measured signal. The figure 7 shows the obtained result. The result shows a

good correlation between FRP and maximum K emission peak and a further

investigation on no saturated data set (at airborne scale or laboratory scale) is

suggested.

Figure 7. HYPER-SIM.GA data of Fire 1 (Table 1). (a) True colour composite (R=621 nm, G =569 nm, B=511 nm), highlighting the flaming fire location and the moderate smoke production, (b) SWIR false colour composite (R=2224 nm, G=1565 nm and B=1250 nm), and (c) comparison of the FRP and AKBD metrics.

4.4 Comparison with laboratory data

The experimental data were acquired on summer 2003. The experimental setup is showed

in figure 11. The fuel bed was constructed atop a 1.5 -1.2 m tray, filled with sand to a depth

of 4 cm and mounted on digitally-logged scales with 0.005 kg precision. The remote sensing

instruments were mounted 11.5 m above the fuel bed, viewing directly downwards and

aligned so that their fields of view were centred on the middle of the fuel bed, providing a 2

m diameter circular FOV for the spectroradiometer and a 4 - 3 m field of view (pixel size 1.27

-1.27 cm) for the MIR camera (Wooster et a., 2005). Between three and eight fires were

conducted per day, and horizontal and vertical video records and logs of the

meteorologicalconditions were obtained for each. (Wooster et a., 2005). Fires were ignited

via application of a flame to the upwind edge of the fuel bed, and the MIR camera, digital

scales and spectroradiometer data logged at 1 Hz, 1 Hz and 0.2 Hz, respectively over the

fire duration. The data analysed are referred as RUN1 and RUN4 measured on 14 July

2003.

Figure 8 Experimental geometry, where a 11.5 m high scaffold tower (main picture) allowed the remote sensing instruments (upper inset) to view vertically downward onto the fuel bed (lower inset)(Wooster et al. 2005).

4.6 Data processing

The two data set (RUN1 and RUN4) were processed. As regard the Visible spectral range

the first step consisted in verifying the correspondence between flaming phases and K

emission. Figure 9 shows an example of thermal camera image (snapshot) and

corresponding acquired spectrum for RUN4

Figure 9. IR image of early stage burn and corresponding K emission spectral profile.

For each ―Run‖, the K emission doublet values were retrieved in order to investigate the

difference or band ratio metrics. A comparison between AKBD metric and respectively

temperature and FRP, was firstly performed on RUN1. As showed in figure 10 a) and b), a

good correspondence was found.

Figure 10 RUN 1 analysis: (a) FRP compared to AKBD; (b) Temperature max compared to AKBD. Time is expressed in second from 00:00.

Secondly, RUN 4 was analysed. This Run , longer then RUN1 confirmed the good correspondence between FRP, temperature and intensity of K peak (figure 11).

Figure 11 RUN 4 (a) FRP compared to AKBD; (b) Temperature max compared to AKBD. Time is expressed in second from 00:00.

5 Discussion

In this study we have focussed the thermal emission signature from burning vegetation in the

VIS and SWIR spectral range, at both laboratory-scales and at the scale of real wildfires.

We have developed calibration procedure for hyperspectral sensor on board of airplane.

We have focused on the potassium emission line spectral signature present in the VIS

spectral range with an higher spectral resolution compared to previous studies.

We are able to distinguish separate K emission lines at 766.5 nm and 769.9 nm. A band

differencing approach was used to quantity the magnitude of the K emission line signature

above the Planckian fire-emitted radiation signal, and using this 'AKBD' metric we

demonstrate the first quantitative relationships between K-line signature strength and the

existing remotely sensed fire measures of fire radiometric temperature and fire radiative

power.

Application of these methods to data from a new hyperspectral imaging system (HYPER-

SIM.GA) indicated that K-emission line signatures are apparent even in the presence of thick

smoke that apparently obscures the fire from view in the VIS spectral region.

We conclude that potential future developers of airborne fire detection and mapping sensors

should investigate possibilities for operating in the VIS spectral region at the K-emission line

wavelengths, which in some cases could be a cheaper solution when compared to sensing

in the longer wavelength MIR or TIR spectral regions.

Sensors optimized for the K bands may be used to detect flaming area, through smoke,

supporting the activities of fire suppression.

In particular the use of small sensors on board of UAV (unmanned air vehicle) may reduce

risk and cost of these activities.

Further, by using the same VIS-SWIR sensor, post fire analysis may be carried on (e.g.

burned area mapping, evaluation of infrastructure damage, and so on).

6. Acknowledgements

This work was supported by the Organisation for Economic Co-operation and Development

(OECD), the European Space Agency, the NERC National Centre for Earth Observation

(NCEO; NE/F001444/1), and by equipment loans from the NERC Equipment Pool for Field

Spectroscopy (EPFS) and the NERC Field Spectroscopy Facility (FSF), whose staff are

gratefully thanked for their advice and cooperation. Gareth Roberts, George Perry and

others who assisted from King‘s College London are thanked for their participation in the

laboratory scale experimental data collection. For the airborne data collection we thank the

participants of the AirFire Project (ESA contract C/N 2009), which was led by Kell S.R.L and

provided the ultra-light Allegro aircraft We further thanks Agostino Fiorani, Antonio Bartoloni

and the whole team of the Kell S.R.L who organized the Airfire airborne campaign.

Selex-Galileo are thanking for their strong support in supplying the HYPER-SIM.GA sensor

and Demetrio Labate, Michele Dami, Tiziano Mazzoni, Leandro Chiarantini, Francesco

Butera for information related to data processing and the and the whole Galileo Avionaca

team who developed the HYPER. A special thanks goes to the late Fabrizio Aversa for

leading the project and to whom this work is dedicated.

With regard to INGV Remote sensing team, lead by Maria Fabrizia Buongiorno, we thank

Valerio Lombardo as the work package leader. An anonymous referee is thanked for the

careful and constructive comments that helped improve the content of the manuscript.

7 References

Aversa, F. (2006). AIRFIRE Campaign an airborne campaign for the validation and

calibration of fire monitoring system based on satellite data processing. Campaign Concept

Document, 24/04/2006.

Lee, J. L., Hoppel, K.,(1989). Noise Modeling and estimation of remotely sensed images,

IGARSS ‘89 2: 1005-1008.

Dennison, P. E., Charoensiri, K., Roberts, D. A., Peterson, S. H., & Green, R. O. (2006).

Wildfire temperature and land cover modeling using hyperspectral data. Remote Sensing of

Environment, 100, 212−222.

Fiorani, A., Canestro, A., Aversa F., Amici, S. & Lombardo, V.( 2007). AIRFIRE-FIN - 01,

22/10/2007 ESA contract C/N20090. Available at

http://earth.esa.int/campaigns/DOC/AIRFIRE-FIN.pdf.

Vodacek, A., Kremens, R.L., Fordham, A.J., Vangorden, S. C., Luisi D., Shott, J.R. &

Latham, D.J. (2002). Remote optical detection of biomass burning using a potassium

emission signature. International Journal of Remote Sensing, vol. 23, NO.13,2721-2726This

looks even more familiar. Journal of Maximising Citations Reviews, 113, D23112,

doi:10.1029/2008JD010717

Wooster M. J., Roberts G., and Perry G. L. W. Retrieval of biomass combustion rates and

totals from fire radiative power observations: FRP derivation and calibration relationships

between biomass consumption and fire radiative energy release, JOURNAL OF

GEOPHYSICAL RESEARCH, VOL. 110, D24311, doi:10.1029/2005JD006318, 2005.

8. Follow-up

The results of the research has been submitted at Remote Sensing of Environment Journal

and is under evaluation process.

The study will be submitted at one of the following conferences EGU conference in Wien

2011 or ERSEAL Edinburgh 2011.

Ultimately the research lead to a jointly-proposed UK-Italian enhanced wildfire

characterisation proposal for a airborne and then potentially spaceborne mission, for

example, the participation at the proposal of TES-GAP mission in the frame of ESA-Explorer

8.

The study is preparatory for the of Working Package (FIRE) under responsibility of S.Amici,

for the ASI-AGI project, funded by Italian, Space Agency.

9. Satisfaction

The OECD Co-operative Research Programme fellowship has increased indirectly my

career opportunities. Thank to this collaboration I have had opportunity to focus on a field of

research that may offer new opportunities of job. Further I have had occasion to work in a

team that represent the excellence in this field of research. The organization at King‘s

College of London was perfect and I have had no problems. I had all facilities I need and I

had a great support by supervisor.

10. Advertising the Co-operative Research Programme

I learnt about the Co-operative Research Programme by Prof. Martin Wooster during our first

short collaboration on 2009.

In order to make it more visible I suggest to advertise the call in the scientific conferences.