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

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1 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 6 th 2010-September 21 st 2010

2 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; 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

3 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 nm with a spectral resolution of 1.2 nm, whilst the SWIR head covers 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

4 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 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.

5 Fire No. Date (2006) Location 1 4 Aug Magliano/ Campagnano Coordinates (lat/long) N E Data Collection Local Time (GMT+2hrs) 13:42hrs Vegetation canopy type Bushes 2 14 Aug Magliano / Campagnano N E :32-17:16 hrs Mixed vegetation 3 14 Aug Manziana / Oriolo N E Aug Cerveteri N E , 17:42 19:47hrs 15:18-15:30hrs Broad-leaved woodland orchard and cropped fields 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 m 2 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 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.

6 a) b) Figure 2 Wildfire imaged at 17:36 GMT on 14 August 2006 in Manziana/Oriolo, Italy ( 42 11, ) 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 O 2 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 K i -Bkg (1) where, Max Band K i 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.

7 Figure 3 August 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 Magliano/Campagnano 16:32. a) The Hyper RGB image (R = 621 nm,g =569 nm, B = 511 nm) is compared to the AKBD ( b). 2 1 Figure 5 August 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 Cerveteri wildfire, extended flaming area are detected by AKBD respect to VIS image.

8 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.

9 4.4 Comparison with laboratory data The experimental data were acquired on summer The experimental setup is showed in figure 11. The fuel bed was constructed atop a m tray, filled with sand to a depth of 4 cm and mounted on digitally-logged scales with 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 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 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.

10 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.

11 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 nm and 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

12 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: 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, Fiorani, A., Canestro, A., Aversa F., Amici, S. & Lombardo, V.( 2007). AIRFIRE-FIN - 01, 22/10/2007 ESA contract C/N Available at 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, This looks even more familiar. Journal of Maximising Citations Reviews, 113, D23112, doi: /2008jd 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: /2005jd006318, 2005.

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