1 Purpose
Within Exupéry VFRS we provide an eruption alert level (AL) estimation. An important parameter that influences AL is the radiant flux that can be monitored using thermal remote sensing techniques. Processed data with corresponding metadata are provided to the Exupéry database in the form of XML and GEOTIFF files. Additional to anomaly characteristics, images provide an overview situation of the volcano’s surrounding area – one can monitor the general weather situation, erupted ash or SO2 cloud. Information acquired with thermal anomaly monitoring can be easily accessed through web GIS interface. In order to understand what is needed to successfully integrate thermal remote sensing into the Exupéry VFRS, the rest of this section presents the theoretical background and a literature overview of this field.
- Atmosperic transmission (in background in orange) shows two atmospheric windows (one in MIR and one in TIR). Within this windows corves of blckbodies at 700 K (MIR) and 300 K (TIR) reach their maximum.
- Detected signal is a linear combination of radiances and not of temperatures.
- An example of NTI histogram (left enalarged scale on Y ascis) for AVHRR data (used chanell 3b and 5) – hotspots occur already by the NTI of -0.9. NTI was originally developed for MODIS, where treshold during night can be set to -0.8 on a global scale to avoid false alarms.
1.1 Theoretical background
Monitoring volcanic activity from space using imaging systems in infrared spectra (IR) has been used for more than 30 years. It was first used already in 1965 as data from the Nimbus I HRIR were analyzed for Kilauea and Mauna Loa volcano [1]. A great step forward was made by [2] that used Landsat Thematic Mapper (TM) for studying active volcanoes. New methods were developed in the last years that are specialized for the specific analysis of volcanic phenomena, e.g. active lava flows [3], lava domes [4], lava-lakes [5] and fumaroles [6]. Mainly polar orbiting satellites have been utilized but the new geostationary satellites enable reliable volcanic monitoring as well [7], [8]. Early on resolution and repeat cycles were not as good as they are now but it has been shown by several authors that thermal anomalies due to volcanic activity can be detected, monitored, and measured. During the last decade most observation of volcanic eruptions were carried out using data from AVHRR, GOES, MODIS, ASTER, BIRD, and ETM+. All systems differ in spatial resolution ranging from 4×4 km for GOES all the way down to 60×60 m pixels for the ETM+. Availability of similar scenes varies also strongly, with geostationary systems sending images very 15–30 min and ASTER every 16 days.
Volcano thermal anomalies (lava lakes and lava flows) have typical temperatures in the range of 400 K to 1000 K [9]. The peak emission of radiance for blackbody surfaces of such temperatures is between 3 and 5 μm according to Wien’s Displacement Law (this part of spectra belongs to medium infra red – MIR). For a temperature of 300 K, which is the expected temperature to be measured by Earth orbiting radiometers, the peak of radiance emission is located at approximately 10 μm (this wavelength belongs to thermal infra red spectra – TIR). Thermal anomaly detection algorithms from remote sensing use this behaviour to detect thermal anomalies that usually cover only a small part of the sensor pixel area. For example, given a lava lake at 500 K that covers a quarter of the pixel area, an observation in the MIR spectra would result in a temperature of 450 K. TIR observation would in contrast be just 390 K given that the background temperature equals 300 K.
The sensitivity of the MIR spectra to thermal anomalies is so high that it reveals small (even smaller than 0.1% of the pixel area) sub-pixel anomalies that do not have any significant impact upon the TIR temperature. The thermal anomaly detection is yet not as easy as it seems because i.e. during daytime the sunlight glints might cause anomalies in the MIR, etc. Many algorithms based on temporal comparison or contextual analyses have been proposed in the last years to automatically detect thermal anomalies, e.g. [10], [11]. A simple and effective thermal anomalies detection algorithm was developed for MODIS sensor and it is based on the threshold of so called normalized thermal index NTI [12]:
NTI = (MIR – TIR) / (MIR + TIR).
Because of the influence of the solar irradiation on the MIR spectra, the NTI has to be defined in another way during the daytime. It was proposed to use a channel at 1.6 μm to correct MIR data and adjust the NTI threshold [13]. Once the thermal anomalies are detected they should be characterized by physical parameters. A standard way for its characterization is the dual band method [14]. The dual-band technique is based on a system of two equations (rad is measured radiance in channels 1 and 2, p is part of the area covered by a thermal anomaly, R is Planck’s function of a given wavelength λ and background temperature TB or thermal anomaly temperature TH).
radMIR = p • R(λMIR, TH) + (1 – p) • R(λMIR, TB)
radTIR = p • R(λTIR, TH) + (1 – p) • R(λTIR, TB)
The system contains three variables (p, TH, TB) and it is nonlinear because of the Planck’s function characteristics. It can be solved if one of the variables is assumed or additional equation with independent measurement is used. In addition to the temperature and area of the thermal anomaly, radiant flux and effusion rate provide interesting information too. Radiant flux P can be easily estimated if the area and effective radiant temperature is known (assuming that lava emissivity can be estimated) from Stefan-Boltzmann’s law:
Teff = (∑(pi • Ti exp(4))) exp (1/4),
P = A • ε • σ • Teff exp(4),
where Teff is the effective temperature, ε is the emissivity and σ the Stefan-Boltzmann constant. Two alternative approaches have been developed to estimate the radiant flux from MODIS – one is based on the temperature in the MIR channel [15] and the other one uses only MIR radiances [16].
The effusion rate is more difficult to determine because lava flow densities can be very heterogeneous, other petro-physical parameters have to be estimated from appropriate data, thus relative errors of 50 % are not unusual.
However, it is not just the effusion rate that might contain gross errors. Volcanic emission can significantly attenuate the signal in especially TIR spectra, meteorological clouds might cover the anomaly completely. The possibility of sun glint has to be considered and removed if present during daytime otherwise a huge anomaly is detected. In addition, the general atmospheric conditions and geometry between the satellite instrument and the Earth’s surface influences the satellite measurements, thus all of these effects have to be considered in the preprocessing in order to reduce noise in the results [17].
1.2 Literature
We first provide a list of the literature cited in the theoretical background section. Some might have deeper interest into this subject, thus we suggest some additional literature.
[1] S.J. Gawarecki, R.J.P. Lyon, W. Nordberg, “Infrared spectral returns and imagery of the Earth from space and their application to geological problems: scientific experiments for manned orbital flight,” American Astronautical Society, Science and Technology Series, Vol. 4, pp. 13–133, 1965.
[2] D.A.Rothery, P.W. Francis, C.A. Wood, “Volcano monitoring using short wavelength infrared data from satellites,” Journal of Geophysical Research, Vol 93, pp. 7993–8008, 1988.
[3] R. Wright, L.P. Flynn, A.J.L. Harris, “Evolution of lava flow-fields at Mount Etna, 27–28 October 1999, observed by Landsat 7 ETM+,” Bulletin of Volcanology, Vol. 63, pp. 1–7, 2001.
[4] T. Kaneko, A. Yasuda, T. Ishimaru, M. Takagi, M.J. Wooster, M.J., T. Kagiyama, “Satellite monitoring of Japanese volcanoes: a prototype AVHRR-based system. Advances in Environmental Monitoring and Modelling,” Vol. 1, pp. 125– 133, 2002.
[5] C. Oppenheimer, P.W. Francis, “Remote sensing of heat, lava and fumarole emissions from Erta ’Ale volcano, Ethiopia,” International Journal of Remote Sensing, Vol. 18, pp. 1661–1692, 1997.
[6] A.J.L. Harris, D.S. Stevenson, “Thermal observations of open degassing conduits and fumaroles at Stromboli and Vulcano using remotely sensed data,” Journal of Volcanology and Geothermal Research, Vol. 76, pp. 175–198, 1997.
[7] B.R. Hirn, C. Bartola, G. Laneve, E. Cadau, F. Ferrucci, “SEVIRI Onboard Meteosat Second Generation, and the Quantitative Monitoring of Effusive Volcanoes in Europe and Africa,” IEEE International Geoscience & Remote Sensing Symposium proceedings, 2008.
[8] A.J.L. Harris, E. Pilger, L.P. Flynn, H. Garbeil, P.J. Mouginis-Mark, J. Kauahikaua, C. Thornber, “Automated, high temporal resolution, thermal analysis of Kilauea volcano, Hawaii, using GOES satellite data,” International Journal of Remote Sensing, Vol. 22, pp. 945–967, 2001.
[9] T. Simkin, L. Siebert, Volcanoes of the World, 2nd edition, Tucson, Geoscience Press, 1994.
[10] A.J.L. Harris, S.E.J. Swabey, J. Higgins, “Automated thresholding of active lavas using AVHRR data,” International Journal of Remote Sensing, Vol. 16/18, pp. 3681 – 3686, 1995.
[11] J. Dehn, K. Dean, K. Engle, “Thermal monitoring of North Pacific volcanoes from space,” Geology, Vol. 28/8, pp. 755–758, 2000.
[12] R. Wright, L. Flynn, H. Garbeil, A.J.L. Harris, E. Pilger, “Automated volcanic eruption detection using MODIS,” Remote Sensing of Environment, Vol. 82/1, pp. 135–155, 2002.
[13] University of Hawaii, “The Daytime MODVOLC Algorithm,” 2008. URL modis.higp.hawaii.edu/daytime.html (visited October 15th 2008)
[14] J. Dozier, “A method for satellite identification of surface temperature fields of subpixel resolution,” Remote Sensing of Environment , Vol. 11, pp. 221–229, 1981.
[15] Y.J. Kaufman, C.O. Justice, L.P. Flynn, J.D. Kendall, E.M. Prins, L. Giglio, D.E. Ward, W.P. Menzel, and A.W. Setzer, “Potential global fire monitoring from EOS-MODIS.”
[16] Wooster M.J.[1], Zhukov B., and Oertel D., “Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products,” Remote Sensing of Environment, vol. 86, Jun. 2003, pp. 83-107.
[17] V. Lombardo, A.J.L. Harris, S. Calvari, and M.F. Buongiorno, “Spatial variations in lava flow field thermal structure and effusion rate derived from very high spatial resolution hyperspectral (MIVIS) data,” Feb. 2009.
Additional literature:
[18] K. Briess, H. Jahn, E. Lorenz, D. Oertel, W. Skrbek, B. Zhukov, “Fire recognition potential of the Bi-spectral InfraRed Detection(BIRD) satellite,” International Journal of Remote Sensing, Vol. 24, pp. 865–872, 2003.
[19] D. Coppola, D. Piscopo, T. Staudacher, and C. Cigolini, “Lava discharge rate and effusive pattern at Piton de la Fournaise from MODIS data,” Journal of Volcanology and Geothermal Research, vol. 184, Jul. 2009, pp. 174-192.
[20] A.G. Davies, J. Calkins, L. Scharenbroich, R.G. Vaughan, R. Wright, P. Kyle, R. Castańo, S. Chien, and D. Tran, “Multi-instrument remote and in situ observations of the Erebus Volcano (Antarctica) lava lake in 2005: A comparison with the Pele lava lake on the jovian moon Io,” Nov. 2008.
[21] J. Dehn, K. Dean, and K. Engle, “Thermal monitoring of North Pacific volcanoes from space,” Geology, vol. 28, Aug. 2000, pp. 755-758.
[22] P.H. Freeborn, M.J. Wooster, G. Roberts, B.D. Malamud, and W. Xu, “Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data,” Remote Sensing of Environment, vol. 113, Aug. 2009, pp. 1700-1711.
[23] L. Giglio, J.D. Kendall, “Application of the Dozier retrieval to wildfire characterization: a sensitivity analysis,” Remote Sensing of Environment, Vol. 77, pp. 34−49, 2001.
[24] L. Giglio, J. Descloitres, C.O. Justice, and Y.J. Kaufman, “An Enhanced Contextual Fire Detection Algorithm for MODIS,” Remote Sensing of Environment, vol. 87, Oct. 2003, pp. 273-282.
[25] A.J. Harris and D.S. Stevenson, “Thermal observations of degassing open conduits and fumaroles at Stromboli and Vulcano using remotely sensed data,” Journal of Volcanology and Geothermal Research, vol. 76, Apr. 1997, pp. 175-198.
[26] A. Harris, S. Swabey, and J. Higgins, “Automated thresholding of active lavas using AVHRR - data,” International Journal of Remote Sensing, vol. 16, 1995, p. 3681.
[27] A.J.L. Harris, J.B. Murray, S.E. Aries, M.A. Davies, L.P. Flynn, M.J. Wooster, R. Wright, and D.A. Rothery, “Effusion rate trends at Etna and Krafla and their implications for eruptive mechanisms,” Journal of Volcanology and Geothermal Research, vol. 102, Nov. 2000, pp. 237-269.
[28] C.O. Justice, L. Giglio, S. Korontzi, J. Owens, J.T. Morisette, D. Roy, J. Descloitres, S. Alleaume, F. Petitcolin, and Y. Kaufman, “The MODIS fire products,” Remote Sensing of Environment, vol. 83, Nov. 2002, pp. 244-262.
[29] Y.J. Kaufman, C.O. Justice, L.P. Flynn, J.D. Kendall, E.M. Prins, L. Giglio, D.E. Ward, W.P. Menzel, and A.W. Setzer, “Potential global fire monitoring from EOS-MODIS.”
[30] A. Koltunov and S. Ustin, “Early fire detection using non-linear multitemporal prediction of thermal imagery,” Remote Sensing of Environment, vol. 110, Sep. 2007, pp. 18-28.
[31] V. Lombardo and M. Buongiorno, “Lava flow thermal analysis using three infrared bands of remote-sensing imagery: A study case from Mount Etna 2001 eruption,” Remote Sensing of Environment, vol. 101, Mar. 2006, pp. 141-149.
[32] V. Lombardo, M.F. Buongiorno, D. Pieri, and L. Merucci, “Differences in Landsat TM derived lava flow thermal structures during summit and flank eruption at Mount Etna,” Journal of Volcanology and Geothermal Research, vol. 134, Jun. 2004, pp. 15-34.
[33] V. Lombardo, M. Buongiorno, and S. Amici, “Characterization of volcanic thermal anomalies by means of sub-pixel temperature distribution analysis,” Bulletin of Volcanology, vol. 68, Jun. 2006, pp. 641-651.
[34] J.T. Morisette, L. Giglio, I. Csiszar, and C.O. Justice, “Validation of the MODIS - active fire product over Southern Africa with ASTER - data,” International Journal of Remote Sensing, vol. 26, 2005, p. 4239.
[35] R. Ressl, G. Lopez, I. Cruz, R. Colditz, M. Schmidt, S. Ressl, and R. Jiménez, “Operational active fire mapping and burnt area identification applicable to Mexican Nature Protection Areas using MODIS and NOAA-AVHRR direct readout data,” Remote Sensing of Environment, vol. 113, Jun. 2009, pp. 1113-1126.
[36] G. Roberts and M. Wooster, “Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 46, 2008, pp. 1200-1218.
[37] G. Roberts, M.J. Wooster, G.L.W. Perry, N. Drake, L. Rebelo, and F. Dipotso, “Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI imagery,” Nov. 2005.
[38] D.A. Rothery, D. Coppola, and C. Saunders, “Analysis of volcanic activity patterns using MODIS thermal alerts,” Bulletin of Volcanology, vol. 67, Jul. 2005, pp. 539-556.
[39] R.G. Vaughan, M. Kervyn, V. Realmuto, M. Abrams, and S.J. Hook, “Satellite measurements of recent volcanic activity at Oldoinyo Lengai, Tanzania,” Jun. 2008.
[40] Wooster M.J.[1], Zhukov B., and Oertel D., “Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products,” Remote Sensing of Environment, vol. 86, Jun. 2003, pp. 83-107.
[41] R. Wright and E. Pilger, “Radiant flux from Earth's subaerially erupting volcanoes,” International Journal of Remote Sensing, vol. 29, 2008, p. 6443.
[42] B. Zhukov, K. Briess, E. Lorenz, D. Oertel, W. Skrbek, “Detection and analysis of high-temperature events in the BIRD mission,” Acta Astronautica, Vol. 56/1–2, pp. 65–71, 2005.
[43] B. Zhukov, E. Lorenz, D.Oertel, M. Wooster, G. Roberts, “Spaceborne detection and characterization of fires during the Bi-spectral Infrared Detection (BIRD) Experimental Small Satellite Mission (2001–2004),” Remote Sensing of Environment, Vol. 100, pp. 29–51, 2006.
[44] B. Zhukov, E. Lorenz, D.Oertel, M. Wooster, G. Roberts, Experience of detection and quantitative characterization of fires during the experimental small satellite mission BIRD, DLR-Forschungsbericht 2005-04, 2005.