INT. J. REMOTE SENSING,

2003,

VOL.

24,

NO.

7, 1579–1586

Landsat 7 night imaging of the Nissyros Volcano, Greece A. GANAS National Observatory of Athens, P.O. Box 20048, 11810 Athens, Greece; e-mail: [email protected]

E. LAGIOS Space Applications Research Unit in Geosciences, University of Athens, Panepistimiopolis, 15784 Athens, Greece

(Received 6 July 2001; in final form 1 November 2002 ) Abstract. A night-time image of Nissyros from the ETMz (Enhanced Thematic Mapperz) scanner onboard the Landsat 7 satellite was acquired in October 2000. The image was processed to compute surface temperature on the volcano while contemporaneous field measurements on the island’s surface were collected. We confirm that the thermal sensor of Landsat 7 can map (a) the ‘orographic effect’ on land surface temperatures (temperature falling with increasing elevations) and (b) the crater surface temperature within an accuracy of 0.4–2‡ C. In addition, the low-temperature fumarolic activity of the volcano could not be detected on the mid-infrared bands (5 and 7). However, there are some high-frequency temporal variations of surface temperature inside the main crater that cannot be mapped because of the revisit capability of the sensor (16 days).

1.

Introduction Many workers have used Landsat 5 Thematic Mapper (TM) data to estimate the surface temperature of volcanic surfaces (e.g. Harris and Stevenson 1997). In addition, the advent of ETMz (Enhanced Thematic Mapperz) sensor onboard the Landsat 7 satellite offers the capability of detecting emitted energy from the Earth’s surface at the spatial resolution of 60 m every 16 days. This capability gave us the opportunity to study small volcanoes with crater diameters ranging between 100 and 250 m such as those of the Nissyros volcano, Aegean Sea, Greece (figure 1). Nissyros is a collapsed stratovolcano at the eastern end of the South Aegean volcanic arc (e.g. Lagios et al. 1998), and shows both seismic unrest and fumarolic activity, accompanied by recent gas and hydrothermal explosions (Papadopoulos et al. 1998). The crater region is relatively flat (figures 1 and 2) so there is a horizontal datum for detecting temperature anomalies. The purpose of our work is to study the effectiveness of Landsat 7 as a volcano monitoring tool when combined with simple image processing tools. This Letter presents our preliminary results. We chose to process night-time imagery in order to (a) remove the solar heating signal evident in the day scenes and (b) maximize the temperature difference between the crater and the surroundings (figure 2). International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2003 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/0143116031000066279

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Figure 1. Map showing ground localities during October 2000 campaign. Background contours are from the 1:50 000 scale map sheet ‘Nissyros’ of the Hellenic Army Geographical Service. The black circle indicates the caldera rim. The black box shows the extent of figure 2. The inset at lower left shows the location of the area within the Greek territory.

2.

Field work Two field campaigns were conducted in September 2000 and October 2000, respectively. The campaigns aimed at collecting ground temperature data, twice a day, to calibrate the surface temperatures computed from band 6 of the ETMz sensor. The land surface data were obtained inside the Stefanos crater, and at the two geothermal wells nearby. We also measured sea surface temperatures before and after the sampling of land surface temperatures. In addition, we collected local

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(a)

(b)

Figure 2. (a) Enlarged portion of figure 1 showing the ground temperature sampling localities around the main crater. Points A, B and C are located inside the crater and spaced 30 m along the north–south direction, whilst point D is 30 m to the east. Point BAR shows the location of the local meteorological station. (b) Field photograph of the Nissyros caldera showing the Stefanos crater. View is to the west.

meteorological data (air temperature, humidity, atmospheric pressure, wind speed, wind direction) from a meteorological station established within the framework of the GEOWARN project (www.geowarn.org), located at the point BAR in figure 2 (27‡ 10’ 03.1@ E, 36‡ 34’ 46.4@ N, elevation 114 m). The sampling points were established in a grid during the first campaign by use of a hand-held GARMIN

13:56 A 31.2 31.8 33.0 34.5

B 33.6 37.0 42.3 50.0

C 35.0 39.3 45.8 51.9

D 33.6 35.3 37.8 41.2

20 Oct 2000 Depth 2 cm 4 cm 7 cm 10 cm

22:16 A 22.2 25.6 28.0 31.9

B 25.2 31.6 36.3 43.9

C 28.3 36.3 41.8 48.6

D 24.2 29.8 35.5 35.1

13:35 Meteorol. Data Air temp.~19.7 Hum.~48% Pr~1000 HPa no clouds 41.2 km h{1

14:36 Meteorol. data Air temp.~20.1 Hum.~46% Pr~1000 Hpa no clouds 47.4 km h{1

13:25 Well A

14:55 Well B

28.6

28.5

20:20 SST 21.6

21:50 Meteorol. Data Air temp.~18.1 Hum.~50% Pr~1002 HPa no clouds 40 km h{1

22:30 Meteorol. Data Air temp.~17.6 Hum.~49% Pr~1003 Hpa no clouds 51.4 km h{1

21 Oct 2000 Depth 2 cm 4 cm 7 cm 10 cm

12:20 A 28.0 28.1 29.1 30.6

B 27.0 27.0 27.2 42.5

C 33.6 37.1 42.8 49.8

D 30.0 30.6 30.7 30.9

11:50 Meteorol. Data Air temp.~17.5 Hum.~44% Pr~1005 Hpa no clouds 36.8 km h{1

21 Oct 2000 Depth 2 cm 4 cm 7 cm 10 cm

22:50 A 19.8 24.6 28.1 30.0

B 20.9 27.5 40.0 47.6

C 25.8 31.1 39.0 47.6

D 20.0 26.1 30.9 34.9

22:26 Meteorol. Data Air temp.~16.2 Hum.~44% Pr~1006 HPa no clouds 34.8 km h{1

11:35 Well A 23.3

23:06 Meteorol. data Air temp.~15.9 Hum.~45% Pr~1005 Hpa no clouds

22:15 Well A 20.6

12:12 SST 23.4–23.5

15:30 SST 23.3

no wind 11:20 Well B 23.4

10:40 SST 22.5

14:45 SST 22.8

21:47 SST 22.4

23:40 SST 21.8

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20 Oct 2000 Depth 2 cm 4 cm 7 cm 10 cm

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Table 1. Temperature ground data. A, B, C and D are sampling points shown in figure 2. Sea surface temperature (SST) is measured at point FARO in figure 1. Shaded text indicates ground measurements during the satellite overpass. All temperatures are in ‡C. Time is local (GMTz3 h).

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12-channel GPS, with a planimetric accuracy of 4–6 m. At each point, ground temperatures were recorded at specified depth intervals: 2, 4, 7 and 10 cm (table 1). All points share the same elevation: 90 m. The WGS84 positions of the sampling points inside the crater are as follows: . . . .

point point point point

A: 27‡ 10’ 04.5@ E, 36‡ 34’ 42.1@ N; B: 27‡ 10’ 04.4@ E, 36‡ 34’ 41.0@ N; C: 27‡ 10’ 04.2@ E, 36‡ 34’ 40.0@ N; D: 27‡ 10’ 05.6@ E, 36‡ 34’ 40.9@ N.

The field measurements were conducted by use of digital thermometers with piercing probes because surface temperatures at the fumaroles do not exceed 103‡ C. In October 2000, we used the FLUKE2 Type K thermocouple 80PK-5A. The thermometer was calibrated against pots of hot water and was found to measure the water boiling point with an accuracy of ¡0.3‡ C. The October measurements are shown in table 1. The temperature profiles of the crater points are shown in figure 3. The September 2000 data were not used in further processing because of a failure in acquiring the ETMz data by the ESA ground station in Matera (Italy). 3.

Image processing The ETMz data were processed by the use of PCI software and ERDAS Imagine 8.4. Fast-L7A imagery was acquired at the Level 1G (Systematic) Product. The scene ID was L71045209_20920001020 (path 045, row 209 at the night world reference system). The atmospheric correction software ATCOR2 (Richter 1996) processed both thermal bands. ATCOR2 uses the theoretical approach by Singh (1988). The calibration coefficients for the low gain band 6 (20 October 2000) were as follows: offset~0, gain~0.00668 (mW cm{2 sr{1 mm{1 ). Then we geocoded the

Figure 3. Plot showing the temperature profile with depth at selected points inside the Stefanos crater, on 19:28 GMT of 20 October 2000. The location of the points is shown in figure 2.

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(a)

(b)

Figure 4. (a) Landsat 7 surface temperature map of the island of Nissyros, Aegean Sea. (b) Temperature map of the crater region. Black lines are elevation contours at 20 m intervals. Note the thermal anomaly indicated by red pixels inside the Stefanos crater.

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product using a first-order polynomial and nearest-neighbour resampling. Ground control points were selected from a 1:10 000 scale map coastline. We found that the low-gain band mapped both sea and land surface temperatures accurately while the high-gain band overestimated our ground measurements by 7–9‡ C. Based on our local meteorological data and the relatively clear conditions (visibility ranging between 15 and 25 km) during the night of the overpass we selected the US 1976 Standard atmospheric model to perform the atmospheric correction assuming constant atmospheric conditions over Nissyros. The US Standard model was also used during the October, mid-latitude experiments of Wukelic et al. (1989). The 20 m elevation contours were also digitized and overlain to show the temperature distribution with relief (figure 4(b)). Then, we checked if our assumption for the ground spectral emissivity (0.98) was correct. In theory, for surfaces in the emissivity range 0.97–0.99 (water, vegetation covered areas) the ATCOR2 calculated ground temperature deviates less than 0.5‡ C from the kinetic temperature. This accuracy is comparable to the noise level of ETMz band 6. The temperature result can be checked if the scene contains calibration targets (ideally water surfaces of known temperature). Our results (figure 4(a)) confirmed that. The sea surface temperature measured at Nissyros main port (Mandraki, figure 1) 1 h before the time of the overpass was 21.6‡ C, while the satellite sensor derived temperature is 22‡ C. Note that we have not filtered our results in order to remove outliers and smooth out SSTs, because of the temperature differences reaching more than 2‡ C along the seashore. We think this temperature difference is real and not an artefact of image processing. Moreover, filtering is more likely to affect pixels at the open sea (figure 4) that are not of interest. However, emissivities for rhyolitic volcanic surfaces such as Nissyros range between 0.95 and 0.97 (Harris and Stevenson 1997). Therefore, we expect that ATCOR2 may underestimate land surface kinetic temperatures up to 1.5‡ C (Geosystems 2000). 4.

Discussion and conclusions A qualitative result in our temperature map is the ‘reproduction’ of the orographic effect in land surface temperature (figure 4; compare with figures 1 and 2). It is reasonable to assign a high degree of confidence in this result as in day-time imagery temperature also falls with increasing elevation (e.g. Warner and Chen 2001). A second result was the absence of any thermal anomaly in the shortwavelength infrared bands (5, 7) of ETMz, indicating low temperature fumarole activity (e.g. Rothery et al. 1988), as indeed was measured in the ground (table 1). Despite the overall agreement of the image processing results with our ground data there are some notable differences. This is evident inside the Stefanos Crater area where the difference exceeds 2‡ C in several localities. Table 1 shows that only the 2 cm temperature of point A (the most northern point) agrees with our ATCOR calculations. The next closest point is D (24‡ C ground versus 22‡ C ETMz). Points B and C differ by 3 and 6‡ C, respectively, from the satellite sensor derived temperature. We attribute this temperature difference to a combination of three effects: (a) the smoothing effect of the ETMz pixel, (b) the lower spectral emissivity of the crater’s surface than the one used during data processing (0.98) and (c) the high energy flux in the south side of the crater (Brombach et al. 2001). The latter effect may be the most influential as it is related to an east–west fracture zone that crosses the crater and creates the vertical gradients seen in the profiles of

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figure 3 (points B and C). This interpretation is enhanced by the ground measurements of the next day (table 1, 21 October 2000). Points B and C show temperatures 4.3 and 2.5‡ C less than the previous day, however, still higher than points A and D. We also note that these high-frequency temporal variations of surface temperature cannot be mapped because of the revisit capability of the sensor (16 days). More research is needed to determine the role of other factors that have contributed to the high-frequency effect, such as (a) local variations in air temperature during the day, (b) local variations in wind speed, (c) fluctuations of the water table, and (d) variations in heat flux. Acknowledgments This research was funded by GEOWARN (IST 1999-12310, DGXIII). Wumme Dietrich, Carlo Cardellini, Irene Nikolaou, Vassilis Sakkas and Yannis Bakopoulos are thanked for useful suggestions. We also thank four anonymous reviewers for comments. References GEOSYSTEMS, 2000, ATCOR2 for ERDAS Imagine, User Manual (version 1.7), a Spatiallyadaptive Fast Atmospheric Correction Algorithm (Germany: Geosystems GmbH), 93 pp. BROMBACH, T., HUNZIKER, J. C., CHIODINI, G., CARDELLINI, C., and MARINI, L., 2001, Soil diffuse degassing and thermal energy fluxes from the southern Lakki plain, Nisyros (Greece). Geophysical Research Letters, 28, 69–72. HARRIS, A. J. L., and STEVENSON, D. S., 1997, Thermal observations of degassing open conduits and fumaroles at Stromboli and Vulcano using remotely sensed data. Journal of Volcanology and Geothermal Research, 76, 175–198. LAGIOS, E., CHAILAS, S., GIANNOPOULOS, J., and SOTIROPOULOS, P., 1998, Surveillance of Nissyros Volcano: establishment and remeasurement of Radon and GPS networks. Proceedings of the 8th International Congress of The Geological Society of Greece, Patras, 27–29 May, (Athens: ERE) vol. 32, pp. 215–227. PAPADOPOULOS, G. A., SACHPAZI, M., PANOPOULOU, G., and STAVRAKAKIS, G., 1998, The volcanoseismic crisis of 1996–97 in Nissyros, SE Aegean Sea, Greece. Terra Nova, 10, 151–154. RICHTER, R., 1996, A spatially adaptive fast atmospheric correction algorithm. International Journal of Remote Sensing, 17, 1201–1214. ROTHERY, D. A., FRANCIS, P. W., and WOOD, C. A., 1988, Volcano monitoring using short wavelength infrared data from satellites. Journal of Geophysical Research, 93, 7993–8008. SINGH, S. M., 1988, Brightness temperature algorithms for Landsat Thematic Mapper data. Remote Sensing of Environment, 24, 509–512. WARNER, T. A., and CHEN, X., 2001, Normalization of Landsat thermal imagery for the effects of solar heating and topography. International Journal of Remote Sensing, 22, 773–788. WUKELIC, G. E., GIBBONS, D. E., MARTUCCI, L. M., and FOOTE, H. P., 1989, Radiometric calibration of Landsat Thematic Mapper Thermal Band. Remote Sensing of Environment, 28, 339–347.