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Exploration and monitoring geothermal activity using Landsat ETM + images
Journal of Volcanology and Geothermal Research 275 (2014) 14–21
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Exploration and monitoring geothermal activity using Landsat ETM + images A case study at Aso volcanic area in Japan Md. Bodruddoza Mia a,⁎, Jun Nishijima b, Yasuhiro Fujimitsu b a b
Department of Geology, Faculty of Earth and Environmental Science, University of Dhaka, Dhaka-1000, Bangladesh Department of Earth Resources Engineering, Faculty of Engineering, Kyushu University, Fukuoka 819-0395, Japan
a r t i c l e
i n f o
Article history: Received 2 September 2013 Accepted 10 February 2014 Available online 22 February 2014 Keywords: Aso volcano Landsat ETM + image Hydrothermal alteration Thermal anomaly Radiative heat flux Heat discharge rate
a b s t r a c t Thermal activity monitoring in and around active volcanic areas using remote sensing is an essential part of volcanology nowadays. Three identical approaches were used for thermal activity exploration at Aso volcanic area in Japan using Landsat ETM + images. First, the conventional methods for hydrothermal alteration mapping were applied to find the most active thermal region after exploring geothermal indicator minerals. Second, we found some thermally highly anomalous regions around Nakadake crater using land surface temperature estimation. Then, the Stefan-Boltzmann equation was used for estimating and also monitoring radiative heat flux (RHF) from the most active region of about 8 km2 in and around Nakadake crater in the central part of the Aso volcano. To fulfill the required parameter in the Stefan-Boltzmann equation for radiative heat flux, the NDVI (Normalized differential vegetation index) method was used for spectral emissivity, and the mono-window algorithm was used for land surface temperature of this study area. The NDVI value was used to divide land-cover in the study area into four types: water, bare ground, mixed and vegetated land. The bare land was found within the most active region. Vegetation coverage area showed an inverse relationship with total RHF in this study as health of thermally stressed vegetation supports this relationship. The spatial distribution of spectral emissivity ranged from 0.94 to 0.99 in our study. Land surface temperature was estimated using a mono-window algorithm and was highest LST in 2008 and lowest in 2011. The results of RHF showed that the highest pixel RHF was found to be about 296 W/m2 in 2008. Total RHF was obtained of about 607 MW in 2002 and the lowest was about 354 MW in 2008. The RHF anomaly area was found the highest in 2002 and was lowest in 2011. The highest total heat discharge rate (HDR) obtained about 3918 MW in 2002 and lowest total HDR about 2289 MW in 2008 from this study area. But in the case of Nakadake crater alone, the higher thermal activity was observed in 2008 and was less in 2004. The study showed that Landsat thermal infrared is the best option for thermal activity exploration and monitoring at Aso volcano as well as in any active volcano region considering high efficiency and low cost. © 2014 Elsevier B.V. All rights reserved.
1. Introduction The study area, Aso volcano, consist of a large caldera of about 18 × 24 km with 10 central cones of basaltic and basaltic–andesite ejecta, situated in central Kyushu Island, Japan (Fig. 1). The central cone, Nakadake, is the most active volcano in Japan, where repeated historic magmatic eruption occurred (Terada and Sudo, 2012). The eruption processes involved strombolian activity, phreatomagmatic explosions and ash emission (Ono et al., 1995; Miyabuchi, 2009). The Nakadake crater erupted characteristically black ash during the active eruption period (Ono et al., 1995). The other surface thermal features in Aso ⁎ Corresponding author. Tel.: +880 1818240937; fax: +880 28615583. E-mail addresses: [email protected] (M.B. Mia), [email protected] (J. Nishijima), [email protected] (Y. Fujimitsu).
http://dx.doi.org/10.1016/j.jvolgeores.2014.02.008 0377-0273/© 2014 Elsevier B.V. All rights reserved.
volcano, known as west zone geothermal features (WZGF), are the Yunotani, Yoshioka and Jigoku-Tarutama hot springs areas. Historically, the fumaroles of this volcano emit a significant amount of volatiles and thermal energy throughout the years. Specially, the Nakadake crater releases significant amounts of volcanic gas, including 200–400 tons/day of SO2 during calm periods (Terada and Sudo, 2012). There is a hot crater lake in Nakadake 200 m in diameter, locally known as Yudamari. The lake water temperature remains at about 60 °C throughout the year in calm periods which is much higher than ambient temperature. Terada et al. (2008) estimated heat flow by applying the model of Ryan et al. (1974) to the observation that the heat discharge rate across the lake surface is almost constant at approximately 220 MW, with the exception of an abrupt increase to 280 MW that coincided with a rapid decrease in the water level in August 2007. Terada and Sudo (2012) studied a few new fumaroles formed at the Yoshioka hot spring since
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Fig. 1. Location map of the study area showing aspect and altitude based on SRTM DEM data. Heat loss study was done within the bold black rectangle area in and around the Nakadake crater.
June 2006 and obtained heat discharges of about 4.6 × 106 J/s, which is almost 20 times higher than that of the previous natural thermal activity in these fumaroles area. Although there are many studies for thermal activity evaluation based on ground geophysical and airborne imaging or video camera imaging, there is no research solely based on satellite thermal infrared data in this study area. Remote sensing is an essential part of volcanology nowadays for exploration and monitoring geothermal systems. Hydrothermal alteration is related to previous geothermal activity i.e., high temperature and pressure from magmatic eruption, interaction or overburden pressure that altered the originally formed minerals to altered mineral deposits. So, the exploration of hydrothermal alteration could indicate the previous thermal activity in and around the volcanoes. The monitoring of hydrothermal alteration
regions could also give an indication of thermal activity of any active volcanoes. The bare regions of volcanoes show the most alteration mineral deposits and can be mapped using satellite images, which also indicate geothermal active zones of any volcanoes. Actually, hydrothermally altered minerals are living fossils of previous thermal activity that changed the original mineral to a specific altered mineral with certain physical conditions of the environment such as high temperature and pressure. So, remote sensing data could be used to map geothermal indicator minerals over large areas, often identifying new areas of interest. There are many studies around the world related to hydrothermal alteration mapping using multispectral Landsat and Aster satellite images (e.g. Abdelsalam et al., 2000; Ramadan et al., 2001; Madani et al., 2003; Yetkin, 2003; Ramadan and Kontny, 2004; Mia and Fujimitsu, 2012).
Table 1 Summary results of this study. Year
2002 2004 2006 2008 2011
Area (%)
100 81.31 80.42 79.13 79.09
Ambient temp. (°C)
Landcover (km2) Water
Bared land
Mixed land
Vegetated
0.44 0.02 0.06 0.18 0.09
2.87 1.73 1.36 1.22 1.26
2.82 3.60 4.13 4.03 3.61
1.81 2.60 2.39 2.51 2.99
13.4 21.7 16 17.3 14.3
Atmospheric transmissivity
0.88 0.81 0.85 0.89 0.93
LST (°C)
RHF (W/m2)
Min
Max
Min
Max
11.48 20.79 15.76 11.41 10.29
51.44 49.91 54.45 62.2 51.57
0.18 0.69 0.02 0.01 0.07
235.28 181.38 243.95 296.16 228.67
Total RHF (MW) (Area adjusted)
Total RHF (MW) in Nakadake crater
606.51 465.79 603.89 354.26 405.71
10.47 5.57 11.52 13.41 9.06
Notes: The area of 2002 image is covered as 100% of our study area and others image have around 80% of our study area as there are image gaps after 2003 in case of Landsat ETM + image. We used this area percentage to adjust in case of total heat flow as well as landcover study. Ambient temperature was acquired from AMEDAS Aso san station hourly data. Atmospheric transmissivity was calculated using NASA web atmospheric parameter correction calculator and obtained above 80% in all our images acquisition time.
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Thematic mapping using multispectral Landsat satellite images covers the visible and infrared spectrum of hydrothermal alterations (Hunt, 1979; Hunt and Ashley, 1979). In this study, the conventional hydrothermal alteration mapping methods using Landsat ETM + image were applied to explore geothermal indicator minerals to find the most active areas in Aso volcano. Thermal monitoring of active volcanoes is very important not only to understand geothermal systems but also to detect precursory signals of future eruptions. A fumarolic eruption could happen with unpredictable hazards to surrounding populations living within potentially active volcanoes. Monitoring for eruptions from active fumaroles could prevent the surrounding environment from thermal, ash and lava flow hazards. Ground based monitoring for heat flow may not be possible due to unstable ground as well as national park areas around the active volcanoes but remote measurement especially using satellite thermal infrared data could be a good solution. In this case, satellite monitoring has succeeded in detecting large thermal anomalies using Landsat and ASTER satellite images in and around thermally active regions around the world. Landsat TM/ETM + has a long history of use for thermal feature studies of volcanoes (Harris et al., 2009; Savage et al., 2010; Mia and Fujimitsu, 2012). Landsat TM has a single thermal band with 30 m in resolution (after 25 February 2010, the thermal infrared band was used to process at 30 m instead of 60 m) and can be downloaded from USGS archives free of cost upon request. As it is only possible to analyze the radiative portion of total heat loss using satellite infrared data directly, we can multiply the estimated radiative heat flux (RHF) using the identified relationship coefficient between radiative heat loss and total heat loss from geothermal or fumarolic areas to estimate heat discharge rate (HDR), as obtained in two previous studies (Harris et al., 2009; Mia et al., 2013). Practically, the HDR from any active fumarolic area is the summation of convective, conductive and radiative components of heat losses without solar heat load. RHF is the radiative portion of surface heat loss that passes as electromagnetic waves from a geothermal field without the effect of solar heating. Radiative geothermal heat flux (GHFR) means the heat only comes from the subsurface, without direct or indirect effects of solar or diffuse convective steaming, via electromagnetic radiation to a satellite sensor. The application of satellite remote sensing for detecting and quantifying thermal anomalies as well as heat losses due to volcanic activity has been scientifically used in recent years (Harris et al., 2009; Savage et al., 2010; Mia et al., 2012; Mia and Fujimitsu, 2013). In this paper, the applicability of Landsat thermal infrared data was demonstrated for thermal activity exploration and monitoring in one of the most active volcanoes in Japan to evaluate the geothermal resources as well as for future eruption indication. The prime objectives of this study are (a) to delineate thermally active regions using the conventional hydrothermal alteration and thermal anomaly methods, (b) to estimate the radiant component of heat flux from 2002 to 2011 by Landsat ETM + thermal infrared data and (c) finally to estimate the HDR of the study area after multiplying the total radiative heat loss by the relationship coefficient between RHF and HDR. To do this, the paper is organized as follows. The first section presents an introduction about motivation, the study area, previous studies and objectives; the second step presents the geologic settings of Aso volcano; and the third section explains about materials and methodology used in this study. In the fourth section, the outcome of this study is discussed in detail. Finally, we draw conclusions in the fifth section, and list the limitations of this study in sixth section.
successively flowed into valleys between the Basement Mountains, filled up them and formed pyroclastic-flow plateaus. Between each of the four large pyroclastic-flow units, eruptions produced numerous tephra fallout layers. Post-caldera cones have arisen near the center of the caldera since the Aso-4 eruption (Ono and Watanabe, 1985). There are at least 17 cones visible on the surface, consisting of both lava and pyroclastics (Hoshizumi et al., 1997). The central post-caldera cones vary depending on their rock chemistry, which ranges from basalt to rhyolite (Ono and Watanabe, 1985). Nakadake is the only active central cone in Aso volcano since ca. 22–21 ka (Miyabuchi et al., 2004) and formed an old edifice (agglutinate and lava), a young edifice (pyroclasts and lava) and a still younger pyroclastic cone (Ono and Watanabe, 1985). The old edifice is the main cone of Nakadake volcano, rising about 900 m from the caldera floor (Miyabuchi et al., 2008). The young edifice is believed to have formed during Holocene time, and the youngest pyroclastic cone rose subsequently inside the young volcanic edifice of Nakadake crater (Ono and Watanabe, 1985). The active Nakadake crater formed in the youngest pyroclastic cone is a composite of seven craterlets aligned N-S (Miyabuchi et al., 2008). The most characteristic event of Nakadake during active periods is continuous fallout of black sandy ash, known as ash eruption (Ono et al., 1995). Strombolian eruptions have also occurred during intense active periods by scattering red-hot scoria clasts around the vent in Aso volcano (Miyabuchi et al., 2008). The lake water temperature ranged from 49 to 82 °C in the Nakadake crater from 2003 to 2005 (Miyabuchi et al., 2008). The Nakadake Crater Lake was a peak-activity volcanic lake during the period of 2003–2005 on the basis of the physico-chemical classification scheme for volcanic lakes (Pasternack and Varekamp, 1997). 3. Materials and Methods Landsat Enhanced Thematic Mapper plus (ETM +) images were used for this study (path/row: 112/37). The Landsat sensor bearing satellite passed through this study region from 10:35 to 10:40 am of all acquired images. A total of 5 sets of images were obtained from the USGS Earth Resource Observation Systems Data Center, which were both radiometrically and geometrically corrected. All images were 8 multi-spectral bands: 4 VNIR (visible and near infrared), 2
2. Geologic settings of Aso volcano Aso is one of the largest caldera volcanoes in the World, located in central Kyushu, southwestern Japan. The pyroclastic-flow deposits of the Aso caldera are divided into four distinct units: Aso-1 (270 ka), Aso-2(140 ka), Aso-3 (120 ka) and Aso-4 (90 ka) in ascending order (Ono et al., 1977). Miyabuchi (2009) demonstrated that the flows
Fig. 2. Image processing flow chart of this study.
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Fig. 3. Location of active region of Aso volcano using black bold rectangle: (a) Band composite map (R: G: B = 4:7:2) shows hydrothermally altered region as blue color; (b) Band Ratio (R: G: B = 7/4:4/3:5/7) map shows hydrothermally altered iron ions in the red region; (c) Principal component analysis map shows hydroxyl hydrothermal region as brighter pixels (Table 2); (d) Principal component analysis map shows iron-oxide hydrothermal region as brighter pixel (Table 2); (e) Landcover map prepared using NDVI value shows almost bare region as active region; and (f) Thermal anomaly map shows some active regions as higher LST in the central part in and around the Nakadake crater.
SWIR (shortwave infrared) and 1 TIR (thermal infrared) (NASA, 2009). The imageries were acquired during summer or end of summer season on 16 October 2002, 03 September 2004, 25 September 2006, 16 October 2008 and 23 September 2011 respectively. The Landsat ETM + image has a single channel of thermal infrared at 30 m resolution. The local meteorological air temperature data was obtained from the nearest AMEDAS meteorological station's website (i.e., Aso san station). The NASA (National Institute of Water and Atmospheric Research) website based calculator for atmospheric parameter correction was used to obtain the atmospheric transmissivity at the time of image acquisition and obtained above 80% in all years of the study time (Table 1). The flow chart was followed for the image processing steps for heat loss estimation using Landsat images (Fig. 2). Initially, we analyzed all images for atmospheric correction and reflectance value calculation of all visible, NIR and SWIR bands explained in our previous study (Mia et al., 2012), where the dark object subtraction method was applied
for atmospheric correction process. Then, in second steps, three approaches were taken for detection of active regions of Aso volcano: hydrothermal alteration mapping using the conventional techniques, delineation of thermal anomaly zone and landcover mapping. The conventional hydrothermal alteration mapping techniques were applied in the first stage for delineating geothermal indicator minerals i.e., band composite, band ratio and principal component analysis; the methods are explained in one our previous study in details (Mia and Fujimitsu, 2012). In case of thermal anomaly detection, the mono-window algorithm for estimating land surface temperature was applied where we needed to input the spectral emissivity, ambient temperature and atmospheric transmissivity at the time of image acquisition. The emissivity of the study area was estimated using NDVI (Normalized Differential Vegetation Index) method. For this, the vegetation index (NDVI) was estimated, which is a process for calculating the vegetation index of any region, that is the ratio of reflectance value of red (band 3) and near infrared
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Table 2 4) region of electromagnetic (band Principle component analysis. Input band
spectrum. The NDVI value ranges
Eigenvector loading for hydroxyl mapping Band 1
Band 4
Band 5
Band 7
PC1 PC2 PC3 PC4
0.089 0.319 −0.903 −0.273
0.606 −0.719 −0.260 0.219
0.719 0.328 0.340 −0.509
0.327 0.523 −0.021 0.787
Input band
Eigenvector loading for iron-oxide mapping
PC1 PC2 PC3 PC4
Band 1
Band 3
Band 4
Band 5
0.095 −0.340 −0.527 −0.774
0.207 −0.697 −0.366 0.580
0.632 0.578 −0.492 0.159
0.741 −0.254 0.589 −0.199
Eigenvalue (%)
4. Results and Discussion
84.929 12.695 2.105 0.271 Eigenvalue (%) 79.837 16.802 2.912 0.449
Notes: In case of hydroxyl type of minerals, the Band 5 is highly reflective and the Band 7 is high absorption in general shown using high or moderate eigenvector loading with opposite sign in PCA analysis for Landsat ETM + images. On the other hand, for ironoxide type of minerals, the Band 1 is highly reflective and the Band 3 is high absorption in general shown using high or moderate eigenvector loading with opposite sign in PCA analysis for Landsat ETM + images.
from -1 to +1, higher than the value of 0.5 indicated the vegetated region and lower the value of 0.2 is the bared region. Then, the spectral emissivity was estimated using the NDVI based emissivity calculation method of Valor and Caselles (1996). Normally, the emissivity value ranges from 0.7 to 0.99 for the real earth surface. The land surface temperature was estimated where the mono-window algorithm was applied because the Landsat ETM + image has only one band of thermal infrared band (Qin et al., 2001). Finally, according to the Stefan-Boltzmann's law, the radiative heat flux (RHF) was estimated from the active region in Aso volcano by using the following Eq. (1) (Bromley et al., 2011; Mia et al., 2012). 4 4 Q r ¼ τσε Ts –Ta
of the relationship coefficient of HDR and RHF ( 6.46), obtained from two previous studies of volcano fumaroles (Harris et al., 2009; Mia et al., 2013).
ð1Þ
Where, Qr = radiative heat flux (W/m2), τ = atmospheric transmissivity, σ = Stefan-Boltzmann constant, ε = emissivity, Ts = land surface temperature (LST) (K) and Ta = ambient temperature (K). The ambient temperature from the AMEDAS meteorological Aso san station's hourly data was used here. The total heat discharge rate of the study area has been calculated after multiplying the above total RHF using the identified mean value
The conventional hydrothermal alteration mapping techniques such as band composite, band ratio and principal component (PC) analysis were used to detect the active regions of Aso volcano area using Landsat ETM + image of 2002. The band composite map (R: G: B = 4:7:2) displayed greenish blue region in and around the Nakadake crater as hydrothermally altered zone (Fig. 3a). According to Kaufmann (1988), the band ratio map (R: G: B = 7/4:4/3:5/7) illustrated the red region as hydrothermally altered minerals containing iron ions in and around the Nakadake crater of Aso volcano (Fig. 3b). The principal components transformation on unstrentched Bands 1, 4, 5, 7 of the Aso volcano are shown in Table 2. The PC4 image showed with a high or moderate eigenvector loading with strong positive for Band 7 and negative for Band 5, resulted dark pixel as hydroxyl-bearing altered minerals; and after negation this PC4 image, we obtained these minerals as bright pixel in and around the Nakadake crater (Fig. 3c). On the other hand, the principal components transformation on unstrentched Bands 1, 3, 4, 5 of the Aso volcano are also shown in Table 2. The PC4 image showed with a high or moderate eigenvector loading with strong positive for Band 3 and negative for Band 1, resulted dark pixel as iron oxide stained altered minerals; and after negation this PC4 image, these minerals obtained as bright pixel in and around the Nakadake crater (Fig. 3d). The result of these methods showed that the alteration zone is in and around the Nakadake crater of central Aso volcano region about 8 km 2 . We were also made a landcover map based on NDVI value of this region and found bare ground in and around the Nakadake crater as thermally active region (Fig. 3e). To obtain thermal anomaly, land surface temperature (LST) was estimated using the thermal infrared data of Landsat ETM + image of 2002. The result showed some higher thermal anomaly areas around the Nakadake crater and the highest LST above ambient about 38 °C (Fig. 3f). The anomalous area was found within the Nakadake area about 8 km 2 . This active region of Aso volcano was selected for monitoring geothermal activity from 2002 to 2011 by using 5 sets of Landsat images.
Fig. 4. Land-covers of active Aso volcanic area prepared using NDVI value as water body (NDVI b 0), bare land (NDVI = 0–0.2), mixed land (NDVI = 0.2–0.5) and vegetated (NDVI N 0.5). This NDVI value has been used to derive the spectral emissivity of this study area and obtained within a range from 0.94 to 0.99.
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Fig. 5. Spatial distribution of land surface temperature above ambient of Aso volcanic area.
Initially we mapped land-cover of this study area based on the NDVI results from 2002 to 2011 as water body (NDVI b 0), bare land (NDVI = 0–0.2), mixed land (NDVI = 0.2–0.5) and vegetated land (NDVI N 0.5) (Fig. 4). The vegetated area was increased in overall from 2002 to 2011 (Table 2). The bare land showed a decline trend during the period in this study region. The spectral emissivity in this study area was estimated using NDVI method and obtained the emissivity value in ranges from 0.94 to 0.99. Land surface temperature was estimated using a mono-window algorithm because of single TIR band of Landsat ETM + imageused in this study. The highest LST was obtained about 62 °C in 2008 and lowest was about 10 °C in 2011 (Fig. 5). The highest LST anomaly coverage area was obtained in 2002 and was lowest in 2008. Results of RHF showed that the highest pixel value of RHF was in 2008 about 296 W/m2 (Fig. 6). The highest total RHF anomaly area was found in 2002 and was lowest in 2008. Overall, the anomaly area was decreased all RHF ranges except few during this time in the study area (Fig. 7). The total radiative heat loss (RHL) was obtained highest about 606 MW in 2002 and was lowest about 354 MW in 2008. Otherwise, the total RHL was obtained about 466 MW, 604 MW, and 406 MW in the year of 2004, 2006 and 2011 respectively (Table 1). Heat discharge rate was calculated in this study area after multiplying the total RHL using the identified relationship
coefficient between RHF and HDR i.e., 6.46 (Mia et al., 2013). The highest total HDR of the study area was obtained about 3918 MW in 2002 and was lowest to be about 2289 MW in 2008. Otherwise, we obtained the total HDR about 3009 MW, 3901 MW, 2621 MW in 2004, 2006 and 2011 respectively. Evaluation of the RHF for the Nakadake crater alone was done by using the subset RHF thematic map about 0.09 km 2 of high heat flow region in and around the Nakadake Crater Lake. The result showed that the highest total RHL was about 13.4 MW in 2008 and was lowest about 5.6 MW in 2004. Otherwise, the total RHL of Nakadake crater is obtained about 10.5 MW, 11.5 MW, and 9 MW in 2002, 2006, and 2011 respectively (Table 1). After multiplying the total RHL of this Nakadake crater alone using the identified relationship coefficient of RHF and HDR (i.e., 6.46), the total HDR was obtained about 67.8, 36.2, 74.9, 86.6 and 58.14 MW in 2002, 2004, 2006, 2008 and 2011 respectively. The results showed that the Nakadake crater was more active in 2008 and less active in 2004. In discussion, the followings could be summarized: 1. The conventional hydrothermal altered methods show an efficient technique for identifying the active regions of Aso volcano. Land surface temperature anomaly map confirm the thermally most active regions in Aso volcano with alteration results.
Fig. 6. Spatial distribution of radiative heat flux of Aso volcanic area.
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about 296 W/m2 in 2008. The total RHL was obtained about 607 MW in 2002 and was lowest about 354 MW in 2008. The RHL anomaly area was found the highest in 2002 and was lowest in 2011. The result also showed that the highest total HDR of the study area was about 3918 MW in 2002 and was lowest about 2289 MW in 2008. In case of Nakadake crater alone, the highest thermal activity observed in 2008 and was less in 2004. From this study, we thought that Landsat thermal infrared is the best option for thermal activity monitoring from the Aso volcano as well as in any active volcanic regions in the World considering high efficiency and low cost. 6. Limitation
Fig. 7. RHF anomaly area changes within the study period.
2. From NDVI based land-covers during our study period, the vegetation coverage area was obtained as inversely correlated with total RHL in the study period (Fig. 8). This result supports the inverse relationship between vegetation health and heat flow in active thermal area of a volcano. 3. The radiative heat flow was abnormally high in 2006 after 2002 although few pixels showed the highest RHF as well as LST in 2008. The result of RHF from the Nakadake crater alone showed the highest value in 2008, that indicates abnormal thermal activity of this region in this year. 4. The results inferred that the highest total HDR of the study area showed about 3918 MW in 2002 and lowest total HDR about 2289 MW in 2008. But in case of Nakadake crater alone, the highest HDR was found in 2008 and was lowest in 2004. 5. This exploration and monitoring technique could be used continuously to predict geothermal activity as well as for future indication of eruption in this study area after acquiring Landsat image from USGS archives with free of cost. 5. Conclusion The active Aso volcanic region was efficiently identified using the conventional hydrothermal alteration mapping methods and thermal anomaly mapping. NDVI used to divide land-covers into four types as water, bare ground, mixed and vegetated land of the study area. Vegetation coverage area shows an inverse relation with the total heat flow of the study area as health of thermally stress vegetation support this relationship. The spatial distribution of spectral emissivity ranged from 0.94 to 0.99 in this study. Land surface temperature was estimated using a mono-window algorithm and obtained highest LST in 2008 and lowest in 2011. The results of RHF showed that the highest pixel RHF was found
• The acquired images after 2002 were found with three to four gap lines because of the scan line corrector (SLC) failure of Landsat 7 ETM + sensor on 31 May, 2003, but the images were covered around 80% of the total study area without gaps. • The span of the acquired satellite imageries was about 42 days from 03 September to 16 October, indicating a small potential for seasonal variation in ambient temperature conditions.
Acknowledgements We would like to express our sincere thankful acknowledgement for the MEXT (Ministry of Education, Culture, Sports, Science and Technology, Japan) Ph.D. scholarship support providing the first author during this study. We would also like to show our sincere gratitude and acknowledgement to the G-COE of Kyushu University for funding of this research. We would like to acknowledge the USGS archives for giving us the satellite images with free of cost upon request during this research. Finally, we would like to show our sincere grateful acknowledgement to the Editor Prof. Lionel Wilson and reviewers to improve our manuscript. References Abdelsalam, M.G., Stern, R.J., Berhane, W.G., 2000. Mapping gossans in arid regions with Landsat TM and SIR-C images: the Beddaho Alteration Zone in northern Eritrea. J. Afr. Earth Sci. 30 (4), 903–916. Bromley, C.J., Manen, S.M., Mannington, W., 2011. Heat flux from steaming ground: reducing uncertainties. Proceedings of the 36th Workshop on Geothermal reservoir engineering. Stanford University, California, USA (SGP-TR-191). Harris, A.J.L., Lodato, L., Dehn, J., Spampinato, L., 2009. Thermal characterization of the Vulcano field. Bull. Volcanol. 71, 441–458. Hoshizumi, H., Watanabe, K., Sakaguchi, K., Uto, K., Ono, K., Nakamura, T., 1997. The Aso-4 pyroclastic flow deposit confirmed from the deep drill holes inside the Aso caldera. Programme and abstracts of the Volcanological Society of Japan 1997 (2), p. 5 (in Japanese). Hunt, G.R., 1979. Near Infrared (1.3–2.4um) spectra of alteration minerals-potential for use in remote sensing. Geophysics 44, 1974–1986. Hunt, G.R., Ashley, R.P., 1979. Spectra of Altered Rocks in the Visible and Near Infrared. Econ. Geol. 74, 1613–1629. Kaufmann, H., 1988. Mineral exploration along the Auaba-Levant structure by use of TM data. Concepts, Processing and Results. Int. J. Remote Sens. 9 (10–11), 1639–1658.
Fig. 8. Relationship between total RHL and land coverage of Aso volcanic area: (a) Total RHL in MW of the study period; (b) Land-cover area in square kilometer during the study period.
M.B. Mia et al. / Journal of Volcanology and Geothermal Research 275 (2014) 14–21 Madani, A., Abdel Rahman, E.M., Fawzy, K.M., Emam, A., 2003. Mapping of the Hydrothermal Alteration Zones at Haimur Gold Mine Area, South Eastern Desert, Egypt using Remote Sensing Techniques. Egypt. J. Remote Sens. Space Sci. 6, 47–60. Mia, M.B., Fujimitsu, Y., 2012. Mapping hydrothermal altered deposits using Landsat 7 ETM + image in and around Kuju volcano, Kyushu, Japan. J. Earth Syst. Sci. 121 (4), 1049–1057. http://dx.doi.org/10.1007/s12040-012-0211-9. Mia, M.B., Fujimitsu, Y., 2013. Monitoring heat losses using Landsat ETM + thermal infrared data – A case study at Kuju fumarolic area in Japan. Acta Geophys. 61 (5), 1262–1278. http://dx.doi.org/10.2478/s11600-013-0115-3. Mia, M.B., Bromley, C.J., Fujimitsu, Y., 2012. Monitoring heat flux using Landsat TM/ ETM + thermal infrared data-A case study at Karapiti (‘Crater of the Moon’) thermal area, New Zealand. J. Volcanol. Geotherm. Res. 235-236, 1–10. http:// dx.doi.org/10.1016/j.jvolgeores.2012.05.005. Mia, M.B., Bromley, C.J., Fujimitsu, Y., 2013. Monitoring heat losses using Landsat ETM + thermal infrared data: a case study in Unzen geothermal field, Kyushu, Japan. Pure Appl. Geophys. 170 (12), 2263–2271. http://dx.doi.org/10.1007/ s000024-013-0662-1. Miyabuchi, Y., 2009. A 90,000-year tephrostratigraphic framework of Aso Volcano, Japan. Sed. Geol. 220, 169–189. Miyabuchi, Y., Masuda, N., Watanabe, K., 2004. Geologic history of the western part of post-caldera central cones of Aso Volcano, southwestern Japan, based on stratigraphic relationships between lava flows and airfall tephra layers. Bull. Volcanol. Soc. Jpn. 49, 267–282. Miyabuchi, Y., Ikebe, S., Watanabe, K., 2008. Geological constraints on the 2003–2005 ash emissions from the Nakadake crater lake, Aso Volcano, Japan. J. Volcanol. Geotherm. Res. 178, 169–183. NASA, 2009. Landsat 7 Science Data User's Handbook. Ono, K., Watanabe, K., 1985. Geological map of Aso Volcano (1:50,000). Geological Map of Volcanoes, 4, Geological Survey of Japan (in Japanese with English abstract). Ono, K., Matsumoto, Y., Miyahisa, M., Teraoka, Y., Kambe, N., 1977. Geology of the Taketa district. With geological sheet map at 1:50,000, Geological Survey of Japan, 145 pp. (in Japanese with English abstract).
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Ono, K., Watanabe, K., Hoshizumi, H., Ikebe, S., 1995. Ash eruption of Nakadake crater, Aso volcano, southwestern Japan. J. Volcanol. Geotherm. Res. 66, 137–148. Pasternack, G.B., Varekamp, J.C., 1997. Volcanic lake systematics I. Physical constraints. Bull. Volcanol. 58, 528–538. Qin, Z., Karnieli, A., Berliner, P., 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region. Int. J. Remote Sens. 22 (18), 3719–3746. Ramadan, T.M., Kontny, A., 2004. Mineralogical and structural characterization of alterationzones detected by orbital remote sensing at Shalatein District, SE Desert, Egypt. J. Afr. Earth. Sci. 40, 89–99. Ramadan, T.M., Abdelsalam, M.G., Stern, R.J., 2001. Mapping gold-bearing massive sulfide deposits in the neoproterozoic Allaqi Suture, Southeast Egypt with Landsat TM and SIR-C/X SAR images. Photogramm. Eng. Remote Sens. 67 (4), 491–497. Ryan, P.J., Harleman, D.R., Stolzenbach, K.D., 1974. Surface heat loss from cooling ponds. Water Resour. Res. 10, 930–938. Savage, S.L., Lawrence, R.L., Custer, S.G., Jewett, J.T., Powell, S.L., Shaw, J.A., 2010. Review of Alternative Methods for Estimating Terrestrial Emittance and Geothermal Heat Flux for Yellowstone National Park Using Landsat Imagery. GISci. Remote Sens. 47 (4), 460–479. Terada, A., Sudo, Y., 2012. Thermal activity within the western-slope geothermal zone of Aso volcano, Japan: Development of a new thermal area. Geothermics 42, 56–64. Terada, A., Hashimoto, T., Kagiyama, K., Sasaki, H., 2008. Precise remote-monitoring technique of water volume and temperature of a crater lake in Aso volcano, Japan: Implication for a sensitive window of volcanic hydrothermal system. Earth Planets Space 60, 705–710. Valor, E., Caselles, V., 1996. Mapping Land Surface Emissivity from NDVI: Application to European, African, and South American Areas. Remote Sens. Environ. 57, 167–184. Yetkin, E., 2003. Alteration Mapping by Remote Sensing: Application to HasandaMelendiz Volcanic Complex. (M. Sc. Thesis) Middle East Technical University, Ankara, Turkey 97.
Contents lists available at ScienceDirect
Journal of Volcanology and Geothermal Research journal homepage: www.elsevier.com/locate/jvolgeores
Exploration and monitoring geothermal activity using Landsat ETM + images A case study at Aso volcanic area in Japan Md. Bodruddoza Mia a,⁎, Jun Nishijima b, Yasuhiro Fujimitsu b a b
Department of Geology, Faculty of Earth and Environmental Science, University of Dhaka, Dhaka-1000, Bangladesh Department of Earth Resources Engineering, Faculty of Engineering, Kyushu University, Fukuoka 819-0395, Japan
a r t i c l e
i n f o
Article history: Received 2 September 2013 Accepted 10 February 2014 Available online 22 February 2014 Keywords: Aso volcano Landsat ETM + image Hydrothermal alteration Thermal anomaly Radiative heat flux Heat discharge rate
a b s t r a c t Thermal activity monitoring in and around active volcanic areas using remote sensing is an essential part of volcanology nowadays. Three identical approaches were used for thermal activity exploration at Aso volcanic area in Japan using Landsat ETM + images. First, the conventional methods for hydrothermal alteration mapping were applied to find the most active thermal region after exploring geothermal indicator minerals. Second, we found some thermally highly anomalous regions around Nakadake crater using land surface temperature estimation. Then, the Stefan-Boltzmann equation was used for estimating and also monitoring radiative heat flux (RHF) from the most active region of about 8 km2 in and around Nakadake crater in the central part of the Aso volcano. To fulfill the required parameter in the Stefan-Boltzmann equation for radiative heat flux, the NDVI (Normalized differential vegetation index) method was used for spectral emissivity, and the mono-window algorithm was used for land surface temperature of this study area. The NDVI value was used to divide land-cover in the study area into four types: water, bare ground, mixed and vegetated land. The bare land was found within the most active region. Vegetation coverage area showed an inverse relationship with total RHF in this study as health of thermally stressed vegetation supports this relationship. The spatial distribution of spectral emissivity ranged from 0.94 to 0.99 in our study. Land surface temperature was estimated using a mono-window algorithm and was highest LST in 2008 and lowest in 2011. The results of RHF showed that the highest pixel RHF was found to be about 296 W/m2 in 2008. Total RHF was obtained of about 607 MW in 2002 and the lowest was about 354 MW in 2008. The RHF anomaly area was found the highest in 2002 and was lowest in 2011. The highest total heat discharge rate (HDR) obtained about 3918 MW in 2002 and lowest total HDR about 2289 MW in 2008 from this study area. But in the case of Nakadake crater alone, the higher thermal activity was observed in 2008 and was less in 2004. The study showed that Landsat thermal infrared is the best option for thermal activity exploration and monitoring at Aso volcano as well as in any active volcano region considering high efficiency and low cost. © 2014 Elsevier B.V. All rights reserved.
1. Introduction The study area, Aso volcano, consist of a large caldera of about 18 × 24 km with 10 central cones of basaltic and basaltic–andesite ejecta, situated in central Kyushu Island, Japan (Fig. 1). The central cone, Nakadake, is the most active volcano in Japan, where repeated historic magmatic eruption occurred (Terada and Sudo, 2012). The eruption processes involved strombolian activity, phreatomagmatic explosions and ash emission (Ono et al., 1995; Miyabuchi, 2009). The Nakadake crater erupted characteristically black ash during the active eruption period (Ono et al., 1995). The other surface thermal features in Aso ⁎ Corresponding author. Tel.: +880 1818240937; fax: +880 28615583. E-mail addresses: [email protected] (M.B. Mia), [email protected] (J. Nishijima), [email protected] (Y. Fujimitsu).
http://dx.doi.org/10.1016/j.jvolgeores.2014.02.008 0377-0273/© 2014 Elsevier B.V. All rights reserved.
volcano, known as west zone geothermal features (WZGF), are the Yunotani, Yoshioka and Jigoku-Tarutama hot springs areas. Historically, the fumaroles of this volcano emit a significant amount of volatiles and thermal energy throughout the years. Specially, the Nakadake crater releases significant amounts of volcanic gas, including 200–400 tons/day of SO2 during calm periods (Terada and Sudo, 2012). There is a hot crater lake in Nakadake 200 m in diameter, locally known as Yudamari. The lake water temperature remains at about 60 °C throughout the year in calm periods which is much higher than ambient temperature. Terada et al. (2008) estimated heat flow by applying the model of Ryan et al. (1974) to the observation that the heat discharge rate across the lake surface is almost constant at approximately 220 MW, with the exception of an abrupt increase to 280 MW that coincided with a rapid decrease in the water level in August 2007. Terada and Sudo (2012) studied a few new fumaroles formed at the Yoshioka hot spring since
M.B. Mia et al. / Journal of Volcanology and Geothermal Research 275 (2014) 14–21
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Fig. 1. Location map of the study area showing aspect and altitude based on SRTM DEM data. Heat loss study was done within the bold black rectangle area in and around the Nakadake crater.
June 2006 and obtained heat discharges of about 4.6 × 106 J/s, which is almost 20 times higher than that of the previous natural thermal activity in these fumaroles area. Although there are many studies for thermal activity evaluation based on ground geophysical and airborne imaging or video camera imaging, there is no research solely based on satellite thermal infrared data in this study area. Remote sensing is an essential part of volcanology nowadays for exploration and monitoring geothermal systems. Hydrothermal alteration is related to previous geothermal activity i.e., high temperature and pressure from magmatic eruption, interaction or overburden pressure that altered the originally formed minerals to altered mineral deposits. So, the exploration of hydrothermal alteration could indicate the previous thermal activity in and around the volcanoes. The monitoring of hydrothermal alteration
regions could also give an indication of thermal activity of any active volcanoes. The bare regions of volcanoes show the most alteration mineral deposits and can be mapped using satellite images, which also indicate geothermal active zones of any volcanoes. Actually, hydrothermally altered minerals are living fossils of previous thermal activity that changed the original mineral to a specific altered mineral with certain physical conditions of the environment such as high temperature and pressure. So, remote sensing data could be used to map geothermal indicator minerals over large areas, often identifying new areas of interest. There are many studies around the world related to hydrothermal alteration mapping using multispectral Landsat and Aster satellite images (e.g. Abdelsalam et al., 2000; Ramadan et al., 2001; Madani et al., 2003; Yetkin, 2003; Ramadan and Kontny, 2004; Mia and Fujimitsu, 2012).
Table 1 Summary results of this study. Year
2002 2004 2006 2008 2011
Area (%)
100 81.31 80.42 79.13 79.09
Ambient temp. (°C)
Landcover (km2) Water
Bared land
Mixed land
Vegetated
0.44 0.02 0.06 0.18 0.09
2.87 1.73 1.36 1.22 1.26
2.82 3.60 4.13 4.03 3.61
1.81 2.60 2.39 2.51 2.99
13.4 21.7 16 17.3 14.3
Atmospheric transmissivity
0.88 0.81 0.85 0.89 0.93
LST (°C)
RHF (W/m2)
Min
Max
Min
Max
11.48 20.79 15.76 11.41 10.29
51.44 49.91 54.45 62.2 51.57
0.18 0.69 0.02 0.01 0.07
235.28 181.38 243.95 296.16 228.67
Total RHF (MW) (Area adjusted)
Total RHF (MW) in Nakadake crater
606.51 465.79 603.89 354.26 405.71
10.47 5.57 11.52 13.41 9.06
Notes: The area of 2002 image is covered as 100% of our study area and others image have around 80% of our study area as there are image gaps after 2003 in case of Landsat ETM + image. We used this area percentage to adjust in case of total heat flow as well as landcover study. Ambient temperature was acquired from AMEDAS Aso san station hourly data. Atmospheric transmissivity was calculated using NASA web atmospheric parameter correction calculator and obtained above 80% in all our images acquisition time.
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Thematic mapping using multispectral Landsat satellite images covers the visible and infrared spectrum of hydrothermal alterations (Hunt, 1979; Hunt and Ashley, 1979). In this study, the conventional hydrothermal alteration mapping methods using Landsat ETM + image were applied to explore geothermal indicator minerals to find the most active areas in Aso volcano. Thermal monitoring of active volcanoes is very important not only to understand geothermal systems but also to detect precursory signals of future eruptions. A fumarolic eruption could happen with unpredictable hazards to surrounding populations living within potentially active volcanoes. Monitoring for eruptions from active fumaroles could prevent the surrounding environment from thermal, ash and lava flow hazards. Ground based monitoring for heat flow may not be possible due to unstable ground as well as national park areas around the active volcanoes but remote measurement especially using satellite thermal infrared data could be a good solution. In this case, satellite monitoring has succeeded in detecting large thermal anomalies using Landsat and ASTER satellite images in and around thermally active regions around the world. Landsat TM/ETM + has a long history of use for thermal feature studies of volcanoes (Harris et al., 2009; Savage et al., 2010; Mia and Fujimitsu, 2012). Landsat TM has a single thermal band with 30 m in resolution (after 25 February 2010, the thermal infrared band was used to process at 30 m instead of 60 m) and can be downloaded from USGS archives free of cost upon request. As it is only possible to analyze the radiative portion of total heat loss using satellite infrared data directly, we can multiply the estimated radiative heat flux (RHF) using the identified relationship coefficient between radiative heat loss and total heat loss from geothermal or fumarolic areas to estimate heat discharge rate (HDR), as obtained in two previous studies (Harris et al., 2009; Mia et al., 2013). Practically, the HDR from any active fumarolic area is the summation of convective, conductive and radiative components of heat losses without solar heat load. RHF is the radiative portion of surface heat loss that passes as electromagnetic waves from a geothermal field without the effect of solar heating. Radiative geothermal heat flux (GHFR) means the heat only comes from the subsurface, without direct or indirect effects of solar or diffuse convective steaming, via electromagnetic radiation to a satellite sensor. The application of satellite remote sensing for detecting and quantifying thermal anomalies as well as heat losses due to volcanic activity has been scientifically used in recent years (Harris et al., 2009; Savage et al., 2010; Mia et al., 2012; Mia and Fujimitsu, 2013). In this paper, the applicability of Landsat thermal infrared data was demonstrated for thermal activity exploration and monitoring in one of the most active volcanoes in Japan to evaluate the geothermal resources as well as for future eruption indication. The prime objectives of this study are (a) to delineate thermally active regions using the conventional hydrothermal alteration and thermal anomaly methods, (b) to estimate the radiant component of heat flux from 2002 to 2011 by Landsat ETM + thermal infrared data and (c) finally to estimate the HDR of the study area after multiplying the total radiative heat loss by the relationship coefficient between RHF and HDR. To do this, the paper is organized as follows. The first section presents an introduction about motivation, the study area, previous studies and objectives; the second step presents the geologic settings of Aso volcano; and the third section explains about materials and methodology used in this study. In the fourth section, the outcome of this study is discussed in detail. Finally, we draw conclusions in the fifth section, and list the limitations of this study in sixth section.
successively flowed into valleys between the Basement Mountains, filled up them and formed pyroclastic-flow plateaus. Between each of the four large pyroclastic-flow units, eruptions produced numerous tephra fallout layers. Post-caldera cones have arisen near the center of the caldera since the Aso-4 eruption (Ono and Watanabe, 1985). There are at least 17 cones visible on the surface, consisting of both lava and pyroclastics (Hoshizumi et al., 1997). The central post-caldera cones vary depending on their rock chemistry, which ranges from basalt to rhyolite (Ono and Watanabe, 1985). Nakadake is the only active central cone in Aso volcano since ca. 22–21 ka (Miyabuchi et al., 2004) and formed an old edifice (agglutinate and lava), a young edifice (pyroclasts and lava) and a still younger pyroclastic cone (Ono and Watanabe, 1985). The old edifice is the main cone of Nakadake volcano, rising about 900 m from the caldera floor (Miyabuchi et al., 2008). The young edifice is believed to have formed during Holocene time, and the youngest pyroclastic cone rose subsequently inside the young volcanic edifice of Nakadake crater (Ono and Watanabe, 1985). The active Nakadake crater formed in the youngest pyroclastic cone is a composite of seven craterlets aligned N-S (Miyabuchi et al., 2008). The most characteristic event of Nakadake during active periods is continuous fallout of black sandy ash, known as ash eruption (Ono et al., 1995). Strombolian eruptions have also occurred during intense active periods by scattering red-hot scoria clasts around the vent in Aso volcano (Miyabuchi et al., 2008). The lake water temperature ranged from 49 to 82 °C in the Nakadake crater from 2003 to 2005 (Miyabuchi et al., 2008). The Nakadake Crater Lake was a peak-activity volcanic lake during the period of 2003–2005 on the basis of the physico-chemical classification scheme for volcanic lakes (Pasternack and Varekamp, 1997). 3. Materials and Methods Landsat Enhanced Thematic Mapper plus (ETM +) images were used for this study (path/row: 112/37). The Landsat sensor bearing satellite passed through this study region from 10:35 to 10:40 am of all acquired images. A total of 5 sets of images were obtained from the USGS Earth Resource Observation Systems Data Center, which were both radiometrically and geometrically corrected. All images were 8 multi-spectral bands: 4 VNIR (visible and near infrared), 2
2. Geologic settings of Aso volcano Aso is one of the largest caldera volcanoes in the World, located in central Kyushu, southwestern Japan. The pyroclastic-flow deposits of the Aso caldera are divided into four distinct units: Aso-1 (270 ka), Aso-2(140 ka), Aso-3 (120 ka) and Aso-4 (90 ka) in ascending order (Ono et al., 1977). Miyabuchi (2009) demonstrated that the flows
Fig. 2. Image processing flow chart of this study.
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Fig. 3. Location of active region of Aso volcano using black bold rectangle: (a) Band composite map (R: G: B = 4:7:2) shows hydrothermally altered region as blue color; (b) Band Ratio (R: G: B = 7/4:4/3:5/7) map shows hydrothermally altered iron ions in the red region; (c) Principal component analysis map shows hydroxyl hydrothermal region as brighter pixels (Table 2); (d) Principal component analysis map shows iron-oxide hydrothermal region as brighter pixel (Table 2); (e) Landcover map prepared using NDVI value shows almost bare region as active region; and (f) Thermal anomaly map shows some active regions as higher LST in the central part in and around the Nakadake crater.
SWIR (shortwave infrared) and 1 TIR (thermal infrared) (NASA, 2009). The imageries were acquired during summer or end of summer season on 16 October 2002, 03 September 2004, 25 September 2006, 16 October 2008 and 23 September 2011 respectively. The Landsat ETM + image has a single channel of thermal infrared at 30 m resolution. The local meteorological air temperature data was obtained from the nearest AMEDAS meteorological station's website (i.e., Aso san station). The NASA (National Institute of Water and Atmospheric Research) website based calculator for atmospheric parameter correction was used to obtain the atmospheric transmissivity at the time of image acquisition and obtained above 80% in all years of the study time (Table 1). The flow chart was followed for the image processing steps for heat loss estimation using Landsat images (Fig. 2). Initially, we analyzed all images for atmospheric correction and reflectance value calculation of all visible, NIR and SWIR bands explained in our previous study (Mia et al., 2012), where the dark object subtraction method was applied
for atmospheric correction process. Then, in second steps, three approaches were taken for detection of active regions of Aso volcano: hydrothermal alteration mapping using the conventional techniques, delineation of thermal anomaly zone and landcover mapping. The conventional hydrothermal alteration mapping techniques were applied in the first stage for delineating geothermal indicator minerals i.e., band composite, band ratio and principal component analysis; the methods are explained in one our previous study in details (Mia and Fujimitsu, 2012). In case of thermal anomaly detection, the mono-window algorithm for estimating land surface temperature was applied where we needed to input the spectral emissivity, ambient temperature and atmospheric transmissivity at the time of image acquisition. The emissivity of the study area was estimated using NDVI (Normalized Differential Vegetation Index) method. For this, the vegetation index (NDVI) was estimated, which is a process for calculating the vegetation index of any region, that is the ratio of reflectance value of red (band 3) and near infrared
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Table 2 4) region of electromagnetic (band Principle component analysis. Input band
spectrum. The NDVI value ranges
Eigenvector loading for hydroxyl mapping Band 1
Band 4
Band 5
Band 7
PC1 PC2 PC3 PC4
0.089 0.319 −0.903 −0.273
0.606 −0.719 −0.260 0.219
0.719 0.328 0.340 −0.509
0.327 0.523 −0.021 0.787
Input band
Eigenvector loading for iron-oxide mapping
PC1 PC2 PC3 PC4
Band 1
Band 3
Band 4
Band 5
0.095 −0.340 −0.527 −0.774
0.207 −0.697 −0.366 0.580
0.632 0.578 −0.492 0.159
0.741 −0.254 0.589 −0.199
Eigenvalue (%)
4. Results and Discussion
84.929 12.695 2.105 0.271 Eigenvalue (%) 79.837 16.802 2.912 0.449
Notes: In case of hydroxyl type of minerals, the Band 5 is highly reflective and the Band 7 is high absorption in general shown using high or moderate eigenvector loading with opposite sign in PCA analysis for Landsat ETM + images. On the other hand, for ironoxide type of minerals, the Band 1 is highly reflective and the Band 3 is high absorption in general shown using high or moderate eigenvector loading with opposite sign in PCA analysis for Landsat ETM + images.
from -1 to +1, higher than the value of 0.5 indicated the vegetated region and lower the value of 0.2 is the bared region. Then, the spectral emissivity was estimated using the NDVI based emissivity calculation method of Valor and Caselles (1996). Normally, the emissivity value ranges from 0.7 to 0.99 for the real earth surface. The land surface temperature was estimated where the mono-window algorithm was applied because the Landsat ETM + image has only one band of thermal infrared band (Qin et al., 2001). Finally, according to the Stefan-Boltzmann's law, the radiative heat flux (RHF) was estimated from the active region in Aso volcano by using the following Eq. (1) (Bromley et al., 2011; Mia et al., 2012). 4 4 Q r ¼ τσε Ts –Ta
of the relationship coefficient of HDR and RHF ( 6.46), obtained from two previous studies of volcano fumaroles (Harris et al., 2009; Mia et al., 2013).
ð1Þ
Where, Qr = radiative heat flux (W/m2), τ = atmospheric transmissivity, σ = Stefan-Boltzmann constant, ε = emissivity, Ts = land surface temperature (LST) (K) and Ta = ambient temperature (K). The ambient temperature from the AMEDAS meteorological Aso san station's hourly data was used here. The total heat discharge rate of the study area has been calculated after multiplying the above total RHF using the identified mean value
The conventional hydrothermal alteration mapping techniques such as band composite, band ratio and principal component (PC) analysis were used to detect the active regions of Aso volcano area using Landsat ETM + image of 2002. The band composite map (R: G: B = 4:7:2) displayed greenish blue region in and around the Nakadake crater as hydrothermally altered zone (Fig. 3a). According to Kaufmann (1988), the band ratio map (R: G: B = 7/4:4/3:5/7) illustrated the red region as hydrothermally altered minerals containing iron ions in and around the Nakadake crater of Aso volcano (Fig. 3b). The principal components transformation on unstrentched Bands 1, 4, 5, 7 of the Aso volcano are shown in Table 2. The PC4 image showed with a high or moderate eigenvector loading with strong positive for Band 7 and negative for Band 5, resulted dark pixel as hydroxyl-bearing altered minerals; and after negation this PC4 image, we obtained these minerals as bright pixel in and around the Nakadake crater (Fig. 3c). On the other hand, the principal components transformation on unstrentched Bands 1, 3, 4, 5 of the Aso volcano are also shown in Table 2. The PC4 image showed with a high or moderate eigenvector loading with strong positive for Band 3 and negative for Band 1, resulted dark pixel as iron oxide stained altered minerals; and after negation this PC4 image, these minerals obtained as bright pixel in and around the Nakadake crater (Fig. 3d). The result of these methods showed that the alteration zone is in and around the Nakadake crater of central Aso volcano region about 8 km 2 . We were also made a landcover map based on NDVI value of this region and found bare ground in and around the Nakadake crater as thermally active region (Fig. 3e). To obtain thermal anomaly, land surface temperature (LST) was estimated using the thermal infrared data of Landsat ETM + image of 2002. The result showed some higher thermal anomaly areas around the Nakadake crater and the highest LST above ambient about 38 °C (Fig. 3f). The anomalous area was found within the Nakadake area about 8 km 2 . This active region of Aso volcano was selected for monitoring geothermal activity from 2002 to 2011 by using 5 sets of Landsat images.
Fig. 4. Land-covers of active Aso volcanic area prepared using NDVI value as water body (NDVI b 0), bare land (NDVI = 0–0.2), mixed land (NDVI = 0.2–0.5) and vegetated (NDVI N 0.5). This NDVI value has been used to derive the spectral emissivity of this study area and obtained within a range from 0.94 to 0.99.
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Fig. 5. Spatial distribution of land surface temperature above ambient of Aso volcanic area.
Initially we mapped land-cover of this study area based on the NDVI results from 2002 to 2011 as water body (NDVI b 0), bare land (NDVI = 0–0.2), mixed land (NDVI = 0.2–0.5) and vegetated land (NDVI N 0.5) (Fig. 4). The vegetated area was increased in overall from 2002 to 2011 (Table 2). The bare land showed a decline trend during the period in this study region. The spectral emissivity in this study area was estimated using NDVI method and obtained the emissivity value in ranges from 0.94 to 0.99. Land surface temperature was estimated using a mono-window algorithm because of single TIR band of Landsat ETM + imageused in this study. The highest LST was obtained about 62 °C in 2008 and lowest was about 10 °C in 2011 (Fig. 5). The highest LST anomaly coverage area was obtained in 2002 and was lowest in 2008. Results of RHF showed that the highest pixel value of RHF was in 2008 about 296 W/m2 (Fig. 6). The highest total RHF anomaly area was found in 2002 and was lowest in 2008. Overall, the anomaly area was decreased all RHF ranges except few during this time in the study area (Fig. 7). The total radiative heat loss (RHL) was obtained highest about 606 MW in 2002 and was lowest about 354 MW in 2008. Otherwise, the total RHL was obtained about 466 MW, 604 MW, and 406 MW in the year of 2004, 2006 and 2011 respectively (Table 1). Heat discharge rate was calculated in this study area after multiplying the total RHL using the identified relationship
coefficient between RHF and HDR i.e., 6.46 (Mia et al., 2013). The highest total HDR of the study area was obtained about 3918 MW in 2002 and was lowest to be about 2289 MW in 2008. Otherwise, we obtained the total HDR about 3009 MW, 3901 MW, 2621 MW in 2004, 2006 and 2011 respectively. Evaluation of the RHF for the Nakadake crater alone was done by using the subset RHF thematic map about 0.09 km 2 of high heat flow region in and around the Nakadake Crater Lake. The result showed that the highest total RHL was about 13.4 MW in 2008 and was lowest about 5.6 MW in 2004. Otherwise, the total RHL of Nakadake crater is obtained about 10.5 MW, 11.5 MW, and 9 MW in 2002, 2006, and 2011 respectively (Table 1). After multiplying the total RHL of this Nakadake crater alone using the identified relationship coefficient of RHF and HDR (i.e., 6.46), the total HDR was obtained about 67.8, 36.2, 74.9, 86.6 and 58.14 MW in 2002, 2004, 2006, 2008 and 2011 respectively. The results showed that the Nakadake crater was more active in 2008 and less active in 2004. In discussion, the followings could be summarized: 1. The conventional hydrothermal altered methods show an efficient technique for identifying the active regions of Aso volcano. Land surface temperature anomaly map confirm the thermally most active regions in Aso volcano with alteration results.
Fig. 6. Spatial distribution of radiative heat flux of Aso volcanic area.
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about 296 W/m2 in 2008. The total RHL was obtained about 607 MW in 2002 and was lowest about 354 MW in 2008. The RHL anomaly area was found the highest in 2002 and was lowest in 2011. The result also showed that the highest total HDR of the study area was about 3918 MW in 2002 and was lowest about 2289 MW in 2008. In case of Nakadake crater alone, the highest thermal activity observed in 2008 and was less in 2004. From this study, we thought that Landsat thermal infrared is the best option for thermal activity monitoring from the Aso volcano as well as in any active volcanic regions in the World considering high efficiency and low cost. 6. Limitation
Fig. 7. RHF anomaly area changes within the study period.
2. From NDVI based land-covers during our study period, the vegetation coverage area was obtained as inversely correlated with total RHL in the study period (Fig. 8). This result supports the inverse relationship between vegetation health and heat flow in active thermal area of a volcano. 3. The radiative heat flow was abnormally high in 2006 after 2002 although few pixels showed the highest RHF as well as LST in 2008. The result of RHF from the Nakadake crater alone showed the highest value in 2008, that indicates abnormal thermal activity of this region in this year. 4. The results inferred that the highest total HDR of the study area showed about 3918 MW in 2002 and lowest total HDR about 2289 MW in 2008. But in case of Nakadake crater alone, the highest HDR was found in 2008 and was lowest in 2004. 5. This exploration and monitoring technique could be used continuously to predict geothermal activity as well as for future indication of eruption in this study area after acquiring Landsat image from USGS archives with free of cost. 5. Conclusion The active Aso volcanic region was efficiently identified using the conventional hydrothermal alteration mapping methods and thermal anomaly mapping. NDVI used to divide land-covers into four types as water, bare ground, mixed and vegetated land of the study area. Vegetation coverage area shows an inverse relation with the total heat flow of the study area as health of thermally stress vegetation support this relationship. The spatial distribution of spectral emissivity ranged from 0.94 to 0.99 in this study. Land surface temperature was estimated using a mono-window algorithm and obtained highest LST in 2008 and lowest in 2011. The results of RHF showed that the highest pixel RHF was found
• The acquired images after 2002 were found with three to four gap lines because of the scan line corrector (SLC) failure of Landsat 7 ETM + sensor on 31 May, 2003, but the images were covered around 80% of the total study area without gaps. • The span of the acquired satellite imageries was about 42 days from 03 September to 16 October, indicating a small potential for seasonal variation in ambient temperature conditions.
Acknowledgements We would like to express our sincere thankful acknowledgement for the MEXT (Ministry of Education, Culture, Sports, Science and Technology, Japan) Ph.D. scholarship support providing the first author during this study. We would also like to show our sincere gratitude and acknowledgement to the G-COE of Kyushu University for funding of this research. We would like to acknowledge the USGS archives for giving us the satellite images with free of cost upon request during this research. Finally, we would like to show our sincere grateful acknowledgement to the Editor Prof. Lionel Wilson and reviewers to improve our manuscript. References Abdelsalam, M.G., Stern, R.J., Berhane, W.G., 2000. Mapping gossans in arid regions with Landsat TM and SIR-C images: the Beddaho Alteration Zone in northern Eritrea. J. Afr. Earth Sci. 30 (4), 903–916. Bromley, C.J., Manen, S.M., Mannington, W., 2011. Heat flux from steaming ground: reducing uncertainties. Proceedings of the 36th Workshop on Geothermal reservoir engineering. Stanford University, California, USA (SGP-TR-191). Harris, A.J.L., Lodato, L., Dehn, J., Spampinato, L., 2009. Thermal characterization of the Vulcano field. Bull. Volcanol. 71, 441–458. Hoshizumi, H., Watanabe, K., Sakaguchi, K., Uto, K., Ono, K., Nakamura, T., 1997. The Aso-4 pyroclastic flow deposit confirmed from the deep drill holes inside the Aso caldera. Programme and abstracts of the Volcanological Society of Japan 1997 (2), p. 5 (in Japanese). Hunt, G.R., 1979. Near Infrared (1.3–2.4um) spectra of alteration minerals-potential for use in remote sensing. Geophysics 44, 1974–1986. Hunt, G.R., Ashley, R.P., 1979. Spectra of Altered Rocks in the Visible and Near Infrared. Econ. Geol. 74, 1613–1629. Kaufmann, H., 1988. Mineral exploration along the Auaba-Levant structure by use of TM data. Concepts, Processing and Results. Int. J. Remote Sens. 9 (10–11), 1639–1658.
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