REMOTE SENSING AND GIS BASED GEOTHERMAL EXPLORATION IN SOUTHWEST TENGCHONG, CHINA Ning Zhang1, Qiming Qin1, Lian He1, Hongbo Jiang1 1
Institute of Remote Sensing and GIS, Peking University, Beijing, 100871, China Corresponding author: Ning Zhang. E-mail:
[email protected] ABSTRACT
This work focuses on using remote sensing and geographic information systems (GIS) to identify promising geothermal areas in southwest Tengchong, China. Thematic information, including surface temperature, urban area, and surface slope are derived from Enhanced Thematic Mapper Plus (ETM+) data and digital elevation models (DEM). GIS is applied as a decision-support tool to integrate the thematic information for suitability analysis. The results indicate that combining remote sensing with GIS is an overall effective and accurate method for geothermal exploration. Three developed geothermal fields are successfully extracted in Tengchong, and promising areas are found to the north of study area and warrant further exploration.
areas with remote sensing data. The objective of this paper is to combine remote sensing with GIS to improve the efficiency and accuracy of geothermal exploration. 2. STUDY AREA AND DATA Our study area is located in E98e22Ą-98e32Ą, N24e 55Ą-25e5Ą in southwest Tengchong, Yunnan Province, China (Fig. 1). Large-scale block fracturing, folding, magmatism, and volcanic activities in the upper portion of the earth's crust contribute to abundant thermal source to geothermal resource in Tengchong (Tong and Zhang, 1989). Therefore, it is a prospective area for geothermal exploration.
Key Words: Remote sensing, GIS, Geothermal exploration, Tengchong 1. INTRODUCTION Remote sensing techniques, particularly thermal infrared (TIR) data, can be used to retrieve land surface temperature for large areas and to identify the extent of shallow geothermal resources and geothermal hot springs. As such, it has been widely used in geothermal exploration (Hao et al.,2001;Qiao, 2002; Vaughan et al., 2005; Coolbaugh et al., 2007; Watson et al., 2008). However, surface temperature can be affected by many non-geothermal factors, such as weather, terrain and the impact from human activities. In our previous work (Qin et al, 2011), four geothermal areas in Yunnan province of China were identified based on ETM+ TIR data and geothermal mechanism analysis. However, the artificial thermal influences were still not considered. Therefore, auxiliary data or analysis tools are required to exclude the non-geothermal influences and more operational and efficient method is also in request. Geographic information system (GIS) is a powerful decision-making tool and has been successfully applied for the identification of geothermal prospects (Noorollahi et al., 2007; Yousefi et al., 2010) and for site selection of geothermal wells (Noorollahi et al., 2008). Therefore it can be used to aid the interpretation of promising geothermal
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Fig .1 Study area in Tengchong, China The remote sensing data used in this work were acquired on February 15, 2008 from the ETM+ sensor aboard Landsat 7 satellite. Due to the failure of the Scan Line Corrector (SLC) on May 2003, the ETM+ data in this work are the SLC-off corrected products downloaded from International Scientific Data Service Platform, Computer Network Information Center, Chinese Academy of Sciences (http://datamirror.csdb.cn). The DEM data used in this work are the Version 1 of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), which are downloaded from the Earth Remote Sensing Data Analysis Center (ERSDAC) of Japan (http://www.gdem.aster.ersdac.or.jp/).
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3. METHODOLOGY
IGARSS 2012
The methodology applied to identify promising geothermal areas is based on an integrated interpretation of the remote sensing and DEM data with GIS. GIS ArcMap 9.1 (ESRI, 2005) is applied in this work for suitability analysis by integrating thematic information and for data visualizing as well. The methodology flow chat is illustrated in Fig. 2.
Fig. 2 Methodology flow chat 3.1 Temperature information extraction Considering the geothermal enriched areas usually present a higher temperature than its surrounding areas, temperature anomalies can be used to determine the extent of prospect areas. In this study, surface temperature is retrieved from ETM+ TIR data by using the single-channel method proposed by Jiménez and Sobrino (2003). The formulas are as follows:
Ts
J
G
J >H 1 (\ 1 Lsensor \ 2 ) \ 3 @ G C 2 Lsensor ª O4 º½ Lsensor O1 » ¾ ® 2 « ¼¿ ¯ Tsensor ¬ C1 JLsensor Tsensor
(1)
1
(2) (3)
In Eq. (1), ڙis emissivity calculated by the method proposed by Sobrino et al. (2004). Ts is land surface temperature in K, Lsensor is the at-sensor radiance with unit Wm-2sr-1µm-1, \ 1 , \ 2 , \ 3 are the atmosphere functions which correlate with the atmospheric water vapor content ( Z ), and a new set of atmospheric parameters for this correlation developed by Jiménez-Muñoz et al (2009) are adopted in this study. In Eq. (2 Ȝ LV WKH ZDYHOHQJWK Rf effective radiance (the DYHUDJH ZDYHOHQJWK Ȝ ȝP LV XVHG C1=1.19104h 108Wµm4m-2sr-1, C2=14387.7µmK,Tsensor is at-sensor brightness temperature in K given by (4) Tsensor=K2/ln(1+K1/Lsensor) K1=666.093(Wm-2sr-1µm-1) with K2=1282.7108(K), (Landsat 7 Science Data Users Handbook,2009). 3.2 Urban information extraction Human activities can produce a large quantity of thermal noise, especially in urban areas. Therefore, it is necessary to
subtract the urban area from the high temperature area to reduce anthropogenic thermal influence. In this work, the urban area is extracted from the ETM+ infrared data using the approach developed by Zha et al. (2003). This approach is based on the subtraction of binary Normalized Difference Vegetation Index (NDVI) image from the binary Normalized Difference Built-up Index (NDBI) image. Detailed method discription can be referred to Zha et al. (2003). NDVI can be calculated according to Eq. (5) and NDBI is calculated in Eq. (6). NDVI=(R2-R1)/(R2+R1) (5) NDBI = (R5 - R4)/(R5 + R4) (6) where R1, R2, R4, R5 is the reflectance of ETM+ band 1 (Red), band2 (Near-Infrared), band 4 (Near-Infrared) and band 5 (Mid-Infrared), respectively. This method has an urban area mapping accuracy of 92.6%. Considering the human influence is gradually reduced with the increase of distance to urban area, the distance to urban area is calculated with the Euclidean Distance function in ArcMap 9.1 (ESRI, 2005). An empirical distance of 1000 m is assigned as the maximum distance to urban areas, assuming that the area more than 1000 m away from urban area is not affected by the thermal effect from human activities. 3.3 Slope information extraction Slope is calculated as the maximum rate of change between each cell and its neighbors in a raster image. The formula is as follows: WDQĮ KO (7) ZKHUH Į LV WKH GHJUHH RI VORSH K LV WKH GLIIHUHQFH RI elevation, l is the horizontal distance between two cells. The higher the slope value is, the steeper the terrain is, the more chance for the development of faulted structure. Considering the geological faults data is not available in this study, the slope information is used as an alternative to evaluate the influence of geological structure on geothermal exploration. 3.4 Data integration and suitability analysis Finally, three thematic maps, the surface temperature map, the distance to urban area map and the surface slope map are generated for prospect selection. Since different thematic maps have different scale values, reclassification is applied to reclassify the thematic maps into the same scale. According to the following principles, each map is classified on a scale from 1 to 10 (Table 1). z For temperature layer: the higher the temperature is, the greater the possibility is for geothermal area and a higher value of the class is assigned. z For distance layer: the further from the urban area, the less influence from human thermal influence, and a higher value is assigned. And the area more than 1000 m away is assumed free from human activities and is given the highest value 10. z For slope layer: the steeper the slope is, the greater possibility is for the fault development, and the
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stronger correlation it is with the geothermal anomalies, and a higher value is assigned. Table 1 Reclassification scales and reassigned values of different thematic layers Temperature Distance Slop New Scales reclassification Reclassification reclassification Value (Unit: Celsius) (Unit: meter) (Unit: degree) 1 7.71 - 11.61 0 - 82.24 0 - 0.51 1 2 11.61 - 13.02 82.24 - 246.71 0.51 - 1.10 2 3 13.02 - 14.23 246.71 - 383.77 1.10 - 1.64 3 4 14.2 - 15.29 383.77 - 516.92 1.64 - 2.23 4 5 15.29 - 16.36 516.92 - 626.57 2.23 - 2.90 5 6 16.36 - 17.428 626.57 - 732.31 2.90 - 3.60 6 7 17.42 - 18.56 732.31 - 818.46 3.60 - 4.39 7 8 18.56 - 19.83 818.46 - 920.28 4.39 - 5.40 8 9 19.83 - 21.25 920.28 - 998.60 5.40 - 6.89 9 10 21.25- 25.86 998.606.89 - 10.03 10 Considering the different influence of thematic layers in geothermal exploration, each layer is weighted according to its importance by the weighed overlay model (BonhamCarter, 1994) in ArcMap 9.1. Surface temperature is the main indicator to identify geothermal areas with TIR remote sensing, therefore it is given the maximum weight of 0.5; the strong correlation between geothermal occurrences and faults development in Tengchong attributes to a weight of 0.4 for slope layer; the layer of distance to urban area is assigned a weight of 0.1. The suitability analysis is then carried out and the suitability map (Fig. 3) is produced with value ranges from 1.3 to 9.8.
Fig. 3 Weighted overlay results of thematic layers In the Fig.3, the greater the value is, the greater possibility exists for an area as a geothermal prospect. In this case, an appropriate threshold should be assigned to select the most promising area with a relative large area extent. Different thresholds have been tried from 6 to 9, and the different results are presented in Fig. 4.
Fig. 4 Selection results of promising geothermal areas with different thresholds
It is easy to find out in Fig.4 that the promising areas are gradually diminished with the increase of threshold value. Too small a threshold value (threshold 6) will include too much promising areas in the final selection and add to the further work for validation. On the contrary, too large a value (threshold 8 or 9) may over exclude some prospective areas. Finally, a balanced threshold of 7 is determined for the prospect selection which extracts an area with high suitability and a relatively complete extent at the same time. In this way, 168 promising points are finally selected with a total area of 4.429 km2. 4. VALIDATIONS The validation of selected areas is carried out by comparing with the recorded geothermal fields and by field investigation as well. The results are shown in Fig. 5.
Fig. 5 Validation results of geothermal prospects Three developed geothermal fields in southwest of Tenchong, namely Nonghuan, Xiaqiluo and Langpu geothermal fields, have been successfully extracted (Fig. 5). The Nonghuan geothermal field is extracted at the southwest corner of study area with a ribbon distribution pattern. The extracted area is 0.61 km2 with an extraction accuracy of 87.1%. The Xiaqiluo geothermal field is found in an inclined “V” distribution (indicated by a red “V”shaped dotted line in Fig. 5) which is consistent with our previous result (Qin et al., 2011). The famous geothermal district, Rehai hot field, is marked by a red dot in Fig. 5. The extracted geothermal area in Xiaqiluo region is 2.04 km2 which is larger than its recorded first grade geothermal area (2 km2). A field investigation was also conducted in this region, and we found that a small number of villages were misidentified as geothermal areas. Before overlay analysis, the urban area map with spatial resolution of 30 m should be sampled to 60 m to be comparable with the temperature result. During this process, the villages with small area are missed to be excluded as urban area and are left as geothermal areas, which might explain the larger geothermal area extracted than recorded. Langpu geothermal field is detected in the south of study area with an area of 0.55 km2 compared with 0.6 km2 recorded first grade geothermal area (Yunnan Geology and Mineral Resources Bureau, 1990; Yang et al., 2003). The extraction
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percentage of geothermal area in Langpu is 91.6%. In addition, some promising geothermal areas are also discovered in the north study area. They present relative small area and require further investigations in future. 5. CONCLUSIONS Our work is a combination use of remote sensing and GIS in an early stage of geothermal exploration in southwest Tengchong of China. ETM+ remote sensing data are used to retrieve the surface temperature and extract the urban area; DEM data are used to obtain the slope information as an indicator of geophysical structure. GIS (ArcMap 9.1) is applied for distance analysis, creating thematic maps and integration of thematic data for suitability analysis. Finally, the geothermal promising areas are selected and validated. Three geothermal fields are successfully extracted in south part of study area, while the prospective geothermal areas in the north part are nearly unexplored and deserve further exploration. The results also indicate that combing remote sensing with GIS is an overall effective and accurate method for geothermal exploration. The methodology applied in this work has efficiently reduced the extent for geothermal exploration and locate the promising geothermal areas for further exploration. 6 ACKNOWLEDGEMENTS The authors would like to thank the financial support of the National Natural Science Foundation of China (41071221) and the R&D Special Fund for Public Welfare Industry of China (Meteorology)(No. GYHY 200806022). 7. REFERENCES [1] Bonham-Carter, G.F., 1994. Geographical Information Systems for Geoscientists: Modeling with GIS. Computer Methods in the Geosciences, 13. Pergamon, New York [2] Coolbaugh, M.F., Kratt, C., Fallacaro, A., Calvin, W.M., Taranik, J.V., 2007. Detection of geothermal anomalies using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA. Remote Sensing of Environment 106, 350-359. [3] ESRI, 2005. Using ArcMap 9.1. Environmental Systems Research Institute, Redlands, CA [4] Hao Y., Gao X., Zhang Y., 2001. The applied research of remote sensing to terrestrial heat resource exploration in xiazhuang area of hebei province. Remote Sensing for Land & Resources 47, 19-24 (In Chinese with English abstract) [5] Jiménez-Muñoz J. C., Cristóbal J., Sobrino J. A., Sòria G., Ninyerola M., Pons X., 2009. Revision of the SingleChannel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. IEEE Transactions on Geoscience and Remote Sensing, 47, 339-349
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