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Remote sensing extraction of geothermal anomaly based on terrain effect correction

  • role： First author第一作者
  • Affiliation：

    School of Earth Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China

  • Email：zhouty5@mail2.sysu.edu.cn
  • Introduction：周桃勇,1995年生, 男, 硕士研究生, 研究方向为资源环境遥感。E-mail: zhouty5@mail2.sysu.edu.cn
ZHOU Taoyong1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China

  • Email：wzhengh@mail.sysu.edu.cn
  • Introduction：王正海，1971年生, 男, 副教授, 研究方向为遥感地质与矿产资源定量评价。E-mail: wzhengh@mail.sysu.edu.cn
WANG Zhenghai1*,  
  • Affiliation：

    School of Earth Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China

QIN Haoyang1,  
  • Affiliation：

    School of Earth Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China

ZENG Yaqi1

реферат

Geothermal exploration in mountain regions strongly relies on the successful identification of thermal anomaly in the regions, which can be performed through the extraction of geothermal anomaly information from thermal infrared remote sensing data. However, changes in Land Surface Temperature (LST) in mountain regions are significantly affected by topography in addition to other factors, such as latitude and surface property. This effect strongly weakens the efficient identification of geothermal anomaly over the LST image retrieved from thermal infrared remote sensing data, which consequently limit the application of remote sensing technique to geothermal resource exploration in mountain regions with rough terrain. This study examines the effects of terrain on LST changes in the mountain region of Longchuan in Southern China to establish an efficient approach, which can correct the effects of terrain on the LST changes for the geothermal exploration in the region.LST was retrieved using mono-window algorithm based on Landsat ETM+ remote sensing data. The effects of terrain on LST distribution were then analysed. The statistical analysis showed a parabolic relationship between LST and aspect. Moreover, the southeast-facing slope had the highest average LST and standard deviation, where LST was significantly and positively correlated with slope gradient. To reduce the impact of terrain on LST distribution, the area was divided into sunny slope, shady slope and transitional slope. The LST in the sunny slope was particularly corrected to its horizontal surface equivalent by the linear regression equation between LST and slope gradient. Geothermal anomalies were then extracted from the LST of these three subareas, with the consideration of geologic structure and land cover.Results showed that the spatial variation amplitude of LST evidently decreased because the significant temperature difference among different terrain conditions has become small in subareas. Four possible geothermal anomalies were recognised in which high temperature areas were closely related to faults and showed little variability in land cover. Comparative analysis with known hot springs indicated that they were likely caused by geothermal activities.In conclusion, topography mainly affects LST spatial distribution by controlling incoming solar radiation. The solution of aspect-based partition and gradient correction presented in this article can also effectively reduce topographic effects. It helps improve the recognition accuracy of geothermal anomalies with remote sensing technology. The solution may also provide an enlightening insight into the forecast evaluation of geothermal resources in mountain regions. Moreover, further analyses of the relationship between LST and other factors controlled by terrain are necessary in future research, especially the physical properties of underlying surfaces, such as land use, soil moisture and vegetation.

ключеви́че слова́

geothermal resource;land surface temperature;terrain effect;Landsat ETM+;thermal infrared;aspect;anomaly extraction

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