<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1794-6190</journal-id>
<journal-title><![CDATA[Earth Sciences Research Journal]]></journal-title>
<abbrev-journal-title><![CDATA[Earth Sci. Res. J.]]></abbrev-journal-title>
<issn>1794-6190</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1794-61902019000400265</article-id>
<article-id pub-id-type="doi">10.15446/esrj.v23n4.77810</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Determination of Hydrothermal Prospects in the Geothermal Region of Paipa (Boyacá, Colombia), Using Remote Sensing and Field Data]]></article-title>
<article-title xml:lang="es"><![CDATA[Determinación de Prospectos Hidrotermales en la Región Geotérmica de Paipa (Boyacá, Colombia), Utilizando Sensores Remotos y Datos de Campo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Calderón-Chaparro]]></surname>
<given-names><![CDATA[Rafael Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vargas-Cuervo]]></surname>
<given-names><![CDATA[German]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>23</volume>
<numero>4</numero>
<fpage>265</fpage>
<lpage>282</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1794-61902019000400265&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1794-61902019000400265&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1794-61902019000400265&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Geothermal resources have been found with the help of conventional geological, geochemical, and geophysical techniques. However, in some cases, difficult access to research areas hinders its application, which in turn increases operating costs and restricts areas of exploration. Remote sensors with thermal infrared are an important tool in the exploration of geothermal resources due to the low cost and high efficiency in the study of large geographic areas. In this research, thermal images of remote satellite sensors are used and integrated with existing geological-geophysical information for the determination of exploratory prospects of hydrothermal vents in the geothermal region of Paipa, Boyacá. The satellite thermal images used are Landsat 7 ETM+ and Landsat 8 OLI/TIRS. Radiometric calibration, atmospheric correction, surface thermal emissivity calculation, a single-channel, and Split window algorithm were applied to retrieve the land surface temperature. Soil temperature, surface temperature, air temperature, relative humidity, atmospheric pressure, and thermal imagery of the geothermal manifestations were also used. The field data contributed to the correction of the thermal bands, and the soil temperature data helped to create a subsurface temperature map at one-meter depth. Once the primary and secondary data was available, in a GIS, an unweighted spatial model was implemented, which used four input indicators (satellite temperature index, soil temperature index, structural lineaments index, and iso-resistivity index), to determine the areas most likely to find geothermal fluids. Six areas of exploratory interest for the extraction of hydrothermal fluids were determined, of which two are already known, and the remaining four are proposed as prospects. The results allowed us to conclude that thermal remote sensors are a useful tool for the mapping of geothermal anomalies in the Paipa region, and by combining these anomalies with geological-geophysical information, it is possible to determine specific areas of exploration.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Los recursos geotérmicos han sido encontrados con ayuda de técnicas de campo, geológicas, geoquímicas y geofísicas, las cuales, en algunas ocasiones, el difícil acceso al área de investigación, dificulta su aplicación, aumentando los costos de operación y restringiendo espacialmente las áreas de exploración. Los sensores remotos con infrarrojo termal, son una herramienta importante en la exploración de recursos geotérmicos, debido al bajo costo y alta eficiencia en el estudio de grandes áreas geográficas. En esta investigación se utilizaron e integraron imágenes termales de sensores remotos satelitales, con información geológica-geofísica existente, para la determinación de prospectos exploratorios de fuentes hidrotermales en la región geotérmica de Paipa, Boyacá. Las imágenes térmicas satelitales utilizadas son Landsat 7 ETM+ y Landsat 8 OLI/TIRS. También, se utilizaron datos de campo, de temperatura de suelo, temperatura de superficie, temperatura de aire, humedad relativa, presión atmosférica e imágenes térmicas de las manifestaciones geotérmicas. Las bandas termales Landsat fueron calibradas radiométricamente, posteriormente corregidas por efectos atmosféricos y emisividad de la superficie, aplicando algoritmos de un solo canal y Split Window, para Landsat 7 ETM+ y Landsat 8 TIRS, respectivamente. Los datos de campo contribuyeron a la corrección de las bandas termales, y los datos de temperatura de suelo, ayudaron a la creación de un mapa de temperatura subsuperficial a un metro de profundidad. Una vez se contaba con los insumos primarios y secundarios, en un SIG, se implementó un modelo espacial no ponderado, el cual utilizó cuatro variables de entrada (índice temperatura satelital, índice temperatura de suelo, índice lineamientos estructurales e índice iso-resistividad), para determinar las zonas de mayor probabilidad de encontrar fluidos geotérmicos. Se determinaron seis áreas de interés exploratorio para la extracción de fluidos hidrotermales, de las cuales, dos son ya conocidas y las cuatro restantes se proponen como prospectos. Los resultados permitieron concluir que los sensores remotos termales son una herramienta útil para el mapeo de anomalías geotérmicas en la región de Paipa, y combinando estas anomalías con información geológica-geofísica, es posible determinar áreas puntuales de exploración.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Remote sensing]]></kwd>
<kwd lng="en"><![CDATA[Thermal infrared]]></kwd>
<kwd lng="en"><![CDATA[Temperature]]></kwd>
<kwd lng="en"><![CDATA[Geothermal]]></kwd>
<kwd lng="en"><![CDATA[Prospects]]></kwd>
<kwd lng="en"><![CDATA[Paipa]]></kwd>
<kwd lng="es"><![CDATA[Sensores Remotos]]></kwd>
<kwd lng="es"><![CDATA[Infrarrojo Termal]]></kwd>
<kwd lng="es"><![CDATA[Temperatura]]></kwd>
<kwd lng="es"><![CDATA[Geotermia]]></kwd>
<kwd lng="es"><![CDATA[Prospectos]]></kwd>
<kwd lng="es"><![CDATA[Paipa]]></kwd>
</kwd-group>
</article-meta>
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