<?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>0120-6230</journal-id>
<journal-title><![CDATA[Revista Facultad de Ingeniería Universidad de Antioquia]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.fac.ing.univ. Antioquia]]></abbrev-journal-title>
<issn>0120-6230</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-62302024000300045</article-id>
<article-id pub-id-type="doi">10.17533/udea.redin.20231030</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Landslide susceptibility assessment in scarce-data regions using remote sensing data]]></article-title>
<article-title xml:lang="es"><![CDATA[Evaluación de la susceptibilidad a deslizamientos en regiones con escasez de datos utilizando sensores remotos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aristizábal-Giraldo]]></surname>
<given-names><![CDATA[Edier Vicente]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruiz-Vásquez]]></surname>
<given-names><![CDATA[Diana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia Sede Medellín Departamento Ambiental y de Geociencias]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad EAFIT  ]]></institution>
<addr-line><![CDATA[Medellín, ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2024</year>
</pub-date>
<numero>112</numero>
<fpage>45</fpage>
<lpage>59</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-62302024000300045&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-62302024000300045&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-62302024000300045&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Landslides triggered by rainfall are among the most frequent causes of natural disasters in mountainous terrains. However, landslide susceptibility assessments are often limited due to the scarcity of reliable observations. Due to this lack of data, especially in developing countries, remote sensing is used for landslide susceptibility analysis. This study presents the application of remote sensing data and a logistic regression model to assess landslide susceptibility in a basin on a remote terrain in the northern Colombian Andes, where a rainstorm on May 18th, 2015, triggered more than 40 landslides and an associated debris flow afterwards. The methodology applied is based on free access remote sensing tools, since the study area is considered a scarce-data zone. The results show that free remote sensing tools provide enough information to run a model as logistic regression and achieve a successful first approach to the landslide susceptibility map of complex terrains as the study area. This suggests that the proposed methodology could be implemented in several regions with similar characteristics based only on free access information.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Los movimientos en masa provocados por lluvias son una de las causas más frecuentes de desastres naturales en terrenos montañosos. Sin embargo, las evaluaciones de susceptibilidad de movimientos en masa a menudo son limitadas debido a la escasez de observaciones confiables. Debido a esta falta de datos, especialmente en los países en desarrollo, los sensores remotos pueden ser usados para el análisis de la susceptibilidad de movimientos en masa. Este estudio presenta la aplicación de un modelo de regresión logística con datos de sensores remotos para evaluar la susceptibilidad a movimientos en masa en una cuenca en un territorio apartado en el norte de los Andes colombianos; donde el 18 de mayo de 2015 una tormenta desencadenó más de 40 movimientos en masa y un flujo de escombros asociado posteriormente. La metodología aplicada se basa en herramientas de sensores remotos de libre acceso, ya que el área de estudio se considera una zona de escasez de datos. Los resultados muestran que las herramientas gratuitas de sensores remotos proporcionan suficiente información para ejecutar un modelo como regresión logística y lograr un primer acercamiento exitoso al mapa de susceptibilidad a movimientos en masa para territorios complejos como el área de estudio. Esto sugiere que la metodología propuesta podría implementarse en varias regiones con características similares basadas únicamente en información de libre acceso.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Scarce data region]]></kwd>
<kwd lng="en"><![CDATA[remote sensing]]></kwd>
<kwd lng="en"><![CDATA[logistic regression]]></kwd>
<kwd lng="en"><![CDATA[landslide susceptibility]]></kwd>
<kwd lng="en"><![CDATA[tropical and complex terrains.]]></kwd>
<kwd lng="es"><![CDATA[Región con datos escasos]]></kwd>
<kwd lng="es"><![CDATA[Sensores remotos]]></kwd>
<kwd lng="es"><![CDATA[Regresión logística]]></kwd>
<kwd lng="es"><![CDATA[susceptibilidad a deslizamientos]]></kwd>
<kwd lng="es"><![CDATA[terrenos tropicales y complejos.]]></kwd>
</kwd-group>
</article-meta>
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