<?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>1390-6542</journal-id>
<journal-title><![CDATA[Enfoque UTE]]></journal-title>
<abbrev-journal-title><![CDATA[Enfoque UTE]]></abbrev-journal-title>
<issn>1390-6542</issn>
<publisher>
<publisher-name><![CDATA[Universidad UTE]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1390-65422020000300069</article-id>
<article-id pub-id-type="doi">10.29019/enfoque.v11n3.592</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Temperatura superficial y estado de la vegetación del bosque de Polylepis spp, distrito de San Marcos de Rocchac, Huancavelica &#8211; Perú]]></article-title>
<article-title xml:lang="en"><![CDATA[Surface temperature and states of the vegetation of the forest of Polylepis spp, district of San Marcos de Rocchac, Huancavelica &#8211; Peru]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quispe Reymundo]]></surname>
<given-names><![CDATA[Bimael J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Révolo Acevedo]]></surname>
<given-names><![CDATA[Ronald H]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional del Centro del Perú  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Perú</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional del Centro del Perú  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Perú</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<volume>11</volume>
<numero>3</numero>
<fpage>69</fpage>
<lpage>86</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S1390-65422020000300069&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S1390-65422020000300069&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S1390-65422020000300069&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El efecto de la temperatura superficial sobre el estado de la vegetación en el bosque de Polylepis spp es el objetivo de esta investigación. Como metodología se utilizó 9 imágenes satelitales del Sensor Landsat 8 OLI/TIRS, evaluadas mediante la percepción remota, aplicando correlación . Pearson e hipótesis estadística . student. Como resultado se obtuvieron las relaciones durante los 9 meses del año 2018 - 2019 entre LST &#8211; NDVI r=0.11, t=0.29; LST &#8211; NDWI r=-0.43, t=1.27; LST - SAVI r=0.13, t=0.34 y LST - MSI r=0.56, t=1.77; la relación promedio de 9 imágenes clasificado en valores Jenks Natural Breaks entre LST &#8211; NDVI r=0.99, t=47.12; LST &#8211; NDWI r=-0.98, t=28.93; LST &#8211; SAVI r=-0.99, t=65.39 y LST - MSI r=0.99, t=30.39; y el efecto del &#8220;TVX&#8221; para NDVI(Este:-0.0778/0.0549; Oeste:0.6434/-0.0120), NDWI(Oeste:-0.6128/-0.0463; Este:0.3057/0.0474), SAVI (Oeste:0.4089/0.0232; Este:-0.0073/-0.0011) y MSI(Este:0.5565/0.1856; Oeste:1.3993/0.0362). En conclusión, se confirma que en los 9 meses que duró el monitoreo no existió correlación estadística pero que en promedio de las 9 imágenes clasificados en Jenks Natural Breaks sí existe correlación; TVX confirmó la influencia de temperatura superficial sobre el estado de vegetación dentro del bosque a través del tiempo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The effect of surface temperature on the state of the vegetation in the forest of Polylepis spp and to relate it is the objective of this research. As methodology 9 satellite images of the Landsat 8 OLI / TIRS Sensor were used, evaluated using remote sensing, applying Pearson r correlation and statistical t student hypothesis. The following results were obtained: the relationships during the 9 months of the year 2018 - - 2019 between LST - NDVI r = 0.11, t = 0.29; LST - NDWI r = -0.43, t = 1.27; LST - SAVI r = 0.13, t = 0.34 and LST - MSI r = 0.56, t = 1.77; the average ratio of 9 images classified in Jenks Natural Breaks values &#8203;&#8203;between LST - NDVI r = 0.99, t = 47.12; LST - NDWI r = -0.98, t = 28.93; LST - SAVI r = -0.99, t = 65.39 and LST - MSI r = 0.99, t = 30.39; and the effect of &#8220;TVX&#8221; for NDVI (East: -0.0778 / 0.0549; West: 0.6434 / -0.0120), NDWI (West: -0.6128 / -0.0463; East: 0.3057 / 0.0474), SAVI (West: 0.4089 / 0.0232; East: -0.0073 / -0.0011) and MSI (East: 0.5565 / 0.1856; West: 1.3993 / 0.0362). In conclusion, it is confirmed that during the 9 months that the monitoring lasted, there was no statistical correlation and that on average of the 9 images classified in Jenks Natural Breaks there is a correlation; TVX confirmed the influence of surface temperature on the state of vegetation within the forest over time.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Polylepis]]></kwd>
<kwd lng="es"><![CDATA[LST]]></kwd>
<kwd lng="es"><![CDATA[Estado de la Vegetación]]></kwd>
<kwd lng="es"><![CDATA[TVX]]></kwd>
<kwd lng="es"><![CDATA[Jenks Natural Breaks]]></kwd>
<kwd lng="en"><![CDATA[Polylepis]]></kwd>
<kwd lng="en"><![CDATA[LST]]></kwd>
<kwd lng="en"><![CDATA[Vegetation States]]></kwd>
<kwd lng="en"><![CDATA[TVX]]></kwd>
<kwd lng="en"><![CDATA[Jenks Natural Breaks]]></kwd>
</kwd-group>
</article-meta>
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