<?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>2007-1132</journal-id>
<journal-title><![CDATA[Revista mexicana de ciencias forestales]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. de cienc. forestales]]></abbrev-journal-title>
<issn>2007-1132</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-11322017000300030</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estimación de la densidad forestal mediante imágenes Landsat ETM+ en la región sur del Estado de México]]></article-title>
<article-title xml:lang="en"><![CDATA[Landsat ETM+ imaging for the estimation of the forest density in the southern region of the State of Mexico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Acosta Mireles]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Miranda]]></surname>
<given-names><![CDATA[Ramiro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Romero Sánchez]]></surname>
<given-names><![CDATA[Martín Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Hernández]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez Ángel]]></surname>
<given-names><![CDATA[Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias Campo Experimental Valle de México- CIR-Centro ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias Centro Nacional de Investigación Disciplinarias en Conservación y Mejoramiento de Ecosistemas Forestales ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Tecnológico Superior de Venustiano Carranza  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<volume>8</volume>
<numero>41</numero>
<fpage>30</fpage>
<lpage>55</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-11322017000300030&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-11322017000300030&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-11322017000300030&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: En el contexto de los mecanismos de mitigación del cambio climático, el monitoreo forestal constante es importante porque los bosques proporcionan información clave. La estimación de parámetros por medio de imágenes de satélite en combinación con información derivada de inventarios permite mantener información actualizada de la estructura del bosque a costo relativamente accesible. Para lograrlo es necesario utilizar modelos que construyan asociaciones válidas entre datos de sensores remotos y de campo. El objetivo de este estudió fue analizar la relación entre el área basal (AB), el volumen (V) y la biomasa (B) derivadas del Inventario Forestal y de Suelos del Estado de México y los datos espectrales provenientes de imágenes Landsat ETM+. El mejor modelo para estimar AB, V, y B incorporó como variable independiente la banda infrarrojo medio (IRM), que presentó la más alta corrección con los datos de campo. Los modelos de regresión ajustados resultantes sirvieron para estimar con precisión el AB, V y B. Todos los modelos de regresión fueron altamente significativos al 95% de confiabilidad, con coeficientes de determinación (R2adj) de 0.52, 0.54 y 0.60 para AB, V y B, respectivamente; lo cual hizo posible elaborar mapas de los parámetros forestales. El estimador de regresión presentó el inventario más conservador e intervalos de menor amplitud con respecto al MSA.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: In the context of the mechanisms of climatic change mitigation, constant forest monitoring is important because forests provide crucial information. The estimation of forest stand attributes based on satellite imagery data combined with forest inventory data allows producing accurate information on forest structure, at a relative and accessible cost. However, there is still a need to use models that enable the construction of a valid relationship between remote sensing and field data. Therefore, this study aims to estimate forest attributes such as basal area (AB), volume (V) and aboveground biomass (B) by analyzing and using the relationship between spectral information, from Landsat ETM+ imagery, and tree measurements. Data from plots collected in 2010 by the National Forest and Soils Inventory (INFyS) were used as primary source of information. Results suggest that the best models to estimate AB, V and B were those that used the near infrared band (band 5) as the independent variable. Results also indicated that adjusted regression models shown statistical bases to estimate AB, V and B in a precise manner. All regression models were highly significant (5 %) with determination coefficients (R2  adj ) higher than 0.47.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Biomasa aérea]]></kwd>
<kwd lng="es"><![CDATA[estructura forestal]]></kwd>
<kwd lng="es"><![CDATA[Landsat]]></kwd>
<kwd lng="es"><![CDATA[modelos de regresión]]></kwd>
<kwd lng="es"><![CDATA[parámetros forestales]]></kwd>
<kwd lng="es"><![CDATA[sensores remotos]]></kwd>
<kwd lng="en"><![CDATA[aboveground biomass]]></kwd>
<kwd lng="en"><![CDATA[forest structure]]></kwd>
<kwd lng="en"><![CDATA[Landsat]]></kwd>
<kwd lng="en"><![CDATA[regression models]]></kwd>
<kwd lng="en"><![CDATA[forest parameters]]></kwd>
<kwd lng="en"><![CDATA[remote sensors]]></kwd>
</kwd-group>
</article-meta>
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