<?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>2007-4018</journal-id>
<journal-title><![CDATA[Revista Chapingo serie ciencias forestales y del ambiente]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Chapingo ser. cienc. for. ambient]]></abbrev-journal-title>
<issn>2007-4018</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma Chapingo, Coordinación de Revistas Institucionales]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-40182024000200007</article-id>
<article-id pub-id-type="doi">10.5154/r.rchscfa.2023.06.040</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Operational implications of spatial resolution of drone imagery in vegetation mapping for forestmanagement]]></article-title>
<article-title xml:lang="es"><![CDATA[Implicaciones operativas de la resolución espacial de imágenes de drones en el mapeo de la vegetación para el manejo forestal]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ordóñez-Prado]]></surname>
<given-names><![CDATA[Casimiro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valdez-Lazalde]]></surname>
<given-names><![CDATA[José René]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Flores-Magdaleno]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ángeles-Pérez]]></surname>
<given-names><![CDATA[Gregorio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Santos-Posadas]]></surname>
<given-names><![CDATA[Héctor M. de los]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Buendía-Rodríguez]]></surname>
<given-names><![CDATA[Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias Centro de Investigación Regional Centro Campo Experimental Valle de México]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2024</year>
</pub-date>
<volume>30</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-40182024000200007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-40182024000200007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-40182024000200007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction Drones allow collecting high-spatial resolution images useful for monitoring forest vegetation dynamics in managed forests. There are, however, doubts about the most effective way to use them concerning spatial resolution.  Objective To identify the optimal spatial resolution of multispectral images captured by drones for mapping land cover types in managed temperate forests in Hidalgo, Mexico.  Materials and methods Spectral images were preprocessed at spatial resolutions from 0.2 to 2.5 m, at 0.1 m intervals. Pine, oak, other broad-leaved trees, herbs and bare soil cover were classified with the Random Forest algorithm. The effect of spatial resolution on land cover classification was evaluated using the Kruskal-Wallis non-parametric test followed by a Mann-Whitney-Wilcoxon post-hoc comparison (p &lt; 0.05). Classification errors of land cover classes were analyzed graphically.  Results  0.2 m spatial resolution images provided the highest land cover classification accuracy (96 %) but was statistically similar to that of 0.7 m (p = 0.3984). The lowest accuracy (82 %) was obtained with 2.5 m spatial resolution imagery. Omission and commission errors were lower and consistent in classifications with 0.2 to 1.2 m spatial resolution images.  Conclusion  Multispectral images (0.7 m resolution), acquired with a fixed-wing drone, allowed us to classify the land cover/vegetation types and the exact spatial distribution of pine, oak and other hardwood species in a temperate forest under forest management.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción  Los drones aportan imágenes de alta resolución espacial para el seguimiento de la dinámica de la vegetación en bosques bajo manejo forestal; sin embargo, existen dudas sobre la forma más eficaz de utilizarlas con respecto a la resolución espacial.  Objetivo Determinar la resolución espacial más apropiada de las imágenes multiespectrales obtenidas por drones, para mapear los tipos de cobertura del suelo en bosques templados bajo manejo forestal de Hidalgo, México.  Materiales y métodos  Las imágenes espectrales se preprocesaron en resoluciones espaciales desde 0.2 hasta 2.5 m, a intervalos de 0.1 m. La cobertura de pinos, encinos, otras latifoliadas, herbáceas y suelo desnudo se clasificaron con el algoritmo Random Forest. El efecto de la resolución espacial en la clasificación de la cobertura terrestre se evaluó mediante la prueba no paramétrica de Kruskal-Wallis seguida de una comparación post-hoc Mann-Whitney-Wilcoxon (p &lt; 0.05). Los errores de clasificación de las clases de cobertura se analizaron gráficamente.  Resultados  Las imágenes de 0.2 m de resolución espacial proporcionaron la mayor precisión de clasificación de la cobertura del suelo (96 %), pero fue estadísticamente similar que la de 0.7 m (p = 0.3984). La precisión más baja (82 %) se obtuvo con imágenes de 2.5 m de resolución espacial. Los errores de omisión y comisión fueron menores y constantes en las clasificaciones con imágenes de resolución espacial de 0.2 a 1.2 m.  Conclusiones  Las imágenes multiespectrales (0.7 m de resolución), adquiridas con un dron de ala fija, permitieron la clasificación precisa de los tipos de cobertura y la distribución espacial exacta de pinos, encinos y otras especies de latifoliadas de un bosque templado bajo manejo forestal.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[forest cover]]></kwd>
<kwd lng="en"><![CDATA[vegetation classification]]></kwd>
<kwd lng="en"><![CDATA[multispectral images]]></kwd>
<kwd lng="en"><![CDATA[kappa index]]></kwd>
<kwd lng="en"><![CDATA[Random Forest]]></kwd>
<kwd lng="es"><![CDATA[cobertura forestal]]></kwd>
<kwd lng="es"><![CDATA[clasificación de vegetación]]></kwd>
<kwd lng="es"><![CDATA[imágenes multiespectrales]]></kwd>
<kwd lng="es"><![CDATA[índice kappa]]></kwd>
<kwd lng="es"><![CDATA[Random Forest]]></kwd>
</kwd-group>
</article-meta>
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