<?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>0120-2812</journal-id>
<journal-title><![CDATA[Acta Agronómica]]></journal-title>
<abbrev-journal-title><![CDATA[Acta Agron.]]></abbrev-journal-title>
<issn>0120-2812</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-28122019000200142</article-id>
<article-id pub-id-type="doi">10.15446/acag.v68n2.78945</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Espacialidad volumétrica de madera en plantaciones forestales usando redes neurales artificiales con imágenes de satélite]]></article-title>
<article-title xml:lang="en"><![CDATA[Volumetric spatiality of wood in forest settlement using artificial neural networks with satellite images]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moreno-Arteaga]]></surname>
<given-names><![CDATA[Argemiro José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moreira-Thiersch]]></surname>
<given-names><![CDATA[Monica Fabiana Bento]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Averna-Valente]]></surname>
<given-names><![CDATA[Robería Oliveira]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bernardi]]></surname>
<given-names><![CDATA[Lucas Krohling]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vasconcelos]]></surname>
<given-names><![CDATA[Sophia Lourengo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Thiersch]]></surname>
<given-names><![CDATA[Cláudio Roberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Federal de Sao Carlos  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>68</volume>
<numero>2</numero>
<fpage>142</fpage>
<lpage>150</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-28122019000200142&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-28122019000200142&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-28122019000200142&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La agricultura sustentable de plantaciones forestales exige la estimación permanente de la cantidad o volumen de madera que se está produciendo en un momento dado, lo que es difícil en extensas áreas de bosque, si solo se utilizan procedimientos manuales en campo. En la presente investigación se modelaron redes neuronales artificiales (ANNs, su sigla en inglés) de perceptrón multicapa para la estimación espacial de volúmenes de madera en una plantación de Eucalyptus sp. localizada en el estado de Mato Grosso del Sur, región Centro-oeste de Brasil. Para el efecto se emplearon como variables de predicción bandas espectrales, texturas de las bandas obtenidos con matriz de coocurrencia de niveles de grises e índice de vegetación, que fueron derivados de imagen digital de satélite Spot 6. La ANN resultante con mejor desempeño presentó una precisión de 93.32% y un coeficiente de determinación de 0.9761, con respecto a valores obtenidos con medidas de campo; no obstante, presentó un error cuadrático medio relativo de 16.32% (RMSE de 7.85 m3/ha), pero la distribución de los residuos no fue tendenciosa, por tanto, el modelo fue promisorio para mapear los volúmenes de madera en extensas áreas sin sobreestimar o subestimar la predicción. La red construida mostró mayor precisión y exactitud cuándo se comparó con otros métodos que usaron variables similares de estimación, inclusive en comparación con modelos neuronales que utilizaron solo bandas espectrales e índices de vegetación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The sustainable agriculture of forest plantations demands the permanent monitoring of the quantity of processed wood, thus becoming difficult to monitor large planted areas by using only manual procedures. Therefore, in this research artificial neural networks (RNA) of multilayer perceptrons, were modeled to estimate the spatial of wood volume in a Eucalyptus-sp plantation located in the state of Mato Grosso del Sur in the Central-West region of Brazil. As input variables in the RNA spectral bands, the textures of the bands obtained with Gray Level Co-occurrence Matrices and vegetation index were used, which were derived from digital satellite image Spot 6. The resulting RNA with the best performance presented accuracy of 93.32% and coefficient of determination of 0.9761. However, this network presented a mean square error of 16.32% (RMSE de 7.85 m3ha-1), but with a unbiased distribution of the residuals. So, the model showed to be suitable to monitor the amount of wood in large areas without overestimating or underestimating the prediction. Compared with different machine learning methods using the same variables, the built network seems to have a higher precision and accuracy. Even in the neuronal models using only spectral bands and vegetation indexes, a better performance was evidenced, showing that the textures contribute in the improvement of predictions.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Biomasa]]></kwd>
<kwd lng="es"><![CDATA[dendrometría]]></kwd>
<kwd lng="es"><![CDATA[percepción remota]]></kwd>
<kwd lng="es"><![CDATA[perceptron multicapa]]></kwd>
<kwd lng="es"><![CDATA[textura]]></kwd>
<kwd lng="en"><![CDATA[Biomass]]></kwd>
<kwd lng="en"><![CDATA[dendrometry]]></kwd>
<kwd lng="en"><![CDATA[remote sensing]]></kwd>
<kwd lng="en"><![CDATA[multilayer perceptrons]]></kwd>
<kwd lng="en"><![CDATA[texture]]></kwd>
</kwd-group>
</article-meta>
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