<?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>2663-3981</journal-id>
<journal-title><![CDATA[Revista cartográfica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. cartogr.]]></abbrev-journal-title>
<issn>2663-3981</issn>
<publisher>
<publisher-name><![CDATA[Instituto Panamericano de Geografía e Historia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2663-39812019000100075</article-id>
<article-id pub-id-type="doi">10.35424/rcarto.i98.142</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Geografía del carbono en alta resolución en bosque tropical amazónico del Ecuador utilizando tecnología LiDAR aerotransportada]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Molina S.]]></surname>
<given-names><![CDATA[Xavier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Farjas A.]]></surname>
<given-names><![CDATA[Mercedes]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ojeda M.]]></surname>
<given-names><![CDATA[Juan Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Geográfico Militar del Ecuador  ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Politécnica de Madrid  ]]></institution>
<addr-line><![CDATA[Madrid ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Politécnica de Madrid  ]]></institution>
<addr-line><![CDATA[Madrid ]]></addr-line>
<country>Spain</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>
<numero>98</numero>
<fpage>75</fpage>
<lpage>95</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2663-39812019000100075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2663-39812019000100075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2663-39812019000100075&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La estimación de la biomasa de la vegetación terrestre en bosque tropical es un tema de gran interés para reducir las emisiones de carbono asociadas a la deforestación y la degradación forestal (REDD+). Las estimaciones de densidad de carbono sobre el suelo (ACD) en base a inventarios de campo y datos provenientes de sensores aerotransportados, en especial con sensores LiDAR, han conducido a un progreso sustancial en el cartografiado a gran escala de las reservas de carbono forestal. Sin embargo, estos mapas de carbono tienen incertidumbres considerables, asociadas generalmente al proceso de calibración del modelo de regresión utilizado para producir los mapas. En este estudio se establece una metodología para la calibración y validación de un modelo general de estimación de ACD usando LiDAR en el Parque Nacional Yasuní en Ecuador. En el proceso de calibración del modelo se considera el tamaño y la ubicación de las parcelas, la influencia de la topografía y la distribución espacial de la biomasa. Para el ajuste y validación del modelo se propone un esquema de muestreo estratificado por posiciones topográficas (valle, ladera y cima). La validación del modelo general para toda la zona de estudio presentó valores de RMSE= 5.81 Mg C ha-1, R2= 0.94 y sesgo= 0.59, mientras que, al considerar las posiciones topográficas, el modelo presentó valores de RMSE= 1.67 Mg C ha-1, R2= 0.98 y sesgo= 0.23 para el valle; RMSE= 3.13 Mg C ha-1, R2= 0.98 y sesgo= - 0.34 para la ladera; y RMSE= 2.33 Mg C ha-1, R2= 0.97 y sesgo= 0.74 para la cima. Los resultados muestran el potencial de los datos LiDAR para caracterizar la estructura vertical de la vegetación en un bosque altamente diverso, permitiendo realizar estimaciones precisas de ACD, y conocer patrones espaciales continuos de la distribución de la biomasa aérea y del contenido de carbono en la zona de estudio.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Estimating biomass of terrestrial vegetation in tropical forest is not only a rapidly expanding research issue, but also a subject of high interest for reducing carbon emissions associated with deforestation and forest degradation (REDD+). The aboveground carbon density estimates (ACD) based on field inventories and airborne sensors, especially LiDAR sensors have led to a substantial progress in large-scale mapping of forest carbon stocks. However, these carbon maps have uncertainties generally associated with the calibration of the regression model used to produce these maps. This work establishes a methodology for calibrating and validating a general ACD estimation model using LiDAR in Ecuador&#8217;s Yasuní National Park. The size and location of the plots are considered in the model calibration phase as well as the influence of topography and spatial distribution of biomass. For the adjustment and validation of the model a stratified sampling scheme by topographic positions (valley, slope and ridge) is proposed. The validation of the general model for the study area showed values of RMSE= 5.81 Mg C ha-1, R2= 0.94 and bias= 0.59, while considering the topographical positions, the model showed values of RMSE= 1.67 Mg C ha-1, R2= 0.98 and bias= 0.23 for the valley; RMSE= 3.13 Mg C ha-1, R2= 0.98 and bias= - 0.34 for the slope; and RMSE= 2.33 Mg C ha-1, R2= 0.97 and bias= 0.74 for the ridge. The results show that the stratified sampling methodology taking into account topographic positions, effectively calibrates the general model with field estimates of ACD, reducing RMSE and bias. The results show the potential of LiDAR data to characterize the vertical structure of vegetation in a highly diverse forest, allowing accurate estimates of ACD, and knowing continuous spatial patterns of biomass distribution and carbon stocks in the study area.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A estimativa de biomassa da vegetação terrestre em bosque tropical é um tema de grande interesse para reduzir as emissões de carbono associadas ao desmatamento e a degradação florestal (REDD+). As estimativas de densidade de carbono sobre o solo (ACD) com base em inventários de campo e dados provenientes de sensores aerotransportados, em especial com sensores LiDAR, têm conduzido a um progresso substancial na cartografia de grande escala das reservas de carbono florestal. Entretanto, estes mapas de carbono têm incertezas consideráveis, associadas geralmente a processo de calibração do modelo de regressão utilizado para produzir os mapas. Neste estudo se estabelece uma metodologia para a calibração e validação de um modelo geral de estimação de ACD usando LiDAR no Parque Nacional Yasuní no Equador. No processo de calibração do modelo se considera o tamanho e a localização das parcelas, a influência da topografia e a distribuição espacial da biomassa. Para o ajuste e validação do modelo se propõe um esquema de amostragem estratificada por posições topográficas (vale, encosta e topo). A validação do modelo geral para toda a zona de estudo apresentou valores de RMSE= 5.81 Mg C ha-1, R2= 0.94 e viés= 0.59, enquanto que, ao considerar as posições topográficas, o modelo apresentou valores de RMSE= 1.67 Mg C ha-1, R2= 0.98 e viés= 0.23 para o vale; RMSE= 3.13 Mg C ha-1, R2= 0.98 e viés= - 0.34 para a encosta; e RMSE= 2.33 Mg C ha-1, R2= 0.97 e viés= 0.74 para o topo. Os resultados mostram o potencial dos dados LiDAR para caracterizar a estrutura vertical da vegetação em bosque altamente diverso, permitindo realizar estimações precisas de ACD, e conhecer padrões espaciais contínuos da distribuição da biomassa aérea e do conteúdo de carbono na zona de estudo.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[forest carbon density]]></kwd>
<kwd lng="en"><![CDATA[LiDAR]]></kwd>
<kwd lng="en"><![CDATA[topographic habitats]]></kwd>
<kwd lng="en"><![CDATA[stratified sampling]]></kwd>
<kwd lng="en"><![CDATA[tropical forest]]></kwd>
<kwd lng="es"><![CDATA[Densidad de carbono forestal]]></kwd>
<kwd lng="es"><![CDATA[LiDAR]]></kwd>
<kwd lng="es"><![CDATA[hábitats topográficos]]></kwd>
<kwd lng="es"><![CDATA[muestreo estratificado]]></kwd>
<kwd lng="es"><![CDATA[bosque tropical]]></kwd>
<kwd lng="pt"><![CDATA[Densidade de carbono florestal]]></kwd>
<kwd lng="pt"><![CDATA[LiDAR]]></kwd>
<kwd lng="pt"><![CDATA[habitats topográficos]]></kwd>
<kwd lng="pt"><![CDATA[amostragem estratificada]]></kwd>
<kwd lng="pt"><![CDATA[bosque tropical]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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