<?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>0188-4611</journal-id>
<journal-title><![CDATA[Investigaciones geográficas]]></journal-title>
<abbrev-journal-title><![CDATA[Invest. Geog]]></abbrev-journal-title>
<issn>0188-4611</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Geografía]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-46112020000200111</article-id>
<article-id pub-id-type="doi">10.14350/rig.59975</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Inventario de cuerpos de agua de la Sierra Madre Occidental (México) usando SIG y percepción remota]]></article-title>
<article-title xml:lang="en"><![CDATA[Water Resource Inventory in the Sierra Madre Occidental (Mexico) based on Remote Sensing and GIS]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sandoval]]></surname>
<given-names><![CDATA[Sarahi]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Escobar-Flores]]></surname>
<given-names><![CDATA[Jonathan Gabriel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Ortíz]]></surname>
<given-names><![CDATA[Eduardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional CIIDIR ]]></institution>
<addr-line><![CDATA[Durango Durango]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnico Nacional CIIDIR ]]></institution>
<addr-line><![CDATA[Durango Durango]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<numero>102</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-46112020000200111&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-46112020000200111&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-46112020000200111&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El agua dulce es un recurso fundamental para procesos ambientales y sociales, indispensable para el surgimiento y desarrollo de la vida, por lo que mapear y monitorear las aguas superficiales tiene gran importancia para comprender los procesos hidrológicos y gestionar los recursos hídricos. El presente estudio se realizó en la cadena montañosa más grande de México, la Sierra Madre Occidental (SMO), localizada entre los estados de Chihuahua, Sonora, Sinaloa, Durango, Nayarit, Zacatecas y Jalisco. La SMO tiene un área de 251 648 km2 y cuenta con elevaciones desde 300 m hasta 3 347 m. Debido a su magnitud, orografía y posición geográfica, alberga una gran variedad de ecosistemas, lo que, a su vez, promueve una gran diversidad de especies y constituye la principal fuente de agua para el norte del país. Los objetivos de esta investigación fueron: 1) la detección de cuerpos de agua en la SMO utilizando imágenes de satélite Sentinel-2 de alta resolución espacial, y 2) la realización de un inventario de los cuerpos de agua en los diferentes tipos de vegetación presentes en tal sierra. En esta investigación se utilizaron 120 imágenes del satélite Sentinel-2, que se caracteriza por tener un sensor multiespectral con una resolución espacial de 10 m. A cada una de las imágenes satelitales se le realizó una corrección atmosférica mediante el método de sustracción de cuerpos oscuros. Para la detección y delimitación de cuerpos de agua se aplicó el Índice Diferencial de Agua Normalizado (NDWI, por sus siglas en inglés). Previo al proceso de validación, se realizó una tabulación cruzada entre los cuerpos de agua que se detectaron contra los polígonos de tipos de vegetación que se clasificaron de la siguiente manera: clase bosque, que incluye tipos de vegetación de pino, encino, pino-encino, encino-pino y bosque mesófilo; clase selva, que incluye selva baja y mediana caducifolia; clase bosque con vegetación secundaria herbácea y arbustiva; clase matorral; clase pastizal y clase chaparral. Estos polígonos se obtuvieron de la Serie VI del vectorial de uso de suelo y vegetación del Inegi, escala 1:250 000. Mediante un geo- procesamiento en el programa ArcGIS 10.7 se obtuvo el número de cuerpos de agua (y su superficie) detectados en cada clase de vegetación. La validación en la estimación de superficies de los cuerpos de agua se realizó con la estimación del índice de Kappa y matrices de confusión y errores, de las cuales se calcularon las superficies de cuerpos de agua y sus intervalos de confianza para cada clase de vegetación. Se detectaron 26 394 cuerpos de agua; el tipo de vegetación con más cuerpos de agua encontrados fue el correspondiente a bosques, con 46.86%, seguido por pastizales, con 21.47%. Los cuerpos de agua detectados tuvieron una superficie de entre 43 m2 y 64 km2. Los valores de los píxeles a partir del NDWI encontrados en los cuerpos de agua oscilaron entre 0.1 a 0.8, con una mediana cercana a 0.3, y los cuartiles, entre 0.2 y 0.4. En cuanto a la precisión de la detección de cuerpos de agua en los diferentes tipos de vegetación, los valores de Kappa indicaron acuerdos buenos y excelentes; los tipos de vegetación de bosque de pino-encino y mesófilo tuvieron el menor valor: K = 0.62, lo que se relacionó con sombras que se confundieron con cuerpos de agua (251 sombras). Los valores más altos de Kappa se obtuvieron en los pastizales, con K = 0.91, en donde se detectaron muy pocas sombras que se confundían con cuerpos de agua (13 sombras). La precisión global fue de 0.738, y en la matriz de error se encontró que la clase que presentó mayores errores de comisión fue el pastizal, con un valor de exactitud de usuario de 0.227. Otra clase que tuvo mayor omisión fue el matorral, con un valor de exactitud del productor de 0.351. La detección de los cuerpos de agua contribuye sustancial- mente a los 800 cuerpos de agua reportados previamente para la SMO en la Serie VI del uso del suelo y vegetación en 2016.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Fresh water is a fundamental resource for environmental and social processes, essential for the emergence and development of life. Mapping and monitoring surface water is therefore of great importance for understanding hydrological processes and managing water resources. This study was conducted in the largest mountain range in Mexico, the Sierra Madre Occidental (SMO), spanning the states of Chihuahua, Sonora, Sinaloa, Durango, Nayarit, Zacatecas and Jalisco. The SMO has an area of 251,648 km2 and elevations ranging from 300 m to 3,347 m. Due to its size, orography and geographical location, this region which constitutes the main source of water for northern Mexico, contains a wide variety of ecosystems, which in turn promote high species diversity. The objectives of this study were: 1) to detect water bodies in the SMO using Sentinel-2 satellite images with high spatial resolution, and 2) to make an inventory of water bodies in the SMO by vegetation type. In this study, 120 Sentinel-2 satellite images were used. The satellite has a multispectral sensor with a spatial resolution of 10 m. An atmospheric correction was carried out for each image using the dark object subtraction method. The normalized difference water index (NDWI) was used to detect and delimit water bodies. Before the validation process, the water bodies that had been detected were cross-tabulated against the polygons of the different vegetation types. These vegetation types were classified as follows: forest class, which includes pine, oak, pine-oak, oak-pine and cloud forest; tropical forest class, which includes low and medium deciduous tropical forest; forest with secondary herbaceous and shrubby vegetation class; scrub class; grassland class and chaparral class. The polygons were obtained from the INEGI 1:250,000 vectorial Series VI data on land use and vegetation. The number of water bodies (and their area) detected in each vegetation class were obtained through geoprocessing using the ArGIS 10.7 program. Estimates of the areas of the water bodies were validated by estimating the kappa index, and by means of confusion and error matrices. These were used to calculate the areas of the water bodies and their confidence intervals for each vegetation class. A total of 26,394 water bodies were detected. The vegetation type with the most water bodies was forest, with 46.86%, followed by grasslands, with 21.47%. The water bodies detected had areas ranging from 43 m2 to 64 km2. Pixel values from the NDWI associated with water bodies ranged from 0.1 to 0.8. The median was close to 0.3, and the quartiles were 0.2 and 0.4. The kappa index values indicated good and excellent agreement for the precision of water body detection in the different vegetation types. The lowest value, K = 0.62, was associated with pine-oak and cloud forest vegetation types. This was due to shadows that were mistaken for water bodies (251 shadows). The highest kappa index values, K = 0.91, were obtained for grasslands, where very few shadows (13 shadows) were confused with water bodies. The overall precision was 0.738, and the error matrix showed that the class with the most errors of commission was grassland, with a user accuracy value of 0.227. The class that had the most errors of omission was scrub, with a producer accuracy value of 0.351. This study makes a substantial contribution to the 800 water bodies previously reported for the SMO in the Series VI data for land use and vegetation from 2016.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Índice Diferencial de Agua Normalizado]]></kwd>
<kwd lng="es"><![CDATA[Sentinel-2]]></kwd>
<kwd lng="es"><![CDATA[agua]]></kwd>
<kwd lng="es"><![CDATA[percepción remota]]></kwd>
<kwd lng="es"><![CDATA[tipos de vegetación]]></kwd>
<kwd lng="en"><![CDATA[Normalized Difference Snow Index]]></kwd>
<kwd lng="en"><![CDATA[Sentinel-2]]></kwd>
<kwd lng="en"><![CDATA[water]]></kwd>
<kwd lng="en"><![CDATA[remote sensing]]></kwd>
<kwd lng="en"><![CDATA[vegetation type]]></kwd>
</kwd-group>
</article-meta>
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