{"id":286,"date":"2021-01-18T17:57:00","date_gmt":"2021-01-18T16:57:00","guid":{"rendered":"https:\/\/geopard.tech\/?p=286"},"modified":"2024-12-01T21:40:34","modified_gmt":"2024-12-01T20:40:34","slug":"uso-de-datos-raster-recortados-para-la-agroindustria","status":"publish","type":"post","link":"https:\/\/geopard.tech\/es\/blog\/use-of-cropped-raster-data-for-agribusiness\/","title":{"rendered":"Uso de datos r\u00e1ster recortados para la agroindustria"},"content":{"rendered":"

Los datos r\u00e1ster recortados seg\u00fan los l\u00edmites del campo parecen muy sencillos. Algunas fuentes de datos son r\u00e1steres con p\u00edxeles y una resoluci\u00f3n de 3 m\/10 m\/30 m, otras son vectores con pol\u00edgonos o multipol\u00edgonos.<\/p>\r\n\r\n\r\n\t\t

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\"Datos<\/figure><\/div>
\"Geopard<\/figure><\/div>\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t
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<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\n\r\n\r\n\r\n

Definir con precisi\u00f3n los l\u00edmites de los datos r\u00e1ster recortados es complejo. La mayor\u00eda de los programas de SIG y agricultura de precisi\u00f3n generan por defecto un r\u00e1ster pixelado. Una evaluaci\u00f3n precisa de los datos cerca del l\u00edmite del campo permite comprender mejor, por ejemplo, las condiciones de la cubierta vegetal y el valor de la pendiente.\u00a0<\/p>\r\n\r\n\r\n\r\n

Ejemplos de im\u00e1genes rasterizadas pixeladas:<\/p>\r\n\r\n\r\n\r\n

\"Raster\r\n
Raster pixelado de infrarrojo cercano<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
\"Raster\r\n
Raster pixelado de elevaci\u00f3n<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

\u00bfEs posible mejorarlo y hacerlo m\u00e1s preciso?<\/p>\r\n\r\n\r\n\r\n

S\u00ed, GeoPard hace eso e incluso pone los datos a disposici\u00f3n para una mayor integraci\u00f3n a trav\u00e9s de una API. Algunos ejemplos:<\/p>\r\n\r\n\r\n\r\n

    \r\n
  • Recorte de im\u00e1genes satelitales sin procesar (vistas en rojo, verde y azul, e infrarrojo cercano) basado en el l\u00edmite del campo:<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n
    \"r\u00e1ster\r\n
    GeoPard Agriculture RGB r\u00e1ster recortado<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
    \"R\u00e1ster\r\n
    GeoPard Agricultura Raster recortado en infrarrojo cercano<\/em><\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

     <\/p>\r\n

      \r\n
    • Recorte de im\u00e1genes satelitales con \u00edndice de vegetaci\u00f3n como WDRVI basado en el l\u00edmite del campo:<\/li>\r\n<\/ul>\r\n

       <\/p>\r\n\r\n\r\n\r\n

      \"r\u00e1ster\r\n
      Gr\u00e1ster recortado WDRVI de eoPard Agriculture<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

       <\/p>\r\n

        \r\n
      • Recorte del conjunto de datos de topograf\u00eda digital (elevaci\u00f3n y rugosidad) basado en el l\u00edmite del campo:<\/li>\r\n<\/ul>\r\n

         <\/p>\r\n\r\n\r\n\r\n

        \"R\u00e1ster\r\n
        Raster recortado de elevaci\u00f3n de GeoPard Agriculture<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
        \"r\u00e1ster\r\n
        GeoPard Agricultura Rugosidad r\u00e1ster recortado<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

        C\u00f3mo se ve en la interfaz de GeoPard y c\u00f3mo se puede integrar en su soluci\u00f3n de tecnolog\u00eda agr\u00edcola:<\/p>\r\n\r\n\r\n\r\n

        \"Raster\r\n
        Raster recortado NIR de GeoPard Agriculture<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
        \"Puesto\r\n
        Puesto de relevo en GeoPard Agriculture<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
        \"GeoPard\r\n
        GeoPard Agricultura WDRVI<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

        En GeoPard comprendemos el valor de estos detalles y trabajamos constantemente para mejorar la soluci\u00f3n.<\/p>\r\n

        \u00bfQu\u00e9 son los datos r\u00e1ster?<\/h2>\r\n

        Los datos r\u00e1ster son un tipo de datos de imagen digital representados por una cuadr\u00edcula de p\u00edxeles o celdas, donde cada celda corresponde a una ubicaci\u00f3n espec\u00edfica en la superficie terrestre. A cada p\u00edxel de una imagen r\u00e1ster se le asigna un valor que representa un atributo o caracter\u00edstica particular de esa ubicaci\u00f3n, como la altitud, la temperatura o la cobertura del suelo.<\/p>\r\n

        Se utiliza habitualmente en sistemas de informaci\u00f3n geogr\u00e1fica (SIG) y aplicaciones de teledetecci\u00f3n para representar y analizar diversos tipos de datos espaciales. Puede obtenerse de diversas fuentes, como im\u00e1genes satelitales y a\u00e9reas, c\u00e1maras digitales y sensores terrestres.<\/p>\r\n

        A menudo se almacena en diversos formatos, como GeoTIFF, JPEG y PNG, dise\u00f1ados para comprimir y almacenar los datos de forma eficiente. El software SIG y las herramientas de procesamiento de im\u00e1genes permiten manipular y analizar los datos, por ejemplo, realizando c\u00e1lculos sobre los valores de los p\u00edxeles o aplicando filtros para realzar ciertas caracter\u00edsticas.<\/p>\r\n

        Algunos ejemplos de aplicaciones son la elaboraci\u00f3n de mapas de uso y cobertura del suelo, el an\u00e1lisis de los cambios en la vegetaci\u00f3n a lo largo del tiempo y la predicci\u00f3n del rendimiento de los cultivos en funci\u00f3n de factores ambientales.<\/p>\r\n

        \u00bfC\u00f3mo se utilizan los datos r\u00e1ster en la agricultura de precisi\u00f3n?<\/h2>\r\n

        Es un componente esencial de la agricultura de precisi\u00f3n, ya que proporciona informaci\u00f3n detallada sobre la salud de los cultivos, las propiedades del suelo y los factores ambientales que pueden utilizarse para tomar decisiones m\u00e1s fundamentadas sobre el manejo de los cultivos. A continuaci\u00f3n, se muestran algunos ejemplos de c\u00f3mo se utilizan los datos r\u00e1ster en la agricultura de precisi\u00f3n:<\/p>\r\n

          \r\n
        • An\u00e1lisis de la salud de los cultivos:<\/strong> Los datos de teledetecci\u00f3n, ya sean im\u00e1genes satelitales o de drones, permiten generar capas de datos que muestran \u00edndices de vegetaci\u00f3n como el NDVI (\u00cdndice de Vegetaci\u00f3n de Diferencia Normalizada) o el NDRE (Borde Rojo de Diferencia Normalizada). Estos \u00edndices ayudan a identificar \u00e1reas del campo con vegetaci\u00f3n sana, as\u00ed como \u00e1reas donde los cultivos pueden estar sufriendo estr\u00e9s debido a enfermedades, plagas o deficiencias nutricionales.<\/li>\r\n
        • An\u00e1lisis del suelo:<\/strong> Los datos del suelo, como el contenido de humedad o la textura, se pueden recopilar mediante sensores que generan capas de datos. Estas capas ayudan a identificar \u00e1reas del terreno con caracter\u00edsticas de suelo variables, lo que permite tomar decisiones sobre la fertilizaci\u00f3n, el riego y otras pr\u00e1cticas de manejo del suelo.<\/li>\r\n
        • An\u00e1lisis ambiental:<\/strong> Las capas de datos que muestran factores ambientales como la temperatura, la precipitaci\u00f3n y la velocidad del viento pueden utilizarse para modelar el crecimiento de los cultivos y predecir el rendimiento. Estas capas tambi\u00e9n pueden ayudar a identificar \u00e1reas del campo propensas a la erosi\u00f3n, las inundaciones u otros problemas ambientales.<\/li>\r\n
        • Aplicaci\u00f3n de tasa variable:<\/strong> Se puede utilizar para generar mapas de prescripci\u00f3n para la aplicaci\u00f3n de dosis variables de insumos como fertilizantes o pesticidas. Al aplicar los insumos en dosis variables seg\u00fan las necesidades de las diferentes \u00e1reas del campo, los agricultores pueden reducir el desperdicio y optimizar el crecimiento de los cultivos.<\/li>\r\n<\/ul>\r\n

          En general, los datos r\u00e1ster son una herramienta crucial en la agricultura de precisi\u00f3n, ya que proporcionan informaci\u00f3n detallada sobre las condiciones de los cultivos y del suelo que puede utilizarse para tomar decisiones m\u00e1s fundamentadas sobre la gesti\u00f3n de los cultivos.<\/p>","protected":false},"excerpt":{"rendered":"

          Los datos r\u00e1ster recortados en funci\u00f3n de los l\u00edmites del campo parecen muy sencillos. Algunas fuentes de datos son r\u00e1steres con p\u00edxeles y una resoluci\u00f3n de 3 m\/10 m\/30 m, otras son\u2026<\/p>","protected":false},"author":210157960,"featured_media":1352,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1377,1366],"tags":[1618],"class_list":["post-286","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-crop-monitoring","category-topography","tag-crop-monitoring"],"acf":[],"yoast_head":"\nUse of Cropped Raster Data for Agribusiness - GeoPard Agriculture<\/title>\n<meta name=\"description\" content=\"Cropped raster data based on field boundary sounds very simple. 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