{"id":4152,"date":"2022-03-22T07:05:58","date_gmt":"2022-03-22T06:05:58","guid":{"rendered":"https:\/\/geopard.tech\/?p=4152"},"modified":"2023-08-25T14:54:43","modified_gmt":"2023-08-25T12:54:43","slug":"subvencion-del-estado-de-renania-del-norte-westfalia","status":"publish","type":"post","link":"https:\/\/geopard.tech\/es\/blog\/grant-from-the-state-of-north-rhine-westphalia\/","title":{"rendered":"Im\u00e1genes hiperespectrales para la agricultura. Subvenci\u00f3n del estado de Renania del Norte-Westfalia."},"content":{"rendered":"

Nos complace anunciar que el proyecto \u201cMarco de inteligencia artificial para la estimaci\u00f3n cuantitativa de las propiedades del suelo mediante im\u00e1genes satelitales hiperespectrales\u201d fue seleccionado para recibir financiaci\u00f3n parcial del Ministerio de Medio Ambiente de Renania del Norte-Westfalia y la Uni\u00f3n Europea en el marco del programa REACT-UE Innovaci\u00f3nUmweltwirtschaft.NRW<\/a> programa. La subvenci\u00f3n est\u00e1 financiada por el Fondo Europeo de Desarrollo Regional \/ Europ\u00e4ischen Fonds f\u00fcr regionale Entwicklung (EFRE).<\/p>\r\n

\"el<\/p>\r\n

El uso de inteligencia artificial y estad\u00edstica en este proyecto permiti\u00f3 determinar la correlaci\u00f3n entre datos hiperespectrales y de suelo (p. ej., nitr\u00f3geno, pH), facilitando un enfoque m\u00e1s preciso y escalable para el an\u00e1lisis del suelo. El pr\u00f3ximo lanzamiento de sat\u00e9lites hiperespectrales con intervalos de revisita frecuentes y acceso continuo a nuevas im\u00e1genes sin demoras presenta varias ventajas, especialmente en la gesti\u00f3n de nutrientes para la agricultura sostenible. Esta tecnolog\u00eda ayuda a evaluar el contenido y la disponibilidad de nutrientes en el suelo, lo que permite a los agricultores adaptar sus estrategias de fertilizaci\u00f3n. Esto se traduce en una mejor absorci\u00f3n de nutrientes por las plantas, una reducci\u00f3n del impacto ambiental y un ahorro de costes.<\/p>\r\n\r\n\r\n

\u00bfQu\u00e9 son las im\u00e1genes hiperespectrales?<\/h2>\r\n

Las im\u00e1genes hiperespectrales son una potente t\u00e9cnica de teledetecci\u00f3n que captura el espectro electromagn\u00e9tico con alta resoluci\u00f3n espectral.<\/p>\r\n

A diferencia de las im\u00e1genes satelitales tradicionales, que suelen constar de tres o cuatro bandas (roja, verde, azul y, a veces, infrarroja cercana), las im\u00e1genes hiperespectrales recopilan cientos o miles de bandas espectrales estrechas en las regiones visible, infrarroja cercana e infrarroja de onda corta. Cada banda proporciona informaci\u00f3n \u00fanica sobre las propiedades de la superficie del \u00e1rea capturada.<\/p>\r\n

Se adquiere mediante sensores instalados en plataformas a\u00e9reas o espaciales. Estos sensores utilizan espectr\u00f3metros para medir la intensidad de la radiaci\u00f3n reflejada o emitida en m\u00faltiples bandas estrechas.<\/p>\r\n

Al recopilar una amplia gama de datos espectrales, los sensores hiperespectrales pueden detectar diferencias sutiles en las firmas espectrales de diversos materiales, lo que permite un an\u00e1lisis muy detallado y preciso.<\/p>\r\n

Aplicaciones de las im\u00e1genes hiperespectrales<\/h2>\r\n
    \r\n
  • Monitoreo ambiental:<\/strong> Desempe\u00f1a un papel fundamental en el monitoreo y la evaluaci\u00f3n de la salud de los ecosistemas. Ayuda a identificar el estr\u00e9s de la vegetaci\u00f3n, monitorear los cambios en la cobertura del suelo, detectar especies invasoras y medir par\u00e1metros de calidad del agua como la concentraci\u00f3n de clorofila o la turbidez en lagos y r\u00edos.<\/li>\r\n
  • Agricultura:<\/strong> Esta herramienta facilita la agricultura de precisi\u00f3n al proporcionar informaci\u00f3n detallada sobre la salud de los cultivos, el contenido de nutrientes, los niveles de humedad y la detecci\u00f3n de enfermedades. Los agricultores pueden tomar decisiones basadas en datos sobre riego, fertilizaci\u00f3n y control de plagas, lo que se traduce en una mayor productividad y un menor impacto ambiental.<\/li>\r\n
  • Geolog\u00eda y exploraci\u00f3n minera<\/strong>Es fundamental para cartografiar formaciones geol\u00f3gicas, identificar dep\u00f3sitos minerales y caracterizar tipos de rocas. Ayuda a los ge\u00f3logos a detectar alteraciones en la composici\u00f3n mineral y a cartografiar \u00e1reas potenciales para la exploraci\u00f3n, lo que contribuye a una actividad minera m\u00e1s eficiente y espec\u00edfica.<\/li>\r\n
  • Silvicultura<\/strong>Contribuye a la gesti\u00f3n y el monitoreo forestal. Permite la identificaci\u00f3n de especies arb\u00f3reas, la cuantificaci\u00f3n de la biomasa forestal, la detecci\u00f3n de estr\u00e9s en los \u00e1rboles y la evaluaci\u00f3n de los da\u00f1os causados por incendios forestales. Esta informaci\u00f3n ayuda a la gesti\u00f3n forestal sostenible, la conservaci\u00f3n de la biodiversidad y los sistemas de alerta temprana de incendios forestales.<\/li>\r\n<\/ul>\r\n

    Beneficios de las im\u00e1genes hiperespectrales<\/h2>\r\n

    Su principal ventaja reside en su capacidad para proporcionar informaci\u00f3n espectral detallada, lo que permite discriminar materiales con gran precisi\u00f3n.<\/p>\r\n

    Esto conlleva una mejor clasificaci\u00f3n y cartograf\u00eda de los tipos de cobertura terrestre, una identificaci\u00f3n m\u00e1s precisa de sustancias espec\u00edficas y una mejor comprensi\u00f3n de los procesos ambientales.<\/p>\r\n

    Los datos hiperespectrales tambi\u00e9n pueden analizarse utilizando algoritmos avanzados y t\u00e9cnicas de aprendizaje autom\u00e1tico para extraer informaci\u00f3n valiosa y automatizar la interpretaci\u00f3n de im\u00e1genes.<\/p>","protected":false},"excerpt":{"rendered":"

    Nos complace anunciar que el proyecto \u201cMarco de inteligencia artificial para la estimaci\u00f3n cuantitativa de propiedades del suelo mediante im\u00e1genes satelitales hiperespectrales\u201d fue seleccionado para financiaci\u00f3n parcial\u2026<\/p>","protected":false},"author":210249426,"featured_media":4161,"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":[1661,1365],"tags":[1599],"class_list":["post-4152","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-satellite-imagery","category-news","tag-remote-sensing"],"acf":[],"yoast_head":"\nHyperspectral imagery for Agriculture. 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