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<idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;"El gobierno nacional, a través de la Unidad de Planificación Rural Agropecuaria (UPRA), busca promover el uso eficiente del suelo y los recursos naturales mediante el diseño de instrumentos a escalas semidetalladas que identifiquen los suelos con capacidad productiva en el marco del concepto de crecimiento verde (UPRA, 2016). En este contexto, y considerando la importancia de los cultivos y su relevancia en Colombia, se ha implementado un sistema de monitoreo para este cultivo.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;El desarrollo metodológico de este proyecto incluye el diseño e implementación de un sistema de monitoreo satelital de coberturas para la detección de áreas productivas del cultivo de papa a nivel nacional en Colombia. Este sistema se basa en el procesamiento de imágenes satelitales ópticas y de radar, utilizando datos de Sentinel 1, Sentinel 2 y NICFI (Planet). Mediante el entrenamiento de algoritmos de clasificación de inteligencia artificial con procesamiento en la nube, se cuantifica el área productiva y su ubicación espacial. La información principal utilizada incluye límites departamentales, municipales y veredales, y cartografía básica a escala 1:25.000.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt; Esta capa delimita semestralmente las áreas agrícolas en estado productivo del cultivo de papa, utilizando un enfoque basado en monitoreo satelital de coberturas, procesamiento en la nube y algoritmos de inteligencia artificial. Dichas zonas se identifican por presentar alto contenido de biomasa vegetal y vigor foliar, así como dinámicas temporales y geográficas asociadas al manejo agrícola. El objetivo es proporcionar información espacial crítica sobre la ubicación, extensión y cambios en las áreas productivas del cultivo de papa, apoyando así la toma de decisiones y la planificación de políticas agrícolas efectivas.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Con lo anterior, se obtiene como producto final seis capas geográficas de tipo polígono del monitoreo del cultivo de papa a nivel nacional para los años 2021, 2022 y 2023, que contiene información de ubicación municipal y departamental, el intervalo de análisis se realiza semestralmente y dos capas geográficas de cambios entre los tres periodos obtenidos en el año a través de una diferencia asimétrica.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Categorías:&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;- Aumento: Los polígonos adicionales entre el periodo 1 y el periodo 2, se clasifican como aumento.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;- Disminución: Los polígonos que desaparecen entre el periodo 1 y el periodo 2, se clasifican como disminución."&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
<idPurp>El objetivo del producto es reportar la delimitación de las áreas productivas de papa para el segundo semestre de 2021, determinadas a partir de la detección mediante monitoreo satelital, inteligencia artificial y procesamiento en la nube. Esta capa vectorial permite conocer la ubicación espacial, la extensión y los cambios en las áreas productivas a lo largo del tiempo, con el fin de apoyar las políticas institucionales.</idPurp>
<idCredit>Instituto Geográfico Agustín Codazzi (IGAC). (2012). Cartografía básica, escala 1:100.000.
Unidad de Planificación Rural Agropecuaria (UPRA). (2024). Monitoreo del cultivo de papa nacional para el periodo 2021-2023, escala 1:25.000.</idCredit>
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<statement>Para obtener el monitoreo de papa se ejecutaron los siguientes pasos:
1. Preparación de los datos: Se realiza un armonización entre las imágenes Sentinel-2 y Planet efectuando una fusión entre ellas.
2. Obtención de muestras manuales (polígonos): a través de una revisión visual en las zonas con presencia de papa en estado productivo.
3. Entrenamiento: A partir de las muestras se realiza el entrenamiento de modelos, clasificación y empleando Bosques aleatorios (RF), dividiendo las muestras en un 70% en entrenamiento y un 30 % para validación.
4. Obtención de clasificaciones: Obtención las clasificaciones iniciales por zonas a las cuales se les realiza una máscara de cultivo.
5. Cálculos de las métricas de exactitud: Donde se realizan muestras de evaluación para la validación y exactitud (general, de productor y kappa) del modelo.
6. Aplicación de filtrados: Aplicación de filtrados de tipo modal con el fin de mejorar el efecto de sal y pimienta.
7. Exportación de los resultados: (de ráster a vector), para así finalmente cuantificar áreas en hectáreas del cultivo.
8. Entrega: Esta información precede a ser entregada al especialista temático que hace evaluaciones de calidad y exactitud de las capas temáticas, además de organizar la información en Geodatabase para su disposición final.</statement>
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