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<CreaDate>20200624</CreaDate>
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<resTitle>Ciq_Region_100K</resTitle>
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<idAbs>&lt;p&gt;Corresponde a la capa de cicatrices por quemas en la Amazonía colombiana, generada mediante monitoreo mensual continuo desde marzo de 2017, a escala 1:100.000. La capa se produce a partir de mosaicos de imágenes Sentinel-2 procesados en la plataforma de análisis geoespacial en la nube Google Earth Engine, calculando la mediana por píxel en ventanas temporales de 30 días para reducir nubosidad, bruma, sombras y datos atípicos. Sobre estos mosaicos se derivan índices espectrales (NDVI, NBR, NBR2, SAVI y MNDWI) que, junto con puntos de calor de los sensores MODIS y VIIRS, sirven como insumo para una clasificación supervisada mediante el algoritmo de machine learning Random Forest, delimitando los polígonos de cicatrices de quema.&lt;/p&gt;
&lt;p&gt;La capa resultante se depura y valida en ArcGIS Pro, eliminando polígonos superpuestos con cicatrices mapeadas en meses anteriores, territorios artificializados y superficies de agua, y verificando la calidad temática y topológica (adyacencia entre polígonos y área mínima cartografiable). Posteriormente, las cicatrices se clasifican según el tipo de cobertura vegetal afectada mediante la intersección con la capa Bosque/No Bosque (SMByC–IDEAM, 2024) y con el mapa de coberturas de la tierra del SIMCOBA para la Amazonía colombiana (Instituto SINCHI, 2024). Finalmente, se generan estadísticas de área quemada por Unidad Espacial de Referencia (UER) y los resultados se publican en el portal del Sistema de Información Ambiental Territorial de la Amazonia colombiana – SIATAC, para apoyar la gestión de monitoreo, prevención y control de las quemas en la región.&lt;/p&gt;</idAbs>
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<keyword>Tema</keyword>
<keyword>Cicatrices de quema</keyword>
<keyword>100K</keyword>
<keyword>Region</keyword>
<keyword>Monitoreo Ambiental</keyword>
<keyword>Histórico</keyword>
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<idPurp>La capa cicatrices de quema por región (Histórico) corresponde a la capa de cicatrices por quemas en la Amazonía colombiana, generada mediante monitoreo mensual continuo desde marzo de 2017 hasta la actualidad, a escala 1:100.000. La capa se produce a partir de mosaicos de imágenes Sentinel-2 procesados en la plataforma de análisis geoespacial en la nube Google Earth Engine, calculando la mediana por píxel en ventanas temporales de 30 días para reducir nubosidad, bruma, sombras y datos atípicos. Sobre estos mosaicos se derivan índices espectrales (NDVI, NBR, NBR2, SAVI y MNDWI) que, junto con puntos de calor de los sensores MODIS y VIIRS, sirven como insumo para una clasificación supervisada mediante el algoritmo de machine learning Random Forest, delimitando los polígonos de cicatrices de quema.</idPurp>
<idCredit>Instituto Amazónico de Investigaciones Científicas - SINCHI. Modelos de Funcionamiento y Sostenibilidad del Laboratorio SIG y SR.</idCredit>
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