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Home > Sciences & Environment > The environment > Applied ecology > Identificación de complejos de humedales a partir del uso de imágenes de satélite y Machine Learning
Identificación de complejos de humedales a partir del uso de imágenes de satélite y Machine Learning

Identificación de complejos de humedales a partir del uso de imágenes de satélite y Machine Learning


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About the Book

Los Planes de Manejo Ambiental de Humedales son instrumentos de planificación del territorio que reconocen que los humedales son ecosistemas estratégicos que prestan bienes y servicios a la sociedad, de ahí su condición de socio-ecosistemas. A través de los mismos se pretende proteger, conservar o recuperar sus condiciones ecosistémicas, hidrológicas, geomorfológicas, biodiversidad; así como el suministro de los servicios ambientales. La identificación, delimitación, georreferenciación y zonificación ambiental de los humedales es la primera etapa del plan de manejo. Este ejercicio es especialmente difícil cuando se trata de áreas geográficas extensas, con presencia de una gran cantidad de humedales en el territorio; como es el caso del Municipio de Piamonte en el Departamento del Cauca-Colombia. En la formulación del Plan de Manejo de Humedales de la Cuenca Alta del Río Caquetá, una alternativa viable consiste en el uso de las nuevas herramientas tecnológicas provistas por los Sistemas de Información Geográfica (SIG) y más recientemente por la Inteligencia Artificial (AI). Con el propósito de clasificar las imágenes SAR Sentinel-1B (GRD), previamente procesadas en la plataforma Google Earth Engine (GEE) y correspondientes al año 2021; se empleó el algoritmo de agrupamiento K-Means para la clasificación automática de áreas de humedales potenciales. Esta técnica no supervisada se basa en la identificación de características similares dentro del conjunto de datos, agrupando en clúster o grupos los datos más similares entre sí. Se trabajó con un total de 70 escenas de radar, considerando las bandas polarimetrícas VV y VH del Sentinel-1B, así como los índices espectrales NDVI y NDWI y variables del terreno, tales como la pendiente, TWI, curvatura, TPI e índice de convergencia.


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Product Details
  • ISBN-13: 9789999322898
  • Publisher: Eliva Press
  • Publisher Imprint: Eliva Press
  • Height: 229 mm
  • No of Pages: 180
  • Returnable: Y
  • Returnable: Y
  • Weight: 299 gr
  • ISBN-10: 9999322891
  • Publisher Date: 11 Sep 2024
  • Binding: Paperback
  • Language: Spanish
  • Returnable: Y
  • Returnable: Y
  • Spine Width: 10 mm
  • Width: 152 mm


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Identificación de complejos de humedales a partir del uso de imágenes de satélite y Machine Learning
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