Boletín de Investigaciones Marinas y Costeras

Vol. 54 Núm. 1 (2025)
Articulos de investigación

Valor y límites de los datos de registro botánico de la GBIF en el mapeo de la extensión de manglar mediante la clasificación KNN de imágenes de Sentinel-2

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  • Julien Andrieu
Julien Andrieu
French Institute of Pondicherry
DOI: https://doi.org/10.25268/bimc.invemar.2025.54.1.1313

Publicado 2025-01-01

Palabras clave

  • Open Data, Mangrove, Mapping, Accuracy assessment, GBIF

Cómo citar

1.
Andrieu J. Valor y límites de los datos de registro botánico de la GBIF en el mapeo de la extensión de manglar mediante la clasificación KNN de imágenes de Sentinel-2. Bol. Investig. Mar. Costeras [Internet]. 1 de enero de 2025 [citado 7 de enero de 2025];54(1). Disponible en: https://boletin.invemar.org.co/ojs/index.php/boletin/article/view/1313

Derechos de autor 2024 Julien Andrieu

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Resumen

En el contexto actual, que enfatiza la importancia de los datos de acceso abierto, este artículo busca realizar la primera evaluación de la efectividad de la base de datos de la Global Biodiversity Information Facility (GBIF) para entrenar clasificadores de imágenes satelitales multiespectrales de acceso abierto con el fin cartografiar la extensión de manglares. Se eligieron seis áreas en Australia, Benín, Brasil, Colombia, Malasia y EEUU por su densidad de datos de presencia de manglar y su proximidad con otras áreas de vegetación boscosa húmeda. Se utilizaron tres conjuntos de datos de acceso abierto: imágenes Sentinel-2, el visualizador de alta resolución de Google Earth y datos de presencia descargados del sitio de la GBIF. En primer lugar, se procesó un algoritmo de k-vecinos más cercanos (KNN), entrenándolo con píxeles circundantes de los datos de la GBIF. Luego, se compararon los resultados con los de una clasificación no supervisada (K-means). La exactitud de los dos métodos se evaluó mediante matrices de error de un proceso de fotointerpretación doble-ciego de imágenes de muy alta resolución obtenidas de Google Earth. El algoritmo KNN obtuvo índices kappa de 0.85 a 0.94, muy similares a los del método no supervisado (de 0.95 a 0.96). La GBIF posee un conjunto de datos adecuado para lugares con información, por lo que se invita a la comunidad a llenar los principales vacíos geográficos en su base de datos.

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