Machine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama
DOI:
https://doi.org/10.57188/manglar.2024.010Resumo
En este trabajo usamos Machine Learning (Randon Forest) como herramienta para clasificar la biomasa y calcular los índices de vegetación buscando identificar las características de la cobertura vegetal en la cabecera del desierto Atacama. Se busca establecer la correlación entre los índices de vegetación y la precipitación, a fin de conocer su confiabilidad sobre la climatología en esta región. Fue importante el análisis geoespacial basado en Google Earth Engine (GEE) y el procesamiento de imágenes Landsat 5 ETM y Landsat 8 OLI/TIRS, para el período 1985 - 2022, lo que permitió caracterizar el cambio climático. El NDVI, SAVI, GVI y RVI han sido probados y validados en sistemas áridos. El NDVI responde positivamente a la precipitación en temporada húmeda y en forma débil en la temporada de lluvias invernales. Se confirma que el NDVI alto corresponde al verano, después de una sequía prolongada. Hacia los años 2020 y 2022, se registra un aumento de cobertura vegetal en lugares de mayor temperatura, evidenciando cambio climático y reflejado en los índices de biomasa.
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Direitos de Autor (c) 2024 Santos Gómez, Edwin Pino-Vargas, Germán Huayna, Jorge Espinoza-Molina, Karina Acosta-Caipa, Fredy Cabrera-Olivera
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