Índices de vegetación para análisis hidrológicos y estudio de dinámicas vegetacionales
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Date
2024
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Universidad de Concepción
Abstract
Una forma de evaluar la respuesta de los ecosistemas forestales ante cambios ambientales asociados al cambio climático es a través de la utilización de índices de vegetación. El objetivo de este estudio es analizar las características de los índices de vegetación utilizados en técnicas de percepción remota y su aplicabilidad en dinámicas hidrológicas, mediante una revisión bibliográfica. Se revisaron de artículos tipo review, para luego generar una base de datos de artículos que estudiaran los índices de vegetación con herramientas de teledetección, analizándolos con la inteligencia artificial Chat GPT-4o. Posteriormente, se realizó una nueva búsqueda de artículos que aplicaran los índices de vegetación en estudios de modelación hidrológica y se analizaron. Se obtuvo que los índices más utilizados en estudios científicos son el NDVI, LAI, SAVI y EVI, cada uno con ventajas y limitaciones específicas, y su elección depende de las características del área de estudio y si requiere de correcciones atmosféricas o del suelo, entre otros. La escala de aplicación de estos índices de vegetación depende de la resolución espacial, temporal y espectral de sensores satelitales como Landsat, Sentinel y MODIS, o de UAVs que ofrecen mayor resolución espacial. Esto permite cuantificar las dinámicas vegetacionales y mejorar la estimación de evapotranspiración, reduciendo las incertidumbres asociadas a modelación hidrológica. A escala local, la utilización de dendrómetros, mediante la medición del crecimiento del tronco de árboles, complementa esta información y permite una estimación de la evapotranspiración más robusta. Sin embargo, la integración de teledetección y dendrómetros con parámetros hidrológicos de montaña como la acumulación y el derretimiento de nieve es un desafío poco explorado y que ofrece un potencial relevante para investigaciones futuras en Los Andes Chilenos.
Vegetation indices can be used to evaluate the response of forest ecosystems to environmental changes associated with climate change. The objective of this study is to analyze the characteristics of the vegetation indices used in remote sensing techniques and their applicability in hydrological dynamics, through a bibliographic review. First, review articles were reviewed, to generate a database of articles that studied vegetation indices with remote sensing tools, analyzing them with Chat GPT-4o artificial intelligence. Subsequently, a new search for articles that applied vegetation indices in hydrological modeling studies was carried out and analyzed. It was found that the most used indices in scientific studies are NDVI, LAI, SAVI and EVI, each with specific advantages and limitations. Their choice depends on the characteristics of the study area and whether it requires atmospheric or soil corrections, among other considerations. The scale of application of these vegetation indices depends on the spatial, temporal and spectral resolution of satellite sensors such as Landsat, Sentinel and MODIS, or UAVs that offer greater spatial resolution. This information allows us to better understand vegetation dynamics, improving our estimates of evapotranspiration, and overall reducing the uncertainty in hydrological modeling. At a local scale, the use of dendrometers that measure the growth of tree trunks, complements this information and improving the estimation of evapotranspiration. However, the integration of remote sensing and dendrometers, and their relationship with key mountain hydrological processes such as snow accumulation and melt, is an underexplored challenge that offers relevant potential for future research in the Chilean Andes.
Vegetation indices can be used to evaluate the response of forest ecosystems to environmental changes associated with climate change. The objective of this study is to analyze the characteristics of the vegetation indices used in remote sensing techniques and their applicability in hydrological dynamics, through a bibliographic review. First, review articles were reviewed, to generate a database of articles that studied vegetation indices with remote sensing tools, analyzing them with Chat GPT-4o artificial intelligence. Subsequently, a new search for articles that applied vegetation indices in hydrological modeling studies was carried out and analyzed. It was found that the most used indices in scientific studies are NDVI, LAI, SAVI and EVI, each with specific advantages and limitations. Their choice depends on the characteristics of the study area and whether it requires atmospheric or soil corrections, among other considerations. The scale of application of these vegetation indices depends on the spatial, temporal and spectral resolution of satellite sensors such as Landsat, Sentinel and MODIS, or UAVs that offer greater spatial resolution. This information allows us to better understand vegetation dynamics, improving our estimates of evapotranspiration, and overall reducing the uncertainty in hydrological modeling. At a local scale, the use of dendrometers that measure the growth of tree trunks, complements this information and improving the estimation of evapotranspiration. However, the integration of remote sensing and dendrometers, and their relationship with key mountain hydrological processes such as snow accumulation and melt, is an underexplored challenge that offers relevant potential for future research in the Chilean Andes.
Description
Tesis presentada para optar al título de Ingeniero Ambiental
Keywords
Ecohidrología, Evapotranspiración, Dinámica vegetal