Bibliographic citations
Bryson, M., (2023). Evaluación del vigor de árboles aplicando índices de vegetación a imágenes captadas remotamente en la Universidad Nacional Agraria La Molina [Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/6266
Bryson, M., Evaluación del vigor de árboles aplicando índices de vegetación a imágenes captadas remotamente en la Universidad Nacional Agraria La Molina []. PE: Universidad Nacional Agraria La Molina; 2023. https://hdl.handle.net/20.500.12996/6266
@misc{renati/1116955,
title = "Evaluación del vigor de árboles aplicando índices de vegetación a imágenes captadas remotamente en la Universidad Nacional Agraria La Molina",
author = "Bryson Cabrera, María Lucía",
publisher = "Universidad Nacional Agraria La Molina",
year = "2023"
}
The analysis of the health status of tree vegetation is important at decision moments for its management and use, that is why several methods for its study exists, where the multispectral index application, such the NDVI with RPA technology, is released to observe the tree vigor, however, it is usually expensive, for that reasson, alternative index have been developed for RGB sensors wich use is more accessible. In this study, the NDVI and the optical index: GLI, NGRDI, RG, TGI, VARI and vNGRDI are applied to tree canopies in areas with dense and dispersed tree vegetation using a multiespectral Phantom 4; methodology consisted of classifying tree vigor through supervised classification with the Random Forest algorithm, training classifiers based on a field sample and vegetation index radiometric information. In training the DSM and other parameters such the increasing of the “Maximum number of trees in the forest” and the “Maximum depth of the tree” were incluided, finding that the 2 first helped to increase the precision maps with RGB index on dense vegetation; among all index, the NDVI resulted with the highest kappa index wich did not increased by varying other parameters, indicating that they didn´t help improve the precisión; for dispersed vegetation, the RGB index had acceptable precisión, wich classification could have been affected by factors such tree phenology or ilumination; for dense vegetation, the RGB index achieved greater precision, finding that index with red, green and blue bands performs better than index wich operates with red and green band only.
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