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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/80352

    Título
    UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials
    Autor
    Rodríguez-Puerta, Francisco
    Gómez-García, Esteban
    Martín-García, Saray
    Pérez-Rodríguez, Fernando
    Prada, Eva
    Año del Documento
    2021
    Editorial
    MDPI
    Documento Fuente
    Rodríguez-Puerta, F., Gómez-García, E., Martín-García, S., Pérez-Rodríguez, F., & Prada, E. (2021). UAV-based LiDAR scanning for individual tree detection and height measurement in young forest permanent trials. Remote Sensing, 14(1), 170.
    Abstract
    The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.
    Revisión por pares
    SI
    DOI
    10.3390/rs14010170
    Version del Editor
    https://www.mdpi.com/2072-4292/14/1/170
    Idioma
    spa
    URI
    https://uvadoc.uva.es/handle/10324/80352
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP57 - Artículos de revista [111]
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