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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/67911

    Título
    Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.
    Autor
    García Hidalgo, MiguelAutoridad UVA Orcid
    Blázquez Casado, Ángela
    Agueda Hernández, BeatrizAutoridad UVA Orcid
    Rodríguez Puerta, FranciscoAutoridad UVA
    Año del Documento
    2018
    Documento Fuente
    Forest systems, 27(3), 6. 2018
    Resumo
    Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types. Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out. Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable. Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable. SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD). SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively. SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables. Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebiñal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest.
    Palabras Clave
    RF algorithm
    SVML algorithm
    SVMR algorithm
    ANN algorithm
    LiDAR
    Orthophotography
    Sentinel-2
    ISSN
    2171-5068
    Revisión por pares
    SI
    DOI
    10.5424/fs/2018273-13686
    Idioma
    spa
    URI
    https://uvadoc.uva.es/handle/10324/67911
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
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