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Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods

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Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods

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Valbuena, R.; Maltamo, M.; Packalen, P. (2016). Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods. Revista de Teledetección. (Special Issue):15-25. doi:10.4995/raet.2016.4029.

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/80394

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Title: Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods
Secondary Title: Clasificación de etapas de desarrollo forestal a partir de datos de vuelos lidar nacionales de baja densidad: comparación de métodos de aprendizaje automático
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Abstract:
[EN] The area-based method has become a widespread approach in airborne laser scanning (ALS), being mainly employed for the estimation of continuous variables describing forest attributes: biomass, volume, density, etc. ...[+]


[ES] Los métodos de estimación por áreas son ya habituales para el uso de escaneo láser aerotransportado (ALS) en la predicción de atributos forestales descritos por variables continuas: biomasa, volumen, densidad, etc. ...[+]
Subjects: Airborne laser scanning , Discriminant analysis , Maximum likelihood , Minimum volume ellipsoid , Naive bayes , Support vector machine , Artificial neural networks , Random forests , Nearest neighbour , Escaneo láser aerotransportado , Análisis discriminante , Máxima verosimilitud , Elipsoide de volumen mínimo , Bayesiano ingenuo , Máquinas de vector soporte , Redes neuronales artificiales , Selvas aleatorias , Vecino más próximo , Clases naturales de edad
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Revista de Teledetección. (issn: 1133-0953 ) (eissn: 1988-8740 )
DOI: 10.4995/raet.2016.4029
Publisher:
Universitat Politècnica de València
Publisher version: https://doi.org/10.4995/raet.2016.4029
Description: Revista oficial de la Asociación Española de Teledetección
Thanks:
This research was funded by SMK. Special thanks to Juho Heikkilä and Jussi Lappalainen (SMK), Heli Laaksonen (NLS), and Aki Suvanto (Blom Kartta Oy) for their support at different stages of this study, including the delivery ...[+]
Type: Artículo

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