Métodos estadísticos paramétricos y no paramétricos para predecir variables de rodal basados en Landsat ETM+: una comparación en un bosque de Araucaria araucana en Chile
Year: 2010
Type: article
Abstract: The Araucaria araucana forests have a high level of both ecological and scientific importance, because they are long-lived and endemic.Although there have been several ecological studies conducted concerning A. araucana forests, none has produced quantitative models.We compared parametric and non-parametric statistical methods for predicting stand variables from Landsat ETM+ derived variables from two A. araucana stands in south-central Chile.The assessed parametric methods were multiple linear regressions (MLR), generalized least squares with a non-null correlation structure (GLS), linear mixed-effects models (LME), and partial least squares (PLS); while the non-parametric methods were: k-nearest neighbor (k-NN) and most similar neighbor (MSN).In descending order, number of trees per ha (N), stand gross volume (V), stand basal area (G), and dominant height (H dom ) were the most difficult variables to be modeled by all the methods.LME with known random effects (i.e., LME1) performed best, achieving a root mean square showing differences (RMSD) for N and V of 18.31 and 4.08 % versus 33.06 and 33.05 % for the second-best method, respectively.However, within the parametric methods, LME1 cannot be used for predicting new observations with no data.After LME1, GLS performed the best; also accounting for the spatial correlation of the data.Parametric methods achieved lower errors.Furthermore, differences were greater among non-parametric than those among parametric methods, with a difference of 10-15 % between k-NN and MSN.Although, given our results, we favor parametric methods; we point out that non-parametric methods are also useful, and the choice between parametric and non-parametric methods depends on the ultimate objective of the study. Key words: k-
Source: Bosque (Valdivia)
Language: Spanish
Subfield: Forestry
Domain: Life Sciences
SDG: Life on land
Open Access status: diamond
OpenAlex ID: W2075374695