Applicability of Kriging to Predict Spatial Distribution of Carbon Stocks of Acacia mangium Plantations

Tiryana, Tatang; Tatsuhara, Satoshi; Shiraishi, Norihiko

doi:10.20659/jfp.14.1_17

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…When data for each independent variable are available at every location, such as those derived from a digital elevation model (DEM), it is possible to produce a map showing continuous predictions over space as demonstrated by Kupfer and Farris (2007) for predicting basal area as well as Kimsey et al (2008) for predicting site index. When such data are unavailable, however, it is still possible to produce a surface map of a predicted variable by using geostatistics as demonstrated by Tiryana et al (2009) for predicting carbon stocks in the study area. The last issue is that GWR may produce multicollinearity among local coefficients that can invalidate their interpretation (Wheeler and Tiefelsdorf, 2005).…”

Section: Discussionmentioning

confidence: 99%

Modeling Spatial Variation in Stand Volume of <i>Acacia mangium </i>Plantations Using Geographically Weighted Regression

Tiryana

Tatsuhara

Shiraishi

2010

FORMATH

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Stand volume can be estimated from other stand variables by using multiple linear regression (MLR) or other ordinary regression models. MLR, however, only produces global parameter estimates that cannot reveal spatial variations in stand variables. In this study, we used a geographical weighted regression (GWR) method to investigate local spatial variations in the relationship between stand volume, stand age, and basal area of Acacia mangium plantations, and to examine whether a GWR model could provide better prediction accuracy than an MLR model. Stand data and geographical coordinates were obtained from 247 plantation sample plots. We analyzed the data using MLR and GWR methods by formulating a linear model that relates stand volume to stand age and basal area. Performance of the GWR model was compared with the MLR model in terms of their parameter estimates and goodness-of-fit statistics. We found that the GWR model was not only able to reveal local spatial variations in the relationship between stand volume, stand age, and basal area, but it also produced better prediction accuracy than the MLR model. The GWR model reduced AIC by 2%, increased R 2 adj up to 3%, and reduced RMSE by 14%, compared with those of the MLR model. The GWR model, therefore, could be useful for modeling spatial variations in stand attributes that cannot be revealed by ordinary regression models.

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Section: Discussionmentioning

confidence: 99%