Abstract
An understanding of how plantation productivity varies spatially is important for forest planning, management and projection of future plantation yields and returns. The 300 Index is a volume productivity index developed for Pinus radiata D.Don that has been widely used within New Zealand to assess site productivity. Although the 300 Index is routinely characterised at the stand level, little research has investigated if remotely sensed data sources can be used in combination with environmental layers to precisely predict this metric at fine spatial resolution. This study uses an extensive dataset obtained from P. radiata plantations in the central North Island, New Zealand. Using this dataset, the objective of this research was to compare the precision of parametric and non-parametric models of the 300 Index that included explanatory variables extracted from aerially acquired light detection and ranging (LiDAR), satellite imagery (RapidEye) at 5-m resolution or environmental layers and combinations of these three data sources. Models were constructed both with and without stand age as an explanatory variable as managers may not always have access to stand age. A total of 28 models (14 data sources × two model methods) were constructed using data from 433 plots. Precision and bias of these models was determined using an independent dataset of 60 plots. Of the non-parametric methods tested (k-most similar neighbour (k-MSN), k-nearest neighbour (k-NN)), k-NN using an optimised value of k-most precisely predicted the 300 Index for 11 of the 14 constructed models. The use of k-NN was found to be more precise than parametric models when age was not available but of overall similar precision to parametric models when stand age was available as a predictor. For models including stand age, the inclusion of LiDAR resulted in the most precise model (mean R 2 = 0.789; root mean square error (RMSE) = 2.48 m3 ha−1 year−1) while for models without stand age, metrics extracted from both satellite imagery and environmental layers produced the most precise model of the 300 Index (R 2 = 0.65; RMSE = 3.21 m3 ha−1 year−1). Results clearly show that models constructed from LiDAR provide the most precise means of estimating the 300 Index. However, in many situations, LiDAR is too expensive to acquire or stand age, which is used as a reference for linking LiDAR to 300 Index, is not available as an independent variable. Under these circumstances, results show that precise models can be constructed from variables derived from the combination of satellite imagery and environmental surfaces.
Highlights
An understanding of how plantation productivity varies spatially is important for forest planning, management and projection of future plantation yields and returns
These analyses showed that, at a common age and stand density, Site Index is only weakly related to basal area, demonstrating that Site Index provides only a partial measure of site productivity for Pinus radiata D.Don (P. radiata) (Kimberley et al 2005)
Variation in the ratios was greatest for the Green Ratio (GR) with values ranging 31-fold
Summary
An understanding of how plantation productivity varies spatially is important for forest planning, management and projection of future plantation yields and returns. Understanding and quantifying how environmental factors influence tree growth and site quality is of considerable importance to forest managers. Despite the prevalent use of Site Index by forest managers and researchers, a site quality metric based on volume, rather than height, may more accurately reflect site quality as volume directly links to stand productivity and integrates both height and diameter growth. This contention is reinforced by previous analysis of stand density trials covering a wide range in Site Index. These analyses showed that, at a common age and stand density, Site Index is only weakly related to basal area, demonstrating that Site Index provides only a partial measure of site productivity for Pinus radiata D.Don (P. radiata) (Kimberley et al 2005)
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