Use of a process-based model to describe spatial variation in Pinus radiata productivity in New Zealand

Kirschbaum, Miko U.F.; Watt, Michael S.

doi:10.1016/j.foreco.2011.05.036

Cited by 36 publications

(27 citation statements)

References 49 publications

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“…This is consistent with previous research that showed air temperature to be the most important determinant of P. radiata growth in New Zealand (Jackson and Gifford 1974;Hunter and Gibson 1984;Watt et al 2010), with air temperature in most locations in New Zealand, including the central North Island, sub-optimal for growth under current climatic conditions (Kirschbaum and Watt 2011). Models of Site Index for plantation species growing outside of New Zealand have also frequently found air temperature to be an important determinant of Site Index (Sharma et al 2012), but rainfall is at least as important as air temperature in many drier regions (Mohamed et al 2014;Sabatia and Burkhart 2014).…”

Section: Discussionsupporting

confidence: 92%

Multi-sensor modelling of a forest productivity index for radiata pine plantations

Watt

Dash

Watt³

et al. 2016

N.Z. j. of For. Sci.

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Background: 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. Methods: 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. Results: 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 m 3 ha −1 year

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Section: Discussionsupporting

confidence: 92%

Multi-sensor modelling of a forest productivity index for radiata pine plantations

Watt

Dash

Watt³

et al. 2016

N.Z. j. of For. Sci.

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“…Air temperature during spring was the environmental variable with the greatest influence on Site Index. This is consistent with previous research that showed that air temperature was the most important determinant of P. radiata growth in New Zealand (Jackson and Gifford, 1974;Hunter and Gibson, 1984;Watt et al, 2010), with air temperature in most locations in New Zealand, including the central North Island, sub-optimal for growth under current climate (Kirschbaum and Watt, 2011). Models of Site Index for plantation species growing outside of New Zealand have also frequently found air temperature to be an important determinant of Site Index (Sharma et al, 2012) but in many drier regions rainfall is at least as important as air temperature (Mohamed et al, 2014;Sabatia and Burkhart, 2014).…”

Section: Discussionsupporting

confidence: 92%

Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning

Watt

Dash

Bhandari³

et al. 2015

Forest Ecology and Management

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“…and the use of multivariate interpolation methods or mixed models, thus combining the most important parameters of the forest stands. For example, several environmental parameters have been used to model the spatial variability in Pinus radiata productivity in New Zealand (Kirschbaum & Watt 2011). Moreover, remote sensing data may be incorporated into the analysis to obtain an improved accuracy of predictions.…”

Section: Discussionmentioning

confidence: 99%

Making objective forest stand maps of mixed managed forest with spatial interpolation and multi-criteria decision analysis

Destan¹,

Yilmaz²,

Şahin³

2013

iForest

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© iForest -Biogeosciences and Forestry IntroductionInformation about extant forest communities is of considerable importance in forest management planning. The stand is the smallest production and silvicultural unit, and the most crucial part of this information. The location of planning units, their size and silvicultural features are used to select management objectives and determine the working cycle, management regulations and silvicultural goals. Thus, the precision of decisions to be implemented and the planning to be realized are both closely related to the accuracy of the stand maps. Moreover, map accuracy depends on the type of forest inventory, the sample size, and the techniques used to evaluate the inventory results.Stand maps have previously been produced subjectively (Feng et al. 2006) in conjunction with current forest management practices. In conventional management planning, stands are defined through the planning process. The type of of the management objectives is a component of this process (Gunnarsson et al. 1998). However, the use of computers has changed this conventional approach, and spatially and temporally dynamic description and treatment units can readily be produced (Holmgren & Thuresson 1997). Thus, dynamic forest planning can be achieved with the use of geopositioned field plot data and computers (Wallerman et al. 2002).The sampling methods used in forest inventories vary according to different countries' forestry objectives and forest structures. Systematic sampling methods are recommended for large and homogeneous forest areas and have been implemented in all production and conservation areas of Turkey since 1964. However, if this sampling method is implemented with an insufficient number of sample plots and/or in heterogeneous forests, the results will be highly questionable because they may fail to reflect the true forest composition. Although Sherman (1996), Aurbi & Debouzie (2000), Flores et al. (2003) andD'Orazio (2003) used remote sensing data to improve the results of such inventories, the desired outcome was never achieved in practice.Spatial interpolation methods, which have been classified by Li & Heap (2008) as nongeostatistical, geostatistical and combined, came into use at the end of the 1960s and have been investigated for use in forest management. As described in Akhavan et al. (2010), Guibal (1973 was the first study to use kriging in the forest inventory process. Jost (1993) also used geostatistical methods to compare conventional inventory results based on systematic sampling with the results of the kriging method. Geostatistically based methods that utilize textural information are frequently used to analyze remote-sensing (RS) images (Zawadzki et al. 2005). The quality and quantity of these methods have increased through advances in the computer sciences and in the science of geographical information systems. These methods allow the use of a variety of ecological and technical parameters in the assessment of inventory results. Spatial interpolation and multi-...

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Use of a process-based model to describe spatial variation in Pinus radiata productivity in New Zealand

Cited by 36 publications

References 49 publications

Multi-sensor modelling of a forest productivity index for radiata pine plantations

Multi-sensor modelling of a forest productivity index for radiata pine plantations

Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning

Making objective forest stand maps of mixed managed forest with spatial interpolation and multi-criteria decision analysis

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