Pinus radiata from Hyperspectral satellite image data€¦ · statistical analysis software: Best...

Mapping foliar nutrition in Pinus radiata from Hyperspectral satellite image data

PROJECT NUMBER: PNC074-0708 AUGUST 2009

SUSTAINABILITY & RESOURCES

This report can also be viewed on the FWPA website

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Melbourne VIC 3000, AustraliaT +61 (0)3 9927 3200 F +61 (0)3 9927 3288

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Mapping foliar nutrition in Pinus radiata from Hyperspectral satellite image data

Prepared for

Forest & Wood Products Australia

by

N. Sims, P. Hopmans, S. Elms and D. McGuire

Publication: Mapping foliar nutrition in Pinus radiata from hyperspectral satellite image data Project No: PNC074-0708 © 2009 Forest & Wood Products Australia Limited. All rights reserved. Forest & Wood Products Australia Limited (FWPA) makes no warranties or assurances with respect to this publication including merchantability, fitness for purpose or otherwise. FWPA and all persons associated with it exclude all liability (including liability for negligence) in relation to any opinion, advice or information contained in this publication or for any consequences arising from the use of such opinion, advice or information. This work is copyright and protected under the Copyright Act 1968 (Cth). All material except the FWPA logo may be reproduced in whole or in part, provided that it is not sold or used for commercial benefit and its source (Forest & Wood Products Australia Limited) is acknowledged. Reproduction or copying for other purposes, which is strictly reserved only for the owner or licensee of copyright under the Copyright Act, is prohibited without the prior written consent of Forest & Wood Products Australia Limited. ISBN: 978-1-920883-83-6 Researcher: Neil Sims CSIRO Sustainable Ecosystems, Clayton, VIC Peter Hopmans Timberlands Research Pty Ltd, Carlton, VIC

Stephen Elms HVP Plantations, Churchill, VIC Don McGuire ForestrySA, Mt Gambier, SA

Final report received by FWPA in August, 2009

Forest & Wood Products Australia Limited Level 4, 10-16 Queen St, Melbourne, Victoria, 3000 T +61 3 9927 3200 F +61 3 9927 3288 E [email protected] W www.fwpa.com.au

grace_davies

New Stamp

Executive Summary

Aims

The objectives of this project were to:

To produce accurate and informative maps of foliar nutrition in Pinus radiata from

Hyperion image data

To examine whether nutrition models can be robustly translated between age classes

To explore the future potential of nutrition monitoring from hyperspectral satellite

images

Methods

Two Hyperion hyperspectral satellite images captured over the Rennick estate near the

Vic-SA border east of Mt. Gambier were acquired on 12 January and 17 February

2008. These images were processed to minimise sensor and atmospheric noise, and

showed reflectance in 168, 10nm wide spectral bands between 400nm and 2500nm.

A preliminary model, created by applying previously published models for N and P

prediction (Sims et al. 2006a) to the 12 January image, was used to stratify sampling

sites in order to encounter the widest possible range of nutrient concentration levels in

the field.

Foliar samples were collected from 100 plots in first thinned (T1; N=54), second

thinned (T2; N=26) and 5 year old (5yr; N=20) age classes distributed throughout the

study area. The concentration of N, P, K, Fe, Zn, Cu and B were assessed using

standard laboratory analyses.

Models were calibrated between spectral and field data using two methods in the R

statistical analysis software: Best Subsets Regression (BSR) and Partial Least Squares

Regression (PLSR). Models were calibrated either on all plots simultaneously, or on

only the T1 Age class alone with subsequent testing of the T1 model fit across all age

classes.

Key Results

The preliminary stratification model provided a poor prediction of N and P

concentration throughout the study area. However, the range of observed values was

consistent with expected concentration levels in this area.

Field data indicate marginal concentration in all plots for N, in 70% of plots for Cu

and 33% of plots for P. Deficient plots were encountered for P (4% of plots), Zn

(10%) and Cu (19%). Concentrations of Fe and B were adequate in 100% of plots,

and for K in 95% of plots.

Preliminary modelling results were similar for the BSR and PLSR methods. PLSR

was chosen for all further analyses, however, because it included all of (and only) the

Hyperion reflectance data, because cross validation methods are better developed for

this technique than for BSR in R, and because PLSR is most commonly used in

literature describing similar work.

Useful models were calibrated across all plots for N (Adj r2 = 0.41; RMSEP =

1.716 g/kg), Fe (Adj r2= 0.41; RMSEP = 11.28 mg/kg) and B (Adj r2= 0.56; RMSEP =

3.523 mg/kg). Models were calibrated on T1 plots for K (Adj r2= 0.68; RMSEP =

1.102 g/kg) and Cu (Adj r2= 0.45; RMSEP = 0.368 mg/kg). The least effective models

were calibrated across all plots for P (Adj r2= 0.28; RMSEP = 0.279 mg/kg) and Zn

(Adj r2= 0.14; RMSEP = 6.225 mg/kg).

Predictions of nutrient concentrations across the estate were strongly influenced by

low canopy cover in stands less than 3 years of age. Deficiencies were predicted in

these areas for all nutrients and, though the true nutrient concentrations are unknown,

it is unlikely that deficiencies are widespread throughout these areas as fertiliser is

applied at establishment.

Maps showing compartment-mean concentration in terms of deficient, marginal and

adequate concentrations indicated a bias towards underestimating concentrations in

areas of low cover, but the proportion of compartments in each critical class

approximated the proportions indicated in the field data.

This study suggests that useful models of nutrient concentration can be calibrated on

field data collected from a range of age classes for several nutrients but that translation

of models to stands less than 3 years of age is unreliable.

Further work

A number of factors limit the utility of Hyperion, currently the only commercially

available satellite hyperspectral data, for operational application for forest nutrition

monitoring. These include the requirement for expert pre-processing of the images to

enable modelling, difficulties in acquiring data due to cloud cover or conflicting

requests for the acquisition of Hyperion data on a single orbit, and Hyperion’s narrow

ii

7km swath width, which may necessitate the acquisition and processing of multiple

images for coverage of larger estates. It may be possible to approximate the results in

this study using multi-spectral satellite imagery such as Landsat, however, and this

may suffice in some circumstances until newer, more suitable satellite hyperspectral

sensors become operational.

A number of hyperspectral satellite launches are due to be deployed in the near future,

including EnMap, which is planned for launch in 2009. EnMap will have similar

spectral characteristics to Hyperion but with a larger swath, higher signal to noise

level and a more frequent revisit time. Studies such as this may assist to improve the

preparedness of forestry organisations to make use of this data in their research and

operational planning.

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Contents1.Introduction........................................................................................................................................... 1

1.1. Project objectives...................................................................................................................... 22. Study area description .................................................................................................................... 23. Materials and method ..................................................................................................................... 4

3.1. Hyperion data collection and preparation ................................................................................. 43.1.1. Plot stratification.................................................................................................................. 7

3.2. Field data collection .................................................................................................................. 83.2.1. Critical concentration levels .............................................................................................. 12

3.3. Modelling................................................................................................................................. 133.3.1. Image data ........................................................................................................................ 13

3.4. Modelling methods.................................................................................................................. 143.4.1. Best Subsets Regression.................................................................................................. 153.4.2. Partial Least Squares Regression .................................................................................... 163.4.3. Outlier identification........................................................................................................... 17

3.5. Method selection..................................................................................................................... 173.5.1. Preliminary models............................................................................................................ 173.5.2. Subset selection................................................................................................................ 183.5.3. Mapping............................................................................................................................. 19

4. Results .......................................................................................................................................... 194.1. Nutrition concentration............................................................................................................ 194.2. Stratification accuracy............................................................................................................. 214.3. Nutrient models....................................................................................................................... 24

4.3.1. Nitrogen............................................................................................................................. 244.3.2. Phosphorus ....................................................................................................................... 314.3.3. Potassium ......................................................................................................................... 374.3.4. Iron .................................................................................................................................... 434.3.5. Zinc.................................................................................................................................... 484.3.6. Copper............................................................................................................................... 534.3.7. Boron................................................................................................................................. 59

5. Discussion and Conclusion .......................................................................................................... 645.1. Nutrition models ...................................................................................................................... 645.2. Model translation between age classes.................................................................................. 655.3. Implications for future monitoring............................................................................................ 66

6. Acknowledgements....................................................................................................................... 697. References ................................................................................................................................... 70

Appendix1. Example field sampling data sheet 73 Appendix2. Names and descriptions of spectral bands used in BSR modelling 74

FiguresFigure 1. The study area at Rennick near Mt Gambier .......................................................................... 3Figure 2. Cross track illumination (a) before correction and (b) following correction............................. 6Figure 3. Comparison of reflectance spectra between raw (green) and pre-processed Hyperion data

(blue), and a typical needle spectrum collected using a spectrometer in the laboratory (red)...... 7Figure 4. Plot centres overlaid over the Hyperion image captured on 17 February 2008 ..................... 9Figure 5. Thinning classes throughout the study area. ........................................................................ 10Figure 6. Year of planting ..................................................................................................................... 11Figure 7. Correlation between nutrient concentrations and cover in T1 plots...................................... 21

iv

Figure 8. Correlation between Observed and Predicted (a) N and (b) P concentration, as predictedfrom a previously calibrated model (Sims et al. 2006a) .............................................................. 22

Figure 9. Location of plots in the 5yr class in compartments with patchy cover (579nm, 651nm and 854 nm as BGR). Dense vigorous vegetation is red. ................................................................. 23

Figure 10. Observed Nitrogen concentration ....................................................................................... 24Figure 11. Predicted vs Observed N values calibrated on All Plots..................................................... 25Figure 12. Descriptive information for N prediction model ................................................................... 27Figure 13. Correlation between pine cover fraction and N prediction error ......................................... 28Figure 14. Predicted N concentration................................................................................................... 29Figure 15. Predicted compartment mean N concentration .................................................................. 30Figure 16. Observed Phosphorus concentration.................................................................................. 31Figure 17. Preliminary P model ............................................................................................................ 32Figure 18. Outlier removed P model .................................................................................................... 32Figure 19. Model diagnostics for P........................................................................................................ 33Figure 20. Correlation between pine cover fraction and P prediction error ......................................... 34Figure 21. Predicted P concentration................................................................................................... 35Figure 22. Predicted compartment mean P concentration.................................................................... 36Figure 23. Observed Potassium concentration .................................................................................... 37Figure 24. Predicted versus observed K (T1, outliers removed).......................................................... 38Figure 25. Model diagnostics for K........................................................................................................ 39Figure 26. K model translated over all plots.......................................................................................... 40Figure 27. Correlation between pine cover fraction and K prediction error ......................................... 40Figure 28. Predicted K concentration................................................................................................... 41Figure 29. Predicted compartment mean K concentration................................................................... 42Figure 30. Observed Iron concentration............................................................................................... 43Figure 31. Predicted versus observed Fe (All plots, outliers removed) ............................................... 44Figure 32. Correlation between pine cover fraction and Fe prediction error........................................ 44Figure 33. Model descriptors for Fe ..................................................................................................... 45Figure 34. Predicted Fe concentration ................................................................................................. 46Figure 35. Predicted compartment mean Fe concentration................................................................. 47Figure 36. Observed Zinc concentration .............................................................................................. 48Figure 37. Correlation between Observed and Predicted Zn concentration (All plots)........................ 49Figure 38. Correlation between pine cover fraction and Zn prediction error........................................ 49Figure 39. Model diagnostics for Zn..................................................................................................... 50Figure 40. Predicted Zn concentration ................................................................................................. 51Figure 41. Predicted compartment mean Zn concentration................................................................. 52Figure 42. Observed Copper concentration ......................................................................................... 53Figure 43. Predicted versus observed Cu (T1) .................................................................................... 54Figure 44. Model diagnostics for Cu ..................................................................................................... 55Figure 45. Translation of Cu model to All Plots.................................................................................... 56Figure 46. Correlation between pine cover fraction and Cu prediction error ....................................... 56Figure 47. Predicted Cu concentration.................................................................................................. 57Figure 48. Predicted compartment mean Cu concentration ................................................................ 58Figure 49. Observed Boron concentration ........................................................................................... 59Figure 50. Predicted versus observed B (All plots) .............................................................................. 60Figure 51. Correlation between pine cover fraction and B prediction error ......................................... 60Figure 52. Model diagnostics for B....................................................................................................... 61Figure 53. Predicted B concentration................................................................................................... 62Figure 54. Predicted compartment mean B concentration.................................................................. 63

v

TablesTable 1. Tukey multiple comparisons of mean N concentration between Age Classes ...................... 12Table 2. Plot descriptions ..................................................................................................................... 12Table 3. Critical concentration thresholds used in this study ............................................................... 13Table 4. Mean nutrient concentrations in outlier and remainder plots ................................................. 17Table 5. Cross validated Root Mean Squared Error’s of Prediction (RMSEP) for each nutrient in

preliminary models. ..................................................................................................................... 18Table 6. Preliminary r2 values for PLS models calibrated and validated on a range of subsets of the

nutrient data................................................................................................................................. 19Table 7. Critical concentrations, the proportion of samples in concentration classes and the mean

concentration levels per age class. ............................................................................................. 20Table 8. Summary information for nutrient models .............................................................................. 64

vi

1. Introduction

The forestry industry spends many millions of dollars annually on fertilising and assessing the

nutrition of plantations. The growth of radiata pine in southern Australia is often limited by

low soil fertility, but site productivity can be maintained by addressing nutrient deficiencies at

various stages during the rotation (Raupach 1967; Raupach et al. 1969). Soil and foliage

analyses have been used as diagnostic tools to identify radiata pine plantations with low

nutrient status and there is a considerable knowledge base for the interpretation of diagnostic

testing and the ameliorative treatment required. However, current methods for assessing plant

nutrition are expensive, labour intensive and potentially dangerous as they often involve

collecting samples by shooting branches from tree crowns. In addition, surveys of plantations

using foliage diagnostic testing indicate that there is considerable spatial variation in the

nutrient status of radiata pine plantations (eg. Turner et al 2001). Samples collected at a

number of localised plots may not be representative of the range and distribution of nutrient

concentrations throughout the wider estate. One option to reduce the risks and cost of

nutrition data collection that can also provide quantitative and spatially registered nutrition

data at fine resolution over entire plantation estates is to predict foliar nutrition concentration

levels from satellite images.

Models of nutrient concentrations in forest areas have previously been developed from

hyperspectral image data (Huang et al. 2004; Serrano et al. 2002; Sims et al. 2006b) including

Hyperion imagery(Coops et al. 2003; Martin et al. 2008; McNeil et al. 2007; Sims et al.

2006a). This work has largely been conducted as experimental research projects which have

included comprehensive but expensive and time consuming sampling programs. Coops

(2002) calibrated useful models for a number of nutrients from Hyperion satellite images of

Pinus radiata plantations despite a considerable time lag of 6 to 18 months between the

collection of foliar samples and image capture. Recommendations of that study included

collecting field data closer to the time of image capture and stratifying plots to cover a wider

range of concentration levels, especially for P (Coops 2002).

Sims et al, (2006a) mapped the concentration of 12 nutrients in exotic pine foliage throughout

a Queensland estate using three adjacent Hyperion images. That study demonstrated the

potential to translate models calibrated on one age class to stands of other ages for a range of

nutrients including N, P, Zn, Fe and B. It also suggested that these models could be used as a

guide to stratifying the location of sampling plots in future studies. Sims et al, (2006a)

employed a comprehensive but time consuming and expensive field data collection program,

and one of the recommendations in that work was that the cost of analysis could possibly be

1

reduced by using standard operating procedures for sample collection. This project attempts

to reduce the time and cost of predicting foliar nutrition and improve the accuracy of the

resultant models by addressing the recommendations from these previous studies.

This report describes linear regression models for 7 nutrients (N, P, K, Fe, Zn, Cu and B)

calculated from Hyperion hyperspectral image data showing a Pinus radiata estate in southern

Australia. These nutrients are known to be potentially limiting to the growth of radiata pine

on coastal sands in the Green Triangle. Satellite remote sensing tools and techniques have

developed rapidly in recent years, and it is now possible to routinely and cost effectively

acquire and process image data for a wide range of resource management applications such as

crop yield prediction. In particular, the availability and quality of hyperspectral satellite

image data, which enables the composition and character of features within pixels to be

scrutinised in fine detail, is likely to increase substantially within the next few years. This

project will enable the industry to adopt these technologies and integrate them into their

operational systems more rapidly and readily. Thus, this project aims to increase the

readiness of plantation growers to adopt remote sensing methods for plantation nutrition

assessment by improving methods for predicting nutrient concentration from hyperspectral

satellite images and developing map products suitable for inclusion in plantation growers’

standard operating procedures.

1.1. Project objectives

The objectives of this report are:

To produce accurate and informative maps of foliar nutrition in Pinus radiata from

Hyperion image data

To examine whether nutrition models can be robustly translated between age classes

To explore the future potential of nutrition monitoring from hyperspectral satellite

images

2. Study area description

This study was conducted on Hancock Victoria Plantations and ForestrySA Pinus radiata

plantations at Rennick on the state border between Victoria and South Australia

approximately 15km east of Mt. Gambier (Figure 1). The study area is characterised by low

relief topography and sandy soils that become deeper towards the north of the study area.

2

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Figure 1. The study area at Rennick near Mt Gambier

Average annual rainfall at Mt. Gambier airport is 709 mm (Bureau of Meteorology data) and

average annual pan evaporation in Mt. Gambier is 1350mm which makes plantations in this

area susceptible to drought stress. Rainfall at Mt. Gambier airport in 2007 and 2008 was

749mm and 630mm respectively. Annual rainfall decreases from south to north across the

study area.

Average monthly rainfall is highest in July (99 mm) and lowest in February (25 mm).

Rainfall in January and February 2008, during the time of image capture and field data

collection, was 6.8 mm (approximately the 10th percentile of monthly total rainfall) and 20mm

(approximately the monthly median) respectively.

The early part of 2008 was characterised by a record heatwave and low rainfall in South

Australia and trees were probably experiencing drought stress conditions during the time of

data collection for this study.

3

3. Materials and method

3.1.Hyperion data collection and preparation

The Hyperion hyperspectral sensor was developed as a validation instrument, to test the

technology, utility and demand for space-born hyperspectral imagery. Hyperion was

launched on the EO-1 satellite in November 2000 and it remains the only commercially

available satellite-based hyperspectral instrument capable of recording spectral image data

from 400 nm to 2500 nm. Each Hyperion scene is nominally 7 km wide and 42 km long and

contains information in 242 spectral bands each 10 nm in bandwidth, at a spatial resolution of

30 m.

Hyperion is a ‘push-broom’ sensor in which all wavelengths are collected simultaneously for

a single row of an image. An image is created line-by-line as the spacecraft moves over the

Earth’s surface (Jupp and Datt 2004). Hyperion consists of two spectrometers, one sensitive

to visible/near infrared wavelengths between 400 – 1000nm (VNIR bands 1-50) and a second

sensitive to short-wave infrared wavelengths from 900 to 2500nm (SWIR bands 51-242). A

filter reflects the VNIR wavelengths to one-spectrometer and transmits SWIR wavelengths to

another (Ungar 2001).

Pixel values in satellite imagery represent the magnitude of energy reflected and emitted

(radiance) from objects in the field of view in one or more wavelengths. In addition to the

physical properties of objects in the scene, pixel values in satellite imagery are influenced by

illumination and atmospheric characteristics, the orientation of the satellite and the condition

or performance of the sensor. Hyperion data also contains a range of noise artefacts, some of

which are common in narrow-band spectroscopy, and others that are specific to the Hyperion

sensor (Datt et al. 2003). For instance, narrow bandwidths restrict the amount of light per

band, and this can be further reduced by atmospheric water absorption in certain spectral

regions causing very poor signal to noise levels of around 50 to 1 where water absorption is

significant (Kruse et al. 2002). This also occurs at the extremes of the ranges of each

spectrometer. Artefacts peculiar to Hyperion images include striping caused by differences in

the sensitivity of individual spectrometer elements across the sensor array, and ‘spectral

smile’ caused by a shift in the centre wavelength across the array, which results in a

systematic change in reflectance brightness across the image (Datt et al. 2003). Minimisation

of these artefacts is essential to accurately model variation in the condition of the objects of

interest on the land surface.

4

This study uses two Hyperion images of the Green Triangle study area captured on 12 January

and 17 February 2008. Pre-processing of the images to minimise the influence of sensor,

atmospheric and illumination conditions on pixel values was performed using a method

modified from Datt et al., (2003):

1. Fix Bad Pixels – Hyperion data contains bands that are not used (bad bands) and bad

pixels resulting from poor detectors in the sensor array. These may be set to an

arbitrary value during initial processing or may contain extreme values in relation to

the remainder of the imagery.

2. Gain/Offset correction – converts raw pixel values to radiance

3. Fix out-of-range data – corrects integer wrap-arounds following rescaling to radiance

4. Interpolate wavelengths – corrects the shift in central wavelength in some bands

across the Hyperion scene (‘spectral smile’)

5. De-spike outliers – adjusts extreme pixel values identified by their mean and mean

deviation from pixel values in the image

6. De-streak – streaks can appear in Hyperion imagery due to poor detector calibration in

the sensor array. Each sensor has a unique pattern of streakiness and must be de-

streaked separately

7. Atmospheric correction – This was performed using the FLAASH atmospheric

correction model in ENVI 4.2 image processing software (Research Systems Inc.

2005). FLAASH converts radiance values to reflectance values by accounting for

factors including the geographic location of the imagery, time and date of image

capture and atmospheric conditions including aerosols and water content

8. Sun angle correction – the influence of solar illumination angle on pixel values is not

accounted for in FLAASH. This was conducted by dividing pixel values by the cosine

of the solar elevation angle

9. Cross track illumination correction – examination of pixel brightness across the

Hyperion image processed following the above steps showed that pixel brightness

reduced systematically from west to east across the image (Figure 2a). Cross track

brightness correction was performed in ENVI using a 3rd order polynomial

interpolation and the multiplicative correction method (Figure 2b).

10. Minimum Noise Fraction (MNF) transformation – this procedure is similar to a

principal components transformation which identifies the main information

5

components in hyperspectral imagery and enables noise reduction in hyperspectral

image data.

11. MNF transformation in this study was performed in two steps separately on each of

the VIS and SWIR. A ‘Forward’ MNF transformation was used to create 1 MNF band

for each image band, with MNF band 1 containing most of the information and noise

levels increasing in each subsequent MNF band. A ‘Reverse’ MNF transformation

was then conducted on bands containing visibly coherent information, which

transforms the image back into the original wavelength band format and reduces noise

levels in the data.

(a) Before correction (b) After applying 3rd order polynomial

cross track correction using multiplicative

correction method

Figure 2. Cross track illumination (a) before correction and (b) following correction

This process results in 168 spectrally calibrated bands, in which the pattern of reflectance over

vegetated areas more closely approximates typical needle reflectance data collected using a

spectrometer in the laboratory. Figure 3 shows the effect of spectral correction on a typical

vegetation pixel, with the blue line representative of the spectral data used in this project.

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Figure 3. Comparison of reflectance spectra between raw (green) and pre-processed Hyperion data (blue), and a typical needle spectrum collected using a spectrometer in the laboratory(red).

Following capture of the 12 January image, work commenced on image calibration and plot

stratification, and arrangements were made for the deployment of field crews for foliar sample

collection. Field crews were mobilised in late January and field data were collected until late

February. A decision was made to use the image captured on 17 February for further

analysis, however, as it was closer to the time of field data collection. Both of these images

were fully pre-processed to reflectance values using the methods described above but they

were not directly calibrated to one another. The resultant processed images are comprised of

reflectance values in168 spectral bands which enables the character of image pixels to be

discriminated from one another in fine detail.

3.1.1. Plot stratification

Foliar samples were collected from 100 plots located throughout the study area, as shown in

Figure 4. Plot were located to encounter the maximum range of N and P concentration as

shown using preliminary maps created by applying previously calibrated models for each of

these nutrients (Sims et al., 2006) to the atmospherically corrected image of 12 January. Plots

were grouped into 3 classes based on their silvicultural stage (Figure 5) and stand age (Figure

6): “T1” (thinned once), “T2” (thinned twice) and “5yr” which were trees 5 years of age,

planted in 2003.

7

Plots in the T1 class were used for model calibration because they are most likely to exhibit

canopy closure, minimising the influence of background characteristics on model

development. Plots in the T2 and 5yr age classes were used for model validation only, to

examine how effectively models calibrated in the T1 age classes describe nutrient

concentration variations in these age classes.

3.2. Field data collection

Fully expanded, current-year needles were collected from the leading shoot, on the northerly

aspect of the crown’s upper 3rd, from 10 trees in T1 and T2 stands, or 20 trees in the 5yr class.

Measure trees were located within a 20 m radius from the plot centre. Samples from each tree

were combined on an equal weight basis to obtain one composite foliage sample

representative of the nutrient status at each plot. Composite samples were dried at 70°C,

finely ground and analysed for essential plant nutrients including total N (by the Dumas

combustion method using a LECO-CN analyser) and total P, K, Fe, Zn, Cu, and B (on a

nitric-perchloric acid digest by ICP-AES).

In addition to foliar data, notes describing the general biophysical characteristics of stands,

including estimates of understorey and canopy cover to the nearest 5% and the condition of

the plants, key understorey species and groundcover composition were collected at each plot.

An example field sheet is shown in Appendix 1.

8

Figure 4. Plot centres overlaid over the Hyperion image captured on 17 February 2008

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Figure 5. Thinning classes throughout the study area.

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Figure 6. Year of planting

11

Field notes collected at the time of foliage sample collection indicate that thinning had

recently occurred at 6 plots, and that they had been classified into an incorrect thinning class.

This included 4 plots originally included in T1 which were in fact T2, and 2 plots originally

included in T2 which were in fact T3. ANOVA of N concentration by Age Class, followed

by a Tukey multiple comparison of mean concentration levels between groups shows

significant differences between age classes T1, T2 and 5yr (Table 1).

Table 1. Tukey multiple comparisons of mean N concentration between Age Classes

AgeClasses diff lwr upr

AdjustedP

T1-5yr -1.310 -2.478 -0.142 0.021

T2-5yr 1.428 0.049 2.806 0.039

T3-5yr 0.448 -1.997 2.892 0.964

T2-T1 2.738 1.609 3.867 0.000

T3-T1 1.758 -0.554 4.070 0.200

T3-T2 -0.980 -3.405 1.445 0.717

The non-significant difference between the T3 and 5yr classes (Table 1) is probably due to the

small sample size of the T3 group (N=2). In fact the physical characteristics of individual

trees and stands are substantially different between these age classes, and conditions in the T3

class are most likely to be similar to those in T2. Consequently, the T3 plots were added to

the T2 age class. The final plot numbers are shown in Table 2.

Table 2. Plot descriptions

Age class No. Plots identified No. Plots Final

T1 30 Calibration30 Validation

54

T2 20 Validation 26

5yr 20 Validation 20

3.2.1. Critical concentration levels

Diagnostic criteria used to evaluate tree nutrient status in terms of low, marginal or

satisfactory (adequate) for radiata pine plantations are shown in Table 3. These criteria were

defined on the basis of long-term tree nutrition research in New Zealand (Will 1985) and

Australia(Boardman et al. 1997; McGrath and Robson 1984; Raupach 1967; Raupach et al.

1969; Turner and Lambert 1986; Turner et al. 2001). Seasonal variation in nutrient

concentrations are not taken into account.

12

Table 3. Critical concentration thresholds used in this study g/kg mg/kg

N P K Fe Zn Cu BDeficient (<) 10 1 3.5 20 10 2 10

Adequate (juv >) 18 1.3 5 30 15 3 15Adequate (adlt.

>) 15 1.3 5 30 15 3 15

Concentrations of nutrients at or below deficient levels correspond to the development of

visual symptoms of deficiency and tree growth limited by nutrient supply, as per Will (1985).

Adequate concentrations are defined as the minimum desirable levels for satisfactory growth

of radiata pine. Concentrations between deficient and adequate are classed as marginal,

indicating some constraint on growth due to low nutrient availability. Potential growth

responses to fertilizer are expected to decline as concentrations of macronutrients (N, P, K)

approach satisfactory levels.

In the case of nitrogen, the critical value of 10 g/kg associated with N deficiency in radiata

pine applies to plantations of all ages. In contrast, two concentrations are used to indicate

adequate nitrogen status depending on age class: 18 g/kg before canopy closure

(approximately 5yrs of age) and 15 g/kg in mature, thinned stands. These values are

determined by satisfactory growth as well as form (Carlyle et al. 1989; Hopmans et al. 1995).

The higher value for older stands is based on findings from post-thinning fertilizer trials

which showed that volume responses to N fertilizer were generally less than 10% when pre-

treatment levels of N in foliage exceeded 18 g/kg. This upper threshold value is consistent

with post-thinning responses of radiata pine across a wide range of sites (Turner et al. 2001).

3.3. Modelling

3.3.1. Image data

A number of studies have used products of reflectance data for modelling purposes including

transforming reflectance spectra to represent pseudo-absorption(Coops et al. 2003; Serrano et

al. 2002), derivative spectra which describe regions of spectral change (Coops et al. 2003;

Datt 1999) and calculating vegetation indices which compare brightness between image

bands. Indices can be targeted to show physiological, physical or chemical properties of

vegetation with which there is a known spectral correlation. The Normalised Difference

Vegetation Index ([nir-red]/[nir+red]), for instance, is founded in the reflectance

characteristics of healthy green leaves in which red energy is strongly absorbed by

chlorophyll and pigments, and near infra-red energy is strongly reflected due to the internal

cellular structure (Tucker 1979). Many other indices can be calculated from hyperspectral

13

data (Hansen and Schjoerring 2003; Thenkabail et al. 2000) several of which have been

correlated with nutrient concentration levels in plant foliage.

Overall, the literature indicates that each of these techniques has particular advantages in

certain modelling conditions and for particular objectives. Models using derivative

reflectance, for instance, have been shown to potentially improve the accuracy of

discrimination of soil minerals in hyperspectral image data (Debba et al. 2006), but also

illustrate that derivative transformations are highly sensitive to data noise and may not be

suitable for use with the inherently noisy Hyperion data. There are many possible ways in

which spectral data can be transformed before analysis, but a number of recent nutrient

modelling studies have successfully used reflectance data only (Christensen et al. 2004;

Hansen and Schjoerring 2003; Martin et al. 2008). Recent research indicates that the spectral

format of the data has no significant impact on calibrations using Partial Least Squares

Regression (Reeves 2009), one of the most commonly used modelling methods in foliar

nutrient studies (Christensen et al. 2004; Coops 2002; Hansen and Schjoerring 2003;

Jorgensen et al. 2007).

3.4. Modelling methods

The high dimensionality of the Hyperion images used in this study can be problematic for

some statistical process, especially where the number of predictor variables (168 spectral

bands) is large relative to the number of observations (54 calibration plots in T1, 100 plots

overall). This situation is described as “P>>N” (Van De Geer and Van Houwelingen 2004)

and can lead to “overfitted” models, in which variables are selected for inclusion in a model

because random noise within them explains part of the variation in nutrient levels. Overfitted

models explain a large proportion of the variation of concentration levels amongst the dataset

on which the model is calibrated, but poorly describe variations in concentration levels in

independent datasets.

In addition, predictor variables may exhibit high multi-colinearity (highly correlated with one

another), which results in many of the potential predictor variables having very similar

predictive power. Van De Geer and Van Houwelingen (2004) note that “if P>>N, it is

impossible to discover the relevant relations from the data and use these in an efficient way

for classification or prediction”, and that “one should avoid any (biological) interpretation of

the set of explanatory variables that are thus selected and their regression coefficients”. The

set of potential predictor variables is therefore best selected based on known biological,

physical or physiological relationships with the dependent variable.

14

The development of the open source statistical computing language R has provided access to

many of the latest analysis techniques, and is now regarded as the de-facto standard tool for

statistical analysis. In addition, the free availability of R enables analytical codes to be shared

and implemented by any potential users. The source code and executables for R are freely

available at: http://www.r-project.org. All statistical data manipulation and analysis in this

project was conducted in R.

Two regression modelling methods are commonly used when P >>N: Stepwise Multiple

Linear Regression (SMLR) and Partial Least Squares (or Projection of Latent Structures)

Regression (PLSR). SMLR, in the context of this project, describes variations in nutrient

concentrations using a combination of several wavelength variables. Stepwise regression

extracts a subset of predictors from a larger set of potential predictor variables that best

describe variations in the dependent variable. Stepwise predictor selection is one method for

reducing the chances of an overfitted model (Tabachnick and Fidell 1996).

One of the characteristics of R is that it is ‘object-oriented’, which enables datasets, models

and analyses to be readily transformed and interrogated. In R, one is first required to build a

model between the dependent and predictor variables and subsequently perform a stepwise

analysis of that model ‘object’ to define the subset of final predictor variables. Object

orientation introduces some problems for SMLR analyses with large numbers of potential

predictor variables, however. In particular, the number of predictor variables included in the

initial linear model is restricted to the number of variables that describes all of the variation in

the dependent variable which, in this project, includes only about the first 30 predictor

variables. An alternative to SMLR in R is Best Subsets Regression.

3.4.1. Best Subsets Regression

Best Subsets Regression (BSR) conducts an exhaustive search of a large number of potential

predictor variables to find the best subset(s) of predictor variables for a linear prediction of the

dependent variable (http://cran.r-project.org/web/packages/leaps/leaps.pdf). The subset of

predictor variables identified by BSR can then be used in multiple linear regression

modelling. The bands selected using multiple linear regression are not necessarily those with

known theoretical correlations with the process of interest (Grossman et al. 1996). MLR has

several advantages, however, including its simplicity and that it may incorporate only

wavelengths that are highly correlated with the process of interest, which can improve

modelling results amongst highly variable plots (Coops 2002).

15

The BSR modelling method used in this project included procedures to assess the optimum

number of predictor variables to include in the models. This involved comparing models that

included between 3 and 10 predictor variables, and selecting the model with the largest cross

validated r2. This process was abandoned, however, because it usually indicated that models

with more parameters tended to predict more accurately. To minimise the likelihood of

overfitting, and to provide consistency between nutrients, the final BSR models were limited

to 4 parameters.

As a further means to prevent overfitting only 38 image bands and transformations of them

were included as potential predictor variables in the BSR modelling process. This subset

includes 28 image bands in key regions of the reflectance spectrum between 437nm and

2355nm, 7 vegetation indices created from ratios of image bands, and 3 outputs from Linear

Spectral Unmixing (LSU) processing, which describe the proportion of pine crowns, bare soil

and shaded areas in each pixel. The names and descriptions of the spectral dataset used in

BSR modelling are shown in Appendix 2.

3.4.2. Partial Least Squares Regression

Partial Least Squares Regression (PLS) creates ‘factors’, similar to a Principal Components

Analysis, from all the predictor variables that describe structures within the predictor and

dependent variable datasets (Garthwaite 1994). PLS is especially suited to modelling where

P>>N because each factor constitutes a single predictor variable. PLS has an added advantage

over multiple variable selection methods in that in PLS all variables are used so covariance

between the variables can result in increased signal to noise levels in the derived factors.

PLS in this project was conducted between individual nutrients and the 168 reflectance

parameters measured by Hyperion. Indices and LSU outputs were not included. The

wavelength data were mean-centred and scaled by their root mean square before PLS analysis

to minimise the influence of differences in the magnitude of reflectance on the resultant

model.

For both BSR and PLS modelling, the primary models were developed between the image

data collected on 17 February 2008 and the field data collected in March 2008. Modelling

was attempted at two scales for each technique. First, calibration was attempted with all

plots, including T1, T2 and 5yr classes. If calibration across all plots resulted in a poor fit

(below about r2=0.3) then the model was calibrated and validated only on the T1 plots.

Models calibrated on T1 plots were subsequently tested for fit on all plots.

16

3.4.3. Outlier identification

A conservative approach to outlier removal has been used in this project, in which the

tendency is to retain data points rather than eliminate them because they are poorly predicted

or to improve overall predictive accuracy. Potential outlier plots were identified using the

‘Influence Measures’ algorithm in R, which identifies points that strongly influence the

regression fit of a linear model. More information on the Influence Measures package can be

found here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/influence.measures.html.

Potential outliers were also examined visually on cross plots showing Observed and Predicted

concentration levels for each nutrient.

Data points identified as being influential were examined in terms of their observed and

predicted concentration values. Comparison of concentration levels in outliers and the

remainder of plots (Table 4) showed no differences between these two groups for N, P, K, Zn

and Cu but outliers were lower for Fe (5yr) and higher for B (T1). Thus, deletion of these

points could not be justified on the basis of differences in observed concentrations with the

possible exception of Fe and B.

Table 4. Mean nutrient concentrations in outlier and remainder plots

N P K Fe Zn Cu B

Number of outliers 9 10 3 12 13 3 4

Mean nutrientconcentration of outliers 14.5 1.6 7.1 47 20 2.9 32

Mean nutrientconcentration of

remainder13.4 1.4 7.6 63 18 2.4 25

F-value NS NS NS *** NS NS **

3.5. Method selection

3.5.1. Preliminary models

A preliminary comparison of the effectiveness of the BSR and PLS modelling methods was

conducted by comparing root mean squared errors of prediction (RMSEP) for models

calibrated on T1 plots for each nutrient, before outlier removal (Table 5). These RMSEP

values were calculated using ‘Leave One Out’ cross validation which involves dividing the

dataset into 54 segments (1 per plot), calibrating the model on 53 segments (the calibration

segments) and testing it on the 54th (the validation segment). The validation segment moves

sequentially through the plots during each of 54 iterations of the process and the average

17

RMSEP is reported. This method provides good estimates of the accuracy of models

calibrated on small datasets (Martens and Dardenne 1998) as in this case.

Both BSR and PLS methods provide similar results amongst nutrients (Table 5) with slightly

higher accuracy estimates for the BSR method than for the PLS method. Given the similarity

of results, all further models in this project were calibrated using PLS, which is preferred for

analysing spectral data in the international literature (Christensen et al. 2004; Hansen and

Schjoerring 2003; Jorgensen et al. 2007) and for which model selection and cross validation

methods are more fully developed and integrated in R, and because model calibration is based

on all of, and only, the reflectance data contained in the calibrated Hyperion image.

Table 5. Cross validated Root Mean Squared Error’s of Prediction (RMSEP) for each nutrient in preliminary models.

BSR PLS UnitsN 1.188 1.401 g/kg

P 0.253 0.284 g/kg

K 1.130 1.238 g/kg

Fe 10.647 11.77 mg/kg

Zn 5.809 6.668 mg/kg

Cu 0.339 0.368 mg/kg

B 3.219 3.449 mg/kg

3.5.2. Subset selection

Assessing the accuracy of models calibrated on T1 plots for predicting nutrient concentrations

in T2 and 5yr plots is one of the main objectives of this project. Table 6 shows r2 values for

three groups of preliminary models:

calibrated on T1 Plots and validated on T1 Plots

calibrated on T1 Plots and validated across All Plots

calibrated on All Plots and validated on All Plots

In general, the best results in terms of predicting nutrient concentrations amongst all age

classes are from models calibrated and validated on All Plots, with the exception of K and Cu.

These latter models did not effectively translate from the T1 plots to other age classes. For

brevity, only the models identified with an asterix in Table 6 are described below).

18

Table 6. Preliminary r2 values for PLS models calibrated and validated on a range of subsets of the nutrient data.

PLS(r2)

cal T1 T1 All

val T1 All All

N 0.35 0.15 0.48*

P 0.05 0.02 0.28*

K 0.64* 0.1 0.17

Fe 0.07 0.26 0.24*

Zn 0.01 0.01 0.1*

Cu 0.41* 0.03 0.03

B 0.54 0.31 0.53*

3.5.3. Mapping

Continuous scale maps were created by back transforming the calibrated Hyperion image

using model coefficients in IDL and ENVI image processing software. Predicted ranges of

nutrient concentration were restricted to the full observed range extended by 1 standard

deviation either side of the minimum and maximum levels. Predicted values greater than the

maximum were converted to the maximum value, and predicted values less than the minimum

were converted to null values. Predictions were applied to all Pinus radiata compartments.

For the calculation of compartment mean concentration value, the digitised compartment

boundaries were ‘shrunk’ to exclude edge pixels.

4. Results

4.1. Nutrition concentration

Concentration levels for most nutrients are generally low with a large proportion of plots

considered to be either deficient or marginal (Table 7). N was marginal in all T1 and T2

plots, and in 75% of 5yr plots. Other nutrients with marginal concentrations in substantial

proportions of plots included P (33% of plots), Zn (17%) and Cu (70%). Deficient

concentrations of Zn and Cu occurred in 10 and 19 percent of plots respectively.

Concentrations of K, Fe and B were generally adequate.

19

Table 7. Critical concentrations, the proportion of samples in concentration classes and the mean concentration levels per age class.

Critical concentration levels g/kg mg/kg

N P K Fe Zn Cu BDeficient (<) 10 1.0 3.5 20 10 2 10

Adequate (juv >) 15 1.3 5.0 30 15 3 15Adequate

(adlt.>) 18 1.3 5.0 30 15 3 15

Percentage of plots

Deficient 0 4 0 0 10 19 0Marginal 100 33 5 0 17 70 0

Adequate 0 63 95 100 73 11 100Adequate (juv) 25 . . . . . .

Mean Concentrationg/kg mg/kg

T1 13.0 1.5 7.8 65 18 2.6 26T2 14.7 1.3 6.6 63 17 2.3 245yr 13.9 1.5 7.7 46 20 2.1 22

Correlations among nutrients in T1 plots were generally weak, though several were

statistically significant (Figure 7). Strong correlations occur between Nitrogen and Potassium

(r = 0.39), and between the micronutrients Iron and Zinc (r = 0.44) and between Zinc and

Copper (r = 0.40).

There is a strong and significant correlation (r = 0.71, P< 0.001) between estimates of canopy

cover from the field notes (‘cover’) and the linear spectral unmixing output of pine cover

fraction (‘lsu_pine’; Figure 7). Our opinion is that experienced field staff, such as were

involved in this project, can make accurate estimates of canopy cover to the nearest 5%, and

we therefore view this correlation as supporting the accuracy of the lsu_pine output. The

lsu_pine layer (hereafter referred to as ‘pine cover fraction’), is used in further analysis

because it provides continuous cover information across the entire estate.

Pine cover fraction is significantly correlated with all nutrients in this analysis apart from Zinc

(Figure 7). Strong correlations with pine cover fraction (r > 0.4) occur for Nitrogen,

Potassium, and Boron. This suggests that the concentration of these nutrients than N, K and

B may influence the amount of foliage or structure of tree crowns in each pixel. Significant

correlations between nutrient concentrations and pine cover fraction are generally positive

apart from Boron and Iron which are negative, though the correlation for Iron is weak.

20

Figure 7. Correlation between nutrient concentrations in T1 plots and between nutrient concentrations and indices of cover as assessed in the field (‘cover’) or pine cover fractionestimated from a linear unmixing of the Hyperion data (‘lsu_pine’). Plots of data are indicated in the lower left half of the figure and their respective correlation coefficients are shown in the upper right half of the figure.

4.2. Stratification accuracy

Previously calibrated models of N and P provided poor predictions of N and P concentrations

observed in this study (Figure 8a and b). Both the absolute values and the range of predicted

values differ substantially between observed and predicted levels. In both models there is a

distinct group of outliers are in the 5yr class. A review of the field sheets indicates that many

of these plots have low canopy cover (around 15%) and are in compartments with patchy

cover characteristics (Figure 9).

21

10 12 14 16 18

46

810

12

Observed N (g/kg)

Pre

dict

ed N

(g/k

g)

1_01 1_02 1_031_04

1_05 1_06

1_07

1_08

1_091_10

1_111_12

1_13

1_14

1_151_16

1_17

1_18

1_19 1_20

1_21 1_22

1_23 1_24

1_25

1_26 1_271_28

1_29

1_30

1_311_32

1_331_34

1_35

1_36

1_37

1_381_39

1_40

1_411_42

1_43 1_441_451_46

1_47

1_481_49

1_501_51

1_52

1_531_54

1_551_56

1_571_58

1_59

1_60

2_01 2_02

2_03

2_04

2_052_06

2_07 2_08 2_09

2_10

2_112_12

2_13

2_14

2_15

2_16

2_172_18

2_19

2_20

5_01

5_02

5_03 5_04

5_05

5_06

5_07

5_08

5_09

5_10

5_11

5_12

5_13

5_14

5_15

5_16

5_17

5_18

5_195_20

(a)

1.0 1.5 2.0 2.5

0.8

1.0

1.2

1.4

1.6

Observed P (g/kg)

pred

icte

d P

(g/k

g)

1_01

1_021_031_04

1_051_06

1_071_08

1_091_10

1_11

1_12

1_13

1_14 1_151_16

1_17

1_181_19

1_20

1_21

1_221_23

1_24

1_25

1_26

1_271_28 1_29

1_30

1_31 1_32

1_331_34

1_351_36

1_37

1_38

1_39

1_40

1_411_42

1_43 1_441_45

1_46

1_471_48

1_49

1_50

1_51

1_52

1_53

1_54

1_551_56

1_57 1_58

1_59

1_602_012_02

2_03 2_04

2_052_06

2_07

2_08

2_09

2_10

2_11

2_12

2_13

2_14

2_15

2_16

2_17

2_18

2_19

2_20

5_01

5_02

5_035_04

5_05

5_06

5_07

5_08

5_09

5_10

5_11

5_12

5_13

5_14 5_15

5_16

5_17

5_18 5_195_20

(b)

Figure 8. Correlation between Observed and Predicted (a) N and (b) P concentration, as predicted from a previously calibrated model (Sims et al. 2006a)

22

[_

[_

[_

[_

[_

[_

[_

[_

[_[_

[_

[_5 _ 75 _ 7

5 _ 95 _ 9

5 _ 85 _ 8

5 _ 65 _ 65 _ 55 _ 5

5 _ 1 45 _ 1 4

5 _ 1 65 _ 1 65 _ 1 55 _ 1 5

5 _ 1 15 _ 1 1

5 _ 1 05 _ 1 0

5 _ 1 25 _ 1 2

5 _ 1 35 _ 1 3

497500

497500

500000

500000

5815

000

5815

000


Figure 9. Location of plots in the 5yr class in compartments with patchy cover (579nm, 651nm and 854 nm as BGR). Dense vigorous vegetation is red.

23

4.3. Nutrient models

4.3.1. Nitrogen

Figure 10 shows descriptive information for the observed N concentration data. This Figure

contains 4 panels. Panel ‘a’ shows empirical density curves of concentration by age class.

Panel ‘b’ is a frequency histogram showing the percentage of the total number of samples

contained within each concentration range. Panel ‘c’ shows boxplots of the nutrient

concentrations in each age class. The boxplots show the median (dot), interquartile range

(box), extreme values (whiskers) and outliers (points above or below the whiskers where they

occur). Panel ‘d’ is a summary map showing T1 plots in their relative spatial orientation,

labelled by broad concentration classes (upper 3rd of concentration values are shown in green,

mid 3rd in yellow and the lower 3rd in red). This format is repeated for all nutrients.

(a)

N.g.kg.

Den

sity

0.000.050.100.150.200.25

10 15 20

5yr T10.000.050.100.150.200.25

T2

(c)

N.g

.kg.

10

12

14

16

18

5yr T1 T2

(b)

N.g.kg.

Per

cent

ofT

otal

010203040

10 12 14 16 18

5yr T1010203040

T2

(d) T1 only

East (000's of m)

Nor

th (0

00's

of m

)

5800

5805

5810

5815

492 494 496 498

Figure 10. Observed Nitrogen concentration

24

N concentration is slightly bimodal in the 5yr age class (Figure 10a and b) indicating the

possibility of recent fertilisation. Coops (2002) found that stratifying plots by time since

fertilising increased the accuracy of predictive models. Fertiliser data were not available for

this stuffy, however. Concentration levels are slightly skewed towards higher levels in the T2

age class but approximately normally distributed amongst T1 (calibration) plots (Figure 10b).

Median N concentration is lowest in T1 (12.42 g/kg) and highest in T2 (15.48 g/kg; Figure

10c), but there is considerable overlap in concentration ranges between age classes. There is

no apparent spatial bias in concentration levels amongst T1 plots (Figure 5d).

The N model calibrated over all age classes provides a reasonably good prediction (Figure 11;

Adj r2: 0.41; RMSEP = 1.716 g/kg). There is little clustering of age classes, but this model

tends to underestimate higher predicted concentrations. Plot 5_07 appears to be an outlier

(Figure 11) but removal of this point has a negligible effect on predictive power.

10 12 14 16 18

1012

1416

Observed

Pre

dict

ed

1_01 1_02 1_031_04

1_05

1_06

1_07

1_08

1_09

1_101_11

1_121_13

1_14

1_15 1_16

1_17

1_18

1_19

1_201_211_22

1_23

1_24

1_25

1_261_29

1_30

1_35

1_36

1_37

1_38

1_39

1_40

1_41

1_42

1_43

1_441_45

1_461_47

1_48

1_491_50 1_51

1_52

1_53

1_54

1_551_56

1_57

1_58

1_59

1_60

2_01 2_02

2_032_04 2_05

2_06

2_072_08

2_09

2_10

2_112_12

2_13

2_14

2_15

2_16

2_172_18

2_19

2_20

1_271_28

1_311_32

1_33

1_34

5_01

5_02

5_035_04

5_05

5_06

5_07

5_08

5_09

5_10

5_11

5_12

5_135_14

5_15

5_16 5_17

5_18

5_195_20

Adj.r2 0.4P <0.0001

Figure 11. Predicted vs Observed N values calibrated on All Plots

25

Figure 12 shows descriptive information for the PLS model for N prediction. Figure 12a

shows change in the r2 of the model for the training (black) and cross validation (red) datasets.

The best PLS model for N prediction has 5 components. Figure 12b shows the loading value

for each wavelength in the factors calculated from the spectral data. Theoretically, when

viewed in this way, each factor generated by partial least squares regression should resemble

the reflectance spectrum for each of the constituents of the image associated with nutrient

concentration (Goutis and Fearn 1996)in decreasing order of influence. Component 1, shown

in black on Figure 12b, and component 2 in red resemble a typical vegetation reflectance

spectrum with maximum variation in the near infra red between about 700nm and 1200nm.

Reflectance in this region is associated with structural characteristics of the foliage in each

pixel (which includes influences of tree form on foliage visibility). Figure 12b therefore

suggests that the model is influenced by the quantity of visible foliage within each pixel,

which is supported by the high correlation between pine cover fraction and N concentration

(Figure 7).

Model residuals for each plot are shown in Figure 12c. The absence of structured patterns in

the residuals indicates a suitable model fit (Quinn and Keough 2004). The regression

coefficients (the multiplier used to predict nutrient concentration from each wavelength) are

shown in Figure 12d, with coefficients near zero having very little influence on the model.

Wavelengths strongly influencing the prediction of N tend to be located in the visible range,

below about 750nm. Wavelengths near the NIR plateau around 1000nm, and around 1450nm

in a region of water absorption are also influential. The format of Figure 12 is repeated for all

nutrients.

26

0 1 2 3 4 5

0.0

0.1

0.2

0.3

0.4

(a)number of components

R2

trainCV

(b)wavelength

load

ing

valu

e

X426.8 X925.4 X1517.8 X2203.8

-0.3

-0.1

0.1

0.2

0.3

(c)plot

Res

idua

l

1_01

1_20

1_46

2_01

2_14

1_34

5_20

-4-2

02

4

(d)wavelength

Reg

ress

ion

Coe

ffici

ents

X426.8 X925.4 X1517.8 X2203.8

-0.3

-0.1

0.1

0.3

Figure 12. Descriptive information for N prediction model

The map of predicted N concentration (Figure 14) shows very little spatial bias across the

estate. Some influence of remnant striping in the calibrated Hyperion images remains visible

as faint diagonal stripes of lower predicted concentration in the southern part of the estate, but

these effects are minor over all.

Predicted mean N concentration is marginal for most compartments (Figure 15) which

accords with observed N concentration levels shown in Table 7. Deficient mean N

concentration is predicted for several compartments. These compartments often include only

on a few remaining pixels following data truncation below the minimum observed value

observed minus one SD (eg. compartments at 495000 E, 581000 N; Figure 14).

27

Comparison with Figure 6 indicates an association between low predicted compartment-mean

N and compartments planted after 2003. These compartments were generally planted in 2006

or 2007 (1- 2 years of age at the time of image capture) and had not reached canopy closure.

The predicted deficient levels therefore probably result from low cover, though there is no

clear association between pine cover fraction and prediction error (Figure 13).

0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Pine cover fraction

Pre

dict

ion

erro

r

Figure 13. Correlation between pine cover fraction and N prediction error

Nevertheless, prediction of nutrient concentrations in stands younger than 3 years appears to

be unreliable, at least in the absence of a calibration data set of young stands less than 3 years

of age. There is no clear association between thinning class (Figure 5) or year of planting

(Figure 6) and compartments predicted to have marginal mean concentration, shown in yellow

in Figure 15. Thus, predicted N concentrations for stands of 5 years or more are considered to

adequately reflect actual concentrations within the accuracy limits of the model.

28

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

N (g/kg)High : 22

Low : 8


Figure 14. Predicted N concentration

29

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Mean N (g/kg)< 10.00

10.01 - 15.00

>15.00


Figure 15. Predicted compartment mean N concentration

30

4.3.2. Phosphorus

P concentration is normally distributed in the T1 age class (Figure 16a and b) and bimodal in

the 5yr and T2 age classes, which may indicate the possibility of recent fertilisation. Median

P concentration is slightly higher in T1 (1.51 g/kg) than in the T2 (1.26 g/kg) or 5yr age

classes (1.29 g/kg; Figure 16c) though the ranges overlap considerably. There is no apparent

spatial bias in concentration level classes across the estate (Figure 16d).

(a)

P.g.kg.

Den

sity

0.00.51.01.52.0

0.51.01.52.02.5

5yr T10.00.51.01.52.0

T2

(c)

P.g

.kg.

1.0

1.5

2.0

2.5

5yr T1 T2

(b)

P.g.kg.

Per

cent

of T

otal

010203040

1.0 1.5 2.0 2.5

5yr T1010203040

T2

(d) T1 only

East (000's of m)

Nor

th (0

00's

ofm

)

5800

5805

5810

5815

492 494 496 498

Figure 16. Observed Phosphorus concentration

The P model calibrated over all age classes is highly significant (P < 0.0001; Figure 17) but

explains a relatively small proportion of the variation in P concentration (Adj r2: 0.28; RMSEP

= 0.279 g/kg). Predicted P concentrations for several plots (1_18 and 1_24) were

significantly lower than their observed value. Removal of these plots did not substantially

improve the r2 (Figure 18) but the predicted range of P values more closely approximated the

observed values. Thus, the outlier removed model was selected for mapping.

31

1.0 1.5 2.0 2.5

1.1

1.2

1.3

1.4

1.5

1.6

1.7

Observed

Pre

dict

ed1_01

1_02

1_031_04

1_05

1_06

1_07

1_08

1_09

1_101_11

1_12

1_13

1_14

1_151_16

1_17

1_181_19

1_20

1_21

1_22

1_23

1_24

1_25

1_26

1_29

1_30

1_351_36

1_37

1_38

1_39

1_40

1_411_42

1_43

1_44

1_45

1_46

1_47

1_48

1_49

1_50

1_51

1_521_53

1_541_55

1_56

1_57

1_58

1_591_60

2_01

2_02

2_03

2_04

2_05

2_06

2_07

2_08

2_09 2_10

2_11

2_12

2_132_14

2_15

2_16

2_17

2_18

2_19

2_20

1_27

1_28

1_31

1_32

1_331_34

5_01

5_02

5_035_04

5_05

5_06

5_07

5_08

5_09

5_10

5_115_12

5_13

5_14

5_15

5_16

5_17

5_18

5_19

5_20

Adj.r2 0.22P <0.0001

Figure 17. Preliminary P model

1.0 1.2 1.4 1.6 1.8 2.0 2.2

1.2

1.3

1.4

1.5

1.6

1.7

Observed

Pre

dict

ed

1_01

1_021_03

1_04

1_05

1_061_07

1_08

1_09

1_10

1_11

1_12

1_13

1_14

1_15

1_16

1_19

1_20

1_21

1_22

1_23

1_25

1_26

1_29

1_30

1_351_36

1_38

1_39

1_41

1_42

1_43

1_44

1_45

1_46

1_47

1_48

1_491_50

1_51

1_52

1_53

1_541_55

1_56

1_57

1_58

1_59 1_60

2_01

2_02

2_03

2_04

2_05

2_06

2_07

2_08

2_092_10

2_112_12

2_13

2_14

2_15

2_16

2_17

2_18

2_19

2_20

1_28

1_32

1_33

5_01

5_02

5_035_045_05

5_07

5_08

5_09

5_10

5_11

5_12

5_13

5_14

5_15

5_16

5_17

5_185_19

5_20

Adj.r2 0.28P <0.0001

Figure 18. Outlier removed P model

32

The outlier removed P model was calibrated on all plots and contained 5 components (Figure

19a). This Figure shows negative r2values for models with fewer than 5 components, which

is a consequence of the method used in R to calculate r2:

r2 = (1 - RSS/TSS)

where RSS is sum of squared difference between the regression line and the data, and TSS is

the sum of squared differences between mean of the data and the data points. Negative r2

values result where TSS < RSS, such as may occur when the mean centred wavelength data

used in this project are compared with predictions of the model in the unscaled original

concentration units.

0 1 2 3 4 5

-0.1

0.0

0.1

0.2


R2

trainCV

(b)wavelength

load

ing

valu

e

X426.8 X925.4 X1517.8 X2203.8

-0.2

0.0

0.2

0.4

(c)plot

Res

idua

l

1_01

1_21

1_47

2_02

2_15

5_01

5_20

-0.4

0.0

0.4

(d)wavelength

Reg

ress

ion

Coe

ffici

ents

X426.8 X925.4 X1517.8 X2203.8

-0.0

8-0

.04

0.00

0.04

Figure 19. Model diagnostics for P

33

Component 1, shown in black in Figure 19b, shows little variation over the spectral range,

while component 2, in red, resembles a vegetation spectrum for wavelengths up to around

1500nm. This may indicate a secondary influence of cover on the model, however the low r2

indicates a poor model fit overall. There is little structure in the residuals (Figure 19c).

Figure 19d indicates that this model is strongly influenced by wavelengths around the ‘red

edge’, where the dominant process shaping the spectrum changes from absorption by

chlorophyll and other pigments to reflectance from leaf structural elements at around 750nm

and water absorption features near 2000nm and 2200nm (Curran et al. 1990).

The map of predicted P concentration (Figure 21) shows several regions of low predicted

concentrations, around 0.5 g/kg in the east, south-west and northern regions of the study area.

Predicted concentration levels are moderately high elsewhere. Predicted compartment-mean

P concentrations are adequate for most compartments (Figure 22) with smaller clusters of

marginal and deficient compartments throughout the study area. As was observed for N,

predicted compartment mean concentration levels are low in young stands, though there is

also little correlation between pine cover fraction and prediction errors for P (Figure 20).

0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.0

0.2

0.4

0.6

Pine cover fraction

Pre

dict

ion

erro

r

Figure 20. Correlation between pine cover fraction and P prediction error

34

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

P (g/kg)High : 3.0

Low : 0.5


Figure 21. Predicted P concentration

35

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Mean P (g/kg)0.52 - 1.00

1.01 - 1.30

1.31 - 1.86


Figure 22. Predicted compartment mean P concentration

36

4.3.3. Potassium

Observed K concentration is approximately normally distributed in each age class (Figure 23a

and b). Median concentration is highest in T1 (7.75g/kg) and lowest in T2 (6.74 g/kg; Figure

23c) with extreme low values in T1 and T2. Figure 23d indicates a possible slight bias

towards lower observed concentrations in the south and south east of the study area amongst

T1 plots.

(a)

K.g.kg.

Den

sity

0.00.10.20.30.4

2 4 6 8 10 1214

5yr T10.00.10.20.30.4

T2

(c)

K.g

.kg.

4

6

8

10

5yr T1 T2

(b)

K.g.kg.

Per

cent

ofT

otal

010203040

4 6 8 10 12

5yr T1010203040

T2

(d) T1 only

East (000's of m)

Nor

th (0

00's

of m

)

5800

5805

5810

5815

492 494 496 498

Figure 23. Observed Potassium concentration

Calibration of the K model across all age classes, as for N and P above, resulted in a very poor

model fit and consequently, prediction of K was calibrated on T1 plots only. Removal of a

single outlier from the Preliminary model, plot 1_12 which had a substantially higher

predicted than observed concentration, increased the r2 from 0.59 to 0.67 (Figure 24). Plot

37

1_12 lies near the centre of what is apparently a reasonably homogeneous compartment in the

north east of the study area and it is not clear why this point is poorly predicted.

4 6 8 10

67

89

1011

Observed

Pre

dict

ed

1_011_02

1_03

1_04

1_05

1_06

1_071_08

1_09

1_10

1_11

1_13

1_14

1_151_16

1_17

1_18

1_19

1_20

1_21

1_22

1_231_24

1_25

1_26

1_29

1_30

1_351_36

1_37

1_381_39

1_40

1_41

1_42

1_43

1_441_451_46

1_47

1_481_49

1_50

1_51

1_52

1_531_54

1_55

1_56

1_571_58

1_59

1_60

Adj.r2 0.67P <0.0001

Figure 24. Predicted versus observed K (T1, outliers removed)

The PLS model for K prediction has 4 components (Figure 25a). Component 1, shown in

black in Figure 25b approximates a foliage spectrum which may indicate a substantial

association with the amount of foliage in the pixels. There is no structure in the residuals

(Figure 25c) indicating a suitable model fit (Adj r2= 0.68; RMSEP = 1.103g/kg). This model

is strongly influenced by wavelengths between about 730nm and 900nm (Figure 25d) which

are associated with foliage structure, and wavelengths between about 1200nm and 1500nm,

which are associated with water absorption(Curran 1989).

38

0 1 2 3 4

0.0

0.2

0.4

0.6


R2

trainCV

(b)wavelength

load

ing

valu

e

X426.8 X925.4 X1517.8 X2203.8

-0.2

-0.1

0.0

0.1

0.2

(c)plot

Res

idua

l

1_01

1_10

1_21

1_37

1_47

1_60

-2-1

01

(d)wavelength

Reg

ress

ion

Coe

ffici

ents

- T1

X426.8 X925.4 X1517.8 X2203.8

-0.1

0.0

0.1

0.2

Figure 25. Model diagnostics for K

Figure 26 shows validation of the model calibrated only on the T1 plots across all plots,

labelled by their age classes. While this model is highly significant (P < 0.00001), it explains

only a small proportion of variation in K concentrations at this scale (Adj r2=0.11). This

model is strongly influenced by eight plots in the 5yr and T1 age classes, which have

predicted concentration levels at or above 10 g/kg but with observed values around 5 g/kg in

some cases (Figure 26). Otherwise, a broad correlation is evident in the remaining data

cluster, and a substantially improved r2 value might be achieved by eliminating these outlier

plots. There is a tendency towards higher predicted concentrations in younger stands, though

the there is little correlation between pine cover fraction and prediction errors (Figure 27).

The map of predicted K concentration levels (Figure 28) shows generally moderate to high

concentrations over most of the estate. Observed K concentrations were marginal in 5% of

plots (Table 3), but this model predicts adequate compartment-mean K concentration in all

compartments (Figure 29).

39

4 6 8 10

68

1012

Observed

Pre

dict

ed

111

1

1

1

11

11

1

1

1

11 1

1

1

11

1

11

1

1

1

1

11

1

11

1

1

1

1

1 11

1

11

1

1

1

11

12

22

2

2

22

22

22

2 2

2 2

2

2

22

2

2

22

5

5

55

5

55

5

55

55

5

5

5

55

5

5

1

1

1

1

1

1

1 11

Adj.r2 0.11P 5e-04

Figure 26. K model translated over all plots

0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Pine cover fraction

Pre

dict

ion

erro

r

Figure 27. Correlation between pine cover fraction and K prediction error

40

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

K (g/kg)High : 15

Low : 2


Figure 28. Predicted K concentration

41

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Mean K (g/kg)

>5


Figure 29. Predicted compartment mean K concentration

42

4.3.4. Iron

Fe concentrations are approximately normally distributed in the T1 age class (Figure 30a and

b) but skewed towards lower concentrations in the 5yr age class. Median Fe concentration

was highest in T1 (65.21 mg/kg) and lowest in the 5yr age class (45.51 mg/kg; Figure 30c).

There may be a slight spatial bias to lower observed concentrations in T1 plots towards the

southern part of the study area (Figure 30d).

(a)

Fe.mg.kg.

Den

sity

0.000.010.020.03

20 40 60 80100

5yr T10.000.010.020.03

T2

(c)

Fe.m

g.kg

.

40

60

80

5yr T1 T2

(b)

Fe.mg.kg.

Per

cent

ofT

otal

0102030

40 60 80 100

5yr T10102030

T2

(d) T1 only

East (000's of m)

Nor

th (0

00's

ofm

)

5800

5805

5810

5815

492 494 496 498

Figure 30. Observed Iron concentration

Two outlier plots (1_28 and 2_15) with substantially lower observed concentrations than

predicted levels were identified in preliminary modelling. Removal of these points increased

the r2 from 0.35 to 0.40 and made the range of predicted concentration levels more similar to

the observed levels. Consequently, the outlier removed model is preferred.

43

Calibration of the Fe model across all age classes results in a moderately good model fit

(Adj r2 = 0.4; RMSEP = 11.28 mg/kg; Figure 31), though there is an evident association

between higher prediction error and lower pine cover fraction (Figure 32).

30 40 50 60 70 80 90

4050

6070

Observed

Pre

dict

ed

1_01

1_02

1_03

1_041_05

1_06

1_07 1_08

1_09

1_10

1_11 1_12

1_13

1_14

1_151_16 1_17

1_18

1_19 1_201_21

1_22

1_23

1_24

1_25

1_26

1_29

1_30

1_351_36

1_37

1_381_39

1_40

1_41

1_42

1_43

1_44

1_45

1_46

1_471_48

1_49

1_50

1_51 1_52

1_531_54

1_55

1_56

1_57

1_58

1_591_60

2_012_02

2_03 2_04

2_052_06

2_07

2_08

2_09

2_10

2_112_122_132_14

2_162_17

2_18

2_19

2_20

1_27

1_31

1_321_33

1_34

5_01 5_025_03

5_04

5_05

5_06

5_07

5_08

5_09

5_10

5_115_12

5_13

5_14

5_155_16

5_17

5_185_19

5_20

Adj.r2 0.4P <0.0001

Figure 31. Predicted versus observed Fe (All plots, outliers removed)

0.3 0.4 0.5 0.6 0.7 0.8 0.9

05

1015

2025

30

Pine cover fraction

Pre

dict

ion

erro

r

Figure 32. Correlation between pine cover fraction and Fe prediction error

44

The PLS model for Fe has 4 components (Figure 33a). Loadings for Component 1 are

relatively small compared to those in other components (Figure 33b) and there is no visible

structure in the residuals (Figure 33c). Wavelengths that strongly influence the Fe model

occur near 730nm, around 1400nm, around 2000nm and at very long wavelengths, which are

associated with water absorption (Curran 1989).

0 1 2 3 4

0.0

0.1

0.2

0.3

0.4


R2

trainCV

(b)wavelength

load

ing

valu

eX426.8 X925.4 X1517.8 X2203.8

-0.3

-0.1

0.1

0.3

(c)plot

Res

idua

l

1_01

1_20

1_46

2_01

2_14

5_02

5_20

-20

-10

010

20

(d)wavelength

Reg

ress

ion

Coe

ffici

ents

X426.8 X925.4 X1517.8 X2203.8

-1.0

-0.5

0.0

0.5

Figure 33. Model descriptors for Fe

The map of predicted Fe concentration (Figure 34) shows generally high predicted

concentration levels with the exception of very young stands for which predicted

concentrations are lower. Despite low predicted concentrations at the pixel scale, predicted

compartment mean concentration levels (Figure 35) are generally adequate, with a few

compartments having marginal concentrations. Compartments identified as marginal are

often, but not exclusively, very recently planted stands (Figure 6).

45

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Fe (mg/kg)High : 100

Low : 20


Figure 34. Predicted Fe concentration

46

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Mean Fe (mg/kg)23.91 - 30.00

30.01 - 84.78


Figure 35. Predicted compartment mean Fe concentration

47

4.3.5. Zinc

Observed Zn concentration levels are approximately normally distributed in the T1 and T2

age classes but include extreme high outliers (Figure 36a and b). The 5yr class exhibits a

truncated range of Zn concentrations including an absence of higher concentration levels.

Despite this, median Zn concentration is higher in the 5yr age class (20.92 mg/kg) than in T1

(18.53 mg/kg) or T2 (18.27 mg/kg) age classes (Figure 36c). There is no apparent spatial bias

in the distribution of observed concentration levels amongst T1 plots throughout the study

area (Figure 36d).

(a)

Zn.mg.kg.

Den

sity

0.000.020.040.060.080.10

0 10 20 30 40

5yr T10.000.020.040.060.080.10

T2

(c)

Zn.m

g.kg

.

10

20

30

40

5yr T1 T2

(b)

Zn.mg.kg.

Per

cent

of T

otal

010203040

10 20 30 40

5yr T1010203040

T2

(d) T1 only

East (000's of m)

Nor

th(0

00's

of m

)

5800

5805

5810

5815

492 494 496 498

Figure 36. Observed Zinc concentration

Three plots were identified as potential outliers in the preliminary model (1_55, 1_58 and

2_12), which had the highest observed Fe concentrations (Figure 37). Removal of these plots

did not improve r2 and the preliminary model was preferred. However, this model provides a

poor prediction of Zn concentration amongst all plots (Adj r2 = 0.13; RMSEP = 6.225 mg/kg),

tending to underestimate higher Zn concentrations and overestimate low concentrations, and

with a clear association between higher prediction errors and low pine cover fraction (Figure

38).

48

5 10 15 20 25 30 35

1416

1820

2224

Observed

Pre

dict

ed1_01

1_02

1_03

1_04

1_05

1_06

1_071_08

1_09

1_10

1_11

1_12

1_13

1_14

1_151_16

1_17

1_18

1_19

1_20

1_21

1_22

1_23

1_24

1_25

1_261_29

1_30

1_35

1_361_37

1_38

1_39

1_40

1_41

1_42

1_43

1_441_45

1_46

1_47

1_48

1_49

1_501_51 1_52

1_53

1_54

1_551_56

1_57 1_58

1_59

1_60

2_01

2_02

2_03

2_04

2_05 2_06

2_07

2_082_09

2_10

2_11

2_12

2_132_14

2_15

2_16

2_17

2_18

2_19

2_201_27

1_28

1_311_32

1_33

1_34

5_01

5_02 5_03

5_045_05

5_06

5_07

5_08

5_09

5_10

5_11

5_12

5_13

5_14

5_15

5_16

5_175_18 5_19

5_20

Adj.r2 0.13P 1e-04

Figure 37. Correlation between Observed and Predicted Zn concentration (All plots)

0.3 0.4 0.5 0.6 0.7 0.8 0.9

02

46

810

12

Pine cover fraction

Pre

dict

ion

erro

r

Figure 38. Correlation between pine cover fraction and Zn prediction error

49

The PLS model for Zn contained 5 components (Figure 39a). The first component, shown in

black in Figure 39b, exhibited little variation. Loadings in the first and second components

(Figure 39b), shown in black and red respectively, indicate a strong influence of the quantity

and/or structure of foliage on the model vegetation. Large scores in the residuals (Figure 39c)

are associated with plots with large observed concentrations, and the apparent structure in the

residuals reflects the generally poor model fit. Influential wavelengths in this model are

around 750nm and water absorption features at 1400nm, 1800nm and 2400nm (Figure 39d).

0 1 2 3 4

-0.1

5-0

.05

0.05


R2

trainCV

(b)wavelength

load

ing

valu

e

X426.8 X925.4 X1517.8 X2203.8-0

.20.

00.

10.

20.

3

(c)plot

Res

idua

l

1_01

1_20

1_46

2_03

2_17

5_03

5_20

-10

-50

510

(d)wavelength

Reg

ress

ion

Coe

ffici

ents

X426.8 X925.4 X1517.8 X2203.8

-0.4

0.0

0.4

0.8

Figure 39. Model diagnostics for Zn

The map of predicted Zn concentration (Figure 40) tends to predict relatively high

concentrations except in younger stands, which may be associated with lower canopy cover.

This model predicts generally adequate compartment-mean concentration levels (Figure 41),

with marginal or deficient predicted concentrations occurring primarily in younger stands

(Figure 6), however this prediction is unreliable overall.

50

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Zn (mg/kg)High : 30

Low :2


Figure 40. Predicted Zn concentration

51

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Mean Zn (mg/kg)<10.00

10.01 - 15.00

>15.01


Figure 41. Predicted compartment mean Zn concentration

52

4.3.6. Copper

Observed Cu concentrations are approximately normally distributed in the T1 and T2 age

classes (Figure 42a and b). Median Cu concentration was highest in T1 (2.65 mg/kg) and

lowest in the 5yr age class (2.34 mg/kg; Figure 39c), with an extreme high value observed in

the T2 age class. There was no apparent spatial bias in the distribution of observed

concentration levels amongst T1 plots (Figure 39d).

(a)

Cu.mg.kg.

Den

sity

0.00.20.40.60.81.0

0 1 2 3 4

5yr T10.00.20.40.60.81.0

T2

(c)

Cu.

mg.

kg.

1.0

1.5

2.0

2.5

3.0

3.5

4.0

5yr T1 T2

(b)

Cu.mg.kg.

Per

cent

of T

otal

010203040

1 2 3 4

5yr T1010203040

T2

(d) T1 only

East (000's of m)

Nor

th (0

00's

of m

)

5800

5805

5810

5815

492 494 496 498

Figure 42. Observed Copper concentration

Calibration of the Cu model over all age classes resulted in a significant but poor model fit

(Adj r2 = 0.09) qnd consequently the model was calibrated on T1 plots only (Adj r2 = 0.44;

RMSEP = 0.368 mg/kg). Plot 1_11 has a much larger observed than predicted value (Figure

43), but removal of this point had only a slight effect on predictive power, increasing Adj r2

values to 0.47, and the preliminary model is preferred.

53

1.5 2.0 2.5 3.0 3.5

2.0

2.2

2.4

2.6

2.8

3.0

Observed

Pre

dict

ed

1_01

1_02

1_031_04

1_051_06

1_071_08

1_09

1_10

1_11

1_121_13

1_14

1_15

1_16

1_17

1_18

1_191_201_21

1_22

1_23

1_24

1_25

1_26

1_29

1_30

1_35

1_36

1_37

1_38

1_39

1_40

1_41

1_42

1_43 1_44

1_45

1_46

1_47

1_481_49

1_50

1_51

1_52

1_53

1_54

1_55

1_56 1_571_58

1_59

1_60

Adj.r2 0.44P <0.0001

Figure 43. Predicted versus observed Cu (T1)

The PLS model for Cu prediction in T1 plots has 4 components (Figure 44a). Component 1

strongly resembles a vegetation reflectance spectrum (Figure 44b) though the correlation

between Cu concentration and Pine cover is small (r = 0.25). There is little structure in the

residuals apart from the high residual score for plot 1_11. This model is strongly influenced

by wavelengths around 750nm, 1480nm and 2000nm, which are associated with foliar

structure and water absorption.

54

0 1 2 3 4

0.0

0.1

0.2

0.3

0.4


R2

trainCV

(b)wavelength

load

ing

valu

e

X426.8 X925.4 X1517.8 X2203.8

-0.2

-0.1

0.0

0.1

(c)plot

Res

idua

l

1_01

1_10

1_20

1_30

1_40

1_50

1_60

-0.5

0.0

0.5

1.0

(d)wavelength

Reg

ress

ion

Coe

ffici

ents

- T1

X426.8 X925.4 X1517.8 X2203.8

-0.0

8-0

.04

0.00

0.04

Figure 44. Model diagnostics for Cu

Figure 45 shows validation of the model calibrated on T1 plots across all plots. This model is

weak and non significant (Adj r2 = 0.02, P=0.084). Visual outliers include nine plots in the

T1 and 5yr age classes, and Figure 46 indicates a evident correlation between pine cover

fraction and prediction error.

The map of predicted Cu concentration (Figure 47) shows generally moderate to high

concentration levels with lower concentrations predicted in younger stands. Predicted

compartment-mean concentrations (Figure 48) are generally marginal with deficient

concentrations predicted for several younger stands. Many compartments with adequate

predicted compartment mean were planted between 1991 and 2002 (Figure 6) and were

unthinned at the time of image capture (Figure 5), thus having relatively young, full crowns.

Overall, this prediction is unreliable, however.

55

1.0 1.5 2.0 2.5 3.0 3.5 4.0

1.0

1.5

2.0

2.5

3.0

Observed

Pre

dict

ed

1

11 1

11

11

1

1

11

1

11

1

1111

1

11

1

1

11

1

1

1

11

1

1

1

1 11

11

11

1

1 1

1

1

1

22 2

2

22

2

2 22

2

2 2

2

22

22

22

2

22

5

5

555

55

5

5

5

55

5

5

5

5

5

55

1

1

1

1

1

1

1

11

Adj.r2 0.02P 0.0839

Figure 45. Translation of Cu model to All Plots

0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.0

0.5

1.0

1.5

Pine cover fraction

Pre

dict

ion

erro

r

Figure 46. Correlation between pine cover fraction and Cu prediction error

56

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Cu (mg/kg)High : 3.82

Low : 0.50


Figure 47. Predicted Cu concentration

57

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Mean Cu (mg/kg)< 2.00

2.01 - 3.00

>3.01


Figure 48. Predicted compartment mean Cu concentration

58

4.3.7. Boron

Observed B levels are approximately normally distributed in T1 (Figure 49a and b) but

strongly skewed towards lower levels in the 5yr age class. Median B concentration is highest

in T2 (25.65 mg/kg; Figure 49c) and lowest in the 5yr age class (21.28 mg/kg). The largest

range of B concentration occurs in T1 (25.09 mg/kg) including 3 extreme high values above

35 mg/kg. There may be a slight spatial bias towards higher observed concentrations in the

easterly mid-latitude region of the study area (Figure 49d).

(a)

B.mg.kg.

Den

sity

0.00

0.05

0.10

0.15

20 30 40

5yr T10.00

0.05

0.10

0.15T2

(c)

B.m

g.kg

.

20

25

30

35

40

5yr T1 T2

(b)

B.mg.kg.

Per

cent

of T

otal

010203040

20 25 30 35 40

5yr T1010203040

T2

(d) T1 only

East (000's of m)

Nor

th (0

00's

of m

)

5800

5805

5810

5815

492 494 496 498

Figure 49. Observed Boron concentration

The PLS model for B calibrated across all plots produced a strong and highly significant

prediction (Adj r2 = 0.56; RMSEP = 3.523 mg/kg; P<0.0001; Figure 50). There is some

clustering of age classes which reflects differences in observed concentration levels and,

though prediction errors tend to be higher in areas of low cover ( ) there are no apparent

significant outliers.

59

20 25 30 35 40

2022

2426

2830

32

Observed

Pre

dict

ed

1_01

1_02

1_03

1_04

1_05

1_06

1_071_08

1_09

1_10

1_11

1_12

1_131_14 1_151_16

1_17

1_18

1_19

1_201_21

1_22

1_23

1_24

1_25

1_26

1_29

1_30

1_35

1_36

1_37

1_38

1_39

1_40

1_41 1_42 1_43

1_44

1_45

1_46

1_47

1_481_49

1_50

1_51

1_52

1_53

1_54

1_55

1_56

1_57

1_58

1_59

1_60

2_01

2_02

2_03

2_04

2_05

2_06

2_07

2_08

2_09

2_10

2_112_122_13

2_14

2_15

2_16

2_172_18

2_19

2_20

1_27

1_28

1_31

1_321_33

1_34

5_01

5_02

5_03

5_04

5_055_06

5_075_08

5_09

5_10 5_11

5_12

5_135_14

5_15

5_16

5_17

5_18

5_19 5_20

Adj.r2 0.56P <0.0001

Figure 50. Predicted versus observed B (All plots)

0.3 0.4 0.5 0.6 0.7 0.8 0.9

02

46

8

Pine cover fraction

Pre

dict

ion

erro

r

Figure 51. Correlation between pine cover fraction and B prediction error

60

The model contains 7 components (Figure 52a). The first component, shown in black in

Figure 52b, resembles an absorption spectrum and there is no apparent structure in the

residuals (Figure 52c). Wavelengths that strongly influence this model occur around 450 nm

(visible blue), 730nm, 1450nm, 2000nm and 2400nm.

0 1 2 3 4 5 6 7

0.0

0.1

0.2

0.3

0.4

0.5


R2

trainCV

(b)wavelength

load

ing

valu

e

X426.8 X925.4 X1517.8 X2203.8

-0.2

0.0

0.2

0.4

(c)plot

Res

idua

l

1_01

1_20

1_46

2_01

2_14

1_34

5_20

-6-4

-20

24

68

(d)wavelength

Reg

ress

ion

Coe

ffici

ents

X426.8 X925.4 X1517.8 X2203.8

-0.5

0.0

0.5

1.0

Figure 52. Model diagnostics for B

The map of predicted B concentration (Figure 53) shows generally moderate to high

concentrations around 30 mg/kg. Lower (but adequate) predicted concentrations around 10-

20 mg/kg are widely distributed throughout the study area and these are often associated with

stands planted after 2003 (Figure 6). Predicted compartment-mean concentration levels

(Figure 54) are adequate for all compartments which accords with observed concentration

levels (Table 3).

61

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

B (mg/kg)High : 40

Low : 10


Figure 53. Predicted B concentration

62

490000

490000

495000

495000

500000

500000

5795

000

5795

000

5800

000

5800

000

5805

000

5805

000

5810

000

5810

000

5815

000

5815

000

5820

000

5820

000

Mean B (mg/kg)

>10


Figure 54. Predicted compartment mean B concentration

63

5. Discussion and Conclusion

5.1. Nutrition models

In this study, attempts have been made to create models that are as inclusive as possible, both

in terms of including a range of age classes in each model, and in retaining individual plots

that may be eliminated as outliers in other similar studies. This study has demonstrated that

PLS can be used to create useful models under those circumstances (Table 8).

Table 8. Summary information for nutrient models

Nutrient Components N Subset Adj. r2 RMSEP

N 5 100 All 0.41 1.716

P 5 98 All 0.28 0.279

K 4 53 T1 0.68 1.103

Fe 4 98 All 0.41 11.28

Zn 4 100 All 0.14 6.225

Cu 4 54 T1 0.45 0.368

B 7 100 All 0.56 3.523

The calibration of models with r2>0.4 for N, Fe and B across all age classes indicates that

differences in canopy structure and/or concentration levels for those nutrients between age

classes are not significant, or can be adequately accounted for within the modelling

framework described in this report. It is not clear why Zinc, the least accurately modelled

nutrient in this study, is so poorly predicted using these methods, especially given the strong

and significant correlation between Zinc and Iron (Figure 7) which was well predicted (Adj.

r2 = 0.41). Overall, this study presents a reasonably good agreement between actual and

predicted values for N, P, K, Fe, Cu and B at Rennick, and thus demonstrates the potential to

use this technology as a diagnostic tool for identification of radiata pine plantations with

deficient, marginal or adequate status with respect to these nutrients.

In terms of the status of foliar nutrition in the Rennick estate, this study indicates generally

adequate levels of P, K, Fe, Zn and B and widespread marginal concentrations of N and Cu.

These maps provide considerable detail that may support decision making regarding fertiliser

assessment and applications that can be interrogated at a range of scales. For example, the

field data and predictive maps indicate widespread marginal concentration of N, but an estate

wide application of N may not be practical or affordable. One of the advantages of remote

sensing is that the images show large areas in fine detail, including variations in N

concentration at the sub-compartment level. This level of detail may enable individual stands

64

to be identified for treatment if necessary, though we expect that the smallest area over which

differences in nutrition can be accurately discerned from Hyperion data is about 1ha.

Ultimately, silvicultural activities must be planned in the context of many factors affecting

estate viability, and with the assistance of information such as is provided in this report.

5.2. Model translation between age classes

The concentration of N, Fe and B were accurately predicted across the T1, T2 and 5yr age

classes. Poor predictions of K and Cu resulted from calibrating the models across all plots,

but removal of apparent outliers from those models may increase r2 values and provide a

better prediction amongst remaining plots. The most accurate model calibrated across all age

classes was for Boron (Table 8). A number of previous studies have calibrated accurate

models for B prediction (Coops 2002; Sims et al. 2006a) despite an absence of specific

absorption features from this inorganic molecule . There is a strong and significant negative

correlation between B concentration and pine cover (r = 0.53; Figure 7) and one possibility is

that predictions of B may be indirect; i.e. that the B model is in fact based on variations in

plant cover, which are themselves correlated with nutrient concentration. Boron deficiencies

can cause changes in plant structure and form, changes in tree colour, the death of leading

shoots and excessive growth of lateral branches resulting in a bushy form and stem

deformation (Turner and Lambert 1986). Thus, B deficiency may alter the appearance or

visibility of crowns in image pixels, which may influence the correlations observed in this

study. However, none of the plots sampled in this study were deficient in B, nor were any

deficiencies predicted. One possibility is that some other covariate of B that is not accounted

for in this study is limiting P.radiata growth.

In general, predicted concentration levels in younger stands, especially those planted after

2003 (less than 5 years old at the time of image capture) were low for all nutrients. Foliage

samples were not collected from stands younger than 5 years old and thus it is not known

whether nutrient concentration levels in these compartments are truly low or deficient.

Deficiencies in young trees are unlikely, however, because fertiliser is usually applied at the

time of planting.

The most likely reason for prediction errors in young stands relates to differences in canopy

architecture. Radiata pine stands less than 5 years old do not usually exhibit canopy closure,

which can increase the contribution of background material such as litter and soils to pixel

values relative to pixels in areas where the canopy is closed. This report shows that, for some

nutrients, there is a clear association between low canopy cover and higher prediction errors

amongst plots. This relationship was strongest amongst micronutrients (Fe, Cu, B and Zn)

65

and weaker for the macronutrients (N, P and K). This probably occurs because the spectral

signal for micronutrients, which are present in very low concentrations, becomes swamped by

the influence of other components of the pixels, including canopy cover. The relative

contribution of canopy to chemical signals has not been measured in this study, and further

work should attempt to quantify the biochemical contribution more specifically, such as

through by investigating model residuals in more detail (Serrano et al. 2002).

Despite the correlation between low cover and high prediction errors, there is no

correspondence between overall model accuracy and the strength of the error/cover

relationship. Indeed, the proportion of compartments with predicted marginal and deficient

compartment-mean concentration levels accords well with the observed proportion of plots in

each critical level for all nutrients, as shown in Table 3. In the context of the data presented in

this report, however, we conclude that most models translate poorly to stands younger than 5

years of age, but that suitable models encompassing a range of age classes and silvicultural

stages can be calibrated for several nutrients that are important for tree growth and form in

southern Australia.

5.3. Implications for future monitoring

This project has demonstrated a number of limitations regarding monitoring foliar nutrition

from hyperspectral satellite image data. Presently Hyperion is the sole source of

commercially available moderate resolution hyperspectral image data from space that covers

the full spectral range from 400nm to 2500nm. Hyperion images are low cost with each scene

in this study costing approximately $2500, which equates to approximately $0.09 per ha at the

nominal scene size of 7km by 42km. In fact, the Hyperion images supplied for this project

were approximately 110km in length, which reduces costs to approximately $0.03 per ha. As

mentioned above, however, Hyperion image data contains a number of artefacts, and

extensive pre-processing is required to prepare the images for analysis. In addition, the

narrow scene width of Hyperion images means that several adjacent scenes may be required

to map an entire estate. Considerable extra processing time and expertise may be required to

calibrate adjacent scenes for this purpose, which may make it impractical for many forestry

organisations to perform in-house. An experienced image processing specialist may be able

to prepare a single Hyperion image for analysis in less than one day but these times may be

considerably longer for new users of Hyperion data, or where multiple adjacent images are

required.

Alternatively, hyperspectral image data is commercially available in Australia from the

HyMap airborne sensor (http://www.hyvista.com/technology/sensors). HyMap can provide

66

very high quality image data at sub-metre spatial resolution and fine spectral resolution over

large areas but is relatively very expensive compared to satellite data. A number of other

research-oriented hyperspectral instruments, such as via Airborne Research Australia

(http://www.AirborneResearch.org.au) are currently being evaluated and can be deployed for

commercial activities if necessary. However, most existing alternative providers of airborne

image data appear to have capitalised on the high spatial resolution possible with these

sensors rather than increase the spectral range of their data, and tend to include 4 narrow

bandwidth spectral bands, though 8 band systems are currently under development (Andrew

Malcolm, LR Eye, pers. comm.).

This study also demonstrated an important aspect of monitoring foliar nutrition from

hyperspectral image data that may influence the uptake of this technology within the forestry

industry. One of the ways in which a monitoring system of this kind has been envisaged to

function was for a single model to be calibrated, describing spectral bands and coefficients

required to back-calculate an image into a nutrient concentration map, that could be applied to

new images captured each year. This would considerably simplify the mapping process and

provide the maximum savings in cost and time for industry partners. This study suggests,

however, that models transferred between images captured on different dates in this manner

predict poorly (Section 4.2). The spectral characteristics of each image are influenced by

many aspects of the remote sensing environment including illumination, atmospheric and

sensor calibration conditions, and changes in the land surface itself. Each of these may alter

the absolute and/or relative brightness of pixel values, or the relationship between pixel

brightness and nutrient concentration itself.

It is possible that nutrient prediction models must be empirically calibrated for images

captured over the same location but in a different season or altered illumination conditions.

However, Martin et al., (2008) demonstrated that foliar nitrogen can be predicted from

hyperspectral images (including Hyperion) across a wide range of forest ecosystem types,

including 8 sites in North America, Central America and Australia. Their method included

calibrating new predictive models for subsets of the sites, rather than translating models from

one subset to another. At this time, however, and in the context of the results in this project, it

is more appropriate to describe a method by which similar studies can be conducted to provide

results consistent with those described in this report rather than provide a model to be used for

predicting nitrogen from future images. That method would include the collection of field

data for model calibration, though a sensitivity analysis could indicate the minimum number

of samples required for appropriate model calibration.

67

A number of operational limitations of Hyperion also became evident during the course of

this project. A delay of approximately nine months occurred between tasking the acquisition

of these images and image capture for two main reasons. Initially, persistent cloud cover

prevented the acquisition of a suitable image. Additional sources of Hyperspectral satellite

imagery may have enabled a suitable image to be acquired earlier if observations were

available between Hyperion’s 16 day overpass cycle. Further delays were caused by a

priority Hyperion tasking in the northern United States on the same Hyperion orbit as was

required for this project. Hyperion can acquire only one image per orbit and this delayed

image acquisition for this project for an unknown period of time.

The availability and quality of hyperspectral satellite image data will increase in the near

future if planned hyperspectral mission progress through to successful launch. Several

commercial and research-oriented hyperspectral instruments are planned for launch over the

next few years (Buckingham and Staenz 2008). These include NASA’s HysPIRI which is

currently in the development phase (http://hyspiri.jpl.nasa.gov/), Italy’s PRISMA mission due

for launch in 2010 (http://www.asi.it/en/activity/earth_observation/prisma_) and the joint

German mission EnMap (Environmental Monitoring and Analysis Program), which is

planned for launch in 2012 (http://www.enmap.org/). EnMap will have spectral

characteristics similar to Hyperion, but with a larger swath width (30km), a 3-day revisit time

and an substantially improved signal to noise ratio. High quality hyperspectral satellite image

data will likely be available in the near future, and for the foreseeable future. Studies such as

this may assist to improve the preparedness of forestry organisations to make use of this data

in their research and operational planning.

68

6. Acknowledgements

This work was conducted under an FWPA grant (PNC074-0708). Many thanks to Paul

O’Donnell and Paul G. for the collection of field samples, and to Barrie May for assistance

with interpreting the field data. Thanks also to Jan Verbesselt (CRC Forestry), Andrew

Robinson (Uni Melb), Josh Bowden (CSIRO) Nic Goodwin (SLATS) and Glenn Newnham

(CSIRO) for statistical advice and assistance. Many thanks also to Darius Culvenor for wise

counsel, and for facilitating my time to work on this project. Many thanks also to Jugo Ilic

and others at FWPA for their patience and assistance during the completion of this work.

69

7. References

Boardman, R., Cromer, R.N., Lambert, M.J., & Webb, M.J. (1997). Forest Plantations. In D.J. Reuter & J.B. Robinson (Eds.), Plant Analysis - An Interpretation Manual (pp. 505-566). Collingwood, Australia: CSIRO Publishing

Buckingham, R., & Staenz, K. (2008). Review of current and planned civilian spacehyperspectral sensors for EO. Canadian Journal of Remote Sensing, 187-197

Carlyle, J.C., Turvey, N.D., Hopmans, P., & Downes, G.M. (1989). Stem deformation in Pinus radiata associated with previous land use. Canadian Journal of Forest Research, 19,96-105

Christensen, L.K., Bennedsen, B.S., Jørgensen, R.N., & Nielsen, H. (2004). Modelling Nitrogen and Phosphorus content at early growth stages in spring Barley using hyperspectral line scanning. Biosystems Engineering, 88, 19-24

Coops, N. (2002). Processing and analysis of EO-1 Hyperion imagery to assess canopy nutrient status in Pinus radiata plantations. CSIRO Forestry and Forest Products. Clayton.

Coops, N.C., Smith, M., Martin, M.E., & Ollinger, S.V. (2003). Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data. IEEE Transactions on geoscience and remote sensing, 41, 1338-1346

Curran, P.J. (1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30,271-278

Curran, P.J., Dungan, J.L., & Gholz, H.L. (1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology, 7, 33-48

Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20, 2741-2759

Datt, B., McVicar, T., Van Niel, T.G., Jupp, D.L.B.J., & Pearlman, J. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEETransactions on geoscience and remote sensing, 41, 1246-1259

Debba, P., Carranza, E.J.M., van der Meer, F.D., & Stein, A. (2006). Abundance estimationof spectrally similar minerals by using derivative spectra in simulated annealing. IEEETransactions on geoscience and remote sensing, 44, 3649-3658

Gamon, J.A., Serrano, L., & Surfus, J.S. (1997). The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 112, 492-501

Garthwaite, P.H. (1994). An Interpretation of Partial Least-Squares. Journal of the American Statistical Association, 89, 122-127

Goutis, C., & Fearn, T. (1996). Partial least squares regression on smooth factors. Journal of the American Statistical Association, 91, 627-632

Grossman, Y.L., Ustin, S.L., Jacquemoud, S., Sanderson, E.W., Schmuck, G., & Verdebout, J. (1996). Critique of Stepwise Multiple Linear Regression for the Extraction of Leaf Biochemistry Information from Leaf Reflectance Data. Remote Sensing of Environment, 56,182-193

Hansen, P.M., & Schjoerring, J.K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86, 542-553

70

Hopmans, P., Kitching, M., & Croatto, G. (1995). Stem deformity in Pinus radiata plantations in south-eastern Australia .2. Effects of availability of soil nitrogen and response to fertiliserand lime. Plant Soil, 175, 31-44

Huang, Z., Turner, B.J., Dury, S.J., Wallis, I.R., & Foley, W.J. (2004). Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. RemoteSensing of Environment, 93, 18-29

Jupp, D.L.B.J., & Datt, B. (Eds.) (2004). Evaluation of the EO-1 Hyperion hyperspectral instrument and its applications at Australian validation sites 2001-2003. Canberra, ACT:CSIRO Earth Observation Centre

Kruse, F.A., Boardman, J.W., & Huntington, J.F. (2002). Comparison of EO-1 Hyperion and airborne hyperspectral remote sensing data for geologic applications. In, AerospaceConference Proceedings, 2002. IEEE (pp. 3-1501-1503-1513 vol.1503)

Martens, H.A., & Dardenne, P. (1998). Validation and verification of regression in small data sets. Chemometrics and Intelligent Laboratory Systems, 44, 99-121

Martin, M.E., Plourde, L.C., Ollinger, S.V., Smith, M.L., & McNeil, B.E. (2008). A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sensing of Environment, 112, 3511-3519

McGrath, J.F., & Robson, A.D. (1984). The distribution of zinc and the diagnosis of zinc deficiency in seedlings of Pinus radiata, D. Don. Aust For Res, 14, 175-186

McNeil, B.E., Beurs, K.M.d., Eshleman, K.N., Foster, J.R., & Townsend, P.A. (2007). Maintenance of ecosystem nitrogen limitation by ephemeral forest disturbance: An assessment using MODIS, Hyperion, and Landsat ETM+. Geophysical Research Letters, 34 Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., & Rakitin, V.Y. (1999). Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. PhysiolgiaPlantarum, 106, 135-141

Quinn, G.P., & Keough, M.J. (2004). Experimental design and data analysis for biologists.Cambridge: Cambridge University Press

Raupach, M. (1967). Soil and fertiliser requirements for forests of Pinus radiata. In A.G. Norman (Ed.), Advances in Agronomy (pp. 307-353). New York: Academic Press

Raupach, M., Boardman, R., & Clarke, A.R.P. (1969). Growth rates of Pinus radiata D. Don, in relation to foliar levels of nitrogen and phosphorus for plantations in the south-east of South Australia. Soil Publication No 26, CSIRO, Australia. pp 28. Reeves, J.B. (2009). Does the spectral format matter in diffuse reflection spectroscopy?Applied Spectrsocopy, 63, 669-677

Research Systems Inc. (2005). ENVI: The Environment for Visualising Images. In. Boulder, Colorado: Research Systems, Inc.

Serrano, L., Peñuelas, J., & Ustin, S.L. (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing of Environment, 81, 355-364

Sims, D.A., & Gamon, J.A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. RemoteSensing of Environment, 81, 337-354

Sims, N.C., Culvenor, D.S., Newnham, G., & May, B. (2006a). Assessment of exotic pine foliar chemistry from hyperspectral satellite data. Client Report No. 1735. 105 pages. Ensis. Clayton, Melbourne.

71

Sims, N.C., Mendham, D.S., White, D.S., Culvenor, D.S., Kinal, J., & McGrath, J. (2006b). The potential of hyperspectral remote sensing for assessing foliar nitrogen concentration, leaf area index and water stress in blue gum plantations. Client Report No. 1650. Ensis. Clayton, Victoria.

Tabachnick, B.G., & Fidell, L.S. (1996). Using Multivariate Statistics. New York: HarperCollins

Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoringvegetation. Remote Sensing of Environment, 8, pp. 127-150

Turner, J., & Lambert, M.J. (1986). Nutrition and nutritional relationships of Pinus radiata.Annual Review of Ecololgy and Systematics, 17, 325-350

Turner, J., Lambert, M.J., Hopmans, P., & McGrath, J. (2001). Site variation in Pinus radiata plantations and implications for site specific management. New Forests, 21, 249-282

Ungar, S. (2001). Overview of EO-1: The first 120 days. In, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (CD Rom). Sydney

Van De Geer, S.A., & Van Houwelingen, H.C. (2004). High-dimensional data: p >> n in mathematical statistics and bio-medical applications. Bernoulli, 10, 939-943

Will, G.M. (1985). Nutrient Deficiencies and Fertiliser Use in New Zealand Exotic Forests. New Zealand Forest Research Institute Bulletin, No.97

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Appendix 1. Example field sampling data sheet

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Appendix 2. Names and descriptions of spectral bands used in BSR modelling.Parameter Description437.0 Reflectance467.5 Reflectance528.6 Reflectance538.7 Reflectance579.5 Reflectance711.7 Reflectance732.1 Reflectance742.3 Reflectance813.5 Reflectance932.6 Reflectance973 Reflectance1043.6 Reflectance1063.8 Reflectance1094.1 Reflectance1144.5 Reflectance1447.1 Reflectance1467.3 Reflectance1487.5 Reflectance1507.7 Reflectance1588.4 Reflectance1689.3 Reflectance2022.3 Reflectance2062.6 Reflectance2183.6 Reflectance2213.9 Reflectance2264.3 Reflectance2304.7 Reflectance2355.2 ReflectanceNDVI(803_681) Normalised Difference Vegetation Index (803nm - 681nm)/ (803nm + 681nm)NDVI(772_712) Normalised Difference Vegetation Index (772nm - 712nm)/ (772nm + 712nm)

RE_NDVIRed Edge Normalized Difference Vegetation Index ((750nm - 705nm)/ (750nm + 705nm) ) (Sims and Gamon 2002)

mNDVI_705Modified Red Edge Normalized Difference Vegetation Index (750nm – 705nm)/(750nm + 705nm -2*445nm); (Datt 1999)

NDNINormalised Difference Nitrogen Index; [log (1/1510nm)-log (1/1680nm)]/[log (1/1510nm)+log (1/1680nm)] (Serrano et al. 2002)

PSRIPlant Senescence Reflectance Index – (680nm -500nm)/750nm; (Merzlyak et al.1999)

PRIPhotochemical Reflectance Index; (531nm - 570nm)/ (531nm + 570nm); (Gamon et al. 1997)

lsu_pine Linear spectral unmixing output. The proportion of pine in each pixellsu_bare Linear spectral unmixing output. The proportion of bare ground in each pixel lsu_shade Linear spectral unmixing output. The proportion of shade in each pixel

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