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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
www.fwpa.com.auFWPA Level 4, 10-16 Queen Street,
Melbourne VIC 3000, AustraliaT +61 (0)3 9927 3200 F +61 (0)3 9927 3288
E [email protected] W www.fwpa.com.au
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
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.
iii
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
!
!
Adelaide
Melbourne
0 100 20050 Kilometers
¯Victoria
SouthAustralia
New SouthWales
Mt GambierRennick
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.
6
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427
620
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1007
1195
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1578
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2345
2537
Wavelength (nm)
Ref
lect
ance
(%)
RawPre-Proc.Lab
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
9
490000
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Thinning Class
Unthinned
T1
T2
T3
T4
MGA94, GDA94UTM Zone 54
Figure 5. Thinning classes throughout the study area.
10
490000
490000
495000
495000
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505000
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Planting year
1948-1959
1960-1969
1970-1979
1980-1989
1990-1999
2000-2002
2003
2004
2005
2006
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
(a)number of components
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
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
(a)number of components
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
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
(a)number of components
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
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
(a)number of components
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
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
(a)number of components
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
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495000
500000
500000
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5800
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5815
000
5815
000
5820
000
5820
000
Cu (mg/kg)High : 3.82
Low : 0.50
MGA94, GDA94UTM Zone 54
Figure 47. Predicted Cu concentration
57
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495000
500000
500000
5795
000
5795
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5800
000
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000
5805
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5815
000
5815
000
5820
000
5820
000
Mean Cu (mg/kg)< 2.00
2.01 - 3.00
>3.01
MGA94, GDA94UTM Zone 54
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
(a)number of components
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
MGA94, GDA94UTM Zone 54
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
MGA94, GDA94UTM Zone 54
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
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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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