Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances

$Page 1: Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances$
www.elsevier.com/locate/jag

International Journal of Applied Earth Observation

and Geoinformation 7 (2005) 11–28

Assessing plantation canopy condition from airborne imagery

using spectral mixture analysis and fractional abundances

Nicholas Goodwin a,*, Nicholas C. Coops a,1, Christine Stone b

a CSIRO Forestry and Forest Products, Private Bag 10, Clayton South, 3169 Vic., Australiab Research and Development Division, State Forests of NSW, P.O. Box 100, Beecroft, 2119 NSW, Australia

Received 1 March 2004; accepted 21 October 2004

Abstract

Pine plantations in Australia are subject to a range of abiotic and biotic damaging agents that affect tree health and

productivity. In order to optimise management decisions, plantation managers require regular intelligence relating to the status

and trends in the health and condition of trees within individual compartments. Remote sensing technology offers an alternative

to traditional ground-based assessment of these plantations. Automated estimation of foliar crown health, especially in degraded

crowns, can be difficult due to mixed pixels when there is low or fragmented vegetation cover. In this study we apply a linear

spectral unmixing approach to high spatial resolution (50 cm) multispectral imagery to quantify the fractional abundances of the

key image endmembers: sunlit canopy, shadow, and soil. A number of Pinus radiata tree crown attributes were modelled using

multiple linear regression and endmember fraction images. We found high levels of significance (r2 = 0.80) for the overall crown

colour and colour of the crown leader (r2 = 0.79) in tree crowns affected by the fungal pathogen Sphaeropsis sapinea, which

produces both needle necrosis and chlorosis. Results for stands associated with defoliation and chlorosis through infestation by

the aphid Essigella californica were lower with an r2 = 0.33 for crown transparency and r2 = 0.31 for proportion of crown

affected. Similar analysis of data from a nitrogen deficient site produced an outcome somewhat in between the other two

damaging agents. Overall the sunlit canopy image fraction has been the most important variable used in the modelling of forest

condition for all damaging agents.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Forest health surveillance; Linear unmixing; Image fractions; Digital camera; Pinus radiata

* Corresponding author. Tel.: +61 3 9545 2265;

fax: +61 3 9545 8239.

E-mail addresses: [email protected] (N. Goodwin),

[email protected], [email protected] (N.C. Coops).1 Present address: Department of Forest Resource Management,

2424 Main Mall, University of British Columbia, Vancouver,

Canada. Tel.: +61 3 9545 2234; fax: +61 3 9545 8239.

0303-2434/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.jag.2004.10.003

1. Introduction

A key management task in plantation forestry is the

assessment and monitoring of canopy health and

condition within individual compartments. Australian

softwood Pinus radiata (D. Don) plantations contain a

number of abiotic and biotic damaging agents that

directly impact on tree growth and survival (Will,

.

N. Goodwin et al. / International Journal of Applied Earth Observation and Geoinformation 7 (2005) 11–2812

1985; Bollman et al., 1986; Lewis and Ferguson,

1993). Three critical damaging agents in New South

Wales (NSW), Australia include an aphid Essigella

californica, low soil nitrogen (N) availability and a

fungal pathogen Sphaeropsis sapinea. Currently, the

task of detecting and quantifying the effect of these

damaging agents is challenging due to the geogra-

phical extent of the plantation resource and high

labour costs associated with maintaining forest health

surveillance and monitoring.

Improvements in remote sensing technologies,

particularly in the spatial and spectral resolution of

optical sensors, however, has made the prospect of

using digital remotely sensed imagery to detect and

classify the health status of native forests and

plantations a realistic and attractive option (Franklin,

2000). These approaches commonly utilise forest

crown and canopy indicators based on detection of leaf

pigments (Datt, 1998; Zarco-Tejada et al., 2002), and

biochemicals (Smith et al., 2003), foliage biomass

(Spanner et al., 1990a, 1990b; Coops et al., 1998) and

structure (Lefsky et al., 2002) and relate them to

changes in leaf spectral reflectance; in particular,

variation in red reflectance due to reduced chlorophyll

absorption, decreases in near infrared (NIR) reflec-

tance from reduced cellular integrity and shifts in the

red edge between these two spectral regions (Carter,

1994; Merzlyak et al., 1999; Levesque and King,

1999). However, some of these spectral vegetation

indices do not behave linearly and saturate at low or

high vegetation covers depending on the index applied

(Turner et al., 1999; Levesque and King, 1999). In

addition to using spectral information of leaf

condition, structural change also occurs at an

individual crown or forest canopy scale. Using high

spatial resolution data with image pixels smaller than

the dimensions of individual tree crowns, the

application of variance measures and spatial statistics

can provide information on the physical structure of

individual trees. For example, the spatial variation of

image data (i.e. number of shades of grey levels and

range of brightness values represented in the image)

can be used to determine the level of shadow in a

patchy canopy compared to a ‘bright’ full and dense

canopy (e.g. Gougeon et al., 1999; Levesque and King,

1999; Olthof and King, 2000). In addition high spatial

resolution imagery allows the delineation of indivi-

dual trees which, in combination with an automatic

delineation algorithm, may offer a mechanism for

broad scale assessment of tree crown attributes. For

example, the tree identification and delineation

algorithm (TIDA) (Culvenor, 2002).

The use of fraction images of a range of key cover

types, derived from spectral mixture analysis, offers

an alternative to applying a variety of spectral indices

and correlations with measured leaf and crown-based

attributes. Traditionally, spectral vegetation indices

have been used to infer biophysical vegetation

properties. The appeal of utilizing simple or normal-

ized ratios of spectral channels is its simplicity and its

relationship—either empirically or theoretically—to

biophysical variables. Additionally, an index can be

easily applied to different scenes from sensors on

different satellites through careful processing (Asner

and Warner, 2003). However, spectral indices are

based on values derived from the entire pixel field of

view and therefore do not account explicitly for non-

vegetated components at the sub-pixel scale (Peddle

et al., 2001; Adams et al., 1993) including shadow,

soil, and understory vegetation. This is especially the

case in low stem density or thin open canopies where

the background surface dominates the signal. Another

potential limitation with the use of spectral indices is

that they are often calculated from a small number of

spectral bands, usually two, and thus do not utilize

new and potentially important information in other

channels (Peddle et al., 2001). Consequently, linear

mixture modeling is proving to be a useful approach in

forest health assessment by recognizing the spatially

heterogeneous mixtures of vegetation, soil, shadow

and others in forest canopies rather than a single cover

type. In contrast to vegetation indices, fractional cover

estimates describe a physical property of the land-

scape and lend themselves to straightforward inter-

pretation based on established ecological knowledge

(e.g. Hall et al., 1995; Asner and Warner, 2003).

Linear spectral mixture analysis divides each pixel

into its constituent materials or components using

endmembers which represent the spectral character-

istics of key cover types (Adams et al., 1986; Garcia-

Haro et al., 1999; Smith et al., 1990, 1994).

Endmembers are spectral features recognizable in

an image and constitute abstractions of real objects

that can be regarded as having uniform spectral

properties (Strahler et al., 1986). Tompkins et al.

(1997) list the strengths of a spectral unmixing

N. Goodwin et al. / International Journal of Applied Earth Observation and Geoinformation 7 (2005) 11–28 13

approach as (i) the fact it is a physically based model

that transforms radiance values to physical variables,

which are linked to the subpixel abundances of

endmembers within each pixel; (ii) it provides a means

to detect and represent components that occur entirely

at a subpixel level, such as sparse vegetation and,

finally, it provides quantitative results that can in turn

be incorporated into models of the processes govern-

ing the distribution of materials within the image

scene.

In the most general approach to spectral mixture

analysis, a set of endmembers are selected from an

image dataset that best accounts for the n-dimensional

spectral variance within a constrained, least-squares

mixture model (Adams et al., 1993). Ideally, these

image endmembers can be compared to ground-based

reference spectra for calibration and interpretation.

The abundance of the endmembers within the image

(represented by ‘‘fraction images’’) can be used to

investigate physical processes that are related to

surface abundances. For example, the proportion of

shadow or non-photosynthetic vegetation (NPV)

would be expected to be higher in trees affected by

defoliation compared to healthy (denser) tree crowns.

These fractions therefore would be biophysically

meaningful and more easily interpreted than purely

statistical analytical methods (such as PCA) (Tomp-

kins et al., 1997). Recent applications of spectral

mixture analysis have indicated the technique has

application using both hyperspectral and multispectral

datasets (Atkinson et al., 1997; Schetselaar and Rencz,

1997; Van Der Meer and De Jong, 2000).

The technique has been used in a range of

biophysical studies, for example, to map the fractional

abundances of photosynthetic vegetation (Roberts

et al., 1993, 1998; Drake et al., 1999; Elmore et al.,

2000; Lobell et al., 2002; Theseira et al., 2002),

classify biophysical structural information (Peddle

et al., 1999) as well as numerous soil and geological

applications (Drake et al., 1999; Asner and Heideb-

recht, 2002). There has been limited application of the

technique for the assessment of forest condition

although the technique has been applied to mapping

acid mining tailings (Levesque and King, 2003), and

spider mite (Tetranychus turkestani) damage in cotton

plants (Fitzgerald et al., 2002).

In this paper, we develop a series of robust

relationships between proportions of key image

fractions, derived from high spatial and spectral

resolution imagery, with a range of individual crown

condition attributes. In particular, we explore which

endmembers can be identified within four-channel

spectral imagery and assess the ability to unmix

imagery using the identified endmembers. We then

assess which proportions of image fractions are

correlated with foliar crown-based attributes of forest

health and develop a series of models which estimate

the health and condition of individual crowns. The

work is undertaken within P. radiata plantations in

New South Wales, Australia which are subject to a

range of damaging agents, with the aim to advance the

development of a generic, operational crown-based

index suitable for use in P. radiata stands throughout

NSW and elsewhere.

2. Methods

2.1. Description of study site and damaging agents

The focus site for this work is Carabost State Forest

located in Southern NSW (35.65S, 147.80E, 500 m

elevation above sea level). The area has an annual

rainfall below 700 mm per year. However, the vast

majority falls in winter, with hot and dry summers.

Generally, the area is considered ‘‘marginal’’ in terms

of P. radiata growth and as a result, the plantation is

prone to the adverse influence of damaging agents, in

particular an aphid Essigella californica, low soil

nitrogen (N) availability, and a fungal pathogen

Sphaeropsis sapinea. Personnel from the State Forest

of New South Wales (SFNSW) Forest Health Survey

Unit (FHSU) provided local knowledge as to areas

within the forest estate which had historical evidence

of each agent.

TheaphidE.californica,firstobservedinAustralia in

1998, attacks older P. radiata trees in the mid-upper

crown, progressing upwards to the terminal shoot and

eventually downwards to the lower crown. It is inclined

to progressively advance from mid-whorl out to shoot

tips, causing needles to become chlorotic and abscise

prematurely (May and Carlyle, 2003). The final result is

very thin crowns, especially from mid to upper crown

and dead tops.Younger outerneedlesmay be retainedon

the outer crown as green tufts. Nitrogen deficiency, in P.

radiata, results in severegrowthreductions,with foliage


of N deficient trees typically a uniform pale green,

turning to yellow under severe conditions, with new

needles and shoots short and stunted. The fungal

pathogen, S. sapinea, is among the most common and

widely distributed fungal pathogens of conifers (Sta-

nosz, 1997) and often affects the leader or outer branch

shoot first, resulting in dead tops followed by an

increasing number of dying branches. The lower crown

can remain green and of normal density. Visible needle

symptoms include needles becoming uniformly paler

green, brief wilting then turning yellow, through orange

and red and ultimately being shed.

In this study ‘condition’ is the term used to describe

the physiological status of individual tree crowns.

Good condition implies closed, dense tree crowns

whereas poor condition describes tree crowns which

have open and thin crowns. Tree crowns categorised as

having a poor condition have potentially been affected

by one of the three damaging agents investigated.

2.2. Imagery collection

Imagery was acquired from the Digital Multi-

Spectral Camera II system (Specterra Systems Perth,

WA). The camera consists of four individual 1024 �1024 CCD arrays. Imagery is acquired at 12 bit

digitisation at a spatial resolution of 50 cm from a

suitable single engine light aircraft. The system allows

four independent and replaceable narrow bandwidth

interference filters. For this study we selected four

wavelength filters which allowed discrimination of the

red edge slopes (680, 720, and 740 nm) as well as a

reference or insensitive wavelength at 850 nm. The

imagery was digitally mosaiced using a digital ortho-

photograph as a base map allowing spatial registration

accuracy to be within 5 m (root mean square error,

R.M.S.E.).

Imagery was flown on the 3rd of September 2002

under clear sky conditions with maximum solar zenith

angles. September was chosen as it is just prior to the

emergence of new shoot growth when mature foliage

would have approximately one year’s cumulative

crown damage. Flight lines covered pseudo invariant

features (PIFs) (large sheets of uniform reflectance

material) to assist in image calibration. Calibration

was necessary to remove distortions in the imagery

such as detector offsets and to convert digital numbers

to reflectance.

2.3. Field data collection

Field data collection took place oneweek after image

capture as it was essential to ensure individual tree

crowns werecorrectly identifiedandassessed in thefield

and matched to the respective crowns in the imagery.

When field programs have been undertaken without

access to the high spatial resolution imagery, matching

tree crowns on the imagery has proven to be difficult

(Coops et al., 2003). The field team consisted of three

forest health experts, one from the SFNSW FHSU, with

significant experience in detecting and assessing forest

healthboth inAustralia andoverseas. Inorder toobtain a

representative set of crowns across all damaging agents,

a number of circular, box or transect plots were

established for each damaging agent, resulting in a

large number of individual tree samples per agent

expressing a full range of symptoms.

As E. californica causes needles to fall prema-

turely, the degree of defoliation within a crown is the

major indicator of damage. Consequently, the crown

was divided into four horizontal quartiles allowing

crown transparency (1—needle density) to be assessed

at each quartile and then averaged over the entire

crown. Needle colour can provide an indication of

aphid presence, with each quartile also being assessed

for degree of yellowing. As younger, uninfested

needles may be retained on the outer crown, the

presence of green needle tufts on the outer canopy is

also scored. Within N limited sites, internal crown

variation is not considered an important indicator.

Generally, needle colour (from dark green to light

green and yellowing under severe condition) and

needle size are good indicators of severity. Crowns are

generally small and height stunted, making crown

volume a critical indicator of poor soil nitrogen status.

Key visible indicators of active S. sapinea infection

are the presence of orange and red needles along entire

shoots and branches. Often the leader is affected first,

making identification via these key crown features

important. As the lower crown remains green and

normal density a comparison of the lower to upper

crown can provide an indication of its severity.

At each individual tree crown a set of attributes

were measured and assessed by the field team,

depending on the type of damaging agent. Table 1

provides a summary of the information collected for

each agent.


Table 1

Analysed field attribute information collected for the three damaging agents

Essigella californica

Tree height (m) DBH (cm) Crown transparency of

the upper quartile (%)

Proportion of crown affected (%)

N = 96 N = 96 N = 96 N = 95

Mean 26.2 36.1 32.7 32.7

Minimum 17.7 22.2 15.0 15.0

Maximum 42.0 53.1 95.0 85.0

Range 24.3 30.9 80.0 70.0

S.D. 4.1 6.7 17.6 14.3

Nitrogen deficiencya

Tree height (m) DBH (cm) Colour code Crown volume Crown transparency score

N = 90 N = 90 N = 90 N = 90 N = 90

Mean 9.1 18.3 1.81 15.4 29.11

Minimum 2.3 2.6 1 1.2 15.0

Maximum 15.9 106.0 4 50.4 85.0

Range 13.6 103.4 3 49.2 70.0

S.D. 3.1 23.3 0.8 9.1 13.0

Spheropesis sapineab

Tree height (m) DBH (cm) Leader colour Overall crown colour

N = 78 N = 78 N = 78 N = 78

Mean 26.2 36.1 2.1 0.2

Minimum 17.7 22.2 1 0.03

Maximum 42.0 53.1 6 1

Range 24.3 30.9 5 0.97

S.D. 4.1 6.7 1.9 0.3

a 1: Dark green; 2: light green; 3: yellow–green; 4: yellow.b 1: Dark green; 2: light green; 3: yellow; 4: orange; 5: red; 6: grey.

2.4. Linear spectral unmixing analysis

Linearspectralunmixingisa techniqueusedtodivide

each pixel into its component or endmember spectra

(Ustin et al., 1998). Endmembers represent the spectral

characteristics of cover types, regarded as having

uniform properties (Garcia-Haro et al., 1999). Matrix

inversion is ultimately performed by (Eq. (1)) to find the

best combination of endmembers to explain the mixed

signal of a pixel (Van Der Meer and De Jong, 2000).

Ri ¼Xn

j¼1

f jREij þ ei and 0 �Xn

j¼1

f j � 1 (1)

where Ri is the pixel reflectance; f j, the endmember

image fraction; REij, the reflectance of image end-

member, j, at band i; n, the number of endmembers;

and ei is the residual error for band i.

The method assumes that the reflectance from each

pixel is a linear combination of each endmember and

the fractional abundances are computed on a pixel by

pixel basis (Okin and Roberts, 2000). This assumption

is arguably the most important problem with linear

mixing modelling (Roberts et al., 1993). Non-linear

mixing can be expected in vegetation canopies as

green vegetation transmission is high at certain

wavelengths. However, in the short wave infrared

within coniferous forests, transmission is generally

low, making the assumption reasonable for most

studies (Roberts et al., 1993; Drake et al., 1999).

The image processing software package ENVI 3.6

(RSI, 2003) was used to undertake the unmixing


analysis. A minimum noise fraction (MNF) technique

was used to derive the spectra of the pure cover types

from the imagery by transforming the four spectral

channels into a reduced number containing indepen-

dent information and to segregate noise in the data

(Green et al., 1988). Three new MNF transformed

bands were then analysed to find the most ‘‘spectrally

pure’’ (extreme) pixels in the image using a pixel

purity index (PPI) classifier. Clusters of extreme pixels

in the image were then displayed and using a

combination of ground-based reflectance spectra,

local knowledge and field observations, pure cover

type labels in the image were assigned to each cluster

of extreme pixels.

A large amount of materials are likely to contribute

to the reflectance of the image scene including

different soil types, vegetation species, vegetation

condition and canopy architecture. However, in

reality, a small number of endmembers are capable

of accounting for the spectral variation. For example, a

study by Roberts et al. (1993) demonstrated that over

98% of the spectral variation in AVIRIS imagery was

accounted for by a combination of three basic

endmembers: green vegetation, shade and soil.

Shadow has been shown to be an important end-

member and is likely to be highly correlated with

canopy structure (Peddle et al., 1999). This is

principally due to fragmented canopies being more

likely to contain high amounts of shadow and

therefore correlate with abundances of the biophysical

variables such as biomass, net primary productivity

(NPP) and leaf area index (LAI). Wood and NPV have

also been shown to be valuable endmembers to include

when analysing forested scenes. Levesque and King

(2003) identified wood, shadow, mining tailings and

vegetation as key endmembers that explained the

variability in their multispectral imagery. Likewise a

number of additional studies have selected NPV as an

endmember (Roberts et al., 1993; Fitzgerald et al.,

2002). However, there have been problems with

similarities in NPV and soil spectra.

In this study, three endmembers were selected to

characterise the variance in the imagery: sunlit

canopy, soil and shadow. These endmembers were

selected through an iterative process that involved

examining the spatial mapping of the endmembers

and comparing with local knowledge and field

observations as well as ground-based reflectance

spectra obtained at the same time as the overpass.

Once the endmembers were selected, the image

fractions were computed, based on a model with low

root mean square error average.

A constrained linear spectral unmixing technique

was then used with a high weighting factor to

constrain the image to sum to unity (Fitzgerald et al.,

2002) and stabilise the results (Elmore et al.,

2000). The n-dimensional visualiser tool was

also used to check the separability of the end-

members and refine the regions of interest selected.

Fig. 1 shows an example of field-based spectra

collected during the field campaign of key end-

member spectra, and image based spectra of the

selected endmembers resulting from the MNF and

PPI analysis.

2.5. Crown delineation

The scale at which plantation health is tradition-

ally assessed is at the basic management unit,

usually based on a visual estimation of canopy health

categories within individual compartments. High

spatial resolution imagery enables the identification

of individual crown attributes which improves the

mapping capabilities due to its ability to exploit

visually both spectral and spatial information. An

important factor in the assessment of crown

condition from remotely sensed imagery is the

method used to generate the spectral signature for

each individual crown. When viewing high spatial

resolution imagery of tree crowns there is consider-

able variation in brightness depending on the pixel

position in the crown caused by (i) differences in

illumination, (ii) canopy geometry, (iii) viewing

angle, and (iv) bidirectional reflectance distribution

function (Li and Strahler, 1985). We utilised a

manual technique to identify individual crowns on

the airborne imagery, which involved sampling the

whole tree based on recommendation of Leckie et al.

(1992) who showed that this was the most appro-

priate method for crown attribute modelling. Based

on this result, each of the visible tree crowns sampled

in the field was manually delineated on the DMSI

imagery. Large scale hardcopies of the imagery and

field maps were used to locate each tree. Boundaries

were then manually digitized onto the imagery and

the mean crown image fraction for each endmember


Fig. 1. Endmember spectral characteristics (a) as derived from hand-held spectroradiometer and (b) as obtained from the imagery.

(3) and the root mean square residual was then

extracted.

Field measurements of tree structure and condition

were statistically compared to the image fractions

using the statistical package Statistica (StatSoft Inc,

2000) and stepwise regression techniques used to

assess the significance of each individual fraction

image and forest attributes.


3. Results

Fig. 1 shows three key endmember field-based

spectra collected during the field campaign, and

image-based spectra of the selected endmembers

computed from the MNF and PPI analysis. The two

spectra closely match, indicating the endmembers

Fig. 2. Image fractions for the three damaging agents: (a) Essigella, (b) nitr

(shadow) and third (exposed soil).

identified on the imagery are representative of the

actual scene endmembers. Whilst the shadow end-

member spectra could not be reliably obtained in the

field, the spectra is recognisable on the imagery as an

endmember due to its near zero reflectance across the

four spectral bands. The field spectra obtained for dead

wood (NPV) are also shown and the inability of the

ogen and (c) Sphaeropsis for the first column (sunlit canopy), second


Fig. 3. Schematic representation of the three image fractions within

a crown of poor condition and good condition.

spectra to be clearly discriminated from soil is clear

due in part to the low dimensionality of the four

channel data.

Fig. 2 shows the three computed image fractions

(sunlit crown, shadow and soil) for three subsets of the

damaging agents. The image fraction images show

clear differentiation in abundances between sunlit

canopy, shadow and exposed soil. Individual tree

crowns are identifiable on the sunlit fraction for all

damaging agents. Shadow is also well predicted over

the scenes, providing an inverse image of the sunlit

crowns and demonstrates that shadow is well

distributed throughout the forest stands. High shadow

fractional values are most evident along roads where

the forest edges occur. The soil fractional endmember

shows localised areas of high soil abundance,

particularly on the nitrogen deficient site where the

tree crown coverage is very low and there are large

areas of exposed bright soil.

For each tree identified manually the average

fractional components for each of the three image

fractions were extracted. All three image fractions

should add to unity including an R.M.S. error term that

indicates the residual unexplained error between the

measured and modelled spectral data. Fig. 3 provides a

schematic representation of a P. radiata tree crown in

poor condition and good condition. In this example,

the healthy crown is comprised of a major contribution

of sunlit crown, a moderate contribution of shadow

and a very minor soil component, whereas an

unhealthy crown has an equal proportion of all three,

with soil and shadow having a much larger fraction in

the manually delineated crown than in the healthy

case.

Fig. 4 indicates the relationship between the three

endmembers (sunlit canopy, shadow and soil) and

crown transparency for the upper quartile in E.

californica affected crowns. Each fraction is scaled

between 0 and 1 against the percent crown transpar-

ency. This figure indicates that weak relationships

exist between crown transparency of the upper quartile

and the sunlit canopy and shadow image fractions,

while virtually no relationship between the soil

fraction and crown transparency exists. The figure

indicates that the relationship between transparency

and the sunlit fraction is negative, with a decrease in

the sunlit fraction associated with an increase in crown

transparency. In contrast, the shadow fraction within

crowns increases as crown transparency of the upper

quartile decreases. Table 2 shows, in tabular form,

these results as well as the results for proportion of the

total crown affected by E. californica. The table shows

both sets of results are similar, with slightly less

significant relationships between the image fractions

and the proportion of crown affected.

Fig. 5 shows the relationships between crown

colour and the image fractions at the nitrogen deficient

site. Crown colour is a four class variable for nitrogen

deficient crowns, with dark green representing crowns

unaffected by nitrogen deficiency, through light green,

yellow green and yellow symptomatic of trees

severely affected by nitrogen deficiency. For this

damaging agent the soil image fraction has the highest

correlation with crown colour, with a coefficient of

determination (r2) of 0.44. As the crown colour

changes from dark green to yellow the proportion of

soil image fraction also increases. The sunlit canopy

image fraction has shown a slightly weaker relation-

ship in comparison to the soil fraction and is a negative

one, with crowns more associated with nitrogen

deficiency containing lower proportions of sunlit

canopy fractions. Results for the shadow image

fraction indicates no significant relationship exists

with crown colour (a = 0.05). Table 2 shows the

results for the three tree variables measured at the

nitrogen deficient site and this indicates similar trends

for all the variables. The crown colour results are the


Fig. 4. Scatterplots of the three endmember image fractions (sunlit canopy, shadow and exposed soil) with crown transparency of the upper

quartile for Essigella damaged crowns.

most significant and crown volume is also highly

significant.

For S. sapinea infected crowns, relationships are

evident between overall crown colour and the three

fractional endmembers. Fig. 6 indicates the sunlit

canopy image fraction has the highest coefficient of

determination (r2) at 0.75. This relationship indicates

that as the crown colour progressively changes from a

healthy score of 0–1 for an unhealthy score, the

fractional abundance of sunlit canopy decreases (i.e.

observations suggest crown colour is related to degree

of crown needle shed). The strength of the relationship

for the shadow endmember is also strong, with r2

values around 0.62, while the soil fraction relationship

is slightly weaker (in moderately sized trees relatively

to the smaller, stunted N deficient trees). The

relationships between fractional abundances of sha-

dow and soil increase as the overall crown health

decreases. Table 2 indicates the results for leader

colour are slightly more significant than those for

overall crown colour.

Table 3 presents the results of the multiple stepwise

regression for each of the three damaging agents. For

E. californica the models indicated low coefficients of

determination. Of the tree attributes only crown

transparency in the upper quartile and proportion of

crown affected achieved an r2 above 0.3. The multiple

regression approach allows the significant image


Table 2

Correlations between field variables and image fractions

Damaging agent and

forest attribute

Sunlit

canopy

Shadow Soil

Essigella californica

Crown transparency

of upper quartile

0.30*** 0.24*** 0.01

Proportion of crown affected 0.28*** 0.20** 0.01

Nitrogen deficiency

Crown colour 0.35*** 0.03 0.44***

Crown volume 0.38*** 0.09* 0.23***

Crown transparency 0.36*** 0.09* 0.27***

Spheropsis sapinea

Overall crown colour 0.71*** 0.57*** 0.34***

Leader colour 0.75*** 0.62*** 0.31***

* a < 0.05.** a < 0.001.

*** a < 0.0001.

fractions to be identified and ranked in terms of level

of significance. For E. californica, sunlit canopy was

identified as the most important variable for both

models.

Table 3 also shows the results of the nitrogen-

deficient regression model. The model for crown

colour is the most significant with soil and shadow

image fractions selected (r2 = 0.56). Crown volume is

slightly less significant, with an r2 = 0.44 and a

standard error of 5 m3 with two endmembers, sunlit

canopy and shadow. Likewise overall crown transpar-

ency of the nitrogen deficient crowns also contains

these two endmembers with a similar level of

significance and a standard error of around 7%.

Fig. 7 shows the results of the predicted crown colour

of N deficient trees against the modelled fractions and

demonstrates the same trends in Table 3, with the soil

fraction being highly correlated with crown colour and

explaining the majority of the variance, and using

stepwise regression, the shadow fraction selected

second, containing the remaining variance in the

relationship. The model confirms that as crown colour

changes from 1 (dark green) to 4 (yellow) likewise the

soil and the shadow fractions increase.

The results for S. sapinea are the most significant

for the three damaging agents. The models for overall

crown colour (ranging from 0 to 1) and leader colour

(a six class colour score) are highly significant and the

multiple regression modelling indicates that both the

sunlit canopy and shadow are significant image

fraction variables. Overall, both the canopy colour

and the leader colour models have standard errors of

around 13%. Fig. 8 demonstrates the developed model

produces strong negative relationships between sunlit

canopy and overall crown colour. This is consistent

with expectations that healthier tree crowns have a

higher proportion of sunlit canopy. The fit of the

shadow image fraction values into the model is

slightly weaker with an r2 of 0.77 and indicates

healthier tree crowns have a lower proportion of

shadow.

In summary, the model results in Table 3 indicate,

like the base correlations, that S. sapinea results are

the most significant, followed by the nitrogen affected

crowns and finally E. californica.

4. Discussion

A common issue when using remotely sensed data

to detect vegetation health and condition is mis-

classification due to the vegetation being in a

physically reduced and fragmented configuration. In

damaged or stressed vegetation the amount of foliage

(or leaf biomass) is likely to be reduced along with key

bio-chemicals such as nitrogen, chlorophyll, and other

pigments as well as water content. Furthermore, the

influence of understorey, shadow and soil increases

dramatically as canopies lose their foliage biomass

resulting in more mixed pixels with greater propor-

tions of these fractions than in healthy canopies. For

example, the effect of increased soil reflectance can

result in an adverse effect on indices that target

chlorophyll content (Coops et al., 2003). Conse-

quently, relationships between spectral indices opti-

mised for vegetation health can be ineffective for

certain vegetation types and for selected damaging

agents.

In this study the application of linear spectral

unmixing for assessment of forest condition has

produced promising results and offers several advan-

tages over simple regression methods with spectral

indices (Levesque and King, 2003). For example,

linear unmixing has been shown to be capable of

detecting vegetation cover at low levels (Drake et al.,

1999; Elmore et al., 2000), and the ability to reference

a small number of spectrally stable endmembers (e.g.

vegetation, soil, and shadow) results in developed


Fig. 5. Scatterplots of the three endmember image fractions (sunlit canopy, shadow and exposed soil) with crown colour of nitrogen deficient

crowns.

models being repeatable. However, the application of

this technique over time, between sites, and in forests

of mixed species composition may encounter diffi-

culties. The main limitations are related to plant

phenology, bi-directional reflectance, and on effective

radiometric calibration of remotely sensed data.

In this application individual crowns experiencing

infestation by the aphid (E. californica) were found to

be the most difficult to successfully assess from the

imagery. Generally, symptoms associated with E.

californica infestation have been demonstrated to

primarily affect the chlorophyll content of needles,

producing chlorosis, while the impact upon cellular

structure only occurs in the later stages of infestation.

As a result, the dominance of the red edge spectral

wavelengths rather than chlorophyll sensitive regions

of the spectrum may have hindered the ability to

successfully relate the imagery to aphid damage. For

E. californica, crown transparency of the upper

quartile produced the highest correlation of the crown

attributes. However, the relationships between the

sunlit canopy and soil image fractions showed a lower

correlation. In addition to spectral band selection a

possible cause for the lower detection of E. californica

affected crowns could be due to the presence of green

tuffs on the outer tree crown. Crowns infested with E.

californica experience thinning from the mid to upper

portions of the crown that should be identifiable on the


Fig. 6. Scatterplots of the three endmember image fractions (sunlit canopy, shadow and exposed soil) with overall crown colour of Sphaeropsis

infected crowns.

imagery. However, it is also common for crowns in

later stages of infestation to retain healthy green tufts

of needles or short rejuvenation at the ends of

branches. As a result, it is possible that these healthy

green tufts are saturating some pixels, in effect

masking out the thinning of the infected crowns.

As E. californica infestations are commonly

associated with an increase in crown transparency

in the top of the crown, clear visual assessment by

ground-based forest health specialists is difficult. In

this modelling approach we have assumed the

assessment of levels of E. californica attack estimated

by field specialists are correct and without error. In

reality, of the three damaging agents investigated, E.

californica is the most difficult to assess visually.

The results for nitrogen deficient crowns have

indicated relationships between forest attribute infor-

mation and the image fractions. Crown colour had the

highest correlation with the image fractions, with

higher proportions of soil image fractions for

unhealthier trees; a change in crown colour from

dark green to yellow. Crowns with reduced nitrogen

experience stunted growth, with new shoots and

needles reduced in size. Considerable areas of soil,

primarily between canopies, may be exposed due to

this stunting of the canopy growth. It is therefore

expected that the soil endmember has been included in

the model for crown colour. Crown volume was also

correlated with the image fractions for nitrogen

deficient trees, with the model development indicating


Table 3

Results for image fractions

Damaging agent Health attribute Image

fraction

Level of

significance

Standard error

of estimate

Number of

observations

r2

Essigella californica Crown transparency

of upper quartile

Sunlit canopy <0.001 9.237 89 0.33

Soil 0.087

Proportion of

crown affected

Sunlit canopy <0.001 8.418 88 0.31

Shadow 0.034

Nitrogen deficiency Crown colour Soil <0.001 0.483 83 0.56

Shadow <0.001

Crown volume Sunlit canopy <0.001 5.822 86 0.44

Shadow 0.004

Crown transparency Sunlit canopy <0.001 7.474 86 0.43

Shadow <0.001

Spheropsis sapinea Overall crown colour Sunlit canopy <0.001 0.134 74 0.80

Shadow <0.001

Leader colour Sunlit canopy <0.001 0.863 76 0.79

Shadow <0.001

that inclusion of sunlit canopy and shadow are the

significant factors in crown condition prediction.

The forest attributes modelled for S. sapinea

affected crowns have displayed the highest signifi-

cance using the spectral unmixing approach. For this

damaging agent, overall crown colour and leader

colour models both have very significant correlations.

Fig. 7. Relationship between predicted crown colour on nitrogen deficient

listed in Table 2.

Sunlit canopy and shadow image fractions have been

identified as the significant factors for both models. S.

sapinea often affects the leader and outer branches,

with an increasing number of dying branches and

change in needle colour as the infestation progresses.

These results have demonstrated that the application

of spectral unmixing is successful in detecting

site and Image fractions based on multiple linear regression model


Fig. 8. Relationship between predicted overall crown colour (S. sapinea) and Image fractions based on multiple linear regression model listed in

Table 2.

changes in plantation attributes, which are specifically

designed to assess crown symptoms associated with

trees infected with S. sapinea.

The inclusion of an endmember for NPV may be

important for vegetation health studies with a higher

proportion of dead or dying vegetation being present

in the damaged trees and crowns. A value of the

fractional abundance for this NPV would clearly

provide useful information for the assessment of

vegetation condition. However, previous research has

shown mixed levels of success for unmixing a NPV

endmember due to misclassification with soil (Roberts

et al., 1993; Drake et al., 1999; Okin and Roberts,

2000). In this study, the limited number of bands

prevented the inclusion of a NPVendmember. The use

of four-band high spatial resolution imagery results in

only three endmembers. In this study, sunlit crown,

soil and shadow were chosen as they were the

endmembers that accounted for the spectral variation

in the scene (derived from the low root mean square

error values). Soil was consistently identified as

having the lowest average fractional abundance for the

tree crowns examined and this corresponds to field

observations. The inclusion of a shadow endmember

has been noted by Peddle et al. (1999) to be the most

important forest component for predicting boreal

forest biophysical variables. In the models produced

by this study the results have confirmed these findings.

In a healthy forest it is likely trees will harness as

much light as possible; light for photosynthetic

activity forming dense individual crowns. This will

in effect limit the amount of intra-crown shadowing.

Unhealthy trees, by comparison, will experience

thinning of the crowns, loss of branches and possibly

stunted growth as well as the formation of canopy gaps

as trees die. This will lead to higher shadowing both

between and within unhealthy tree crowns.

The majority of research related to linear unmixing

has focussed on discriminating the proportions of soil,

mineral and vegetation types (Boardman and Kruse,

1994). A recurring issue when using the technique has

been to discriminate substances of similar composi-

tion, for example soils which are composed primarily

of the same base materials. The same issue is relevant

for vegetation studies. With only one dominant

vegetation species present in this study (P. radiata)

this concern is not as important as in other studies

where unmixing species associations is important. The

modelling approach in this study using proportions of

endmembers within each individual tree crown is a

unique one and one which appears to hold much

promise. The approach allows an assessment of which


spectral components are driving the predictions in

each cluster as well as the relative importance of each

unmixed component in the prediction. The relative

increase, (or decrease) for example, of the soil

fractions is logical and allows the effect of thinned

or open crowns to be modelled explicitly.

In terms of applying the technique over large

plantations, the issue of automated crown delineation

is an important operational issue. Accurate corre-

spondence between field-estimated crown attributes

and the identification of the respective crowns on the

imagery was critical to developing the relationships

between the unmixed fractions and health attributes.

Significant advancements in automatic tree delinea-

tion from high spatial resolution imagery have been

made (Culvenor, 2002) and ongoing work is deter-

mining the effectiveness of methodologies defining

operational procedures for using the technique in

conjunction with forest inventory and to confirm their

cost effectiveness.

Acknowledgements

This study is part of a research program applying

remotely-sensed multispectral imagery to the classi-

fication of canopy damage from a range of damaging

agents in P. radiata plantations supported by CSIRO

Forestry and Forest Products, State Forests of NSW

and by Forestry and Wood Products Research and

Development Corporation, Melbourne. The project

relied strongly on members of the State Forest of New

South Wales (SFNSW) Forest Health Survey Unit

(FHSU) lead by Angus Carnegie, with Grahame Price

and Ian Hides (SFNSW). Other members include,

Michael Stanford, Ken Old, Mark Dudzinski (CSIRO

FFP) who undertook field data collection and image

processing. In addition Carnegie, Stanford, Old and

Dudzinski were also involved in initial project

design and jointly developed the field assessment

techniques used in this study for which we are very

grateful. We thank SFNSW for access to the relevant

P. radiata plantations in NSW to undertake the field

work and Dr Laurie Chisholm (University of

Wollongong) for collecting ground-based spectra.

We also greatly appreciate the comments made by the

reviewers (Ray Merton and Mark Dudzinski) of this

manuscript.

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