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

International Journal of Applied Earth Observation and Geoinformation 7 (2005) 11–28 www.elsevier.com/locate/jag

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 b

a CSIRO Forestry and Forest Products, Private Bag 10, Clayton South, 3169 Vic., Australia 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

1. Introduction * 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.

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,

0303-2434/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2004.10.003

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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 geographical 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) reflectance from reduced cellular integrity and shifts in the red edge between these two spectral regions (Carter, 1994; Merzlyak et al., 1999; Le´ vesque 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; Le´ vesque 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; Le´ vesque and King, 1999; Olthof and King, 2000). In addition high spatial resolution imagery allows the delineation of individual 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 normalized 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 nonvegetated 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 landscape and lend themselves to straightforward interpretation 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 characteristics of key cover types (Adams et al., 1986; GarciaHaro 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

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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 governing 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) (Tompkins 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 Heidebrecht, 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 (Le´ vesque 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

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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. The aphid E. californica, first observed in Australia 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 outer needles may be retained on the outer crown as green tufts. Nitrogen deficiency, in P. radiata, results in severe growth reductions, with foliage

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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 (Stanosz, 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 MultiSpectral 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 orthophotograph 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 one week after image capture as it was essential to ensure individual tree crowns were correctly identified and assessed in the field 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 health both in Australia and overseas. In order to obtain 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 prematurely, 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.

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Table 1 Analysed field attribute information collected for the three damaging agents Essigella californica

Mean Minimum Maximum Range S.D.

Tree height (m)

DBH (cm) N = 96

Crown transparency of the upper quartile (%) N = 96

N = 96 26.2 17.7 42.0 24.3 4.1

Proportion of crown affected (%) N = 95

36.1 22.2 53.1 30.9 6.7

32.7 15.0 95.0 80.0 17.6

32.7 15.0 85.0 70.0 14.3

Nitrogen deficiencya

Mean Minimum Maximum Range S.D.

Tree height (m) N = 90

DBH (cm) N = 90

Colour code N = 90

Crown volume N = 90

Crown transparency score N = 90

9.1 2.3 15.9 13.6 3.1

18.3 2.6 106.0 103.4 23.3

1.81 1 4 3 0.8

15.4 1.2 50.4 49.2 9.1

29.11 15.0 85.0 70.0 13.0

Spheropesis sapineab

Mean Minimum Maximum Range S.D. a b

Tree height (m) N = 78

DBH (cm) N = 78

Leader colour N = 78

Overall crown colour N = 78

26.2 17.7 42.0 24.3 4.1

36.1 22.2 53.1 30.9 6.7

2.1 1 6 5 1.9

0.2 0.03 1 0.97 0.3

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

2.4. Linear spectral unmixing analysis Linear spectral unmixing is a technique used to divide 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 ¼

n X j¼1

f j REij þ ei and 0 

n X fj  1

(1)

j¼1

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

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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 independent 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 endmember 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. Le´ vesque 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 endmembers and refine the regions of interest selected. Fig. 1 shows an example of field-based spectra collected during the field campaign of key endmember 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 traditionally 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 considerable 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 appropriate 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

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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.

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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

identified on the imagery are representative of the actual scene endmembers. Whilst the shadow endmember 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

Fig. 2. Image fractions for the three damaging agents: (a) Essigella, (b) nitrogen and (c) Sphaeropsis for the first column (sunlit canopy), second (shadow) and third (exposed soil).

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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 transparency. 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

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Fig. 3. Schematic representation of the three image fractions within a crown of poor condition and good condition.

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 relationship 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

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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 shadow 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

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

0.30***

0.24***

0.01

0.28***

0.20**

0.01

Nitrogen deficiency Crown colour Crown volume Crown transparency

0.35*** 0.38*** 0.36***

0.03 0.09* 0.09*

0.44*** 0.23*** 0.27***

Spheropsis sapinea Overall crown colour Leader colour

0.71*** 0.75***

0.57*** 0.62***

0.34*** 0.31***

* ** ***

a < 0.05. a < 0.001. a < 0.0001.

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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 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 nitrogendeficient 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 transparency 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

A common issue when using remotely sensed data to detect vegetation health and condition is misclassification 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 proportions 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). Consequently, relationships between spectral indices optimised 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 advantages over simple regression methods with spectral indices (Le´ vesque 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

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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 difficulties. 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

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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 information 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

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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 Proportion of crown affected

Sunlit canopy Soil Sunlit canopy Shadow

<0.001 0.087 <0.001 0.034

9.237

89

0.33

8.418

88

0.31

Crown colour

Soil Shadow Sunlit canopy Shadow Sunlit canopy Shadow

<0.001 <0.001 <0.001 0.004 <0.001 <0.001

0.483

83

0.56

5.822

86

0.44

7.474

86

0.43

Sunlit canopy Shadow Sunlit canopy Shadow

<0.001 <0.001 <0.001 <0.001

0.134

74

0.80

0.863

76

0.79

Nitrogen deficiency

Crown volume Crown transparency Spheropsis sapinea

Overall crown colour Leader colour

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 significance using the spectral unmixing approach. For this damaging agent, overall crown colour and leader colour models both have very significant correlations.

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

Fig. 7. Relationship between predicted crown colour on nitrogen deficient site and Image fractions based on multiple linear regression model listed in Table 2.

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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 NPV endmember. 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 composition, 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

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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 correspondence 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 delineation from high spatial resolution imagery have been made (Culvenor, 2002) and ongoing work is determining 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 classification 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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