Image acquisition and pre-processing

The imagery used in this project was acquired using a Digital Multi-Spectral Camera (DMSC; SpecTerra Services, 2004) under cloud free conditions between 11am and 1 pm on 24 March 2003. The imagery was captured over an area of approximately 400 ha at a pixel size of 0.5 m by 0.5 m, which resulted in several pixels per tree crown and a 512-pixel swath width for the complete image. The camera was mounted on a single engine light aircraft. The DMSC had four individual 1024 ×1024 charge- coupled device (CCD) arrays capable of acquiring data at 12-bit digitisation. Flight paths were oriented parallel to the solar azimuth to reduce bi-directional reflectance artefacts and approximately 1,000 meters above the ground.

Narrow bandwidth (approximately 10 nm full width half maximum bandwidth) interference filters were fitted to the DMS camera, which allowed detection at 550 nm, 680 nm, 740 nm and 780 nm wavelengths (Table 4.2). These wavelengths were chosen as they proved successful in characterising MLD symptoms at the leaf scale (Pietrzykowski et al., 2006).

Pseudo-Invariant Features (PIFs – one white and one black sheet; size =3m2) with known spectral properties were placed in the field of view of the camera within the target area. Reflectance spectra were collected from the PIF’s at the time of image capture using a hand-held spectroradiometer (PP Systems Analytical Spectral

Devices, Inc. Boulder, Colorado, USA 1999). An empirical line calibration was then used to convert image intensity values for pixels in the image to reflectance.

Trees sampled and assessed in the field were individually identified in the image and each whole crown was manually delineated into a region of interest (ROI) in ENVI (ENVI 2001) as per the method described by Leckie et al., (1992). Statistics

calculated from each ROI included the mean reflectance and variance in each band for which imagery was captured.

Table 4.2. Reflectance variables investigated for the usefulness in spectrally characterising symptoms of Mycosphaerella. R=Reflectance (nm), Var = Variance, NDVI=Normalised Difference Vegetation Index.

Data Spectral variable

Reflectance mean R550

R680

R740

R780 Reflectance variance RVar550

RVar680

RVar740

RVar780

Reflectance Indices (R740-R680)/60 = Lower slope of red edge (Merton, 1999) (R780-R740)/40 = Upper slope of red edge (Merton, 1999) (R780-R680)/(R780+R680) = NDVI (Lichtenthaler et al., 1996) (R550/R680) (Leckie et al., 1992)

(R680/R550) (Pietrzykowski et al., 2006)

(R780-R680)/100 = Total slope of the red edge (Merton, 1999)

4.2.5 Data analysis

Model development

A range of variables were used in building the model, including the mean reflectance and reflectance variance of the ROI’s at each waveband acquired (550 nm, 680 nm, 740 nm and 780 nm), and a variety of spectral indices that compare reflectance in two or more bands (Table 4.2). Initially many indices were considered as they have been shown, in other work, to be highly correlated with the health of vegetation (e.g. Leckie et al., 1992; Franklin and Raske, 1994; Peñuelas et al., 1995; Peñuelas et al.,

1997; Datt, 1999a). These were modified to utilise the four spectral bands acquired for this study. The red edge indices (upper, lower and total slope) were modified from those developed by Merton (1999), and the NDVI from that of Lichtenthaler (1996). Only indices highly correlated with severity were kept for final model development and are shown in Table 2.2. One index (R680/R550) tested was derived from a previous leaf scale investigation using foliage sampled from this plantation (Pietrzykowski et al., 2006).

An ANOVA was completed on both symptom data sets to investigate the level of variance a given variable contributes to the total variation. Wavelengths and indices that explained the most variation and with no co-linearity were included in a stepwise multiple regression analysis. Normal probability plots were examined to ensure assumptions of normality and homoscedasticity held, and residuals were analysed. Data transformation was not necessary as distributions proved normal, unimodal and symmetric. Subsequently, the most appropriate models chosen were those with the least number of significant variables giving the best coefficient of determination and root mean square error (RMSE). All statistical analyses were completed using SAS Version 8 (SAS Institute Inc, 1999).

Map development

Linear spectral mixture analysis was used to identify the proportion of bare soil, shadows and sunlit crown in the imagery, and a mask was created to eliminate pixels containing less than 40% sunlit crown (ENVI, 2004). It was not possible to manually delineate every crown throughout the image, so a circular filter, with a diameter approximating the average crown diameter, was used in ArcGIS (ArcGIS ESRI, 2001)

to create images representing the mean pixel value and pixel variance at the crown scale. A mask of the model was then applied to the calibrated and registered image to depict predicted defoliation and Mycosphaerella index severity.

Model validation

A bootstrap validation procedure in SAS was completed to test the predictive

accuracy of the model. This is a widely accepted method for model validation when data sets are limited in size (Davison and Hinkley, 1997). The bootstrap-validation method (macro sourced from: http:/merlot.stst.uconn.edu) was applied to the full data set (n=72) and was set at 1000 iterations.

Classification accuracy assessment

Model accuracy was assessed using the predicted severities for the 72 trees. Once the models were applied to the raw DMSI data, the predicted defoliation and

Mycosphaerella index values were extracted from each tree crown. A coefficient of determination was calculated to indicate the fit of the equation to the stimulus- response data. Additionally, an accuracy analysis (confusion matrix) was completed on data classified into classes. Details are given in the following paragraphs.

When considering breaking the continuous data into classes to look at Mycosphaerella

symptom severity it is important to consider critical thresholds where the pathogen is having an effect on the host’s growth. To date, research has identified two thresholds relating to the effect of defoliation (Lundquist and Purnell, 1987; Rapley, 2005; Pinkard et al., 2006; Smith et al., 2006) and leaf necrosis severity in eucalypt growth (Smith, 2006). Results have shown that crown defoliation levels over ~20% begin to

have an effect on stem growth and volume (Lundquist and Purnell, 1987; Pinkard et al., 2006) but if levels remain below ~70%-80%, then damage is not long term and the trees recover (Rapley, 2005; Smith et al., 2006). Beyond ~70% defoliation, tree growth is indefinitely reduced and damage yield is permanent (Smith et al., 2006). By identifying trees with 20% crown severity, forest managers would be alerted to

possible growth reductions and could more intensely monitor their health status. Hence, it is logical to establish an action threshold (e.g. 50%) to allow time for interventions before tree crowns reach a critical ~70%-80% level of damage.

For this study, data were split into three (0-20%, 20-70%, 70%+) and four (0-20%, 20-50%, 50-70%, 70-100%) classes. The fourth class was added to compare level of accuracy with an additional class. A confusion error matrix was produced

(Congalton, 2001). An error matrix is a square arrangement of numbers comparing training data (observed) used to describe each class during classification with the output, classified image (predicted). Two statistics from error matrix analysis are used to describe the classification accuracy of the map in general: (1) the Overall Accuracy (OA) score and; (2) Kappa (or ‘Khat’) statistic from Kappa analysis (Congalton and Green, 1999). The OA score shows the proportion of pixels that have been correctly classified according to the training data. This is calculated from the sum of the correctly classified pixels (the main diagonal in the matrix) divided by the total number of pixels. Kappa analysis attempts to account for chance agreement by incorporating the row and column totals, (which indicate the probability of getting a correct classification by random chance) into the accuracy assessment (Congalton and Green, 1999). The Kappa statistic ranges from -1 to +1, and can be interpreted in terms of the percentage of agreement between the training data and the classified map

(e.g. Strong agreement Kappa = >0.80 (80%); Moderate agreement Kappa = ≥ 0.40 (40%) and ≤ 0.80 (80%); Poor agreement Kappa = < 0.40 (40%) (Landis and Koch, 1977). The Kappa coefficient is calculated as: kappa = (observed agreement – chance agreement) / (1-chance agreement). OA values tend to overestimate the accuracy of classified images (Ma and Redmond, 1995) and Kappa analysis tends to

underestimate map accuracy (Foody, 1992). Therefore, the true predictive accuracy of the image usually exists between the OA and Kappa statistics.

To describe the precision of individual classes the Producer’s and User’s accuracy were calculated. Producer’s accuracy was calculated by dividing the total number of correctly classified pixels by the total number of training pixels in that class (the column total). Producer’s accuracy indicates commission errors, where pixels have been incorrectly included in the target class. User’s accuracy indicates errors of omission, where pixels have been incorrectly omitted from the target class

(Congalton, 2001). For example, a class (e.g. defoliation severity 1) with a producer’s accuracy of 80% and a user’s accuracy of 85%, would indicate that 80% of areas of defoliation severity ‘1’ on the ground have been correctly classified, but that only 85% percent of areas labelled ‘1’ on the map are actually areas of severity ‘1’ on the ground. This map would therefore underestimate the extent of areas with defoliation severity ‘1’.

4.3 Results

4.3.1 Observed spatial pattern of Mycosphaerella Leaf Disease damage