Objective a) Differences in spectral pattern between habitat ty p e

Determine the degree to which spectral reflectance pattern varies between habitat types in the visible and near infrared (NIR) regions o f the spectrum.

Spectra

The graphs o f the mean spectra collected at each study plot (Figure 4:4 to Figure 4:9) illustrate only subtle differences between the spectral responses at study plots from different habitat types. Most spectra exhibit a typical pattern found in vegetation spectra namely a distinctive reflectance feature in the green region followed by an absorption feature in the red region and a marked increase in the NIR resulting in a red edge shoulder between the two regions. Similar patterns o f change over the sampling period show strong similarity between study plots o f different habitat types whereby reflectance in the red region increases as vegetation senesces and reflectance in the NIR decreases steadily (Skidmore 2002; Price

1994; Schmidt & Skidmore 2003).

Notable differences are evident in the green region o f the spectra where the rush pasture/grassland and mixed sedge habitats for example exhibit high reflectance when compared with most o f the other habitat types. The reflectance in the green region o f two of the low sedge study plots is also relatively high. Study plot LS3 exhibits a visibly lesser reflectance in the green region which may be attributable to the differences in the location o f this study plot outwith the same compartment as the others. The compartment where LS3 is located is managed differently and as such contains tussocky vegetation and a relatively greater proportion o f tall grasses all o f which may contribute to a greater degree o f scattering amongst the spectral samples. In addition, whereas some study plots differ greatly between reflectance in the green region, they do not necessarily differ to the same extent in the NIR region. This is the case for LS2 and MC2 or RP3 and MG1 in the July datasets. These results

are similar to Cochrane (2000) where reflectance along the spectrum was compared between various tree species and differences in the green region did not necessarily carry over into other parts o f the spectrum.

Standard deviations are greatest in the NIR region o f the spectra right across the study plots and over the sampling period. This is unsurprising as radiation at these wavelengths is scattered to a greater extent in vegetation spectra due to variations in canopy structure (Schmidt & Skidmore 2003; Spanglet et al. 1998). Diffuse skylight can contribute as much as 5-10% o f the total illumination however (and at shorter wavelengths this value can be even greater at 20-25% o f the total illumination) and tends to fill in shadows and reduce the contrast between surfaces with dissimilar surface textures. Spectra in this study were often collected in conditions o f partial cloud cover and therefore diffuse skylight may well have contributed to the reflectance patterns although this is a difficult problem to avoid. The use o f the Spectralon® panel and white reference spectra would have kept the effects o f subtly varying illumination conditions to a minimum.

Spectra were recorded on days when wind was considered to be a non-contributing factor although no measurements o f local wind speed were made. Wind can affect the structure o f vegetation and therefore the amount o f shadowing present within the Field o f View (FOV).

This in turn can affect the within-habitat spectral variation and lessen the ability to discriminate between habitat types. In addition to caution being taken on the choice o f sample days, the ASD FieldSpec™ employed in this study is a rapid scanner thereby minimizing any very short term variations in canopy spectra affecting the spectral pattern.

Figure 4:10 and Figure 4:11 illustrate the mean spectral patterns between habitat types when the data from the study plots are grouped together. The differences in the green region are highlighted in Figure 4:11 and do provide some indication that spectral signature does vary between habitat type. Similar results in this region were identified by Schmidt and Skidmore (2003) using field spectra collected for salt marsh vegetation types. The relative patterns between habitat spectra do not appear to be consistent, however, and this may highlight the

importance o f time o f data collection on the spectral separability between habitat types. The within habitat variation in spectral response is not illustrated in these graphs but is discussed further under work presented for Objective c).

PCA

Each dataset was gathered over a number o f days and study plots close in proximity to each other were often sampled on the same days. This therefore leads to a degree o f spatial and temporal autocorrelation within the dataset which is difficult to identify and extract (Atkinson

& Emery 1999; Cliff & Ord 1973; Fortin et al. 1989). A principal components analysis o f the meteorological data that applied to each study plot during the August sampling stage was assessed in order to investigate this potential problem further. The August dataset was the only meteorological dataset wholly intact for all o f the sampling days and so was deemed the most suitable for this analysis. The results o f this analysis are shown in Figure 4:43 and the relative locations o f the study plots in feature space do appear to correspond with the order applied in data collection. Figure 4:43 is considered in relation with the PCA on the spectra collected at each study plot in August (Figure 4:14) and although there is a very slight correlation between the sample scores along Axis 1 for both results (Pearson Correlation Coefficient 0.376, P-value 0.000) the patterns between the relative locations o f the study plots in feature space do differ.

2.5

Figure 4:43 PCA perform ed on M eteorological Data for August field spec data collection days (Study plots m eans plotted)

Principal Components Analysis (PCA) was carried out on both the AVS1-42 dataset and the improved upon with larger datasets. The precise relationship between class separability and scale o f spectral resolution in this sense is not explored further in this study though it is an area that does warrant further research.

Insh Marshes is split into compartments that are managed differently whether by varying the intensity o f grazing within the compartment, topping or scrub clearance programmes. As such habitats that fall within different compartments may be subject to different management which results in within-habitat variation in terms o f canopy structure and possibly species composition. This in turn, may result in a greater range o f spectral patterns associated with one habitat type. As the PCA results show relatively large differences between study plots

from the same habitat type, the possibility of vegetation structure differences within each habitat type due to different management techniques was considered. The following table lists the management associated with each study plot as identified in Maier & Cowie (2002).

Table 4:15 M anagem ent within each study plot (M aier & Cowie 2002) S tu d y P lo t M anagem ent

EF1 No stock access

LS1 S heep high grazing; Topping LS2 S heep high grazing; Topping

LS3 S heep low grazing; S crub C le a ra n ce MC1 S heep m edium grazing

M C 2 S heep m edium grazing M C 3 S heep m edium grazing

M C 4 S heep low grazing; S crub cle a ra n ce MG1 S heep m edium grazing

M G 2 S heep m edium grazing MS1 S heep high grazing; T opping M S2 S heep high grazing; T opping

M S 3 S heep low grazing; S crub cle a ra n ce RP1 S heep high grazing; T opping

R P2 S heep high grazing; T opping R P3 S heep high grazing; T opping

The study plots to consider in terms of within habitat management practices are the ‘LS' study plots (Species-rich low sedge), CMC’ (Molinia caerulea sedge mire) and 'M S ' (Mixed sedge). A glance at the patterns between the study plots located within these habitat types (see Figure 4:12, Figure 4:14 and Figure 4:15) often highlight a strong similarity between study plots located within the same management compartment. In addition, those habitat types that share the same management practices also display a large degree of dissimilarity between the study plots, such as ‘RP’ for example (Rush pasture/grassland).

Although PCA is presented here as an effective method to assess the spectral dissimilarity between classes o f spectral data the results remain difficult to interpret. I he effect of the spatial and temporal autocorrelation cannot be quantified and can therefore not be discounted although the results presented in Figure 4:43 suggest that this had a minimal effect. It is neither possible to identify consistent trends along the axes nor to explain with great detail the nature o f the clustering amongst the classes. Also, the data is presented using only the

standard deviations o f sample scores in axis 1 and axis 2 and so, in reality, the overlap and spread is even greater. The results illustrate the degree to which spectral separability between classes may change over just three months and that there may therefore be optimal times to classify between habitat types. The results for the mixed sedge samples for example, illustrate that July may be a less profitable time o f the year to separate this particular habitat type from others as spectral overlap with other habitat types is shown to be greatest at this time o f the year (Figure 4:13). In contrast however, the Equisetum fluviatile samples are most distinct in the August dataset as are data collected in the Myrica gale habitat type.