Composite Image

If it was not possible to separate between grazing classes with a single image, it could be possible when combining images together. To test the potential for be-tween class separation when using a time series, a stack of images from different

5.2 Initial Class Separation 67

parts of growing seasons was created. Three images were used: Landsat The-matic Mapper (TM) 23.05.2004 (spring), Landsat TM 24.07.1994 (summer), and Landsat Enhanced Thematic Mapper (ETM)+ 18.10.99 (late autumn). This multi-temporal data merging was done by combining images of the same area taken on more than one date to create a product useful for visual interpretation.

Merging various combinations of bands from the different dates to create colour composites can aid the interpreter in discriminating the various vegetation types present (Lillesand et al., 2004). Figure 5.6 shows a combination of the three NIR bands from the three different image dates.

Figure 5.6: A composite 3 date image created from a data-stack of 3 dates. The NIR band from each date is shown in; red: 23.05.04, Green: 24.07.94 and Blue: 18.10.99. The compos-ite image shows that the high NIR values are the most dominant feature in the July image, appearing as green and representing vegetated land cover. The blue areas, corresponding to snow and ice, are the most dominant in the October image. The dominating feature in the May image (visualised in red) are reflections from bare rock and soil in the image.

Visualised as green in the figure 5.6, are the areas that dominate the radiance val-ues in the 24th of July image. Those areas are known to be covered in vegetation.

This can indicate that a mid summer image is a good choice for the classification of vegetation using a single date image. Visualised as bright blue, are the radiance values from the 18th of October image. These corresponds to areas with snow and ice (see original image in fig 5.1G). The more exposed areas of Venabygd are covered in alpine grasses, lichen and bare rock. These are appearing as the dominating radiance values from the 23rd of May image (i.e. in red).

ROI separability statistics were calculated from all 18 bands in the image stack, see table 5.7. When comparing these results with those from the single date im-ages (shown in tables 5.1 to 5.6) there has been a considerable improvement in the separability between all grazing classes pairs. LG and VG received 1.88 using transformed divergence, and G and VG were clearly separable from the imperme-able class.

Grazing Quality Classes Jefferies Matusita Trasformed Divergence

Good & Very Good 1.02 1.19

Less Good & Good 1.07 1.21

Less Good & Impermeable 1.57 1.75

Less Good & Very Good 1.66 1.89

Good & Impermeable 1.85 1.98

Very Good & Impermeable 1.94 1.99

Table 5.7: ROI separability. asr images (i.e. 18 bands): 23.05.04, 24.07.94, 18.10.99

Further improvements could be made by adding additional dates or trying other date / image combinations from various parts of the growing season. Despite the promising results obtained from the time series analysis, proceeding analyses in this thesis focused on one mid summer image. This was to reduce the complexity of the situation and make the process more manageable. An image from the mid-dle of the summer was chosen. The image was from the 24th July 1994 as it had the best separation results between grazing quality classes. More importantly, a summer image ensures that all vegetation types are present and are a part of the signal recorded in the image.

5.2.2 NDVI

The normalised difference vegetation index (NDVI) gives a measure of ”green-ness” of the land cover which ranges from -1 to +1, with +1 representing very

”green” land cover types. The NDVI for the mid summer image was calculated and compared to the grazing class polygons. Figure 5.7 shows the results in a histogram.

5.2 Initial Class Separation 69

As expected all the grazing classes have values above 0. The maximum value for the classes lay at around 0.7. Each class had fairly high standard deviations, but the peak values for each class were distinct from each other. The VG grazing quality class had the highest NDVI peak value, followed by the G grazing quality class. The LG class peaked at the lowest value of the three classes, at around 0.35.

The pasture class (beitevoll) was a very small class in the Venabygd area and was not represented clearly in the range of frequencies. It however had a peak similar to that of the VG class. Pasture is made of lush green grass and was expected to have NDVI values closest to the VG class. It was incorporated to give a comparison for very ”green” vegetation. In terms of using NDVI a feature to separate the grazing classes, there was too much overlap between them for this to be used as a separator alone, but did show positive results.

Figure 5.7: NDVI plotted for each grazing quality class. The NDVI gives a measure of

”greenness” which ranges from -1 to +1, with +1 representing very ”green” land cover types.

Each class had a fairly high standard deviation, but the peak values for each class were distinct from each other. The VG grazing quality class had the highest NDVI peak value, followed by G grazing quality. The LG class peaked at the lowest value of the three classes around at 0.35.

Vegetation Classes

The initial grazing class separation results did not shed any strong light on the possibility for separation. The next phase was then to Break the grazing classes into smaller units to try and improve separation. These units corresponded to the

individual vegetation types defined by NIJOS.

Figure 5.8: Histogram plot for each vegetation class within the LG grazing quality class.

Lavhei (2e) has a distribution slightly outside the average for all the others in bands 1,2,

& 3 for the LG grazing class. This could be leading to some of the problems in separating between the grazing classes.

Each vegetation type has its own number and letter code which will be used reg-ularly throughout the results chapter. See chapter 3 for a full list of conversions.

The following three histogram plots from fig 5.8, to 5.10 show the spread of each vegetation type grouped into the three grazing quality classes. Data is shown for each band of the mid summer image dated 24.07.1994.

In the LG grazing quality class (fig 5.8), 2c-Lavhei has a distribution that is slightly different from the others in bands 1, 2, and 3. In band 4 it has a simi-lar distribution to the other vegetation types. Vegetation classes 9c-grasmyr and 9a-rismyr show very similar distributions in all bands. In band 4 it is a differ-ent vegetation type 7b that changes the distribution to the other two seemingly dominate classes.

Having a class with a distribution differing from the majourity can drastically alter the radiance mean of a grazing quality class. Illustrating the data in this way can give an indication of where the problem areas lie with the separation between grazing classes, making them not as visible on the plots. The frequencies

5.2 Initial Class Separation 71

Figure 5.9: Histogram plot for each vegetation class within the G grazing quality class. 4b and 7b have similar distributions in bands 1, 2, 3, 5, and 7, but varying distributions in band 4. 7b has its radiance peak at a lower value in band 4 than that of 2e and 4b.

of many of the vegetation types are very small compared to the dominating classes.

Good separation between grazing quality classes is important, but not between vegetation types of the same grazing class.

In the G grazing quality class (figure 5.9), the dominant vegetation types are 2e-Rishei, 4b-Bl˚abærbjørkeskog and 7b-Bl˚abærgranskog. 4b and 7b have similar distributions in bands 1, 2, 3, 5, and 7, but varying distributions in band 4. 7b has its radiance peak at a lower value in band 4 than that of 2e and 4b.

The VG grazing class in figure 5.10, has vegetation types with similar frequencies.

7c-Enggranskog and 4c-Engbjørkeskog have similar distributions but are slightly aside from the other vegetation types. Band 4 has a large range of radiance values.

11b and 4g are defined in their own grazing quality class called pasture (beitevoll), but are included here for comparison.

No vegetation type singled its self out completely from the rest in any of the grazing quality classes. Separation between any single vegetation classes did not become apparent from these plots.

Figure 5.10: Histogram plot for each vegetation class within the VG grazing quality class.

Vegetation types have fairly similar distributions. Distributions in band 4 cover a large range of radiance values.

5.3 Image Variation

The next set of analyses followed on from the initial separation results which illustrated that the defined grazing and vegetation classes did not have spectral properties that made it easy to separate between them. The second group of anal-yses therefore, looked closer at the dataset to try and understand what was varying in the image and why. Anaylses looked at what lies behind the variation within individual vegetation and grazing classes. This section covers analyses and dis-cussions that look into:

• Spectral variation,

• Terrain variation,

• Terrain variation,

• Atmospheric variation,

5.3ImageVariation73

Figure 5.11: Flow diagram to indicate the procedure taken during analyses of terrain variation and spectral variation. This flow diagram follows on from figure 5.2. The next flow diagram is figure 5.16.

• Illumination variation and

• Effects of NIJOS mapping methods (section 5.4)

Figures 5.11 and 5.16 illustrate the procedures followed for these analyses with flow diagrams.