.
Results
It is not within the scope of this chapter to examine and discuss fully the results obtained so far. For the purposes of illustration only, a highly simplified example of the results from just one 1km2 is presented. The vegetation classification has been reduced to a single dominant vegetation type and the six most common types involved with changes to birch and pine woodland have been selected. Care must be taken in interpreting these simplified results since the pixels are classified only by the dominant vegetation type within a polygon. For example, a polygon with 55% of its pixels in vegetation type A and 45% of type B would be classified as A. However, if the vegetation changes, such that the proportions are reversed, and the polygon is classed as B, it does not imply that all pixels in type A in the polygon have changed to type B; in fact only 10% have changed.
It should also be noted that the category ‘grassy heaths’ included recently burned Landscape ecology and geographic information systems 128
moorland; whether this reverts to heathland depends very much on local grazing pressures. The ‘heath’ class contains both wet and dry heaths.
More detailed estimates of changes in the various vegetation components are obtained by analysing the data using several levels of the vegetation classification.
Maps of the areal cover of birch-, pine- and heath-dominated vegetation for each of the 3 years are shown in Figures 9.8 to 9.10, along with their areas as a percentage of the whole square (Figure 9.11). There have been marked increases over the years for birch (from 9% of the square in 1947 to 27% in 1985), unimproved grassland (3% to 18%) and bog (6% to 14%, after dropping to 3% in 1964). Although pine trees were present in the square in 1947, they were not dominant anywhere, and therefore show as zero cover at this level of classification. By 1985, pine trees had become dominant in 5% of the square’s area. In contrast, the amount
Figure 9.8. Grid square 23—birch (1947–1985).
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of moorland had declined from 46% to 11%; only 2% of this loss was accounted for by changes to other vegetation types not shown such as arable/improved grassland, forestry plantation, tall shrubs and bracken. Only in the case of birch did the rates of change approach linearity.
Figure 9.12 shows some of the different pathways involved in succession to birch and pine woodland from other major types; the numbers show the percentage of the vegetation type at the foot of the arrow that has changed to the vegetation at the head of the arrow between the 2 years.
The changes from other components of the square to birch-dominated vegetation indicates the invasive nature of this tree and its ability to establish in widely differing vegetation types. Also of interest are the changes from moorland to only three other types
Figure 9.9. Grid square 23—Scots pine (1947–1985).
in 1947–1964, but to five other types, including pine dominant, in 1964–1985. These changes cannot be discussed fully here, but some can be related to decreases in grazing
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pressures and/or the amount of burning taking place (e.g. pine establishing on moorland) and to local increases in grazing pressure (e.g. dry heath changing to unimproved grassland, wet heaths to boggy graminoid-dominant vegetation). The aerial photographs showed that some areas were drained before 1964, which partly explains the successions from bogs in Figure 9.12(b).
Fuller discussion of the forces driving these sorts of changes can be found in the literature, e.g. Miles (1988) and Ball et al. (1982).
Figure 9.10. Grid square 23—dry heath (1947–1985).
Discussion
Some points arise from the methodological aspect of this work. Firstly, checks between photo-interpretation in the laboratory and subsequent ground-truthing showed that the majority of the vegetation types of interest defined in the classification scheme can be
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recognized on the photographs selected. For example, dry heath, bracken (in large patches) and grass can consistently be identified, as can birch and pine trees above about 2m in height. However, even experienced observers have difficulty consistently identifying small patches of scrub vegetation. Gorse, broom, juniper and willow were found to occur in patches that were too small to permit conclusive identification all of the time. Where small stands of scrub have apparently persisted to the present day, it is sometimes possible to confirm that the species has remained the same by ageing the present plants from ring counts of stem sections or cores and seeing if their age predates the earlier photograph. This check is less useful for gorse and broom which rarely live for more than 20 years.
Secondly, although a ‘working’ methodology has been developed for studying vegetation successions, a number of improvements could be made. Aerial photography clearly contains the ecological information of interest but the aerial photo-interpretation can be difficult and time consuming; ideally this would be automated. Unfortunately, automated analysis of remotely sensed data (and particularly aerial photography) is not
Figure 9.11. Proportion (%) of square occupied by major semi-natural vegetation types in 1947, 1964 and 1985.
yet at a stage where it can be used satisfactorily for this type of work. Investigations carried out within the framework of this project into alternative sources of data from
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satellite and airborne scanner systems have shown that they do not yet provide adequate resolution for the purpose of modelling vegetation successions. Furthermore, vegetation classification based solely on spectral information is not sufficiently well developed even today for the classes of most interest. Our conclusions support those of Budd (1987).
Improvements are possible with the use of additional information, e.g. texture, which is already incorporated in some automated classification systems, but other factors used during visual photo-interpretation (such as association, shape and size) are more difficult to incorporate.
At present, therefore, even with the currently available image-processing algorithms, satellite data are at best only a supplementary/complementary source of information to standard aerial photo-interpretation. Obviously it will be a long time before we have 40 years of reliable, high-resolution satellite data comparable to the archival aerial photography now available.
Useful improvements can readily be identified in the data-processing aspects of our methodology, particularly with regard to the polygon maps (Figures 9.8 to 9.10);
obviously some of the editing procedures could have been conducted more efficiently if we had had continual access to a suitable image analysis system. However, the procedure which took most man-hours (as opposed to computer time) was in linking the unnumbered polygons in the computer files with their appropriate numbers, and hence vegetation classifications, ascribed during the manual mapping. Ideally, one would display a map on a monitor, use a cursor to select any point within a polygon and then input the original number of the polygon where-upon all the component pixels would receive that coding. The ability to do this would delete all the steps in Figure 9.6 from
‘Convert binary to ASCII’ to ‘Note ASCII numbers…’ and also the steps ‘Edit ASCII polygon…’ and ‘Sort vegetation…’ in Figure 9.7. Our limited investigations suggest that this type of function is not available even on some sophisticated image analysis systems, the classifications either being done during manual digitizing or else the data are received in an already classified form.
In conclusion, it is clear from the preliminary results obtained by this research that, despite constraints imposed by varying scales and quality, it is both possible and practical to use archival aerial photographs to study vegetation successions in upland habitats. The methodology we have developed is functional, although it could be refined, and could potentially be applied to other aspects of remote sensing research.
Acknowledgements
We would like to thank Don French (ITE, Banchory) for writing most of the programs that were essential for this study, Ruth Weaver (Polytechnic Southwest) for her work and advice in conducting the first half of the work described here, and Nigel Brown (ITE, Monkswood) for his guidance and preparation of data on the IIS and SYSSCAN systems.
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