List of Tables
7. DISCUSSION
7.3 CLASSIFICATION METHODS
7.3.1 Hard classifiers
Two previous attempts at mapping land cover using hard classifications of Landsat data produced comparable results for the Eastern Shores (Figure 7.1). A national land cover classification for South Africa (CSIR/ARC, 1998) mapped unimproved grassland, wetlands, and thicket & bushland as the main cover types in the unmasked area of the Eastern Shores. Also mapped were smaller areas of forest and water. Smith (2001) used a more detailed land cover classification in mapping Maputaland, finding the unmasked part of the Eastern Shores to be dominated by hygrophilous grasslands8, woody grasslands9, and sedge & grass swamp. Also present were smaller amounts of open water, plantations and mud flats. Both these classifications produced maps of all land cover types, not just water, and neither attempted to map the temporal fluctuations in surface water. In a third mapping exercise, Walsh (2004) mapped swamp forest in Maputaland but found it very difficult to differentiate swamp forest from other forest types based on spectral information alone. In this case, mapping accuracy was improved by incorporating DEM data.
Both CSIR/ARC (1998) and Smith (2001) found the main Eastern Shores land cover types to be grasslands and wetlands/hygrophilous grasslands. In testing the applicability of hard classifiers to the mapping of surface water, the current study used similar land cover categories, namely wetland and grassland, as well as a water class. Classification of the clusters from the unsupervised cluster analysis was done by referral to field data, aerial
8. Hygrophilous grasslands are waterlogged for most of the year and are found on flat ground and in depressions between dunes.
9. Woody grasslands occur mainly on dune crests, slopes and relatively high lying level plains.
(a)
Unimproved grassland H M I ^ ^ ^ ^ M H B B B B H B B ^ ^ ^ ^ ^ ^ ^ B H 62 V/etlands • I H B B B H 1 8
Thicket & bushland • • • • 1 0 Forest plantations MZ
Water bodies mZ Forest B3 Other land cover 11 ,
0 10 20 30 40 50 60 70 Percentage cover
(b)
Hygrophilous grassland Woody grassland Sedge & grass swamp Open water Plantations Mud Inland evergreen forest Terminalia woodland Beach Swamp forest Other land cover
0 10 20 30 40 50 60 70 Percentage cover
Figure 7.1: Percentage of the study area covered by the land cover classes of (a) the CSIR/ARC (1998) classification, and (b) the Smith (2001) classification. Figures are for the unmasked portion of the Eastern Shores study area only.
photographs and colour composite images. In performing this classification it was apparent that many clusters could not be assigned to any of the three classes but appeared to be mixed classes, containing elements that were partly grass and partly wetland. These mixed classes were assigned to a new class called grassland/wetland. The numbers of clusters allocated to the wetland class seemed to indicate that these environments were a lot more complex than grasslands, which generally tended to contain fewer clusters. It was also noted that there was less confusion between grasslands and wetlands in wetter years. This ties in with the findings of Pillay (2001) who achieved greater accuracies when mapping wetlands of the Midmar sub-catchment using images from the wet season.
The maximum likelihood classification also mapped pixels into classes of water, grassland and wetland. The major problem with this classification lay in the large number of pixels left unclassified. As discussed in the previous chapter, this could have been due to poorly chosen training sites, but it is much more likely that the landscape was too complex to be
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mapped into just three classes. This is similar to the problem experienced with the cluster analysis where considerable confusion existed between grassland and wetland clusters.
The problem with the spectral separation of different land cover types has been commented on by a number of researchers. Smith (2001) found that the maximum likelihood classifier struggled to distinguish hygrophilous grasslands from woody grasslands, but that mapping accuracy could be improved through the incorporation of ancillary DEM and vegetation data. Similarly, Boresjo Bronge & Naslund-Landenmark (2002) discovered that Scandinavian marshes were difficult to map using spectral data alone, and so used ancillary data derived from topographical maps to enhance classification accuracy. The presence of mixed classes was commented on by Jensen et al. (1995), who noted the presence of 'intermediate' clusters in their mapping of aquatic vegetation of the Florida Everglades.
These, they surmised, contained different mixtures of the land cover types they were attempting to map.
Both hard classifiers produced good estimates of the amount of open water in the study area. This was in line with findings by Smith (2001) who reported a water mapping accuracy of 100% for Maputaland using Landsat imagery. Open water proved easy to map because of the uniqueness of its reflectance signal, a fact noted in numerous studies.
Wickware et al. (1991), Lunetta & Balogh (1999) and Frazier et al. (2003) among others, were all able to map water fairly easily using data from near- or mid-infrared bands.
In terms of a hard classification approach, very few of the reviewed studies relied solely on an unsupervised approach to map water and/or wetlands. The preference was to use either a supervised maximum likelihood classification or else to employ a hybrid classification technique. Even so, some studies found even the maximum likelihood classification unsatisfactory for mapping wetland-type environments (e.g. Wickware et al, 1991). Smith (2001) surmised that the accuracy of the maximum likelihood classifier could be improved by incorporating ancillary data like elevation and water table heights. Those studies that used hybrid classifications (e.g. Jensen et al, 1995; Lunetta & Balogh, 1999; Munyati 2000) all used clusters from an unsupervised cluster analysis as input into a supervised maximum likelihood classification. This was done in an attempt to overcome the limitations of hard classifiers.
The limitations of hard classifiers found in this study mirror those reported by other researchers. The main reason for the difficulty in applying hard classification techniques to wetland mapping is that the spectral characteristics of these environments represent a continuum from open water with no emergent vegetation through to complete vegetation cover where no water is visible. These complex landscapes do not lend themselves to hard classification and are much better served by sub-pixel classification methods.