The Maputaland Coastal Plain is situated in north-eastern KwaZulu-Natal Province, South Africa. The Maputaland Coastal Plain and underlying aquifer are two separate but inter-linked entities. This area with high permeable cover sands, low relief and regional geology that slopes towards the Indian Ocean, hosts a variety of important wetlands in South Africa (e.g. 66% of the recorded peatlands). The wetlands overlie and in some cases also connect to the underlying regional water-table. The apparent distribution of wetlands varies in response to periods of water surplus or drought, and over the long-term has been reduced by resource (e.g. agriculture, forestry) and infrastructure (e.g. urbanisation) development. Accurate wetland mapping and delineation in this environment is problematic due to the ephemeral nature of wetlands and extensive land-use change. Furthermore the deep aeolian derived sandy soils often lacks soil wetness indicators in the soil profile. It is postulated that the aquifer is the source of water to rivers, springs, lakes and wetlands (and vice versa). However, the role of groundwater in the sustainability of hydro-ecological systems is unclear. Consequently this research attempted to determine spatial and temporal changes in the distribution of these wetlands, their susceptibility to human development, understand the landscape processes and characterise and classify the different wetland types. An underlying assumption of the hydrogeomorphic wetland classification concept in South Africa is that wetlands belonging to the same hydrogeomorphic unit share common features in terms of environmental drivers and processes. Given the above, the objectives of this thesis relating to the north-eastern corner of the Maputaland Coastal Plain are to: 1) Map the distribution of wetlands and their relation to other land-use; 2) Characterise the landscape processes shaping the dynamics of wetland type and their distribution; 3) Classify wetlands by applying hydrogeomorphic wetland classification system. This study used Landsat TM and ETM imagery acquired for 1992 and 2008 (dry) and Landsat ETM for 2000 (wet) along with ancillary data. Wetland type characteristics were described using terrain unit position in the landscape, SRTM DEM, land surveyor elevation measurements along with long-term rainfall records, in situ water-table levels with soil analysis and geology and vegetation descriptions. A conceptual model was used to account for the available data, and output from a hydrology model was used to support the interpretation of wetland distribution and function. Wetlands in the study area include permanent wetlands (swamp forests and reed/sedge wetlands), but the majority of sedge/moist grassland wetlands are temporary systems. The wetland distribution reflects the rainfall distribution and groundwater discharge in lower lying areas. The weathering of the Kosi Bay Formation is a key factor in wetland formation. Because of an increase in clay content with depth, the pore-space and hydraulic conductivity are reduced which causes water to impede on this layer. The nature of the aquifer and regional geology that slope towards the east along with extreme rainfall events in wet and dry periods are contributing drivers of wetland and open water distribution. In 2008 (a dry year) the smaller wetland extent (7%) could primarily identify “permanent” groundwater-fed wetland systems, whereas for the wet year (2000) with larger wetland extent (18%) both “temporary” and “permanent” wetlands were indicated. Comparison between both dry years (1992 and 2008) indicates an 11% decrease in wetland (sedge/moist grassland) and a 7% increase in grassland distribution over time. Some areas that appear to be grassland in the dry years were actually temporary wetland, based on the larger wetland extent (16%) in 2000. The 2008 Landsat TM dataset classification for the entire Maputaland Coastal Plain gave an overall 80% mapping accuracy. Landscape settings identified on this coastal aquifer dominated by dune formations consist of 3 types: plain (upland and lowland), slope and valley floor. Although the wetland character is related to regional and local hydrogeology as well as climate affecting the temporal and spatial variability of the wetlands this research confirms that the patterns and wetland form and function are predominantly shaped by the hydrogeomorphic setting and not the rainfall distribution. The following wetland types were identified: permanent wetlands such as peat swamp forests, peat reed and sedge fens; temporary wetland systems such as perched depressions, and sedge/moist grasslands. The Hydrogeomorphic wetland classification system was applied using a semi-automated method that was 81% accurate. The following hydrogeomorphic units could be identified: one floodplain, i.e., Siyadla River Floodplain, channelled valley-bottoms, unchannelled valley-bottoms, depressions on modal slope values <1%, seepage wetlands on modal slope values 1-2%. However, evaluation of the hydrogeomorphic classification application results suggests that the “flat” hydrogeomorphic class be revised. It did not fit meaningfully on the upland plain area. This research finding concludes wetland function does depend on landscape setting and wetland function is not truly captured by the hydrogeomorphic type classification. Not all depression on the coastal plain function the same way and three types of depressions occurs and function differently, i.e., perched depression with no link to the regional water-table vs. depressions that are linked with the regional water-table on plain, slope and valley floor landscape settings. Overall, this research study made a useful contribution in characterising and classifying wetland type and distribution for a high priority wetland conservation area in South Africa. Applying similar methods to the broader Maputaland Coastal Plain will particularly benefit from the research findings. The importance of using imagery acquired in wet and dry periods as well as summer and winter for a more comprehensive wetland inventory of the study area, is stressed. To manage the effects of climate variability and development pressure, informed land-use planning and rehabilitation strategies are required based on landscape analysis and interpretation.

U.S. Army Corps of Engineers (USACE) regulates fill of jurisdictional waters of the United States including wetlands. Recent USACE regulations set a threshold of impacts to wetlands at one-half acre. Impact area can be determined by Global Positioning System (GPS) measurement of wetland boundary and Geographic Information System (GIS) calculation of impact area. GPS point selection methods include (1) equal time interval, (2) transect and (3) intuition. Four two-acre shapes were measured with each GPS method and brought into GIS for area calculation. Analysis of variance and Root Mean Square Error analyses determine that the transect method is an inferior point selection method in terms of accuracy and efficiency.

A suitable methodology for mapping wetlands in South Africa has not been agreed upon. This investigation aimed at developing a methodology for the accurate and efficient delineation of wetland areas using satellite imagery and other relevant spatial datasets. Both summer and winter LANDSAT ETM+ satellite imagery covering the study area of the Midmar sub-catchment were processed using various image classification techniques. These included the supervised, unsupervised and level slicing classifications. The accuracy of each technique was tested against the only existing verified wetland dataset that covers the study area. A ground truthing exercise was also undertaken. The different classification techniques resulted in different classification accuracies when compared to the verified wetland dataset. Accuracies for the different classification techniques were as follows: unsupervised 20 class classification (summer) 55%, (winter) 39%, unsupervised 255 class classification (summer) 71%, (winter) 47%; supervised classification (summer) 65%, (winter) 41%; level slicing classification (summer) 65%, (winter) 45%. The inaccuracies could mostly be attributed to a change in land cover as there seems to be an overall loss of wetland areas. However, the ground truthing exercise resulted in higher classification accuracies especially with unsupervised 255 class classification. This study concluded that LANDSAT ETM+ satellite imagery was useful for detecting wetlands areas during summer by using a fine classification technique (255 class). A finer classification technique is also suited for the detection of both large and small wetland areas. Major recommendations include: the use of summer imagery in a high rainfall period; the unsuitability of using winter imagery due to the spectral confusions created; the use of high resolution satellite sensors (SPOT) for monitoring purposes while lower resolution sensors (LANDSAT) should be used for mapping; the increased use of topographical modelling for wetland detection; the use of an appropriate scaled land cover database and the use of field verification exercises for comparing classifications. / Thesis (M.Env.Dev)-University of Natal, Pietermaritzburg, 2001.

Global trends of transformation and loss of wetlands to other land uses has deleterious effects on surrounding ecosystems, and there is a resultant increasing need for improved mapping of wetlands. This is because wetland conservation and management depends on accurate spatial representation of these systems. Current approaches to mapping wetlands through the classification of satellite imagery typically under-represent actual wetland area, and the importance of ancillary data in improving the accuracy in mapping wetlands is recognized. This study uses likelihood estimates of wetland occurrence in KwaZulu-Natal (KZN), South Africa, using a number of environmental surrogate predictors (such as slope, rainfall, soil properties etc.). Using statistical information from a set of mutually independent environmental variables in known wetland areas, conditional probabilities were derived through a Bayesian network (BN) from which a raster layer of wetland probability was created. The layer represents the likelihood of wetlands occurring in a specific area according to the statistical conditional probability of the wetland determinants. Probability values of 80% and greater also accounted for approximately 6% of the KZN area (5 520 km²), which is substantially more than the previously documented wetland area in KZN (4% of the KZN area or 4 200 km²). Using an independent test dataset, Receiver Operating Characteristic (ROC) curves with the Area Under Curve (AUC) analysis verified that the final model output predicted wetland area well (AUC 0.853). Based on visual comparisons between the probability layer and ground verified wetland systems, it was shown that high wetland probability areas in the final output correlated well with previously highlighted major wetland and wetland-rich areas in KZN. Assessment of the final probability values indicated that the higher the probability values, the higher the accuracy in predicting wetland occurrence in a landscape setting, irrespective of the wetland area. It was concluded that the layer derived from predictor layers in a BN has the potential to improve the accuracy of the KZN wetland layer by serving as valuable ancillary data. Application of the final probability layer could extend into the development of updated spatial freshwater conservation plans, potentially predicting the historical wetland extents, and as input into the land cover classification process. Keywords: ancillary data, Bayesian network, GIS, modelling, probability, wetland mapping. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2014.

Please read the abstract in the section 00front of this document / Dissertation (MSc (Botany))--University of Pretoria, 2006. / Plant Science / unrestricted