List of Tables
5. METHODOLOGY
5.6 ACCURACY ASSESSMENT
woody, although Table 5.5 shows how each of them represented varieties of classes with similar reflectance characteristics. The endmember values for both the spectral and component datasets were entered into Idrisi in preparation for the unmixing phase of the spectral mixture analysis.
Table 5.5: Details of the image endmembers used in the spectral mixture analysis.
Endmember Water Woody Non-woody
Representing Open water, shade
Plantations, dune forests, bush clumps and other dark green vegetation Grassland and wetland vegetation
5.5.3.2 Linear spectral unmixing
Linear spectral unmixing was carried out with the UNMIX module in Idrisi and was performed on bands 2, 3, 4, and 5 of the spectral data and on the first three components of the component data. Using the values of the three endmember signatures, the spectral reflectance signal from each pixel was unmixed and the percentage cover of each of the endmember land cover classes was calculated. The output from the spectral unmixing was a series of fraction images, one for each endmember, which indicated the percentage cover of the endmember land cover class within each pixel. A residual image showing pixel residual values was created by summing the absolute values of each of the band residuals6. High residual values implied a poor fit between the modelled and actual pixel values.
these rely primarily on ground truth measurements. This prompted the investigation of alternative methods of assessing the accuracy of the image classifications. To this end, daily rainfall records and water table depths from a number of stations in and around the study area were obtained and their suitability for verifying the accuracy of the image classifications was assessed.
In investigating the use of ground water table levels it was noted that there was a close link between groundwater and the amount of surface water in the study area (Figure 5.7). Depth to groundwater readings covering periods close to the study dates were obtained from a borehole located at the south-western edge of Lake Bhangazi (Borehole Dl, 28°07'49"S;
32°31'37"E, 8.01m asl). This was the only borehole in the study area from which readings covering the entire study period were available. Water table heights calculated from these borehole readings are shown in Table 5.6.
surface water bodies area of surface water fluctuates in response to water table and rainfall
Figure 5.7: Conceptual model showing factors controlling surface water on the Eastern Shores.
From Table 5.6 it can be seen that borehole readings had been made at irregular intervals before and after the study dates. In some cases the gap between study date and borehole reading was two months, in others, three weeks. There was only one instance of a borehole reading coinciding with a study date. This situation made the use of borehole data as a verification tool extremely difficult as it was impossible to determine the influence of rain that fell between the study dates and the dates of the borehole readings. For this reason, the use of borehole data for accuracy assessment was not considered a viable option.
Water table fluctuates in response to rainfall
Table 5.6: Water table heights in metres above mean sea level for borehole Dl. The nearest study dates are indicated with arrows while the figures in brackets show the number of days between each study date and the nearest water table measurement.
Date 1991/07/06 2001/01/05 2001/03/01 2001/07/20 2001/08/21 2001/10/01 2001/11/21 2001/12/14 2002/04/24 2002/05/29 2002/06/21 2002/08/26
Water Table Height (m) 7.93
7.98 7.47 7.69 7.75 7.72 7.67 8.03 7.01 6.83 6.81 6.89
Study Date 1991/07/23 (-17) 2001/03/20 (-19) 2001/05/07 (-67) 2002/04/24 (0) 2002/07/13 (-22) 2002/09/15 (-20) 2002/10/17 (-52)
The assumption behind using rainfall data for accuracy assessment is based on the nature of hydrological inputs and outputs on the Eastern Shores. For the study area as a whole, surface and subsurface hydrological inputs and outputs are limited as there are no rivers flowing into the area. Ground and surface water inputs occur entirely though the addition of rainfall, with virtually no groundwater recharge occurring from the nearby Lake St Lucia (Kelbe, 2005, pers. comm.). For individual water bodies on the Eastern Shores, inputs are also controlled by rainfall. This occurs either directly through rain falling onto the water bodies themselves, or indirectly via groundwater flow and surface run-off of rainfall (Figure 5.7). Losses occur only through evaporation and transpiration, with no loss of surface water into the groundwater (Ellery, 2006b, pers. comm.). Rainfall is therefore the main factor driving surface water levels on the Eastern Shores. Given this close relationship between water levels (the dependent variable) and rainfall (the independent variable), comparing the outputs of the spectral mixture analysis against rainfall measurements provides an important means of validating the accuracy of the water mapping. If, through regression analysis, it is found that mapped water extents fluctuate in tandem with variations in rainfall, this would serve to verify the accuracy of the spectral unmixing process.
Daily rainfall records covering the entire study period as well as the months preceding the study dates were obtained for the Cape St Lucia Lighthouse and Charters Creek recording stations. The Cape St Lucia station is located 12 km south of the study area while Charters Creek is situated on the Western Shores, 3 km to the west of the study area. Cumulative rainfall totals for various periods prior to the study dates were computed for both stations and correlated against the amounts of surface water obtained through the image classification. These comparisons of rainfall against surface water were used to assess the accuracy of the image classification. The borehole data was not used.