The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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developed by GeoAnalytika, an affiliate local company of the 
author, the desired accuracy and image quality has been 
achieved. The final method used was actually a modified 
multistep process using IHS and HSL Inversion Method. 
5.2 
Ortho-Image Production 
After creating the pan fused natural colour satellite image, the 
next step is image rectification and registration. This stage 
involves warping the image to correct for geometric errors and 
conform it to a selected map shape in the correct coordinate 
system. These five major steps summarizes the whole ortho 
rectification process namely selection of map projection and 
coordinate system, identification of ground control points 
(GCPs) from the image, collection of ground control points with 
a GPS, rectification using a suitable mathematical model, 
reprojection to a standard map projection and quality control 
and assessment. 
A total of 33 identified GCPs were successfully identified, 
occupied, and established in a local map projection and datum 
as required for the final base map. The distribution of points is 
shown in Figure 2. In order to meet the accuracy requirements 
for the basemap, a high accuracy differential GPS survey was 
conducted. Occupation time per control point was set from 15- 
20 minutes, which ensures that enough satellite observation data 
is acquired to produce a high accuracy position per point. 
Results of the Differential GPS survey results were summarized 
as follows: Maximum RMS error = 0.017 meters, Average RMS 
= 0.009 meters, Maximum Standard Deviation = 0.143 meters, 
and Average Standard Deviation = 0.05 meters. The high 
accuracy survey results ensures that positional errors will be 
kept to a minimum and shall not affect the accuracy of the final 
basemap. 
After all GCPs have been collected and processed, a 
rectification model is then created. The average GCP RMS error 
is 0.89 meters. In addition, the RMS error of the GCPs 
exhibited an unusually high deviation from the mean error with 
differences from a high of 4.78 meters (8 pixels) to a low of 
1.13 pixels (0.68 meters) even with repeated checks of their 
plotted position in the image. It was deduced that the errors 
were inherent in the image because of pre-rectification done by 
the image supplier. An alternative method is using a delauney 
triangulation. This approach fixes and preserves the image point 
locations and uses a linear stretch to warp the pixels in between 
the control points. Thus, there is virtually zero (0) error at the 
control points and the errors are distributed linearly between 
control points. This method assumes that the GCP image points 
have been chosen as carefully as possible to match the actual 
ground feature with minimum doubt. 
The result is a geometrically sound UP Campus base map image 
with sub-meter accuracy and visual quality that can rival 
conventional color aerial photography suitable for large scale 
mapping purposes up to 1:2,000 (Figure 2). 
Figure 2. UP Campus Base Map Showing Boundary and 
Distribution of GCPs in the Project Area 
5.3 Visual Interpretation of High Resolution 
QuickBird Imagery 
A compilation of land titles was used to define the extent of 
university property (Figure 2). The lots are plotted, checked for 
traverse errors, and then consolidated to form the whole 
coverage of the campus using CAD software. The boundaries 
were overlayed on the base map image to determine the extent 
of the study area. Remote sensing softwares were used to view 
and visually interpret the image to determine locations of areas 
occupied or directly utilized by informal settlers. 
Ward and Peters (2007) used visual interpretation of high 
spatial resolution multispectral IKONOS satellite image to 
identify low-income informal homestead subdivisions (IFHS, 
also known as colonias) in peri-urban areas of US metropolitan 
areas. This process of visual interpretation of high spatial 
resolution satellite data is not automated, but requires the 
systematic search, identification, and delineation of the target 
features by the analyst. 
The object of study which is informal settlements is readily 
identifiable on the image because of the high resolution and 
excellent visual quality of the image. There were two identified 
qualified categories of informal settlements for this study. The 
first is the “slum” type characterized by MMUSP (2002) to have 
lack of spatial pattern, smaller structures, irregular boundary 
demarcation, clustering and uneven spread, different reflectance 
and locational attributes. These settlements are considered to be 
the miserable or depressed areas, normally lacking in basic 
services such as electricity, water, and communication lines. 
The second type is a “semi-formal” type characterized by a 
more decent neighborhood similar to a low-cost housing with 
provisions of the basic services and amenities. These 
settlements, although not physically informal, lack necessary 
tenure or permit to legally occupy the property. 
5.4 
GIS Mapping 
Abbott (2003) has pointed out the value of GIS for the 
evaluation of informal settlements. In his study, vegetation 
cover and land use have been quantified by visual interpretation 
of 1:8000 scaled air photos using the Autocad 14 (AutoDesk) 
and ArcView 3.2 (ESRI) softwares. The digitization of the