**5.1.2 Kibera change detection**

The multi-level contextual change detection method we used in this case study was first developed for monitoring various greater and intensive processes on the Earth's surface with middle resolution imagery (i.e. Landsat, SPOT). The result on Kibera proved same contextual logic is efficient also when implemented on very high spatial resolution imagery.

Change detection applied to Kibera informal settlements aimed to obtain an outline of the distribution and extent of major urbanisation processes. Comparison was made for QB 2006-03-27 and GE 2009-07-25 images. Method has proven to be suitable for monitoring changes related to various processes (buildings construction, buildings collapse or disappearance, rooftop renovation, increase/decrease in vegetation) and/or of the coincident description of their trends (see section 5.2.2 for identified informal settlement growth in Raila village area). Although quantitative assessment of change occurrences was not performed due to lack of independent reference data, a detailed visual control was made through comparison of before and after images and, nevertheless, several conclusions can be derived.

Identified pattern of changes (Fig. 11) clearly draw attention to spots where urbanisation between years 2006 and 2009 was most intensive: Kianda north-east, Raila southern border, Mashimoni and Laini Saba northern border and Soweto East eastern tail. According to the

Object-Based Image Analysis of VHR Satellite Imagery for

classification of GeoEye image from 2009.

**5.2.1 Detailed land cover map of Raila village 2009** 

2.5 km2 only.

**5.2 Raila village** 

buildings were considered:

manually digitize the whole image.

individual object in build-up zones.

modelled and mapped (Fig. 12).

Population Estimation in Informal Settlement Kibera-Nairobi, Kenya 425

As we can see from Table 5, population estimation of Kibera can vary from 150 thousand up to 650 thousand people living in the informal settlements according to different population density sources. Nevertheless, both limits are high taking into account Kibera is spread on

MapKibera Project (2008) 0.0951 156,652 IRIN (2006) 0.2000 329,377 AHI US (2005) 0.3000 494,065 Sartori et al. 1 (2002) 0.3300 543,471 Sartori et al. 2 (2002) 0.3900 642,284

Table 5. The estimation of population according to the density acquired from different sources. Housing area of Kibera was calculated from results obtained with object

We made a detailed classification focused on residential housing only on image subset covering the area of Raila village. The main idea was to automatically obtain polygon shapes of each individual residential object or settlement. Although on VHR image individual objects in general can be distinguished, due to the complexity and variety of dense roof surface there was impossible to extract the shape of every individual residential object or each group of objects (settlements). Roofs and their elements themselves present heterogeneity, resulting in distinct spectral variation within areas of homogeneous land cover. Class "buildings\_bright" were possible to detect since they had a very distinct morphological pattern that was contrasted by surrounding (built-up) land cover. Segments of class "buildings\_brown" were sometimes integrated with their surroundings (soil) so they could not be readily distinguished. Some urban elements (like rooftops) are a combination of many different surface materials; this produced a spectral response that was difficult to interpret with routine procedures.

Automatic shape detection of each individual residential object would enable good total population estimation. Two approaches to get classified vector shapes closer to individual

 Manual correction of the shapes of obtained polygons: Small test showed that this is extremely time consuming. Correction took more time than it would take if one would

 Use of additional attributes that are automatically created in the phase of ENVI EX segmentation: Investigation showed that there is no useful relation within additional attributes of all sub-classes of buildings. Therefore additional attributes could not assist to semi-automatic reclassification of some segments in order to obtain better shapes of

Finally, focused to residential build-up objects in Raila area four major object classes were

**Source Density [people/m2] People living in Kibera** 

density of change pattern elements, in addition to the edges of Kibera informal settlement, several changes occurred in the eastern part of Kibera. These are mainly due to larger new buildings constructions or complete rooftops renovations. Additional socio-economic data or bigger events information (like flooding) would be welcomed to associate the rate of more intensive rooftops renovations at some of Kibera villages and compared to the other parts of the settlements.

With external data, for example land cover for a reference point in time, a fairly reliable "from-to" change statistics could be extracted. However in rather homogenous land use areas like Kibera informal settlement is, where most of the land use is residential (section 5.1.1) and where urbanisation direction limits are well known due to formal residential settlement boundaries, such information would not additionally characterize the change pattern. On the example of Raila village change detection in section 5.2.2 we comment some difficulties observed related to detection of change in slum-like areas in a more detail.

Fig. 11. Identified changes between years 2006 and 2009 (Erdas Imagine). Change pattern enclose changes due to new buildings construction, buildings disappearance and larger rooftop renovations.

#### **5.1.3 Kibera population estimation in 2009**

Total surface of Kibera was calculated as a sum of housing areas of all 12 villages throughout whole Kibera from vector results obtained with object-based classification. All the vectorized structures have been computed using ArcGIS software and we assumed that all the structures are used for habitat purposes.

Sum of the residential areas show that total residential area of Kibera is around 1.646,883 m2, which is 358,706 m2 more than total surface from year 1993 given by Sartori et al. (2002), where residential area was 1.333,834 m2. We can see that informal settlement population density has increased from year 1993. Since Kibera is surrounded by well-defined residential boundaries and its drastical expansion therefore was not possible, we can assume that the increase of residential area can attribute to denser housing inside the informal settlements through years.

As we can see from Table 5, population estimation of Kibera can vary from 150 thousand up to 650 thousand people living in the informal settlements according to different population density sources. Nevertheless, both limits are high taking into account Kibera is spread on 2.5 km2 only.


Table 5. The estimation of population according to the density acquired from different sources. Housing area of Kibera was calculated from results obtained with object classification of GeoEye image from 2009.
