**5.2 Raila village**

424 Remote Sensing – Applications

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

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

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

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

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.

parts of the settlements.

rooftop renovations.

**5.1.3 Kibera population estimation in 2009** 

all the structures are used for habitat purposes.

detail.

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

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 buildings were considered:


Finally, focused to residential build-up objects in Raila area four major object classes were modelled and mapped (Fig. 12).

Object-Based Image Analysis of VHR Satellite Imagery for

**5.2.3 Raila village population estimation in 2009** 

terms of typical size of houses in the Raila village (Fig. 14).

2009 for Raila village, Kibera.

Population Estimation in Informal Settlement Kibera-Nairobi, Kenya 427

QB2006-03-27 QB2008-08-10 GE2009-07-25

Change pattern from years 2006-2008. Change pattern from years 2008-2009.

Urban growth pattern through years 2006, 2008 and 2009 for Raila village. Dark red corresponds to 2006-2008 and light red to 2008-2009 period. Fig. 13. Results of change detecion for urban growth pattern through years 2006, 2008 and

great and consequently buildings disappearance is more difficult to detect. Same applies if new building has dark colour rooftop covers (for example, often used blue colour plates). Main change missing occurrences therefore apply to the just described situations, making difficulties for automatic approaches. To some extent this problem was solved so that the threshold for negative changes was reduced during transformation categorisation step.

According to the total residential area in Raila village obtained from object based classification (section 5.1.1, Table 3), population estimation was calculated based on different density per area parameters (Table 6). Additionaly we illustrate this information also in

Fig. 12. Detailed object based classification results for residential areas in Raila village.

#### **5.2.2 Raila village urban growth**

Multi-level contextual change detection approach was applied to Raila village to highlight the extent of major urbanisation processes that can take action even in a short time span. As expected the development on southern border of the village is well recognised (Fig. 13) and different time sequences analysed outlined where and when the growth has taken place.

Method incorporates spatial neighbourhood dependence to control the false change information (i.e. inherent changes due to locally based variability in data). As a result it gives a change pattern of positive and negative changes in imagery spectral space. Positive changes correspond to an increase in digital number values at the same location and negative changes to a decrease in spectral values. Method was developed for middle resolution imagery, but its application to Kibera informal settlement data proved this concept to be efficient on VHR imagery as well. In general, majority of objects transformations regardless of their size (small patches to entire buildings) was identified. The results were evaluated with visual control or before-after imagery comparison. Although quantitative assessment was not possible as more detailed independent data were not available, we estimate that more than 90% of changes associated to buildings appearance and disappearance are captured.

Hence, detailed inspection of identified changes outlined several difficulties related to detect change in slum-like areas. First of them is preservation of shape. Because of the use of multilevel information the objects edges may be shrinked to some degree, causing that change is not recognised at entire object extent but only at part of it. Second, the level of change recognition (detection rate) is different in positive and negative spectral space. New materials (i.e. metal plates) used to cover houses have very strong reflection, so difference from low reflecting brown soil to new buildings with bright cover is obvious and unproblematic. In contrast, for example from rusty rooftops to bare soil this difference is not

Fig. 12. Detailed object based classification results for residential areas in Raila village.

Multi-level contextual change detection approach was applied to Raila village to highlight the extent of major urbanisation processes that can take action even in a short time span. As expected the development on southern border of the village is well recognised (Fig. 13) and different time sequences analysed outlined where and when the growth has taken place.

Method incorporates spatial neighbourhood dependence to control the false change information (i.e. inherent changes due to locally based variability in data). As a result it gives a change pattern of positive and negative changes in imagery spectral space. Positive changes correspond to an increase in digital number values at the same location and negative changes to a decrease in spectral values. Method was developed for middle resolution imagery, but its application to Kibera informal settlement data proved this concept to be efficient on VHR imagery as well. In general, majority of objects transformations regardless of their size (small patches to entire buildings) was identified. The results were evaluated with visual control or before-after imagery comparison. Although quantitative assessment was not possible as more detailed independent data were not available, we estimate that more than 90% of changes associated to buildings

Hence, detailed inspection of identified changes outlined several difficulties related to detect change in slum-like areas. First of them is preservation of shape. Because of the use of multilevel information the objects edges may be shrinked to some degree, causing that change is not recognised at entire object extent but only at part of it. Second, the level of change recognition (detection rate) is different in positive and negative spectral space. New materials (i.e. metal plates) used to cover houses have very strong reflection, so difference from low reflecting brown soil to new buildings with bright cover is obvious and unproblematic. In contrast, for example from rusty rooftops to bare soil this difference is not

**5.2.2 Raila village urban growth** 

appearance and disappearance are captured.

Change pattern from years 2006-2008. Change pattern from years 2008-2009.

Urban growth pattern through years 2006, 2008 and 2009 for Raila village. Dark red corresponds to 2006-2008 and light red to 2008-2009 period.

Fig. 13. Results of change detecion for urban growth pattern through years 2006, 2008 and 2009 for Raila village, Kibera.

great and consequently buildings disappearance is more difficult to detect. Same applies if new building has dark colour rooftop covers (for example, often used blue colour plates). Main change missing occurrences therefore apply to the just described situations, making difficulties for automatic approaches. To some extent this problem was solved so that the threshold for negative changes was reduced during transformation categorisation step.

#### **5.2.3 Raila village population estimation in 2009**

According to the total residential area in Raila village obtained from object based classification (section 5.1.1, Table 3), population estimation was calculated based on different density per area parameters (Table 6). Additionaly we illustrate this information also in terms of typical size of houses in the Raila village (Fig. 14).

Object-Based Image Analysis of VHR Satellite Imagery for

low processing costs.

information is aimed.

as sub-object attributes could be explored and used.

Population Estimation in Informal Settlement Kibera-Nairobi, Kenya 429

and were not under detailed examination of this study. Informal settlements represent a particular housing and living conditions which is from a humanitarian point of view in most cases below acceptable standards (UN-HABITAT, 2004; MKP, 2011; Sartory et al. 2002). Due to informal character and low governmental management services in the past, reliable and accurate data about informal settlements and their population is rarely available. On the other side there is a strong need to transform informal into formal settlements and to gain more control about the actual urbanisation progress. Thus, obtaining spatially and temporally accurate information is a first step to establish proper actions in terms of local or regional planning. For these tasks, conventional data sources, such as maps, statistics or even GIS data are usually obsolete, not available, not as accurate as needed or do not hold the information needed (MKP, 2011; Sartory et al. 2002). The case study presented demonstrates how informal settlements can be approached from VHR satellite image data. Using an object based approach of image analysis detailed land cover/use within informal settlements can be obtained to facilitate GIS-based management tasks and population modelling. The application of automatic, even if simple pixel-based, change detection proved to support real-time observation of informal settlement areas whenever appropriate VHR satellite data are available with relatively

Merits of object-based image analysis in dense informal settlements analysis with VHR remote sensing data have been confirmed in several studies (section 2). However some drawbacks still resist. In case of residential land cover/use map derivation main unsolved difficulty is automatic detection and separation of individual houses. Although small differences in heights of rooftops create visually well distinguished boundaries of objects, the heterogeneity of rooftop material and its small scale changeability often overrule the value of neighbourhood houses boundaries relationship. Current limit of object based analysis is also that still requires substantial post-classification routines and check-over that can be done mainly manually through visual control. What characterise this procedure as time demanding whenever geometrically and semantically correct

Change detection applied revealed great potential for long-term monitoring and informal settlements urbanisation growth analysis. Hence more research is needed to provide sufficient detection rate of spectrally lower magnitude changes that are typical for informal settlements specifics and its reflectance intensity representation on satellite imagery. Due to rooftop material used bright (metal) materials are unproblematic to distinguish from soils, however dark rooftop materials (blue, brown colour) are spectrally closer to bare, unpaved (brown) or vegetated (green) soils. Here object based approaches would prove better option

Valuable population estimation can be made with a relatively low cost if residential area if accurately estimated from high-resolution images, although some considerations exist. Area based population estimation model can be used for the informal settlements in other images of similar resolution knowing the number of people living per surface unit. Zhang (2002) exposed some problems of selection of the scales of remote sensing imagery, reduction of influence of plant cover on remote sensing data, stability of the correlation between population and remote sensing indicator variables and correction of building count. In this research we met all mentioned problems in order to accurately estimate


Table 6. Density of people living in Raila village according to different sources and total residential area obtained from object-based classification of GeoEye 2009-07-25 image.

Fig. 14. Density of people according to different sources per typical size of houses in Raila village.

In Table 6 we presented also the density on 32 m2 unit since this size of house was measured to be a most typical and frequent (medium range size) housing unit in Raila village. It was also observed that size of small buildings is approximately 16 m2, while large buildings reach on average 65 m2. Fig. 14 summarise in addition the above relationship.

When perfoming such population estimation it is assumed that all of the build-up area is used for dwelling, which is not allways true as houses may be also used for other activities. For the possible calibration of the above population densities field survey sampling would be required. Nevertheless, Table 6 clearly outlines how population estimation based on critera of simple density per area based modelling can propose up to 4-times higher (different) population estimation.
