**5. Results**

420 Remote Sensing – Applications

Change pattern after eliminating false changes due to shadows differences.

Fig. 9. Overview of change pattern intermediate results through implemented processing

Population statistics gives very important information for understanding of modern society. Demographic research is one of the main research directions of social science for a better understanding of the interactions between population growth and social, economic and environmental conditions. The collection of population data depends mainly on the census, which is labour-intensive, time-consuming and demands high financial resources. The 2009 Kenya Population and Housing Census reported Kibera's population to be 170,070 (Karanja, 2010). This report was far from the belief of that time that Kibera slum was of the biggest informal urban settlements in the world. Several actors had provided and published over the years increasing estimations of the size of its population, most of them stating that it was the largest slum in Africa with the population exceeding 1 million. According to Davis (2006), a well known expert on urban slums, Kibera had a population of about 800,000 people. International Housing Coalition (IHC, 2007) talked about more than half a million people. UN-Habitat (2004) had released several estimations ranging between 350,000 and 1 million people. These statistics mainly come from analyses of aerial images of the area. IRIN (2006) estimated a population density of 2000 residents per hectare. In 2008 an independent team of researchers began a door-by-door survey named Map Kibera Project (MKP, 2011). A trained team of locals, after having developed an ad-hoc surveying methodology, has so far gathered census data of over 15,000 people and completed the mapping of 5000 structures, services (public toilets, schools), and infrastructures (drainage system, water and electricity supply) in the Kianda village. Considering data collected for Kianda village, the population of the whole Kibera slum

Change pattern over 2006-2009 3-band difference image.

> Change pattern after eliminating small patches due to rooftop renovations.

2006-2009 3-band difference image for Kibera subset (Raila village).

can be estimated between 235,000 and 270,000 people.

Change pattern after automatic multi-level threshold procedure.

**4.4 Population estimation** 

steps.

With object-based (contextual) classification performed on GeoEye image with Feature Extraction module of ENVI EX, it was possible to obtain accurate land cover map and following this, total residential area of Kibera slum and its divisions (villages) with very high accuracy. From this data, those related to build-up areas, were used for population estimation. With multi-level contextual change detection implemented in Erdas Imagine, it was possible to obtain representative change pattern reflecting where in informal settlement intensive urbanisation processes have taken place. Results are presented in the following order: land cover mapping, change pattern identification and population estimation, for Kibera informal settlement (section 5.1) and Raila village (section 5.2), respectively.

#### **5.1 Kibera**

#### **5.1.1 Kibera land cover map 2009**

Object based classification and post-classification on GeoEye 2009-07-25 image was performed for each village in Kibera informal settlement separately. Finally individual results were joined in a land cover map of Kibera informal settlement (Fig. 10).

Object-Based Image Analysis of VHR Satellite Imagery for

residential classes only.

imagery.

**5.1.2 Kibera change detection** 

conclusions can be derived.

Population Estimation in Informal Settlement Kibera-Nairobi, Kenya 423

Lindi. Only residential segments were estimated. Since with ENVI EX classification the outline of individual residential objects could not be extracted, we compared only the total sums of areas classified as *residential*. Results are shown in Table 4. The best result (error of

All (semi)automatic classification methods display some errors, but as an approximate solution object based classification on VHR data yielded very good results upon selection of proper segmentation parameter values. Different shapes and colours in informal settlements determine a complex urban formation, which is difficult to differentiate from other land cover types, especially from bare soils and unpaved streets. With the object-based classification dwelling zones were found with high accuracy all over the image, in spite of

> **Area [m2]**

**Coverage [%]** 

3%) was obtained when choosing parameter values for segmentation/merge: 85/85.

their spectral similarities with streets and other urban features, especially bare soils.

Testing rectangle (200m x 300m) 60,000 Manual digitalization 37,913

**Supervised classification ENVI EX (segmentation 85, merge 85) 36,663 97**  Supervised classification ENVI EX (segmentation 85, merge 65) 42,172 111 Supervised classification ENVI EX (segmentation 85, merge 45) 41,174 109

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

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

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

Table 4. Comparison of results of ENVI EX supervised classification using different segmentation parameters with manual digitalization. The total areas were compared for

Fig. 10. Merged final classification results of GeoEye image where all 6 selected land cover classes are shown for all 12 Kibera villages (ESRI ArcGIS).

Vectorized classes of entire Kibera were merged together in order to be able to calculate area of total land use/land cover. As it is seen from Table 3, residential areas cover 2/3 (66%) of the whole Kibera area and are prevailing when compared to other land uses. This can be well confirmed from the visual examination of the satellite images of the discussed area.

Accuracy assessment was done by comparing results of supervised classification with manually digitalized objects. Comparison was done on the area 200 x 300 m in the village


Table 3. Area of different land use types for 12 informal villages and the total sum in Kibera.

Fig. 10. Merged final classification results of GeoEye image where all 6 selected land cover

Vectorized classes of entire Kibera were merged together in order to be able to calculate area of total land use/land cover. As it is seen from Table 3, residential areas cover 2/3 (66%) of the whole Kibera area and are prevailing when compared to other land uses. This can be well confirmed from the visual examination of the satellite images of the discussed area.

Accuracy assessment was done by comparing results of supervised classification with manually digitalized objects. Comparison was done on the area 200 x 300 m in the village

Kianda 117,710 8,144 9,511 16,775 18,005 170,145 Soweto West 49,620 4,089 6,455 15,001 6,177 81,343 Raila 45,657 8,835 17,066 30,886 6,139 108,583 Gatwekera 234,609 17,634 11,718 19,682 23,878 307,520 Kisumi Ndogo 111,472 6,979 6,944 10,891 28,432 164,717 Makina 303,599 53,844 11,992 40,474 34,801 444,710 Kamdi Muru 51,248 1,829 7,541 12,123 8,670 81,412 Mashimoni 96,287 5,520 2,018 7,628 16,900 128,355 Laini Saba 181,211 17,400 13,141 45,545 20,490 277,786 Lindi 159,112 2,5826 11,885 48,215 26,630 271,668 Silanga 150,058 21,569 19,975 34,833 17,298 243,733 Soweto East 174,200 6,238 15,618 29,302 22,823 248,181 **SUM [m2] 1.674,784 177,906 133,863 311,354 230,245 2.528,152 SUM (%) 66.25 7.04 5.29 12.32 9.11 100,00**  Table 3. Area of different land use types for 12 informal villages and the total sum in Kibera.

**bare ground) Shadows Total** 

classes are shown for all 12 Kibera villages (ESRI ArcGIS).

**/ village Residential Trees Green areas Soils (roads,** 

**Land use [m2]** 

Lindi. Only residential segments were estimated. Since with ENVI EX classification the outline of individual residential objects could not be extracted, we compared only the total sums of areas classified as *residential*. Results are shown in Table 4. The best result (error of 3%) was obtained when choosing parameter values for segmentation/merge: 85/85.

All (semi)automatic classification methods display some errors, but as an approximate solution object based classification on VHR data yielded very good results upon selection of proper segmentation parameter values. Different shapes and colours in informal settlements determine a complex urban formation, which is difficult to differentiate from other land cover types, especially from bare soils and unpaved streets. With the object-based classification dwelling zones were found with high accuracy all over the image, in spite of their spectral similarities with streets and other urban features, especially bare soils.


Table 4. Comparison of results of ENVI EX supervised classification using different segmentation parameters with manual digitalization. The total areas were compared for residential classes only.
