Joint Research Centre (JRC)

Transcription

1 Joint Research Centre (JRC) IPSC - Institute for the Protection and Security of the Citizen SES - Support to External Security Unit Ispra Italy

2 EC JRC IPSC SES ISFEREA WUF 4, Nanjing 3-6 November 2008 Supporting slum mapping using very high resolution satellite data

3 Outline Advances in satellite technology Describing slums spatially EO based indicators Conclusions

4 Advances in satellite technology Landsat 5 (1984) Landsat 7 (1999) SPOT 4 (1998) IKONOS (2000)

5 Describing slums spatially Slum households are often grouped at hazardous locations Geologically hazardous zones (slopes or floodplains) Highly polluted areas (garbage dumps, industrial sites) Other high-risk zones (railroads, flight corridors, energy transmission lines) Slums are often characterised by specific housing types and housing pattern Small sized, single-storey housing Mix of roofing material (corrugated iron, plastic sheets, ) Haphazard housing and road pattern Narrow and unpaved road network Few open spaces

6 The role of remote sensing Remote sensing provides a synoptic overview Area-wide analysis of an entire city Identification of hazardous locations Slopes & floodplains using digital elevation models High-risk zones through mapping of traffic infrastructure and industrial sites Description of settlement pattern Dwelling size and type Roof materials Orientation of buildings and road network Absence/presence of vegetation

7 Presence/Absence of Vegetation Presence of vegetation in cities is limited, because land is scarce and expensive Vegetation in cities is often present in residential areas as private luxury or as public open spaces Absence of vegetation can an indicator for slums The identification of vegetation is often based on vegetation indicators widely used in environmental context

8 Example: Casablanca Residential Area Slum Area

9 Shadows as markers for built-up structures Shadows contain some information about builtup structures Presence (3D structures cast shadow) Height (function of sun elevation) Shape/size (footprint of the bulding) Detection based on radiometric properties dark areas next to bright roofs

10 Example: Casablanca Presence of shadows is an indicator for: Multi-storey buildings Planned areas with open spaces Problems Vicinity of buildings Varying sun angle Shadows cast by vegetation

11 Size of built-up structures Slum areas are often recognized by a smaller size of the buildings compared to formal settlement pattern Automated estimation of the average size of built-up structures Diameter of the surface plant, i.e. the surface of the roofs visible in the remotely-sensed data

12 Example: Nairobi Kibera Slum 4.2 m Business District 39 m Residential 7.8 m

13 Variety of built-up structures Slum areas are characterised by a limited variety of building shapes Automated estimation of the number of classes of built-up volumes present in the area Extraction based on the number of local maxima present in the image shadows

14 Example: Nairobi -20 Pattern 01 Pattern Pattern DMP Contrast (closing) DMP Contrast (closing) DMP Contrast (closing) Size (m ) Size (m) Size (m )

15 Alignements of built-up structures Presence of preferential alignments in settlement patterns is an indicator for planning activities Absence of preferential alignments is an indicator for haphazard building activity typical for slums Automated estimation of alignments based on the building shadows

16 Example: Nairobi Pattern Pattern Pattern

17 Combination of Indicators Combination of indicators improves the separability Example: Casablanca Indicators for 10 slum and 10 non-slum quarters Vegetation Presence Shadow size Slums are characterized by small buildings and little vegetation

18 Conclusion Remote sensing CAN NOT provide Identification of slum households based on the all five slum indicators Remote sensing CAN provide City-wide, objective and robust information Indirect slum descriptors: Hazardous locations Small buildings Narrow roads with haphazard orientation Little open spaces This spatial definition of slums, focusing on the location and the structure of slum quarters, is complementary to the UN Habitat definition