SEVENTH FRAMEWORK PROGRAMME THEME [ENV.2010.2.1.5-1] [Assessing vulnerability of urban systems, populations and goods in relation to natural and man-made disasters in Africa]
Grant agreement no: 265137 Project acronym: CLUVA Project title: "CLimate change and Urban Vulnerability in Africa"
Deliverable reference number and title: D2.7 Green infrastructure maps for selected case studies and a report with an urban green infrastructure mapping methodology adapted to African cities Due date of deliverable: 31/05/2012 Actual submission date: 25/06/2012
Start date of project: 01/12/2010
Duration: 36 months
Organisation name of lead contractor for this deliverable: University of Manchester Revision [1]
Project co-funded by the European Commission within the Seventh Framework Programme (2007-2013) Dissemination Level PU Public PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)
1
X
Note about contributors The following organisations contributed to the work described in this deliverable: Lead partner responsible for the deliverable: The University of Manchester Deliverable prepared by: Name of contributor(s): Dr Gina Cavan & Dr Sarah Lindley Partner responsible for quality control: CSIR Deliverable reviewed by: Ingo Simonis Other contributors: Addis Ababa University: Kumelachew Yeshitela Addis Ababa University: Alemu Nebebe Addis Ababa University: Tekle Woldegerima Ardhi University: Riziki Shemdoe Ardhi University: Deusdedit Kibassa Technical University Munich: Stephan Pauleit Technical University Munich: Florian Renner Technical University Munich: Andreas Printz Technical University Munich: Katya Buchta University Gaston Berger, Saint Louis: Adrien Coly University Gaston Berger, Saint Louis: Fatimatou Sall University Gaston Berger, Saint Louis: Ndèye Marème Ndour University of Ouagadougou: Youssoufou Ouédraogo University of Ouagadougou: Bani Saïdou Samari University of Ouagadougou: Bakary T. Sankara University of Yaoundé: Rodrigue Aimé Feumba University of Yaoundé: Jean Noel Ngapgue University of Yaoundé: Monique Tatsaa Ngoumo University of Yaoundé: Maurice Tsalefac University of Yaoundé: Emmanuel Tonye
2
SUMMARY This report presents results from the first stage of the Task 2.2 work programme: urban characterisation and green infrastructure mapping. The term green structure map is used in this document to differentiate between outputs from this stage and outputs being covered in a later Task 2.2 deliverable (D2.9). The work generates new data resources for CLUVA case study cities using a methodology which has considerable novelty in the context of African cities. This report is produced with inputs from representatives from all of the CLUVA African case study partners. The report begins by explaining the concepts behind Urban Morphology Type (UMT) maps and the value of these maps as a means of characterising urban areas. UMTs are classifications which combine facets of urban form and function. When mapped, UMT units provide biophysically relevant geographical units for understanding climate-related impacts and adaptations. Since UMT units can be seen as “integrating spatial units linking human activities and natural processes” (Gill et al., 2008: 211) they also have relevance for planning purposes. Thus, the results provide a framework not only for CLUVA Task 2.2 but also for other tasks within the project with a clear link to the requirements of decisionmakers and other stakeholders working in urban areas. By definition UMT maps provide a way of understanding green structures within urban areas. They do this through providing a basis for extracting ‘green’ UMTs - those which are wholly or mainly associated with vegetated land. This is illustrated for Dar es Salaam in Figure S1. However, the report also shows that these UMT-derived green structure maps alone are an imperfect representation of green structures within urban areas since they fail to account for green structures which occur within other urban morphology classes, such as residential zones. Analysing the land cover characteristics of urban morphology units allows the production of ‘enhanced’ green structure maps (Figure S2). These enable a more sophisticated assessment of green structures, including those not traditionally considered in green infrastructure planning. This more holistic assessment is vital for understanding city-wide urban ecosystem services and their potential for climate adaptation. The production of the enhanced green structure maps therefore takes the research activities of Task 2.2 into the assessing stage of the work programme. The report documents work completed by the deliverable deadline in each case study city, with future work highlighted in the conclusion. All case study cities have produced UMT maps – three at a provisional stage and two at a draft final stage. These provide the basis for the green structure mapping deliverable. However, additional enhanced green structure maps have already been produced for Dar es Salaam with that process on-going for Addis Ababa, complemented by work using an alternative methodology. Land cover mapping work will continue for the other three case study locations and a timeline for production is shown in the conclusion. All work has been supported with manuals, workshop sessions and a dedicated week-long meeting. The activities of the task provide new data and research results for the specific CLUVA case study cities but also provide a template for other cities located within similar climate zones. 3
Figure S1: UMT-based green structure map for Dar es Salaam – UMT classes which are principally associated with green structures
4
Figure S2: Enhanced green structure map (including water cover in this example) for Dar es Salaam based on combined UMT and UMT-based land cover assessment. 5
CONTENTS 1
Introduction ................................................................................. 8
2
Concepts and Methodology ...................................................... 10 2.1 Urban Morphology Types .................................................................. 10 2.2 Land surface cover.............................................................................. 12 2.3 Process for characterising the urban environment.............................. 12 2.3.1 UMT classification scheme .................................................... 13 2.3.2 Map units ................................................................................ 14 2.3.3 Verify units ............................................................................. 15 2.3.4 Land cover classification ........................................................ 16 2.3.5 Map land cover ....................................................................... 17 2.3.6 Analyse and verify .................................................................. 18 2.3.7 Produce green structure maps ................................................. 18
3
CLUVA urban characterisation and green structure mapping case studies ......................................................................................... 20 3.1 Addis Ababa case study...................................................................... 21 3.1.1 UMTs and their creation in Addis Ababa............................... 21 3.1.2 Land Surface Cover Assessment ............................................ 35 3.1.3 Conclusion .............................................................................. 41 3.2 Dar es Salaam case study.................................................................... 43 3.2.1 UMTs and their creation in Dar es Salaam............................. 43 3.2.2 Land Surface Cover Assessment ............................................ 52 3.2.3 Issues and limitations.............................................................. 65 3.2.4 Conclusion .............................................................................. 65 3.3 Other case studies ............................................................................... 67 3.4 Ouagadougou Case Study................................................................... 67 3.4.1 UMT categorisation and mapping .......................................... 67 3.4.2 Benefits of the UMT approach ............................................... 70 3.4.3 Issues and limitations.............................................................. 70 3.4.4 Conclusion .............................................................................. 70 3.5 Douala Case Study.............................................................................. 71 3.5.1 UMT categorisation and mapping .......................................... 71 3.5.2 Benefits of the UMT approach ............................................... 76 3.5.3 Land surface cover assessment ............................................... 76 3.5.4 Issues and limitations.............................................................. 76 3.5.5 Conclusion .............................................................................. 77 3.6 Saint Louis Case Study....................................................................... 77 3.6.1 UMT categorisation and mapping .......................................... 77 3.6.2 Benefits of the UMT approach ............................................... 87 3.6.3 Issues and limitations.............................................................. 88 6
3.6.4 Conclusion .............................................................................. 89
4
Discussion................................................................................... 90
5
Conclusion and next steps ........................................................ 96
6
References .................................................................................. 97
7
Appendix .................................................................................... 98 7.1 Metadata for UMT datasets: An example of Dar es Salaam .............. 98 7.2 Detailed list of contributors ................................................................ 99
7
1
INTRODUCTION
Task 2.2 Vulnerability and adaptation potential associated with urban ecosystems has the following specific objectives: 1. To analyse, quantify and map important ecosystem services of the urban green structure that increase the resilience of African cities to climate change; 2. To assess the impacts of climate change on urban green structure & its ecosystem services; and, 3. To evaluate the prospects for urban green structure as a measure for adapting African cities to climate change. Associated research activities can be split into three stages broadly associated with the three objectives of Task 2.2 (Figure 1.1). This report outlines the methodology and associated results that are central to achieving the first objective. Specifically, it outlines the methodology and implementation processes involved in urban characterisation and mapping current green structures1, for the CLUVA case study cities. Thus, it enables understanding of the current baseline, and focuses on characterising the city and its green space (what and where). In turn this leads to an understanding of green infrastructure as “an interconnected network of green space that conserves natural ecosystem values and functions and provides associated benefits to human populations” (Benedict and McMahon, 2002). Among the functions are provisioning, regulating and social ecosystem services2, which are at the centre of the work of Task 2.2. Evidence based green infrastructure planning can then take place as the final stage of work with a goal to maximise positive ecosystem services within the CLUVA case study cities within the context of climate change. The remit of this work is specifically urban ecosystem services, i.e. those which are relevant to urban areas and their immediate peri-urban environs. The characterisation methodology includes: interpretation of existing maps, satellite images and aerial photographs; agreement on appropriate typologies; classification of urban areas into morphology types; and assessment of surface cover properties. The framework applied is the same for all case study cities (classification schemes, principles for data production, etc.) but the specifics of delivering it may differ (e.g. which data and software to use, and not all morphologies/ cover types will exist in each city). Section 2 of this report briefly outlines the methodological framework underpinning the analysis. It is illustrated with a European example to explain the full methodology including how the results from the CLUVA case study cities can be used for subsequent stages of work. Section 2 explains how the methodology was applied in each case study city before presenting the study results in Section 3. Results to date are shown for all five CLUVA case study cities. The current status of results in each city varies due to reasons explained in Section 3. Whilst some 1
Note that in the DoW deliverable D2.7 uses the term ‘green infrastructure’ maps. For the purposes of this report the maps are subsequently referred to as green structure maps. The reason for the change of terminology is in order to differentiate the interim data outputs from the green infrastructure planning output due to be produced in Stage 3 of the Task 2.2 work. 2 Further explanation of ecosystem services is given in Deliverable D2.6.
8
results are shown for each city, they are explained in particular detail for two case studies, Addis Ababa and Dar es Salaam. For Addis Ababa, the emphasis is on production of the UMT map and for Dar es Salaam the emphasis is on the subsequent land cover assessment stage. Section 4 makes some provisional comparisons between case study results to be further explored following the production of final datasets, and written up in the form of academic papers. The section concentrates on identifying cross-cutting points, limitations and further recommendations. Section 5 concludes the report. It provides a timetable for remaining work and outlines the next steps for Task 2.2. Figure 1.1: Main themes associated with Task 2.2 objectives
9
2
CONCEPTS AND METHODOLOGY
The methodology requires an urban typology classification and mapping exercise to be done before green structure mapping, the analysis of ecosystem services, and assessments of relationships with urban climate and climate change. As previously noted, the underlying framework is the same for all case study cities, whilst the implementation of the methods may differ as a result of the different local teams carrying out the analysis, and the local characteristics of their city. This section of the report outlines the framework for the analysis. The specific implementation is covered separately in Section 3. Characterising the urban and peri-urban environment involves two main stages: classification and mapping of urban typology, and classification and mapping of the associated land surface cover. 2.1
URBAN MORPHOLOGY TYPES
Urban Morphology Types (UMTs) are the basis for creating the spatial framework that underpins Objectives 1-3 (Figure 1.1). Essentially UMTs are the foundation of a classification scheme which brings together facets of urban form and function. Their application allows the delineation of geographical units which are functional in terms of their biophysical processes. Connections to urban functions (land uses) allows biophysical functions to be combined with a planning orientated perspective. Thus UMT units can be seen as “integrating spatial units linking human activities and natural processes” (Gill et al., 2008: 211). Such an approach is often necessary because biophysical units such as discrete green spaces may not be very well represented by existing administrative units. Similarly existing land use frameworks do not normally consider aspects of urban form and structure together. The specific guiding methodology used in this research originated from a UK research project, Adaptation Strategies for Climate Change in the Urban Environment (ASCCUE), funded by the UK Engineering and Physical Sciences Research Council (EPSRC). This methodology, outlined in Gill et al. (2008), has been specifically adapted to the context of African cities, whilst still utilising the same general principles. In Germany, the UMT approach has been in use since the early 1980s under the name of “urban structural types”, i.e. which can be distinguished by their characteristic pattern of built and open spaces. The approach assumes that “the physical features of these entities and the various (human) activities they accommodate largely determine the social and environmental quality of the urban system” (Pauleit and Duhme, 2000). As urban morphology/ structural units and types are the expression of past and recent human decisions on the use and form of land, they offer the potential to serve as interfaces between natural and social sciences, on the one hand, and planning on the other (Figure 2.1). The approach has been increasingly adopted for urban ecological studies in Europe (e.g. Pauleit and Breuste, 2011), however, its application in the context of African cities has considerable novelty. Examples of urban morphological assessments in African cities do exist, eg. in North Africa, and whilst these conform to some of the same general principles they do not have the scope of the current assessment (Moudon, 1997).
10
Figure 2.1: Urban morphology types as interfaces between science and planning (adapted from Breuste 2006).
The UMTs developed in CLUVA include ecological and social parameters which allow areas to be fully characterised in order to assess the contribution of the different UMTs to the provision of ecosystem services and functions. For example this may be for food provision, flood mediation, pollution and temperature reduction, firewood and biomass production, biodiversity, etc. The data can also be used as the basis for developing guidance for green infrastructure planning in the case study cities. Thus, the UMT units and their attributes allow the Task 2.2 team to carry out analysis and modelling for Objective 2, and provide a framework for the development of green infrastructure plans in Objective 3. The UMT maps can be used as the basis for producing a green structure map for each CLUVA city through extracting the UMT classes which primarily relate vegetation structures. Since UMTs are biophysically relevant units which take account of the boundaries of natural and vegetated zones this is a straightforward process. Final drafts of the UMT-based green structure maps on this basis have been completed for two case study cities, Addis Ababa and Dar es Salaam (see section 3). Whilst this provides a good starting point for assessing ecosystem services these maps alone provide an incomplete picture of green structures. Complementary land surface cover assessment is required in order to recognise the green structures which are present within other urban morphological units. Datasets combining urban morphology and land surface cover can support the production of ‘extended’ green structure maps which provide a rich database to support the assessment of ecosystem services and, ultimately, the generation of green infrastructure plans.
11
2.2
LAND SURFACE COVER
Assessing city-wide ecosystem services requires an idea of the green structures within all urban land types – not just those which are wholly or mainly ‘green’ (e.g. parks). This is particularly important as many UMT categories contain green structures as part of their overall character. For example, residential urban typologies will include urban structures and impervious surfaces such as roads in addition to green structures in the form of street trees and through urban gardens. Land cover assessment is therefore necessary to provide the basis for developing a picture of the surface cover properties across the city (with a particular emphasis on green structures). The land cover assessment also aims to: Develop datasets which can be used to help analyse primary data, e.g. data collected from monitoring and survey exercises aiming at understanding hazard-exposure or vulnerability; Develop inputs which can be used to model impacts – including green infrastructure adaptations, e.g. providing estimates of evapotranspiring and pervious cover for current and prospective climate impact modelling; Provide a basis for assessing land cover change, e.g. to understand non-climate related pressures on green space and green structures; and, Provide a means of assessing spatial differences in green structure densities and characteristics (i.e. ideally not just providing an average land cover per UMT class). Assessment of land cover also provides a range of possible indicators which can immediately feed into WP3, for example: Density of tree cover; Proportion of cover associated with evapotranspiration; Proportion of pervious/impervious cover; and, Proportion of water bodies.
2.3
PROCESS FOR CHARACTERISING THE URBAN ENVIRONMENT
The characterisation methodology involves working through seven activities that include the generation of a UMT classification scheme, its application through the mapping of UMT units and the assessment of land cover within UMT units and classes. The stages allow basic UMT-based green structure maps and extended green structure maps to be produced for the case study cities (Figure 2.2). These seven processes are each summarised in further detail below. This section is not intended to be a step-by-step guide on how to undertake the methodology, but provides a broad overview of the key steps involved. For the purposes of explaining the methodology and its connections to the next stage of work, this section illustrates outputs for the main steps using the example of a case study application in Greater Manchester, UK (Gill et al., 2007; 2008). Materials providing specific information (e.g. on how to map UMTs) have been produced as part of the research capacity building activities in WP4. It has proved helpful to use a worked example, even one developed from a European perspective, to develop a shared understanding of terms and processes. How the methodology is interpreted in each case study city is shown in Section 3. 12
Figure 2.2: Process for characterising the urban environment and its outputs
2.3.1
UMT classification scheme
A set of Urban Morphology Type classes developed in a UK context are shown in Table 2.1. They broadly relate to a UK land use classification scheme. This example was a useful starting point, to demonstrate the idea of ‘urban morphology’. However, the urban morphology classes for African contexts are clearly different. Therefore the first task involves devising a suitable UMT classification scheme for each CLUVA case study city; remembering at this point that the emphasis is on function and structure rather than pure urban land cover or pure urban land use. Whilst urban morphology classes to use in each case study city can be informed by existing national or local land-use classifications urban morphology classes have distinct elements. This means that urban land use classifications alone do not fully represent urban morphology. Within every city it is expected that there are some classes which have stronger links to built structures and those which have stronger links to vegetation structures. It is important to include classes at both end of the 13
spectrum as each has a role in the assessment of urban ecosystem services. Not all classes will apply to each case study city, but the aim is to maintain consistency of the classes where possible. In designing the UMT classification scheme it is also useful to consider practical issues of what can be detected through visual analysis of remote sensing data (orthorectified aerial photography) as the primary method of applying the scheme. In practice, local teams can also apply expert knowledge and additional datasets to help in the mapping task. Table 2.1: Urban Morphology Types developed for Greater Manchester, UK (Gill et al., 2008). 1/ FARMLAND 1.2 Unimproved Farmland 1.1 Improved Farmland
2/ WOODLAND 2.1 Woodland 3/ MINERALS 3.1 Mineral workings and quarries 4/ RECREATION AND LEISURE 4.1 Formal Recreation 4.2 Formal open space 4.3 Informal open space 4.4 Allotments 5/ TRANSPORT 5.1 Major Roads 5. 2 Airports 5.3 Rail 5.4 River, Canal 6/ UTILITIES AND INFRASTRUCTURE 6.1 Energy production and distribution 6.2 Water storage and treatment 6.3 Refuse disposal 6.4 Cemeteries and crematoria
2.3.2
7/ RESIDENTIAL 7.1 High density Residential 7.2 Medium density Residential 7.3 Low density Residential 7.4 Rural settlement 8/ COMMUNITY SERVICES 8.1 Schools 8.2 Hospitals 9/ RETAIL 9.1 Retail 9.2 Town centre 10/ INDUSTRY AND BUSINESS 10.1 Manufacturing 10.2 Offices 10.3 Storage and distribution 11/ PREVIOUSLY DEVELOPED LAND 11.1 Disused and derelict land 12/ DEFENCE 12.1 Defence 13/ UNUSED LAND 13.1 Remnant countryside
Map units
Once the UMTs for the case study city have been defined, they need to be mapped. The aim of this process is to produce a new geospatial dataset, containing seamless polygons of each UMT unit, which include specific information for each land parcel within an associated attribute table, such as the UMT code, geometric properties, etc. This geospatial layer provides complete and consistent coverage across the city. Internal consistency in recording of data and coding in attribute tables is essential.
14
Mapping UMTs involves the process of digitising, which transforms information into digital format, such as from a paper map, or creates new data from other geospatial sources, such as from digital imagery. It is most likely that this step will involve on-screen digitising of UMT units from orthorectified aerial photography in GIS or tools like Google Earth (Figure 2.3). On-screen digitising involves creating a map layer on the screen by tracing units with a curser using reference information (aerial photography) as the background. As a first step, typical images of each UMT class (e.g. from transect work) can be captured and kept for reference with a description of its characteristics. This can aid in the identification and digitisation of all UMT units across the city. Examples of these for the case study cities are shown in Section 3. Linear features such as roads and rivers can be used as the outline of units, and matched with administrative units/zones where possible, e.g. for the boundary of the dataset. Once a complete UMT layer has been created through the process of digitisation, it is then suitable for further GIS analysis, and can be combined with other datasets to produce spatial indicators. Figure 2.3: Mapping UMT units using orthorectified aerial photography, an example in a UK context (Gill et al., 2008).
2.3.3
Verify units
Verification includes checking and updating the dataset before finalising, and the production of an appropriate metadata record (see Appendix 1 for an example of a metadata record generated for the Dar es Salaam UMT dataset). After mapping, the UMT units need to be “ground-truthed” and revised where necessary. Field trips may be necessary to verify the UMT units on the ground. Revisions may be needed due to unclear imagery, or because of unexpected classes or changes which have occurred since the date of the orthorectified photography - such changes can be recorded in the associated data attribute table as it 15
is important to ensure that the UMT dataset constructed is a baseline assessment of the units at the date of the orthorectified aerial photography. The UMT units can then be verified by the internal CLUVA teams including those who have not been involved in the mapping stage. A further verification step with local stakeholders is also recommended, such as with local authorities and local groups. This can be helpful both to verify the baseline UMT units, and identify any changes that have occurred since the date of the aerial photography. In addition, verifying the UMTs with stakeholders can help to raise awareness of the CLUVA project and its significance for providing an evidence base for spatial planning. The Addis Ababa team has been particularly successful in this regard (see the case study city report in Section 3 and discussion in Section 4).
2.3.4
Land cover classification
Land cover assessment is necessary to identify and understand the green structures across the whole city. A land cover assessment can be made from the existing UMT dataset. There are several ways that this could be achieved; for example through satellite image processing or through further visual interpretation of the aerial photographs. The latter method – visual interpretation of the aerial photographs – is one which was successfully used in the Greater Manchester example. This approach was also recommended as the CLUVA methodology for the following reasons: It is known to provide useful data for identifying a range of green structure types; It has the capacity to be further developed in order to extend knowledge; It can ensure consistency of method between cities; and, It can be directly supported by existing data resources (i.e. orthorectified aerial photography obtained for the UMT mapping). In addition to this method, some CLUVA partners have decided to undertake remote sensing based assessments of vegetation cover. The analysis of imagery, for example using Normalised Difference Vegetation Index (NDVI) as a measure of biomass, would give a good spatial differentiation across the city – in other words a localised estimate of vegetation cover. This would also be suitable for identifying tracts of vegetation cover. Such analysis would be a good addition to urban morphology data generated using the aerial photograph method in case study cities that have the resources to undertake this assessment e.g. through an Addis Ababa Task 2.2 PhD programme. The Addis Ababa case study provides some provisional results using an imagery-based approach (see Section 3.1) to be compared to the main CLUVA method. The results suggest that remote sensing-based methods cannot easily provide the level of detail that is possible using the main CLUVA methodology. In a similar process to that explained for UMTs, a set of land surface cover classes must be identified and agreed prior to the assessment process. Classes can be identified through visual assessment of aerial photographs and local knowledge. A screen captured example of each land surface cover type can be created with the orthorectified aerial photograph which was used to create the UMT layer. Figure 2.4 provides an example of land surface cover classes. Again, whilst local 16
modifications will be required for some classes, emphasis was placed on the importance of maintaining consistency of classes internally, and where possible, between case study cities. This is further explained in the case study sections. Figure 2.4: Land surface cover classes for Greater Manchester, UK (Gill et al., 2008).
2.3.5
Building
Shrub
Cultivated
Other impervious
Mown grass
Water
Tree
Rough grass
Bare soil / gravel
Map land cover
Once the land surface cover classes have been agreed, an assessment of the composition of land surface cover types within each UMT category can be undertaken. The approach followed in CLUVA involves visual assessment of surface cover at random points within the UMT dataset. This is fully explained in Gill et al. (2008) and the CLUVA land cover assessment manual produced for WP4. It enables a rapid assessment of land surface cover across the city (compared to, for example, detailed digitisation of land cover polygons) and is relatively undemanding in terms of resource requirements. Determining the number/density of random points to assess is important as this affects the scientific rigour of the results. The Greater Manchester study used a standard error threshold of 2.5 to ensure that there is a 95% chance that the actual land surface cover is within ±5% of the estimated land surface cover result. If a single land surface cover class comprises over 50% of a particular UMT class a minimum of 400 random points are required to ensure the standard error threshold is not exceeded (Gill et al., 2008). Overall this previous study used a mean sampling density of around 9 random points per square kilometre. The effect of increasing the number of random points on results was assessed for Dar es Salaam (Section 3.2), and a recommended mean density of points 17
was set at 10 random points per square kilometre (minimum: 100; maximum: 400) for CLUVA assessments. Once the number of points allocated to each UMT class has been determined, mapping can commence (see Section 3). Whilst applying this sampling density is a good starting point for assessments in other cities, it is still necessary to calculate standard errors in order to ensure that the threshold is met in each case. Further considerations for applying the land cover assessment method in African cities, such as the influence of season, is discussed in the context of the Dar es Salaam case study (see Section 3.2). 2.3.6
Analyse and verify
Summary statistics can be used to generate the count of each land surface cover type, and its proportion of the full point total can be determined for each UMT category. The proportional surface cover in all UMT categories can also be compared (Figure 2.5). Figure 2.5: Proportional land surface cover analysis, an example from Greater Manchester, UK (Gill et al., 2008). 1.0 0.9
Proportional cover
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
re to ta w il n ce m an nt re uf ac tu di rin st g rib u o di tio ffi su ce se n & s d st & or de ag re m re e na l ic nt tl an co d un t ry sid e
im pr un ove im d fa pr rm ov la ed m nd in f a er rm al l a wo nd w rk oo in gs dl a & nd qu fo rm ar ri e al fo s rm recr e al in op atio fo n en rm s al op pac e en sp ac al e lo tm e m n ts aj or ro ad s ai en rp er o gy rts pr od uc r r ai iv tio w l er at n ,c er & an st di al or st rib ag ut e io & n t ce re rea m tm fu et se en er t di ie sp s hi & gh os c a re de l m n m at ed sity or re ia de si ns de lo it y nt w ia re de l s id ns en ity tia re l si de nt ia sc l ho o ho ls sp ita ls
0.0
UMT building
2.3.7
other impervious
tree
shrub
mown grass
rough grass
cultivated
water
bare soil / gravel
Produce green structure maps
Once finalised land cover data are available they can be used as the basis of producing extended green structure maps. Figure 2.6 shows how this information can be used to generate estimates of building mass or evapotranspiring cover for a city to act as spatial indicators. Data can also be used as inputs into climate impact models. Validated models provide a foundation for prospective studies, including assessing current and future ecosystem services, e.g. for moderation of surface temperatures (Figure 2.7). This provides evidence for green infrastructure planning. It is anticipated outputs along these lines will be produced for selected CLUVA case study cities. This evidence can then be used in the green infrastructure planning stage of Task 2.2. 18
Figure 2.6: Results from applying the land cover assessment: examples of building mass and evapotranspiration maps from Greater Manchester, UK (Gill et al., 2007).
Figure 2.7: Results from climate impact models: examples of (a) surface temperature modelling in Greater Manchester, and (b) what-if scenario modelling – the impact of adding/subtracting 10% green structure cover on surface temperatures (Gill et al., 2007). (a)
(b)
High density residential
Max surface temp (°C)
40 35 30
current development
25
-10% green
20
+10% green
15 1970s
2020s Low
2020s High
2050s Low
2050s High
Time period and scenario
19
2080s Low
2080s High
3
CLUVA URBAN CHARACTERISATION AND GREEN STRUCTURE MAPPING CASE STUDIES
All five CLUVA case study cities have participated in the urban characterisation and associated green structure mapping work being conducted through Task 2.2. Whilst the teams in each city have all made demonstrable progress towards the achieving aims of the characterising stage of the Task 2.2 programme, each city is currently at a different stage of the research process. The current status of work in each of the case study cities is identified in the sections below. The reasons for the different status of case study outputs are also given. At the kick-off meeting in Ouagadougou in Jan 2011 each case study team was interested to participate in the Task 2.2 urban characterisation work. However, communications between teams proved a considerable obstacle in the first year and therefore work did not progress equally in all case study cities. A pragmatic decision was taken to concentrate activities in Addis Ababa and Dar es Salaam where the research is now the most advanced. Final drafts of the UMT-based green structure maps for both of these case study cities have been generated for the D2.7 deliverable due date. These are illustrated for Dar es Salaam in detail, as an example. For the other case study cities, work was re-started in 2012. Work in Ouagadougou was restarted first following a visit by one of the team from TUM in February. Subsequently, there was agreement in the CLUVA General Assembly (March 2012) for work to also be restarted in Saint Louis and Douala. This decision was made in recognition of the wider value of the activities of this task for CLUVA integration. The original communication issues were overcome through a clearer identification of team members and their roles and responsibilities and by holding a dedicated workshop to bring the whole Task 2.2 team together in April 2012. An important part of this workshop was the opportunity for the Addis and Dar es Salaam teams to demonstrate the results from their existing work. The workshop also used existing CLUVA manuals and provided a chance for in depth discussion. It was also a vital aspect of the workshop that presentations, discussions and training were delivered in both French and English wherever mixed language groups were involved. A clear programme of activity was agreed at the end of the workshop and the team have been in regular communication with Task 2.2 members ahead of the field mission in July 2012 in Dar es Salaam. The following sections present the results for each of the five case study cities. Addis Ababa and Dar es Salaam have completed datasets with additional analyses on-going for a fuller understanding of green structure in the cities. Results for these two case study cities are presented as detailed case studies. Although work in the three additional case study cities is less advanced, the progress and status of work in those cities is also reported. Given the evolution of the work programme it is clear that the different case study cities will be at different stages of work. However, the convergence of all cities is expected by the end of 2012 according the time schedule (see Table 5.1). Following the presentation of the CLUVA case studies, the discussion in Section 4 draws together common themes and issues and looks forward to the next stages of Task 2.2 and the use of the project results more widely within CLUVA. 20
3.1
ADDIS ABABA CASE STUDY
The Addis Ababa case study section is divided into two main sections. The first detailing the work undertaken for the initial UMT production and the second the work to date on alternative land cover assessment work which will complement results generated through the main methodology. The benefits and limitations of applying methods are discussed with an overall conclusion identifying the key points from the work. Currently UMT-based green structure maps have been produced. Land cover assessment work using the CLUVA methodology is on-going and therefore is not included here. 3.1.1
UMTs and their creation in Addis Ababa
3.1.1.1 UMT categorisation and mapping An initial UMT classification scheme was drafted during a workshop in Addis Ababa in June 2011. The CLUVA task 2.2 researchers from EiABC, UM and TUM and stakeholders from the Addis Ababa Environmental Protection Authority and Gulelle Botanic Garden contributed in drafting the UMT scheme. The draft classification was also discussed with the leader of WP3 in the same meeting. The classification scheme was therefore a joint academic and stakeholder derived output which took a range of perspectives from across the CLUVA project. It was decided at the same meeting to test the approach via a pilot study covering a transect across the city (Figure 3.1). The sample transect was designed in such a way that it covered: a representative set of urban morphologies, from the city centre to the urban periphery; and the proposed case study areas within the city. It was also designed to cover areas of the city which had been visited by the whole team during the June 2011 workshop to allow for a shared understanding of how the urban morphology units related to areas on the ground. The transect enabled the classification scheme and techniques to be tested, and allowed the time required for the digitising work to be better assessed. The transect work was carried out using aerial photography for 2006 which was acquired from the Addis Ababa Urban Plan Information Institute. The first aerial photograph3 (available for use in 2006) was taken in December 2005 and a small area in May 2006 for the purpose of developing Land Information System for Addis Ababa. After completion of the digitising, editing and visualising process the 2006 aerial photo pilot study area was completed, ground truthing was done to check the actual urban morphology of the ground and verify the classification. The verification process revealed the high pace of change in the city. The draft UMT units were also presented to stakeholder experts in a workshop held in October 2011 (related to Task 2.2 MS2.4). Verification was therefore also undertaken both through the academic team and the stakeholder group as potential users of the research results. The pilot work revealed the need for some modification of the UMT classification scheme. One reason for modification was for easy communication with local urban planners, the intended users of the research results. Here the use of appropriate terminology and its impact on outputs like 3
The 2006 aerial photo has a resolution of 0.2 m and was taken and mapped by GeoMaps. The Orthophoto Scale is 1:2000 (0.2m Ground sampling resolution).
21
classification schemes particularly matters. Based on urban development history, man-made and natural factors cities could have different land structure. Any classification scheme should be able to grasp and indicate these structures. Therefore UMT classification needs to consider these local situations and put the classes as clearly understandable to local use as possible. For example, consider the UMT sub-class 6.1 (Energy distribution). Initially “Energy production and distribution” was proposed. However, since there is no energy production structure in Addis Ababa this was renamed. Similarly re-labelling was done for sub-class 6.2. The final UMT classification scheme is shown in Table 3.1 with examples and descriptions of the top level classes given in Table 3.2. Figure 3.1 Addis Ababa transect used for the pilot study
The verification process was very important in demonstrating the potential value of the characterisation work to wider stakeholders. The other outcome of the process was an agreement for provision of the remaining aerial photographs for 2006 and the agreement to supply 2011 data to EiABC for internal use the purposes of the CLUVA research activity. The verification process with stakeholders was therefore instrumental for ensuring that the UMT mapping could be extended to cover the whole city and it also brought the idea of using the UMT units for future use in urban planning, a key outcome from the process. The use of 2011 imagery was considered to be preferable to the 2006 imagery due to the pace of change in the city and the value of a more recent baseline for project stakeholders. 22
These developments lead to the decision to create UMT maps for two time periods, 2006 and 2011. Benefits of this decision included the possibility to take account of change over time, both generally and also in terms of the extent and type of green spaces and structures across the city. Analysis of urban land change is helpful for understanding of pressures on urban green structures as part of the ‘Assessing’ stage of the Task 2.2 work programme. However, producing UMT maps for two different time periods also requires much more time resource, explaining why the Addis Ababa team have not progressed to the end of land cover assessment stage. A similar analysis process has been followed for the creation of the 2011 version of the UMT dataset. The second aerial photograph was taken in December 20104 and became available for use in January 2012. The original purpose of collecting the aerial photograph was to develop Real Property Registration and Land Information (Cadastre) System. The source of the aerial photography is “Integrated Land Management Information System Project” of the Addis Ababa City Administration. Both aerial photographs used in the UMT unit delineation are high quality, high resolution products. The draft final version of the 2011 UMT units has been produced to coincide with the production of this deliverable. The verification stages are to be completed before the Dar es Salaam workshop in July 2012. Based on the information collected during ground truthing, the UMT maps will be updated and draft final map will be produced. The latter will be presented to the stakeholders to get feedback on the UMTs and the maps. Therefore the final map is planned to be completed by the end of July 2012. The process of producing the UMT units has been supported by workshop activity and manuals. The local team’s work involved several members of EiABC staff, including the project leader, a local GIS expert and the PhD student allocated to Task 2.2. Appendix 2 lists those involved in the process. The GIS expert (AN) led a team of sixteen local Bachelor intern students of the Architecture and Urban and Regional Planning programme. The internships are intended to provide practical experience as part of their third year Bachelor programme. The UMT work provided interns with conceptual and practical skills, understanding the idea of UMT units and their role and also the practical skills associated with on-screen digitising. Training of the interns was conducted entirely by the local GIS expert, also a lecturer in EiABC. The students did the digitizing using AutoCad 2007. Editing and visualization was done using ArcGIS 9.3 software at a scale of 1:15000. The view scale was important in order to ensure that aerial photography interpretation was kept consistent. Supervision of the digitising, editing and visualization was done by a GIS expert in close consultation with an Ecologist (KY). Through this activity the research capacity building impact of CLUVA has been extended. All datasets were quality assured by a single person to minimise the potential for misclassification or the introduction of errors into the dataset. The GIS lead therefore took on a data cleaning role as well as a supervision role. 4
The 2011 aerial photo was taken and mapped by Hans Luftbild, a German based company. The aerial photo has a resolution of 0.2m and the orthophoto Scale is 1:2000 (0.2m Ground sampling resolution). The total number of aerial photos taken is 482, the physical format of the aerial photo is 104mm by 68.4mm and photo resolution is 7.2 µm. Other information regarding the aerial photo are: color RGB & NIR; image Geometry Accuracy = 15m width) 5.2Bus terminals 5.3 Rail way 5.4 Train station 5.5 Airports 6. UTILITITES AND INFRASTRUCTURE 6.1 Energy distribution 6.2 Water treatment 6.3 Refuse disposal, including landfill 6.4 Cemeteries 7. RESIDENTIAL 7.1 Condominium & multi-storey 7.2 Villa & single storey stone/concrete 7.3 Mud/wood construction 7.4 Mixed 8. COMMUNITY SERVICES 8.1 Education 8.2 Medical 8.3 Religion 9. RETAIL 9.1 Formal shopping area 9.2 Open markets 9.3 Mixed formal and open 10. INDUSTRY & BUSINESS 10.1 Manufacturing 10.2 Offices 10.3 Palace 10.4 Hotel 10.5 Storage and distribution 10.6 Garages 10.7 Mixed 11. BARE LAND
24
3.1.1.2 UMT results Table 3.2 and Figure 3.2 show the high level UMT outputs for Addis Ababa. Figure 3.3 shows the sub-UMT level with Figure 3.4 showing a graph of the proportions of area covered by each class across the city (see also Table 3.3 for summary statistics). Field crops currently make up over one quarter of the land area of the Addis Ababa study area, by far the largest area associated with a single sub-UMT category. Much of the land devoted to field crops exists towards the south and south east. To the north, Addis Ababa is fringed with the exotic plantation species Eucalyptus. Around 44% of the land area of the city is associated with sub-UMT classes which are primarily green in nature5. Over one third of the city is associated with residential urban morphology types and of these housing of mud/wood construction has the largest proportion (46%). Indeed, these areas make up the second highest proportional cover of the city at around 16%. Whilst there is some evidence of an urban core, the maps also illustrate Addis Ababa’s multi-nucleated character (Nvarirangwe, 2008). There is a large proportion of bare land (9%) at least some of which is likely to be associated with development. As can be seen from Figure 3.2, there are distinct parcels of bare land throughout the city with very large areas, some up to 781 hectares, identified to the north east, around the airport and around riverine corridors. Comparison with 2006 data will enable the exploration of UMT change and when combined with land cover assessment it will also be possible to investigate which particular UMT classes experience the most development pressure and assess the impact on green structures in the city. This is important because pressures on green structures come from both climatic and non-climatic drivers. Furthermore, the extent to which green infrastructure related climate adaptations can be built into city planning are also sensitive to these issues. Table 3.2: High level UMT classification scheme for Addis Ababa and a description of the general characteristics of each Top level categorisation and example 1. AGRICULTURE
Description This is a UMT class characterized by field crops and vegetables. The field crops are grown using rain, therefore the land appears green during the rainy season (June to September). After crop harvest (usually in November & December), the land remains bare. During this time, the land could only be used for livestock keeping. The vegetable farm is based on irrigation from nearby rivers. The major vegetables grown include cabbage, tomato, potato, carrot, onion, garlic, and lettuce.
2. VEGETATION
This UMT unit is characterized by a permanent land cover of woody and non-woody vegetation. Plantation is a unit dominated by a uniform plantation of
5
Taken for the puposes of this discussion as 1.1, 1.2, 2.1, 2.2, 2.3, 2.4, 4.1 and 8.3, but note that the extended green structure map will eventually take account of green structures in other UMT classes.
25
Eucalyptus (exotic) trees which are mostly used for fuel wood and construction. Mixed forest is a closed stand of different indigenous trees and shrubs. Riverine vegetation is an open stand of mixed type of vegetation composed of trees and shrubs found along rivers. Grassland usually covers a small area in the upper catchment of the city and is used for livestock feeding.
3. MINERALS
This UMT class is a representative of an excavated site used for quarrying.
4. RECREATION
This UMT class consists of green areas which are used for public recreation and conservation of native flora. Parks are open spaces covered with perennial vegetation (tree, shrub and herbs). They serve as public recreation and wedding ceremony. Stadium and festival sites are open spaces used for sport activities and religious, political and recreational public events.
5. TRANSPORT
The transport UMT is used for public and freight transport terminal. Except the railway unit all the other units are currently functional. The railway structure is in place but has stopped working for about a decade due to old age.
26
6. UTILITITES AND INFRASTRUCTURE
This UMT class is characterized by structures that help the provision of various utilities to the public, industry and business. Energy distribution includes electricity transmission sites. Water treatment is a unit for the treatment of sewerage. Refuse disposal is a site for disposing solid waste collected from residence and business areas. In most cases cemeteries are associated with churches in Addis and therefore contain trees and shrubs. Cemeteries outside the church do not usually contain trees and shrubs.
7. RESIDENTIAL
The different types of residential houses constitute this UMT class. The subclasses are Condominium and multi-storey (when more than 75% of the houses are with two or more storeys and built from concrete), villa and one storey (where more than 75% of the houses are villa type or with only one storey, all built from concrete), mud/wood construction (where more than 75% of the houses are built from mud and wood) and mixed subclass when a residential area contains a mixture of any of the above three subclasses.
8. COMMUNITY SERVICES
In this UMT class are included institutions that provide educational, medical and religious services to the community. Educational institutions are those that provide elementary, secondary or tertiary education. Medical institutions are those providing medical service for both inpatients and outpatients. Religious institutions are churches and mosques for practicing spiritual commitments.
27
9. RETAIL
This UMT class is a commercial area where commodities are exchanged with money. Formal shopping area is when more than 75% of commodity exchange takes place in multi-storey buildings. Open markets are when more than 75% of the commodities are exchanged in open field or in small shops with no storey. The mixed subclass is when multi-storey buildings, small shops and open fields are found together for commodity exchange. This is assessed through building characteristics on the ground.
10. INDUSTRY & BUSINESS
This UMT class consists of subclasses of manufacturing (when more than 75% of the site is occupied by manufacturing industries), offices (when more than 75% of the site is occupied by government and private offices), palaces (when more than 75% of the site is occupied by palaces, hotels (when more than 75% of the sites are occupied by hotels), storage and distribution (when more than 75% of the sites are used for merchandise storage and distribution), and garage (when more than 75% of the site is used for freight terminal and vehicle repairing). When a site is occupied by any mixture of the above subclasses, it is termed mixed.
11. BARE LAND
This UMT class represents a land which has never been occupied any structure or a land which used to be covered by built structure but is now demolished and the land remains bare.
28
Note: The examples may be partially shaded by the UMT unit which are drawn over some of the screen shots. For example this helps to show the differences between individual vegetation units in Group 2 and the mix of units associated with linear features such as roads (in Transport Group 5).
29
Figure 3.2: Map of the high level UMT output for Addis Ababa
30
Figure 3.3: Map of the UMT subclasses for Addis Ababa
31
Table 3.3: Summary statistics of the UMT sub-categories UMT classes
Count of UMT units 110 83 24 210 166 143 17 19 19
Min. Area (Ha) 0.15 0.13 0.17 0.10 0.10 0.10 0.70 0.27 0.30
Max. Area (Ha) 5039.69 26.31 557.75 176.30 493.26 84.89 29.01 8.69 27.29
Mean Area (Ha) 131.42 3.96 140.33 7.82 10.94 5.72 8.74 3.20 5.40
Sum of Area (Ha)
SD Area (Ha) 542.61 5.23 188.00 18.99 45.89 12.20 8.08 2.58 7.32
Var Area (Ha)
1.1 Field crops 14456.02 294427.71 1.2 Vegetable farm 328.46 27.35 2.1 Plantation 3367.93 35343.17 2.2 Mixed forest 1642.20 360.71 2.3 Riverine 1816.57 2106.00 2.4 Grassland 818.43 148.79 3.1 Mineral workings and quarries 148.60 65.29 4.1 Parks 60.73 6.63 4.2 Stadium and festival sites 102.62 53.58 5.1 Major road corridors (>=15 m 12 0.20 2075.93 174.06 2088.72 598.94 358723.09 width) 5.2 Bus terminals 11 0.14 10.06 1.94 21.29 2.85 8.13 5.4 Train station 2 0.24 12.20 6.22 12.43 8.46 71.53 5.5 Airport 2 103.89 515.24 309.56 619.13 290.86 84602.11 6.1 Energy distribution 5 0.71 21.42 7.35 36.74 8.50 72.32 6.2 Water treatment 4 5.37 82.56 33.43 133.70 34.29 1175.86 6.3 Refuse disposal, including landfill 2 5.77 24.37 15.07 30.14 13.15 172.82 6.4 Cemeteries 25 0.24 19.95 4.72 117.98 4.96 24.57 7.1 Condominium & multi-storey 274 0.11 256.84 9.93 2721.40 26.30 691.59 7.2 Villa & single storey 446 0.10 177.90 15.32 6833.17 21.39 457.53 stone/concrete 7.3 Mud/wood construction 727 0.10 332.28 11.23 8162.11 23.22 539.19 7.4 Mixed 7 0.84 7.37 2.21 15.45 2.31 5.32 8.1 Education 112 0.11 29.87 3.17 354.89 4.56 20.75 8.2 Medical 24 0.17 35.78 4.32 103.56 7.91 62.56 8.3 Religion 99 0.12 9.02 2.07 204.60 1.87 3.48 9.1 Formal shopping area 46 0.11 17.52 2.96 136.17 3.90 15.24 9.2 Open markets 8 0.22 2.28 1.28 10.24 0.63 0.40 9.3 Mixed formal and open 11 0.14 57.15 7.56 83.12 16.64 277.02 10.1 Manufacturing 121 0.13 61.98 5.79 700.79 8.52 72.56 10.2 Offices 192 0.13 61.12 4.49 862.38 7.21 52.03 10.3 Palace 2 15.73 28.26 22.00 44.00 8.86 78.51 10.4 Hotel 19 0.28 27.54 4.17 79.21 6.73 45.32 10.5 Storage and distribution 80 0.16 43.71 4.55 363.90 7.42 55.10 10.6 Garages 38 0.11 34.11 3.46 131.58 6.64 44.13 10.7 Mixed 18 0.14 32.52 7.54 135.73 9.14 83.50 11. Bare land 333 0.10 780.82 13.82 4602.74 72.40 5241.15 Unclassified* 157 0.12 37.18 3.92 615.33 5.99 35.92 * Unclassified units require ground verification to determine their UMT class. Note that the minimum size threshold of 1 hectare has not been used for Addis Ababa. This is due to the very small parcels of land associated with some of the urban morphology types that the local team felt it was important to retain.
32
Figure 3.4: Percentage area cover of UMTs of Addis Ababa
3.1.1.3 Benefits of the UMT-based approach The UMT dataset is expected to add value to the existing land-use map used in Addis Ababa as well as providing the basis for the Task 2.2 assessment of ecosystem services and wider CLUVA integration. The land use map of Addis Ababa, which was produced for the implementation of the existing master plan of the city, classifies the city into 8 land use classes. These are: 1. Airport, 2. Mixed use, 3. Stadium festival site, cemetery, slaughter house 4. Industry, manufacturing and storage 5. Urban agriculture 6. Main and sub-centre 7. Forest, green along river and park 8. Reserved area This classification, however, ignores several land uses; for example minerals, transport (only airport indicated), utilities and infrastructure, community services are not represented in the land use plan. The characterization of the environment of Addis Ababa based on UMTs is exhaustive in terms of 33
covering both the built and green structure types with more or less clear subdivision among the UMT subclasses. Therefore, this methodology provides important data and information for the management of the urban ecosystem services based on effective urban planning procedures. Using a common classification scheme allows comparison of UMTs across CLUVA case study cities as well as other cities who have utilized similar methodology (e.g. Manchester). However, as would be expected, due to different topographic situations and land use history, some units are not represented in Addis (e.g. wetlands). Other modifications were also made, for example, previously developed land (UMT class 11) and unused land (UMT class 13) were treated together as bare land (UMT class 11) in the case of Addis. In general about 67% of the major UMT classes of Addis are similar to those of Manchester (Gill et al. 2008). Different techniques are currently employed for assessing the impact of climate change on urban ecosystem, lifelines, settlement and people. Because of this, the results are neither comparable nor integrative to be used for a comprehensive understanding of urban systems. The application of similar or comparable methodology, however, could reduce the above-stated problems. In Addis Ababa, Task 2.1 and 2.3 of CLUVA assess the vulnerability of adobe houses and people to the impact of flooding respectively. Adobe houses (in the case of Addis, houses constructed from mud and wood) are identified as a sub-unit of the residential UMT class and mapped. The mapping now provides opportunity for linking vulnerable communities with areas of hazard-exposure using a biophysically defined spatial unit. Collecting data and integrating the information obtained on adobe houses, vulnerable people and green spaces from the same UMT (e.g. mud/wood construction) helps for comprehensively assessing the impact of climate change on the urban environment. Ecosystem services in urban areas are related to the quantity and quality of green spaces. Quantitative analysis of green spaces requires the characterization of the urban environment. The characterisation of urban environment based on UMT units helps analysing the extent and distribution of green spaces. Therefore, characterising the urban environment of Addis Ababa based on UMTs helps to identify the amount and distribution of evapotranspiring surface covers within the different UMTs and therefore the ecosystem services that the green spaces within each UMT would provide. For the assessment of ecosystem services like carbon sequestration, surface temperature and runoff regulation, the proportion of pervious and impervious surface features in different land uses should be identified. Therefore the UMT method provides spatially explicit data for assessing the potential of different urban structures for the provision of ecosystem services. Ultimately this information can feed into green infrastructure planning. Based on the UMT analysis, hotspots (UMT 7.3) for collecting data on adobe houses, vulnerable people and vulnerable ecosystem have been selected. The results of the vulnerability assessment could now be easily integrated for a comprehensive evaluation of the vulnerability of the hotspots. The UMTs of Addis Ababa differ in their ecological performances due to their differences in physical and land use components. Identification of the proportion of pervious and impervious surface cover within each UMT helps assessing the potential of different surface covers to either aggravate the impact of climate change or in adapting to climate change impacts. Based on such 34
analysis, appropriate planning recommendations could be provided for enhancing the adaptation potential of urban structures to the impact of climate change. Therefore, the UMT analysis and the subsequent surface cover analysis of Addis Ababa provide spatial data for planning urban systems which are resilient to flooding. 3.1.1.4 Issues and limitations Some of the key issues reported in the production of the data associated with this deliverable relate to the acquisition of the aerial photo of Addis Ababa. The permission to acquire the aerial photo of Addis Ababa comes from the City Manager of Addis Ababa. The Addis Ababa Urban Plan and Information Institute is responsible to issues the aerial photo under the permission of the City Manager of Addis Ababa. Because of bureaucratic issues, about three months was required to access the initial aerial photo. However, the involvement of the Addis Ababa Urban Plan and Information Institute as a stakeholder the CLUVA project assisted in the process of resolving the data access and permission issues. Involving the stakeholders in the verification process was also valuable in facilitating access to the more recent aerial photography. A condition of the licence was that UMT analysis needed to be undertaken within the local academic institution. This requirement was adhered to. The involvement of student interns was a benefit for the wider research capacity building elements of the CLUVA project. However, this also meant that there was a greater burden on the research team based in EiABC. There was also a need to modify the process identified in the manual documents to fit with the existing skills of the student intern group. Therefore the initial digitising process was carried out using AUTOCAD rather than GIS. Whilst the conversion from CAD to GIS should be straightforward, in practice conversion lead to the creation of sliver polygons, gaps and topological errors which required checking and reassigning into appropriate polygons. This problem significantly prolonged the digitisation process. As would be expected with a student group, there were varying levels of engagement and success with the task in hand, therefore there was also some reassignment of tasks from the less to more able students. This had the effect of further prolonging the digitization task. 3.1.2
Land Surface Cover Assessment
Land surface cover assessment will be carried out for Addis Ababa using the process identified in Gill et al. (2008). The reasons for taking this approach have been identified in section 2.3.4. Application of the methodology is illustrated with the example of Dar es Salaam (see section 3.2) with work for Addis to follow the completion of the UMT dataset. The skills and experience of the Addis team allowed for complementary remote sensing work to be undertaken for the city. This is used as a basis of assessing the land cover classes proposed for Addis Ababa (Table 3.4), as a means of exploring the extent to which the Gill et al. (2008) method could be effectively replaced with a remote sensing based assessment. This extends the work of existing studies using this assessment method as there has so far been no direct comparison of results from the aerial photography method with those from remote sensing. This work therefore provides a valuable 35
addition to the current knowledge base, even though it is recognised that the situation can change between cities. Land cover assessment for Addis Ababa city has been proposed as a base study for the “Climate Change and the Role of Urban Green Space and its Planning: The Case of Addis Ababa. A Ph.D research proposal at the EiABC, AAU. Land cover assessment and mapping are planned to be done using two different methods and data sources, firstly using the UMTs identified using aerial photographs and secondly through the analysis of satellite imagery. The UMT units are only just completed so a full analysis has not yet been possible. However, preliminary efforts to establish the land cover classes and a complementary effort to map land cover using a high resolution satellites imagery (Quick Bird) is discussed in the following section. 3.1.2.1 Land cover assessment using the UMTs The methodology applied to assess the land cover of Greater Manchester (Gill et al., 2008) will be followed for Addis Ababa. In this method land cover types are traced from the UMTs, using a combination of GIS and statistical techniques where first typical land cover types are established using specific defining characteristics. With the help of GIS techniques, each UMT unit is independently selected and the many polygons belonging to same UMT are dissolved into one unit in which random points are distributed. Each point is traced using the ArcGIS select method and named by the appropriate land cover class to which it belongs. A short description and screen captured snapshots of the tentatively agreed land cover classes are presented in Table 3.4. The twelve land cover classes are as per the preliminary agreement in the Munich workshop to ensure consistency between test cities. Despite the need for consistency however, modifications may need to be applied due to the unique nature of land cover types in each city. Table 3.4: Screen captured images of land cover classes of Addis Ababa City Land cover class 1. Built structure type I
Descriptions This land cover represents buildings of well-planned and high rise types.
2. Built structure type II
This land cover represents buildings of informal (generally unplanned) and non-high rise types.
36
3. Forests (other than eucalyptus)
This cover consists trees of mixed types other than eucalyptus which is characterized by areas with contiguous vegetation cover.
4. Field crops
These areas include tef (Ergrostis tef), the most important staple food in many parts of Ethiopia) and wheat farming land which are fed with rainfall once per year. The fields are green only between June and September (rainy season).
5. Vegetable farms
The land cover here is cultivated vegetables. Associated areas are recorded through the UMTs, mostly located along the river sides where the most common types of vegetables, like tomato, potato, cabbage, etc. are grown using irrigation, thus it is green almost throughout the year.
6. Grass lands
These are found adjacent to farms and also as pockets in the forest areas. Compared to what is a grass land else where it is not wide and typical but it varies from the field crops and vegetable farms and is relatively green throughout the year used as grazing land by farmers.
7. Eucalyptus trees
This represents the Eucalyptus dominated forest trees, In many cases eucalyptus and other tree species are found to be in combination and it is likely to be difficult to come up with precise cover class between these two but the aim is to try and differentiate class 3 from class 7.
8. Dark bare ground
This represents bare ground surfaces without any vegetation cover and field patterns. Most of these areas are abandoned old quarry sites,
37
therefore the example in the image contains some shadow.
9. Impervious surfaces
Surfaces which are sealed and do not allow water to pass through like roads, pavements, airports, etc.
10. Bush/shrubs
This represents smaller trees and shrubs with low canopy cover and relatively smaller growth height.
11. Light bare ground
These areas are surfaces with no vegetation cover and no cultivated field patterns. Most represents the freshly opened quarry sites but also depends on the soil type. In the north-west and western part of Addis where there is a dominantly brown soil even if the quarry site is new it will appear as dark bare ground.
12. Water body
This is to be recorded as a land cover class but this is of minor importance in the context of Addis Ababa. Water cover associated with rivers and streams are expected to make negligible contributions to land cover assessments in a small number of UMT classes, e.g. riverine vegetation – here woody vegetation is expected to make up a large proportion of the UMT land cover profile.
3.1.2.2 Land Cover Mapping Using Satellite Imagery (Quick Bird 2007) The discipline of remote sensing has added greatly to the task of understanding the characteristics of the Earth’s surface. Parallel to the increasing availability of remotely sensed data there has been an improvement in the analytical and technical capabilities for interpreting those data. Therefore, satellite images are being increasing found to yield useful information for natural resource management and urban planning activities. Whilst many urban planning and management tasks can 38
now be informed by data generated from the analysis of satellite images, this still cannot generate richness of information which planners often require (Xiao and Zhan, 2009). Nevertheless, it is helpful to explore the extent to which remote sensing can generate comparable information for some of the CLUVA Task 2.2 work. Techniques can also be used to provide further inputs for the assessment of ecosystem services in relation to the wider objectives of the PhD analysis. In this study use of satellite images to map the land cover of Addis Ababa is needed for two main purposes: To compare the two methods i.e. the use of manually digitized orthophotos with the automatically developed one from the satellite imagery in detecting the details of urban land cover and its change; and, To investigate the impact of using different techniques for estimating the volume of biomass within the urban environment6. The comparative work has a number of sub-objectives associated with it. For example, to recommend methods for the different stages of Task 2.2, the benefits and limitations between approaches and to assess the value of an alternative method for biomass estimation. The work will also be able to recommend methods for use in subsequent urban planning works, as has been identified earlier. The results are currently exploratory and further analytical work is planned. In due course, the full details of the remote sensing exercise are to be written up separately through the PhD thesis7. A summary of the findings to date are included here, together with an indication of the implications for Task 2.2. Different techniques and image analysis software such as ENVI4.3 and ArcGIS 10 has been tried to establish the twelve tentatively agreed land cover classes (see Table 3.4). For practical reasons (handling large data sizes) and ease of operation land cover classification was carried out using the ArcGIS10 Maximum likelihood classification supervised classification tool. The input raster image used for this classification was Quick bird satellite image (2007) prepared for the Addis Ababa City boundary (Figure 3.5). After repeated exercises the reflectance values of many of the proposed classes proved to be confused and only the following seven classes were identified (Figures 3.6 and 3.7). Even within these classes there are misclassified images like the cultivated/vegetable farms covered by well grown crops with grasses and shrubs. Similarly field crop areas without grown crops but which show distinct field patterns have been confused with bare ground. Use of more reliable method by combining images with high resolution (quick bird) with the multi spectral bands (Landsat ETM+) using ERDAS Imagine software is planned to enable higher accuracy and possibly more land cover classes. However, initial results suggest that remote sensing cannot easily replicate the level of detail possible using the main CLUVA methodology.
6
This also connects to work to estimate woody biomass which is ongoing in Dar es Salaam, led by the TUM team. This will include a full reporting and justification of methods and inclusion of a deeper discussion of findings, including documenting the appropriate error statistics.
7
39
Figure 3.5: The Quick Bird Image 2007 of Addis Ababa City
Figure 3.6: Land surface cover types of Addis Ababa (identified from Quick Bird Image 2007)
40
Figure 3.7: Land Cover map of Addis Ababa (based on Quick Bird Image 2007)
Table 3.5: Total area of each land cover class Class Name Bare Building Cultivated Grass Road Shrub Trees Total
3.1.3
Pixel Count .59m resolution
Area Km2
Percent of total area
90,667,190 224,170,291 210,023,379 235,123,812 260,650,727 433,914,428 158,499,450 1,613,049,277
32 78 73 82 91 151 55 562
5 14 13 15 16 27 10 100
Conclusion
The Addis Ababa case study has illustrated the potential of the UMT approach to urban characterisation. It is especially noteworthy that the research process has been undertaken with a strong connection to local stakeholders and this has helped to improve data access and the potential for CLUVA results to be taken into the realm of local institutional decision-making. As well as 41
following a previously designed methodology, the work in Addis Ababa has also extended knowledge by providing a template for urban morphological assessment in other cities in its climate zone and by providing the basis for a comparison of methods for the next stage of the Task 2.2 work programme. The basis for green infrastructure planning has been improved in the city and there is a solid spatial framework through which ecosystem services can be assessed which relate back to meaningful planning units for the city.
42
3.2
DAR ES SALAAM CASE STUDY
The Dar es Salaam case study is the second of the detailed case studies considered in this report. Again the section is divided into two parts with the first detailing the work undertaken for the production of the final draft of the UMT-based green infrastructure map, and the second the work to date on land cover assessment. In the case of Dar es Salaam most effort in terms of the land cover assessment only uses the main CLUVA methodology and the provisional results from this process are shown in full. This enables a good examination of the expected outputs from that process in other case study cities. Again the section includes a discussion of the benefits and limitations of applying methods, with an overall conclusion identifying the key points from the work.
3.2.1
UMTs and their creation in Dar es Salaam
3.2.1.1 UMT categorisation and mapping The UMT classification was decided through a face-to-face meeting of the teams in June 2011 in Dar es Salaam. The classification was developed from that initially constructed from Addis Ababa with the help of the Task 2.2 team and a member of WP3. It resulted in the scheme presented in Table 3.6. The process used in Dar es Salaam differed slightly from that used in Addis Ababa. Here, a preexisting land use map was available as a starting point for the UMT delineation. Therefore the classification was done using the available Land Use Map (LUM) units sourced from the City Council produced with UN Habitat. The dataset was produced in 2008. The process followed involved deciding how the land use classification scheme could be mapped onto the UMT classes. It was based on LUM level 4 which describes various land use categories in more than 2100 units (Table 3.6). Orthorectified aerial photographs for 2008 (resolution of 10m) were collected from the Ministry of Lands, Housing and Human Settlement Development and used to gain additional information for understanding the equivalent UMT class for each unit. Identification was done following the use of an area or a building and its green structure surroundings as well as types and number of buildings and their surroundings. The aerial photographs were particularly important for understanding green structure UMT class types and were also required for the second stage of the analysis. The orthorectified aerial photographs are known to have been taken during the dry season though the date is not specified. The reclassification of UMT units was initiated with the editing process using ArcMap by the Ardhi PhD student (DK). A short training course was conducted during the CLUVA workshop held in Dar es Salaam in June 2011 to assist with this task. For this and the subsequent GIS-related tasks, the student was supported by local GIS experts from Center for Information and Communication Technology (CICT) of the Ardhi University (BS). Through the process a PhD student was also exposed to some GIS knowledge which contributed significantly to the mapping task. Table 3.6: UMT Class level two with classifications from Land Use Map 43
UMT class level 2 1.1 Horticulture 1.2 Field crops 1.3 Mixed farming 2.1 Plantation 2.2 Mixed forest 2.3 Riverine 2.4 Mangrove 2.5 Forest 2.6 Bushland 3.1 Mineral workings and quarries 4.1 Parks 4.2 Stadium and festival sites 4.3 Botanic Garden 4.4 Beach 4.5 Other open space 4.6 Sports grounds 5.1 Major road corridors 5.2 Airports 5.3 Rail 5.4 Port 5.5 Bus stations 6.1 Energy production and distribution 6.2 Water tanks and treatment 6.3 Refuse disposal, including landfill 6.4 Cemeteries 7.1 Condominium & multi-storey (>75%) 7.2 Villa & single storey stone/concrete (>75%) 7.3 Mud/wood/sand brick construction (>75%) 7.4 Mixed (if threshold does not apply) 7.5 Scattered settlement 8.1 Education and culture (universities, schools) 8.2 Medical 8.3 Religion 8.4 Institutional 8.5 Public/private Hall 9.1 Malls 9.2 Formal shopping area (>75%) 9.3 Open markets (>75%) 9.4 Mixed formal and open 10.1 Manufacturing (>75%) 10.2 Offices (>75%) 10.3 Storage and distribution (>75%) 10.4 Garages (>75%) 12.1 River 13.1 Marsh/swamp 14.1 Hotels 14.2 Entertainment 15.1 Military
Description and examples from LUM 2008 level 4 Horticulture Cultivation of crops Cultivated crops, bush Cultivation of crops Forest River valley; green belt; hazard land Forest Bushland Quarries, salt pans Open space; parks Stadiums Botanic garden Open space Open space; recreational centres; clubs, playgrounds Archery ranges; rifle ranges; sports ground Feeder road; trunk road Airport; aerodrome Railway line; railway facilities; railway stations Ports and dockyards; Bus station; daladala stop Electricity generation station; Sewage treatment Dumping sites Cemeteries Apartment houses; quarters; other dwellings Residential/commercial Scattered settlement Art gallery, museum; University; secondary school; library Hospital; dispensary; health centre Religious (church, mosque) Prisons; police station; court of law; fire station Community hall Exhibition hall; shopping malls Shops and offices Processing; industrial; manufacturing Banks; Parastatal offices; Government & embassy offices Fuel storage; filling station; godowns; wholesale warehouse Motor vehicle repair; workshops Rivers Swamps; estuaries Hotels Casinos; bars and night clubs Army Barracks; quarter; FFU Barracks
44
In addition to the re-classification of LUM level 4 categories into UMT categories (carried out by DK), further processing of the LUM was required in order to produce a UMT map. This was carried out by UM. Firstly, small roads existing in the land use map needed to be removed. This is because the UMT major road category threshold is roads greater than 15 metres width. Roads with a width smaller than 15 metres are part of the character of other UMTs, rather than a distinct category or feature themselves e.g. streets in residential areas. This process was undertaken using editing tools in ArcMap8 (Figure 3.8). Secondly, very small units less than 1 hectare in size needed to be integrated with surrounding UMTs, as these were also too small to be recognised as distinct UMT units. This process was undertaken using the eliminate tool in ArcMap9. Figure 3.8: Processing the existing Land Use Map into UMT categories – removing small streets (mainly from residential areas). Screenshots to show before (left) and after (right).
UMTs were verified through ground-truthing and expert verification. Orthorectified aerial photography for 2008 was available for around 26% of the city, mostly on the north western part of Dar es Salaam, and field verification were also conducted for this area (Figure 3.9). Secondly, expert opinion was sought to comment on the UMT units. In this process the Ardhi Task 2.2 lead (RS) reviewed results in the Munich workshop, which involved checking selected UMT units (that had been previously identified by UM and TUM as requiring verification) and verifying them with RS’s local knowledge supported by visual assessment of historical imagery viewed in Google Earth. Local experts at Ardhi University also commented on the UMT delineation at an internal meeting held on 16th May 2012.
8
Full details about the editing undertaken to remove the small roads in the LUM data can be found at: http://uomgeospatial.wordpress.com/2012/02/27/get-rid-of-all-those-small-streets-in-my-data/ 9 Units were processed according to if they had already been categorised into UMTs. There were originally 812 units under 1 hectare in size. Polygons without UMT classes were merged to the neighbouring polygon; polygons which had been categorised into UMTs were merged with neighbouring UMTs of the same category.
45
Figure 3.9: Verification of Dar UMT units - an example from a field trip in Dar es Salaam
3.2.1.2 UMT Results Examples of each of the UMT categories are provided in Figure 3.10. The UMT map for Dar es Salaam is shown in Figure 3.11. The final draft map indicates the UMT units that were verified using the orthorectified aerial photography from 2008 and field surveys. The unverified extent will be considered later through analysis of historical imagery on Google Earth together with local expert input at the Dar meeting in July 2012. Summary statistics of the UMTs are provided in Table 3.7 and Figure 3.12 displays the percentage contribution of each UMT category to the total area of Dar es Salaam. Of all the UMTs in Dar es Salaam, mixed farming contributes the largest proportion, of 41% of the total area, mainly found on the outskirts of the city. Scattered settlement is the second largest UMT, contributing to 17% of the total area in Dar es Salaam. This UMT is found between areas in the city centre and the most outlying areas of the city. Villa and single storey stone/concrete contribute 11% of the total area of Dar es Salaam. Although this UMT is mostly found in planned areas, there are other unplanned areas which also fall into this UMT category. Mixed residential UMT contributes 9% and this includes residential and commercial land uses and different building materials, consisting mainly of cement and sand blocks. These UMTs are found throughout the city and tend to decrease towards the outskirts of the city: they are found in both planned and unplanned areas. Field crops contribute 46
4% and these UMTs are found mostly on the outskirts of the city. Bushland contributes just less than 4%, and some areas of bushland are also used for mixed farming so the percentage area of bushland may be slightly higher. Military, marsh/swamps, riverine, and mud/wood/sand brick construction UMTs have between 13% each. Military areas are spread across the city from the city centre in Upanga to the outer-most parts of the city in Bunju. Mud/wood/sand brick construction UMTs are mostly found in the vicinity of the city, and with time this UMT is changed to either mixed or villa and single storey stone and concrete. Major road corridors are around 1% and all other UMTs are less than 1% of the total area of Dar es Salaam. In general, the UMTs of Dar es Salaam do not follow specific patterns because its urban morphology is very heterogeneous, certainly compared to European cities. In some areas residential areas are located within or very close to industrial areas, such as Tabata Reli, Keko, and along Nyerere Road. Urbanization due to rapid population growth and other factors has led to people constructing houses in areas not zoned for residence. Often, the land use plan and regulations governing urban land use management are violated and in many cases recreational land uses, especially open spaces designated as parks or playground are encroached. This makes UMT delineation a challenge but also highlights the need for stronger planning frameworks. A number of UMTs especially those under transition are not fully developed and therefore, are expected to change in the near future. These UMTs include mixed farming, scattered settlement and field crops. The government has surveyed some of these areas specifically for residential use and the process of allocating plots to home builders is on-going. Currently these UMTs have few inhabitants. Recently more than 2400 plots were surveyed in Mabwe Pande which is categorized as mixed farming, and the plots are being given to victims of flash floods which swept across the city in December 2011. With time, and as owners of these plots start to develop them, the UMT will eventually change. As has been noted for Addis Ababa, UMT transition takes place rapidly in Dar es Salaam. Therefore assessment of change is included within the remit of the Dar es Salaam PhD student’s research work (DK). The associated PhD research objective is to establish the dynamism of green spaces and to better understand the pressures on green structures within the city. For example, some UMTs are known to be under particular pressure from population change and so this work will quantify changes in characteristics between two different time frames. The Mixed farming UMT class is one such class that is hypothesised to be associated with high rates of loss due to the expansion of residential and commercial areas. This encroachment process is fuelled by the rapid population growth in the city. Recent growth of Dar es Salaam’s population is estimated at 4% per annum (URT Census, 2002). Similarly to mixed farming, the scattered settlement UMT is also under transition due to various development activities taking place. With time, this UMT is transformed to either mixed, condominium or villa and single storey stone/concrete. In comparison, the rate at which area categorized as scattered settlement is diminishing is faster than the rate at which mixed farming is 47
being transformed. This is because expansion of the city starts from the city centre moving outward, and accordingly formally scattered settlements come closer to the city centre. If the city expands at its current rate and no planning protection is established these UMTs (together with mixed farming) are likely to be converged within the urban centre. Figure 3.10: Aerial images of selected UMT classes (scale 1:1,500, then reduced to 80%) 2.3 Riverine
2.4 Mangrove
5.1 Major road corridor
7.1 Condominium
7.2. Villa & single-storey
7.3 Mud/wood/sand brick
9.2 Formal shopping area
10.1 Manufacturing
13.1 Marsh/swamp
48
Figure 3.11: UMT map for Dar es Salaam (final draft version)
Table 3.7: Summary statistics of UMT data in the city of Dar es Salaam Unverified extent
2008 orthophoto extent (km2) UMT Class 1.1 Horticulture 1.2 Field crops 1.3 Mixed farming 2.2 Mixed forest 2.3 Riverine, valleys 2.4 Mangrove 2.5 Bushland 3.1 Mineral workings & quarries 4.1 Parks 4.2 Stadium & festival sites
Dar es Salaam total urban area % of total Total area area 11.85 0.79 64.73 4.31 611.91 40.76 4.40 0.29 37.01 2.46 4.68 0.31 58.29 3.88
No. of units 0 2 13 2 41 9 12
Min. area 0.04 0.25 0.08 0.01 0.02 0.01
Max. area 0.06 16.47 0.98 5.48 1.64 4.44
Av. Area 0.05 3.81 0.53 0.41 0.35 0.90
Total area 0.10 49.59 1.05 16.81 3.19 10.84
13
0.02
1.20
0.34
4.39
0.82
5.20
0.35
6
0.01
0.49
0.10
0.61
0.07
0.68
0.05
1
0.17
0.17
0.17
0.17
1.76
1.93
0.13
49
Total area 11.85 64.64 562.32 3.35 20.20 1.49 47.45
4.3 Beach 4.4 Other open space 4.5 Sports ground 5.1 Major road corridor 5.2 Airport 5.3 Rail 5.4 Port 5.5 Bus stations 6.1 Energy production & distribution 6.2 Water tanks and treatment 6.4 Cemeteries 7.1 Condominium & multi-storey 7.2 Villa & single storey stone/concrete 7.3 Mud/wood/sand brick construction 7.4 Mixed 7.5 Scattered settlement 8.1 Education and culture 8.2 Medical 8.3 Religion 8.4 Institutional 9.1 Malls 9.2 Formal shopping area 9.3 Open markets 9.4 Mixed formal & open 10.1 Manufacturing 10.2 Offices 10.3Storage & distribution 10.4 Garages 13.1 Marsh/swamp 14.1 Hotels 14.2 Entertainment 15.1 Military Total area
8 31 5 28 0 17 8 3
75%) 8.3 Mall and other constructed shopping area (>75%) 8.4 Mixed market: formal and open (if threshold does not apply) 8.5 Hotels 9.1 River (more than 15 m width) and scanty water 10.1 Administrative centre 10.2 Military centre 10.3 Police centre 11.1. Bare land
72
OBSERVATIONS Most agriculture parcels in Douala are mixed. Swampy or marshy land are usually covered by grasses
It is not up to 15m width, but it is included due to its vital role in providing the linkage between the two parts of the town.
This housing type is more likely to be associated with planned development but can occur in unplanned zones This housing type is more likely to be associated with unplanned development
Constructed and totally covered market. These markets are not totally constructed or covered.
Figure 3.21: Pilot UMT mapping work for the Douala case study
73
Figure 3.22: Percentage area of each Urban Morphology Type unit in Douala
74
Table 3.14: Pilot summary statistics of UMT data in the city of Douala UMT units (sub-units) 1.1. Field crops & vegetable farm 2.1. Mangrove 2.2. Plantation 2.3. Grassland/Marshy or swampy land 3.1 Parks and botanic garden 3.2 Stadium and festival sites 3.3. Beach 4.1. Port 4.2. Rail, rail station 4.3. Airport 4.4. Bus station/Parking 4.5. Major road corridors ≥ 15 m 4.6. Bridge (on the Wouri river) 5.1. Refuse disposal, including landfill 5.2.Cemeteries 5.3. Energy production and distribution 6. Condominium, Villa, single and multi-storey stone ≥75% 6.2 Mud/wood construction >75% 6.3 Mixed construction (if threshold does not apply) 7.1 Religion 7.2 Education 7.3 Medical 8.1. Manufacturing (>75%) 8.2. Offices/storage and distribution (>75%) 8.3. Formal shopping area (>75%) 8.4. Mixed market: formal & open 8.5.Hotel 9.1 River (more than 15 m width) and scanty water 10.1. Administrative center 10.2. Military center 10.3. Police center 11.1. Bare land TOTAL
Number of units 3 3 3 6
320060 1121750 28800 22825
Maxi. area 597387 9033443 130235 1083550
Average area 421888 6176808 80507 611252,83
Total area in square meter 1265664 18530423 241520 3667517
% of total area 0.58 8.52 0.11 1.68
6 3 2 1 1 1 1 7 1 1
16553 105892 93538 3381210 704229 7033670 106433 28340 28340 397748
519831 775464 200376 3381210 704229 7033670 106433 311740 28340 397748
126874,43 422224 146957 3381210 704229 7033670 106433 157894,29 28340 397748
761246,6 1266673 293914 3381210 704229 7033670 106433 1105260 28340 397748
0.35 0.58 013 1.55 0.32 3.23 0.04 0.51 0.01 0.18
3 1
27405 532321
182203 532321
83237,333 532321
249712 532321
0.11 0.24
7
118331
2003590
670181,71
4691272
2.15
10 3
975464 145961
33548900 8365340
8782674,5 3804493,7
87826745 11413481
40.38 5.24
4 33 5 11 1
10632 19779 83780 59863 3544580
33627 327990 198874 5049470 3544580
23376,5 85281,97 131046 879579 3544580
93506 2814305 655231 10142151 3544580
0.04 1.29 0.3 4.66 1.62
1
39817
39817
39817
39817
0.01
4 3 1
25161 10451,45 46 827000
132701 107869 46 827000
68058 44848,15 46 827000
272232 134544,45 46827000
0.12 0.06 21.53
5 3 3 3 140
83169 55711 18294 26997,9 /
1622260 105470 255543 4635900 /
433043 78625,333 130002,7 2222162,6 /
2165215 235876 390008 6666487,9 217478332
0.99 0.1 0.18 3.06 100%
Min. area
75
3.5.2
Benefits of the UMT approach
The UMT approach provides a new dataset for the city of Douala. It will also provide the basis for the Task 2.2 assessment of ecosystem services and wider CLUVA integration. The existing land use map of Douala does not give the same level of information. Furthermore it does not cover the same spatial extent, in particular it fails to recognise the extent of the peri-urban zone which is shown in Figure 3.21. The UMT can also help to analyse the close relationship between land use, urban form, population and poverty. From the UMT map, a vegetation map of Douala can be extracted, and by so doing, ameliorate the existing one and use this new information for proposing a green plan for the town. 3.5.3
Land surface cover assessment
The examples from the detailed case studies are also used to inform the development of appropriate land cover classes for the next stage of work. The proposed land cover classes are given in Table 3.15. This will also be subject to modification following field work activities. Table 3.15: Land surface cover types for Douala Land cover type Type 1. Urban structures I (formally constructed buildings). Type 2. Urban structures II (informally constructed buildings). Type 3. Other impervious surface Type 4. Bare Ground/soil Type 5. Sand Type 6. Water Type 7. Large trees Type 8. Small trees /shrubs Types 9. Palm trees Type 10. Grasses Type 11. Cultivated crops
3.5.4
Observations Constructed with wood, plastic, used steel sheet, cardboard or other informal materials
Issues and limitations
The Douala team have only recently started the UMT mapping process but have already completed a first draft of the UMT map. This gives a good idea about how the characterisation work can be interpreted in the Douala case. Aside from the short time that the team have been involved, there are other particular issues which the team face. For example, the geographical location of the Douala team at Yaoundé rather than in the case study city means that verification work is more time intensive than is the case for teams working in the other case study cities.
76
3.5.5
Conclusion
The UMT is a good approach to assess urban ecosystems in Douala, because of its systemic and analytical nature. The UYI started this work late but is committed to do all the steps before the end of the year so that more benefit can be obtained from applying this innovative methodology. In order to fully realise the UMT map it is necessary for further field work activity using a sufficiently large team. The team consider also on-going communication with European partners and attendance at relevant meetings to be essential for achieving a high quality end product.
3.6
SAINT LOUIS CASE STUDY
The Saint Louis team adopted the UMT approach after the opportunity that was offered following the workshop in Pretoria to participate in the Munich Task 2.2 workshop held at the end of April 2012. Applying the method of the UMT needed some adjustments because of technical constraints but also the particular characteristics of the city of Saint Louis. These modifications are not considered to introduce inconsistencies and instead the work in Saint Louis follows the broad requirements of the method. This section covers the methodological approach followed and the results obtained. It discusses the merits of the approach to Saint Louis before suggesting joint elements of Task 2.2 with the other components of CLUVA of Saint Louis. It ends with the presentation of a series of indicators to consider studying ecosystem services. 3.6.1
UMT categorisation and mapping
In common with the other case study cities, it was necessary to adapt the UMT classification scheme for Saint Louis in order to take account of the distinct human and physical characteristics of the city. In particular modifications were required to account for the amphibious character of the city. Saint Louis is an estuarine settlement with littoral and Sahel features. The present-day characteristics of the city are driven by underlying geomorphic units which formed in the typical sequence of marine transgressions and regressions and Quaternary climatic changes: successive barrier beaches, the dunes, semi-fixed dunes and mud deposits (Michel, 1973). These units have evolved under the combined effect of natural and anthropogenic factors giving a rather atypical morphology in the urban structure in Saint Louis. Of particular note is the influence of the omnipresence of water which geographically structures part of the territory but present-day development is also affected by the urban processes which have taken place. For example, the morphology of Saint Louis is marked by a series of urban extensions based on changes in the population (Figure 3.23). Climatic variability has also influenced the morphology of the city, including the characteristics of ecosystems, the land uses that the city can ultimately support and the options for development
77
Figure 3.23: City expansion in relation to the geographical distribution of ecological units.
In fact the differences compared to the classifications used elsewhere are mainly due to the different nature of ecosystems and the contrasts between them. Ecosystems in Saint Louis have a rather different make up compared to other CLUVA case study cities. Again, this links back to the specific role of water within the city. There is a notable distinction between the dry ecosystems and wetland ecosystems within landscapes in the city of Saint Louis. This is how they were defined as major classes for the city of Saint Louis. The vegetation related classes which are therefore proposed for Saint Louis include: For wetlands: 13.1 Mangrove 13.2 Other Aquatic Vegetation 13.3 Zone de tanne (areas of salt flats in areas formerly occupied by mangroves) 13.4 Flood zone 13.6 Saline zone 13.7 Sub-tidal zone 13.8 Intertidal zone For dry ecosystems: 16.1 Savannah 16.2 Bare soil (as a natural erosion feature) 16.3 Sand dunes 16.4 supra-tidal zone 78
Understanding the urban morphological characteristics associated with historical and more modern built structures in the city needs to account for the spatial morphology (dense, very dense, dispersed) building material (the type of materials used) functions (e.g. residential, administrative, business, services) construction standard (e.g. blocks, units with two or three floors, terraces) Structures that are observed in the housing in Saint Louis are as follows: 7.1 Condominium and multi storey 7.5 Scattered settlement 7.6 Residential Zone to regular terrace 7.7 Residential Zone subdivided to terrace 7.8 Area of dense residential 7.9 Precarious residential zones 7.10 Housing with luxury tile or slate 7.11 Block constructions Table 3.16 provides a fuller list of UMTs with screenshots of examples used to assist the mapping activity. The digitising work was carried out by a local Masters of Geography analyst (FS) with existing mapping and empirical knowledge of the city. Images available on Google Earth were used in the absence of high resolution media from local sources and units generated according to features that could be distinguished from the aerial photography. An image dated 5th December 2011 was used meaning that the UMTs are characterised from the reference point of the cold, dry season in Saint Louis. The work started by firstly mapping urban morphologies associated with ecological units and a pilot UMT map is now completed (Figure 3.24). The different units are to be checked on the basis of existing data and a field visit. Table 3.16: Description of key UMT classes in Saint Louis UMT
Sub Unit
1. Agriculture
1.2 field crops (fruit trees)
Screenshot
Hydro-agricultural developments realized with the support of SAED13. This is usually associated with the cultivation of rice
1.4 Irrigated Culture
13
Description of key characteristics of UMT classes Seasonal fruit farms run by private owners
Societe d'Amenagement et d'Exploitation des Terres du Delta du Fleuve Senegal
79
Soils exploited during rainy seasons 1.5 Rain-fed agriculture SIAR fitting garden (Senegalese Institute of Agronomic Research) 4.3 Botanic garden
They are sites developed with plantations as places of leisure 4.4 Other open spaces 4. Recreation and conservation
They are mainly located along the coast in Langue de Barbarie with different tourism and leisure facilities and associated infrastructure like lodgings
4.5 Hotels
Areas of basic sports mostly football but also Basketball; they can also host other important cultural, social or political events
4.6 Sports ground
5. Transport
Sections of national road (RN2) at the level of Saint-Louis.
5.1 Major road corridors ≥15m
Old fish port 5.4 Port They centralize the services of inter-urban and intra-urban links and offer other services to the passengers with the development of connected activities (trade, catering, etc.) Airport with national connections
5.5 Bus station
5.6 Airport
80
They link the different isles which constitute the city of Saint-Louis: The Isle, Sor and Langue de Barbarie.
5.7 Bridge
6. Utilities and infrastructure
Burial places and which may hold records of the deceased
6.4 Cemeteries
These are buildings that house various services and / or housing staff.
6.5 Blocks
These are areas along the river or the ocean where artisanal fishermen landing or departure.
6.6 Boat access areas
The town centre of Saint-Louis with a rather specific architecture for most of the buildings inherited from colonial era but which host different functions: residences, administration, services and business
7.1 Condominium and multi storey≥75%
Ghetto habitations with no group plan of development and dispersed houses in the area
7.5 Scattered settlement
Habitations in regular districts 7.6 Area of regular dwelling with terrace 7. Residential
Areas of informal dwellings successively occupied through the expansion of the urban tissue and which were parcelled
7.7 Area of parcelled dwelling with terrace
Area of regular dwelling but highly densified through population growth
7.8 Very dense area of dwellings
Dwellings which developed in low points or on salted fields
7.9 Precarious area of dwelling
Areas of dwelling run by estate agencies
7.10 High standing dwelling with tiles or slates
81
Scholar buildings very often secondary 8.1 School 8. Community services
The second university of Senegal hosts a pedagogical and administrative campus, a social campus for the students and some dwellings for the staff
8.5 University
Places of commerce for the exchange of various well developed goods but they tend towards uncontrolled development of the stands
9.4 Mixed formal and open 9. Retail
Commercial services 9.5 Commercial zone Areas associated with the production of goods 10.1Manufacturing
10. Industry and business
Areas bordering the coast which are associated with fish-related processing or traditional preservation activities (dry or salted fish)
10.5 Area of traditional processing of fishrelated goods
Buildings associated with services 10.6 Services The large waterway which acts as the distinctive and structuring physical element of the city of Saint-Louis
12.1 River 12. Open water
Occurring on low lying areas functioning as temporary streams
and
12.4 Basin
13. Wetlands
Now existing in remnant status open to the influences of the sea and the river and with stratified vegetation according to topography, floods and tides.
13.1 Mangrove swamp
82
Predominantly reeds which develop in or around backwaters. They are different from the mangrove which are mostly made up by rhizophora and avicenia
13.2 Other aquatic vegetation
Areas of former mangrove swamp which deteriorated with the drastic climate conditions through times. Nowadays they are bare or host a weak steppe halophyte.
13.3 ‘Zone de tanne’
Low areas periodically exposed to floods and tides during the year.
13.4 Flood zones Areas liable to flood
Spaces where saline efflorescences are observed.
13.6 Saline Zones
Area continually flooded by the tide 13.7 Sub-tidal zone
Area covered by the tidal zone 13.8 Intertidal zone Areas associated with national security 15. Military
15.1 Military
Areas of savannah along tracts not occupied by dunes. 16. Dry Ecosystems
16.1 Savannah
Areas which are not covered by vegetation, and which could be naturally eroded.
16.2 Bare soil
Sand or sand dune zones along the coast and hosting vegetation at the level of nebkas, salsola baryosma and Sporolus spicatus.
16.3 Beach and Sand dunes
83
Flooded area in case of high tides during the year
16.4 Supratidal area
Coastal dune zones with some shrub vegetation
16.5 Vegetated Dune zones
Areas which are subject to reforestation 16.6 Plantation zones
Table 3.17: Area and percentage of land for major and sub UMT classes Main UMT classes (% total)
Sub UMT classes
Area (ha)
%
873.64
100
1.2 Field crops (fruit trees)
152.49
17.45
1.4 Irrigated Culture
445.1
50.95
1.5 Rainfed agriculture
276.05
31.60
67.69
100
4.3 Botanic garden
18.91
27.94
4.4 Other open space
6.64
9.08
4.5 Hotels
32.22
47.60
4.6 Sports ground
9.92
14.66
114.98
100
5.1 Major road corridors
34.68
30.16
5.4 Port
4.96
4.31
5.5 Bus Station
4.12
3.58
5.6 Airport
70.47
61.29
5.7 Bridges
0.75
0.65
61.92
100
6.4 Cemeteries
15.51
25.05
6.5 Blocks
22.1
35.69
6.6 Boat access areas
24.31
39.26
1472.58
100
1. Agriculture (6.89%)
4. Recreation & Conservation (0.49%)
5. Transport (0.91%)
6. Utilities & infrastructure (0.49%)
7. Residential (11.61%) 7.1 Condominium and multi storey≥75% 7.5 Scattered settlement
70.86
4.81
584.16
39.67
7.6 Area of regular dwelling
198.71
13.49
84
with terrace 7.7 Area of parcelled dwelling with terrace 7.8 Very dense area of dwellings 7.9 Precarious area of dwelling 7.10 High standing dwelling with tiles or slates 7.11 Mixed
244.65
16.61
77.57
5.27
210.64
14.30
23.35
1.59
62.64
4.25
207.55
100
8.1 School
16.52
7.96
8.5 Univerisity
191.03
92.04
19.00
100
13.19
69.42
5.81
30.58
30.73
100
3.67
11.94
11.68
38.01
15.38
50.05
2891.99
100
12.1 River
2856.92
98.79
12.4 Basin
35.07
1.21
2625.16
100
732.23
27.89
13.2 Other aquatic vegetation
367.47
14.00
13.3 ‘Zone de tanne’ 13.4 Flood zones Areas liable to flood 13.6 Saline Zones
227.51
8.67
1243.43
47.37
20.22
0.77
13.7 Sub-tidal zone
15.93
0.61
13.8 Inter-tidal zone
18.37
0.70
52.21
100
4267.66
100
16.1 Savannah
228.25
5.35
16.2 Bare soil
5.64
0.13
16.3 Beach and Sand dunes
115.79
2.71
16.4 Supratidal area
3880.51
90.93
16.5 Vegetated Dune zones
34.64
0.81
16.6 Plantation zones
2.83
0.07
8. Community services (1.64%)
9. Retail (0.15%) 9.4 Mixed formal and open market 9.5 Zone commerciale 10. Industry & Business (0.24%) 10.1 Manufacturing 10.5 Area of traditional processing of fish-related goods 10.6 Services 12. Open Water (22.81%)
13. Wetlands (20.70%) 13.1 Mangrove swamp
15. Military (0.41%) 16. Dry ecosystems (33.66%)
85
Figure 3.24 shows sub-level UMTs for Saint-Louis. Open water has been included in the study area summary statistics due to the close relationships between land and water in the city. Even with open water included, around half of the study area of the city is associated with primarily green structure related UMT units14 and around 14% are associated with buildings. Of the building related units, the largest proportion of the study area is associated with scattered settlements (4.6%) and this contributes around one third of the primarily built units and nearly 40% of the area covered by residential zones. Housing associated with precarious zones makes up around 14% of all residential areas in Saint Louis. Dry ecosystems represent the largest green structure type in the study area at around one third of the land area. Most of this is associated with the supra-tidal area. Wetlands cover approximately 23%with mangrove swamp representing 28% of the wetland total. Around 9% of the dry ecosystem area is ‘zone de tanne’ or the former mangrove areas which are now degraded and either bare or supporting other salt tolerant vegetation species. Figure 3.24: Sub-UMTs for Saint Louis
14
Taken as 1.2, 1.4, 1.5, 4.3, 4.4, 13.1, 13.2 13.3, 16.1, 16.4, 16.5 and 16.6 for the purposes of this assessment.
86
3.6.2
Benefits of the UMT approach
Different methods are proposed as part of the study of resilience in urban ecosystems including the UMT methodology. It offers the advantage of highlighting the different components of each structure of the urban fabric and a means to analyse the potential ecosystem services and resilience of associated systems to climate change. This method supports the development of guidelines so that and urban planning can mitigate climate change through ecosystem services. Despite the presence of important ecosystems with high ecological potential, the urban site of St. Louis is actually an economic and Socio-Ecological System (SES) vulnerable to the vagaries of the weather because of its hydro-geographical characteristics. Some actions have been initiated to protect these ecosystems with a view to integrate them into the urban landscape giving rise to various structures in the urban fabric. A new awareness has been developed about the role that these ecosystems play in protecting the city against hazards. The interest of the Saint Louis team in the UMT approach lie in the ability of the framework to help to understand the extent to which ecosystems may be able to protect the city and therefore to make urban structures more resilient in the context of climate change. The definition of UMTs will take account of this overall purpose. The defined ecological services will be connected to real urban ecological units and which connect with other factors which in turn help to explain different vulnerabilities and resilience to climate change. The goal is to find adaptations based on ecosystem services and upgrade policies to protect important ecosystems. The management of ecological and social components through the UMT framework will allow issues associated with each to be harmonised. Indeed the UMT data will help answer questions of vulnerability across the various structures that make up the urban area, to analyse the effectiveness of management options and land management by examining the ability response or resistance of various structures face different hazards and risks but also by speculation of ecosystem services that can mitigate the impact of climate change. The UMT framework acts as a spatial reference framework for incorporating issues of the city of Saint Louis that transcends the neighbourhood level and illustrates them through the observable structures that resulted from the combined effect of past climate change and of urban dynamics. 87
Specific results will be gained for the different UMT classes and relate to specific ecological and social problems within associated zones. Therefore planning activities can be better informed by scientific understanding through these units. The work in Saint Louis on multi-risk management, urban governance that supports urban planning, social vulnerability and ecosystem services around the task 2.2 will all contribute to this overall objective. The team will therefore develop a comprehensive database around UMT units by collecting data on the risks, types of vulnerabilities, institutional inventory and ecological assessment in the context of climate patterns. The data will feed into a masters study, particularly centred on the ecosystem services associated with mangroves and this study will generate further indicators relevant for WPs 2 and 3. Topics will incorporate inputs from Task 1.1 in modelling the dynamics of wetlands (a Task 2.2 objective) considering: precipitation characteristics; changing tides and floods and changing evaporation characteristics. These will be used to analyse the recurrence and extent of risks according to climate models based on natural hazards by incorporating products Task1.1 with sociodemographic data as well for a vulnerability assessment of urban structures than for development of multi-risk management models. There will be further investigations and surveys to link Task 2.2 with the Task 2.3 work on wider vulnerabilities structured around: The vulnerability of property, Institutional vulnerability, Attitudinal vulnerability; and, Physical vulnerability. At the end of these studies it will be possible to establish a vulnerability profile for the city of Saint Louis, a city of water in the heart of the Sahel with complex urban processes and facing multiple environmental hazards, and better understand the role that ecosystem services can play both now and in the future. The profile of the city will be the basis for a better planning for the urban area to make it more resilient to climate change. Recommendations can be made available at the end on the basis of ecosystem services within real ecological units present in the city and taking into account the risk of flooding, salinization, erosion and degradation of biodiversity. This information may ultimately be useful for other cities facing similar issues around the world. 3.6.3
Issues and limitations
The major technical constraint to achieve the common approach in the development of UMT is the availability of satellite images or aerial photographs of good resolution. As with Douala, despite this limitation there has still been sufficient progress since the meeting in Munich to allow the production of a draft UMT map. Ground truthing and other verification activities are to be used to underpin the activity date before further analysis takes place.
88
3.6.4
Conclusion
A full draft of an urban morphology unit map has been produced through which the nature and extent of green structures across Saint Louis can be established as a baseline for the Task 2.2 activity. Achieving the output will enable improved knowledge on the current dynamics, and the development of tools and information useful for planning exercises for conservation and territorial action. The study of UMTs in St. Louis will feed into two important activities: The development strategy of the mangrove in Saint Louis and surrounding areas. The plan developed by the Study Firm Tropis is part of the implementation of Agenda 21. This process led to a sustainable management plan that supports environmental, educational and economic development. Its implementation is currently on hold. The development of the Action Plan for adaptation and mitigation to climate falls within the framework of the Initiative "Cities and Climate Change." UN-HABITAT initiated in 2008 the initiative "Cities and Climate Change" at the global level. In 2009, five pilot cities were identified in Africa: Bobo-Dioulasso (Burkina Faso), Saint Louis (Senegal), Kigali (Rwanda), Mombasa (Kenya) and Walvisbay (Namibia). The initiative of UN HABITAT in Saint Louis seeks initially to assist the City in the formulation of a long term strategy of urban development which integrates climate change related issues.
89
4
DISCUSSION
The Task 2.2 urban characterisation work has generated a large quantity of new data and research findings. These provide an extension to knowledge as well as a solid foundation for the next steps of CLUVA. Research capacity building has been a two-way process and the results presented in this report are the product of considerable inter-disciplinary effort. The findings allow a current baseline to be produced, and a foundation for assessing the rapid pace of change in the cities both retrospectively and prospectively. Delivering the UMT work has involved overcoming various obstacles, some of which are still causing slow progress for some due to factors beyond the immediate control of local teams. Data has been a considerable initial barrier and some teams have also reported extremely challenging situations. Despite the technical and resource challenges facing the case study partner teams, all five case study areas have produced at least a draft UMT map. Once these are finalised they can be used as the basis for generating green structure maps of each city. Draft final UMT-based and enhanced green structure maps are available for Dar es Salaam and draft final UMT-based maps are available for Addis Ababa. For the three other case study cities UMT-based maps are currently provisional. The relative proportions of evapotranspiring cover in the case study areas are shown in Table 4.1 (including proportional cover of green structures and water combined). The data underline the core differences between the geographical settings of the cities. The extent to which the outputs can be analysed and cross compared for this deliverable is limited since it has only been possible to produce some maps a short time before the deliverable due date. Classes are being compared to produce a master UMT file and an agreed legend to use for map production. The different extents of the case study boundaries also make a meaningful comparison difficult for the purposes of this report. Future work will consider how to best tackle this issue. Further work is also planned in order to draw together cross-cutting themes, and to assess synergies and differences between the study areas. This will include some comparison with other data resources, where possible. This additional research activity will be written up through academic articles with any further information documented as appendices to this report. Table 4.1: Proportions of evapotranspiring UMTs and residential UMTs in all five cities (provisional)
Addis Ababa Dar es Salaam Ouagadougou Douala* Saint Louis* *Includes rivers
Green & blue structure (% of total area) 43.7 54.7 19.6 32.9 84.3
90
Residential (% of total area) 34.1 37.9 73.9 47.8 11.6
Included UMTs: For green structure: Addis Ababa: 1.1 Field crops; 1.2 Vegetable farm; 2.1 Plantation; 2.2 Mixed forest; 2.3 Riverine; 2.4 Grassland; 4.1 Parks. Dar es Salaam: 1.1 Horticulture; 1.2 Field crops; 1.3 Mixed farming; 2.2 Mixed forest; 2.3 Riverine, valleys; 2.4 Mangrove; 2.5 Bushland; 4.1 Parks; 4.3 Beach; 4.4 Other open space; 4.5 Sports ground; 13.1 Marsh/swamp. Ouagadougou: 1.1 Horticulture; 1.2 Field Crops; 1.3 Mixture of agriculture & natural spaces; 1.4 Agroforestry territory; 1.5 Agricultural territory; 2.1 Tree Plantation; 2.2 Mixed forest; 2.3 Forest Gallery; 2.4 Grassy Savannah; 2.5 Shrub Savannah; 4.1 Parks; 13.1 Marshy grasslands; 12.1 Temporary streams; 12.2 Lakes. Douala: 1.1 Field crops & vegetable farm; 2.1 Mangrove; 2.3 Grassland/marshy or swampy land; 3.1 Parks; 3.3 Beach; 9.1 River. Saint Louis: 1.2 Field crops; 1.4 Irrigated culture; 1.5 Rainfed agriculture; 4.3 Botanic garden; 4.4 Other open space; 4.6 Sports ground; 12.1 River; 12.4 Basin; 13.1 Mangrove swamp; 13.2 Other aquatic vegetation; 13.3 ‘Zone de tanne’; 13.4 Flood zones areas liable to flood; 13.6 Saline zones; 13.7 Sub-tidal zone; 13.8 Inter-tidal zone; 16.1 Savannah; 16.3 Beach and sand dunes; 16.4 Supratidal area; 16.5 Vegetated Dune zones; 16.6 Plantation zones. For residential areas: Addis Ababa: 7.1 Condominium & multi-storey; 7.2 Villa & single storey stone/concrete; 7.3 Mud/wood construction; 7.4 Mixed. Dar es Salaam: 7.1 Condominium & multi-storey; 7.2 Villa & single storey stone/concrete; 7.3 Mud/wood/ sand brick construction; 7.4 Mixed; 7.5 Scattered settlement. Ouagadougou: 7.1 Condominium & multi-storey high standing; 7.2 Villa & single storey stone/concrete medium standing; 7.3 Mud/wood/ sand brick construction low standing; 7.6 Houses under construction Douala: 6.1 Condominium, villa, single & multi-storey stone; 6.2 Mud/wood construction; 6.3 Mixed construction. Saint Louis: 7.1 Condominium & multi-storey; 7.5 Scattered settlement; 7.6 Area of regular dwelling with terrace; 7.7: Area of parcelled dwelling with terrace; 7.8 Very dense area of dwellings; Precarious dwelling area; 7.10 High standing dwelling with tiles or slates; 7.11 Mixed
91
The UMT work presented in Section 3 has shown that there is some comparability between urban morphology types between cities in different climate zones. However, as expected there are also distinctions which need to be made. Sometimes these distinctions are related to the need to effectively communicate with stakeholders (as was pointed out in Addis) but more often they relate to how different cities and their urban ecosystems have developed over time. It is likely that the UMT classifications may therefore be useful to other cities within the specific climate zone which each city represents. All cities note specific vegetation structures which can be clearly distinguished from aerial photography, even in the case of relatively low quality sources which suggests that the methodology can be further transferred. It is especially useful that the green structure types which have particular significance in some of the case study locations, such as mangrove forests in Saint Louis and Eucalyptus plantation in Addis Ababa can be readily mapped for a number of different time periods. Whilst some of these structure types can be detected from satellite image processing, the provisional investigation for Addis Ababa has shown that this is not likely to generate the wealth of information which is possible through using the CLUVA UMT and land cover assessment approach. There are further analyses which can also be undertaken in order to further explore spatial patterns, e.g. using point-based land cover analysis technique to compare cover properties in different parts of case study cities, such as in the core compared to the urban periphery. It has taken a little time to develop a shared understanding of the concepts behind the study with the CLUVA team. This is partly due to handling complex ideas in several languages. However, the value of the UMT approach has been recognised across the CLUVA consortium in that it provides the chance to complement land use planning activities without competing with them. The methods used are relatively low cost in terms of resources, for example in comparison to the sophisticated image processing techniques which are now emerging from the international literature. However, they still generate information which is robust and which can be used across the consortium. A shared understanding of the baseline ecological and social fabric of the case study areas is an essential element of any study looking into the impacts of climate change on an urban area. For a task working on urban ecosystem services it is doubly important, and something that is recommended as a starting point to any study in this field (McIntyre et al., 2000). Given the high pace of change in cities, the opportunity to develop a recent picture of the areas was also seen to be an advantage over using other readily-available datasets, such as existing land use and land cover maps. As discussed in Section 2.1. It is only through considering urban form and function and the connection between social and ecological states and drivers that a sound basis can be established for green infrastructure planning. There were other advantages raised in the case study discussions too, such as the opportunity to extend the number of classes (for example in Addis Ababa) and the spatial extent of data (for example in the case of Douala). One of most positive signs of the success of a new dataset is its acceptability by stakeholders, and the outcome in Addis Ababa in this regard is particularly encouraging. The work is not without its limitations. For example, some of the units are large, but more detailed analyses can take place within UMT units which can then be related back to the city context in a way which would not otherwise be possible. Ideally there would have been a wider peri-urban area considered in the Task 2.2 work. However, the practical issues of delivering the mapping work have meant that it is already a considerable logistical challenge to deliver new data for an existing urban 92
administrative zone. Similarly it was not possible to use a consistent base year for all of the case study cities (Table 4.2) and the use of dry season imagery can make vegetation structures more difficult to identify. Whilst there could be some argument for using wet season aerial photography, this is less commonly available and it would undermine the consistency of results if different seasons were being used across the different case study cities. The analysis for Dar es Salaam has shown that there is some value in assessing the potential effects of seasonality on land cover results even where the majority of the analysis carried out on data generated in the dry season. The relative susceptibility of land covers to be misclassified has also been assessed with particular care required for the grasses class. The Dar es Salaam results also demonstrate how the detailed land cover assessment can further extend understanding of the patterns of green structures within a city, by not simply considering ‘green’ UMTs but the patterns of green structures present in other urban morphology types (Figure 4.1 and 3.18). Table 4.2: Orthophotography used as the basis for UMT delineation City Ouagadougou Douala Dar es Salaam
Orthophotography Date 2010 2010 2008, 2002 (partial)
Unit delineation
Addis Ababa
2011, 2006
Digitising Digitising From existing land use map Digitising
Saint Louis
Dec 5th 2011
Digitising
93
Progress Pilot UMT units Pilot UMT units Final draft of UMT units (2008) Final draft of UMT units (2011) UMT classification scheme
Figure 4.1: UMT units in Dar es Salaam which are (a) principally associated with green structure and (b) associated with evapotranspiring cover (a)
94
(b)
95
5
CONCLUSION AND NEXT STEPS
This report has presented detailed results from the development and application of an urban morphology methodology to African case studies in different climate zones. Findings are presented for all five CLUVA case study cities. The resultant data, although provisional at this stage, clearly demonstrates the potential to act as a basis for the next stage of Task 2.2 and as a means for linking social and ecological issues through a spatial framework which is suitable for wider use within the CLUVA programme. The Task 2.2 team have supported the process of developing the datasets so that new skills have been developed across the team and there is an added benefit of a shared understanding of the characteristics of the case study cities to assist with the assessing and planning stages of Task 2.2. This includes both similarities and differences in between cities in different African climate zones which will ultimately be helpful in transferring results more widely. It is also significant that clear teams are now identified and strong working relationships developed. This has resulted in a clear plan of delivery has been agreed for the next stage. The Task 2.2 summary table (Table 5.1) below shows the current progress to date and identifies the future timetable for the completion of each of the six steps in each case study city. The next major stepping stone in delivering Task 2.2 is the July 2012 field mission activity in Dar es Salaam. This provides the next opportunity to discuss the work and the details of the analytical phases to follow. At this point Task 2.2 moves onto the challenge of using the results within ecosystem services assessments which also take account of current and potential future climate drivers. Table 5.1: Current progress to date in Task 2.2 in each case study city Key: Green = steps that have been completed to date; Orange = steps to be completed by mid-July 2012 Yellow = steps to be completed by mid-Nov 2012; Pale yellow = steps to be completed by end of 2012
Step 1: UMT classification scheme Step 2: Map the UMT units using orthorectified aerial photography Step 3 Verify Step 4: Decide land cover classification scheme Step 5: Map land cover
Addis Ababa
Dar es Salaam*
Douala
Ouagadougou
Saint Louis
2006, 2011
2008 (2002*)
Section 2010 (2005)
Section 2010/11
2009
2006, 2011
2008 (2002*)
Section 2010 (2005)
Section 2010/11
2009
2006, 2011
2008 (2002*)
Part
Part
Part
2006, 2011
2008 (2002*) 2008 (2002*)
Step 6: Analyse and verify
Part
Note: * 2002 for part of the city to be completed
96
6
REFERENCES
Benedict, M.A. and McMahon, E.T. (2002). Green infrastructure: smart conservation for the 21st century. Renew. Resour. J. 20(3): 12-17. Breuste, J. (2006). Urban development and urban environment in Germany. The Geographer, Delhi: 49 (2): 1–14. Coly A. et al., 2012, D 5.4 - Social vulnerability assessment in the selected cities, 26p. CLUVA project deliverable report. Gill, S.E., Handley, J.F., Ennos, A.R., and Pauleit, S. (2007). Adapting cities for climate change: the role of the green infrastructure. Built Environment 33: 115–133. Gill, S.E., Handley, J.F., Ennos, A.R. Pauleit, S., Theuray, N., and Lindley, S.J. (2008). Characterising the urban environment of UK cities and towns: a template for landscape planning in a changing climate. Landscape and Urban Planning 87: 210–222. Paskoff, R. (1998). The impacts of development on coastal evolution, Paris, Armand Colin 3ed, 260p. Michael, P. (1973). Basins of the Senegal River and Gambia geomorphological study, tome1, Paris, Memoires ORSTOM 63, 810p. Moudon, A.V. (1997) Urban Morphology as an emerging interdisciplinary field Urban Morphology 1, 3-10. Nyarirangwe, M. (2008). The impact of multi-nucleated city morphology on transport in Addis Ababa (2008) in van Dijk, M. P. and Fransen, J Managing Ethiopian cities in an era of rapid urbanization. Eburon Uitgeverij BV. Pauleit, S. and Duhme, F. (2000). Assessing the environmental performance of land cover types for urban planning. Landscape and Urban Planning 52 (1): 1–20. Pauleit, S. and Breuste, J.H. (2011). Land use and surface cover as urban ecological indicators. Chapter 1.1 in. Niemelä J. (ed.) Handbook of Urban Ecology, Oxford University Press, Oxford, pp. 19-30. Sall, F. (2011). Climate change and impacts on wetlands in the city of St. Louis, Master’s thesis, LU / Geography Section, 90p. Xiao, Y. and Zhan, Q. (2009). A review of remote sensing applications in urban planning and management in China, Urban Remote Sensing Event, May, 2009 IEEE digital library. 97
7
APPENDIX
7.1
METADATA FOR UMT DATASETS: AN EXAMPLE OF DAR ES SALAAM
Citation Title Alternative title Creator Publication date Edition Description Topic Keywords Abstract
Purpose
Additional information
Type Data‐relevant time period Status Update frequency Language Further information Spatial domain Projected coordinate system Geographic coordinate system Bounding coordinates
Attribute description Label & definition
UMT_Dar_2008 Urban Morphology Types for Dar es Salaam in 2008 Ardhi University, University of Manchester & Technical University Munich 2012 1 Environment / Planning Urban morphology, land use, planning, ecosystem services This is an Urban Morphology Type dataset for Dar es Salaam, Tanzania. Urban Morphology Types combine urban form and function, and their application allows biophysical functions to be combined with a planning oriented perspective. The data set was developed for the CLUVA project (www.cluva.eu) funded by EU Seventh Framework Programme. Specifically, it was developed by Task 2.2 research. The overall objective of the project is to develop methods and knowledge to manage climate risks, to reduce vulnerabilities and to improve their coping capacity and resilience towards climate changes. The UMT dataset was created based on re‐classifications and editing of the Land Use Map from 2008, sourced from the Dar es Salaam City Council in collaboration with UN Habitat; and interpretation of orthorectified aerial photography from 2008. Digital Vector data 2008 (time unknown) ground condition Complete As needed English www.cluva.eu Arc_1960_UTM_Zone_37S GCS_Arc_1960 Decimal degrees: West: 39.057356 East: 39.307725 North: ‐6.560755 South: ‐6.857618
Projected/local: Left: 506340.920400 Right: 533999.573840 Top: 9274863.698900 Bottom: 924059.934220
FID: unique feature identifier OBJECTID: unique feature identifier LULEVEL4GR: Code in the Land Use Map level 4 UMTClass: Urban Morphology Type class UMTCode: Urban Morphology Type code (numeric)
98
AP_2002: (1) denotes if feature covered by 2002 aerial photography AP_2008: (1) denotes if feature covered by 2008 aerial photography AREA_SQ_KM: Area of unit in kilometres squared UMT_Level1: Urban Morphology Type top level class Threshold: Denotes if feature is smaller than 1 hectare threshold applied Shape_Leng: Denotes length of feature in metres Shape_Area: Denotes area of feature in metres squared Verificati: Notes from verification/ processing stage 1 Verifica_1: Notes from verification/ processing stage 2 Custodian / Distributor Contact name Organisation Email address Telephone number Access & use constraints Access Use constraints Metadata creator Contact name Organisation Email address Telephone number
Sarah Lindley / Riziki Shemdoe University of Manchester / Ardhi University
[email protected] /
[email protected] +44(0)161 275 8685 The dataset can be accessed by requesting it from the custodian. Information about use constraints will be provided on request of the dataset. Gina Cavan University of Manchester
[email protected] +44(0)161 306 6426
Adapted from Go-GEO metadata tool, http://hds.essex.ac.uk/Go-Geo/Tool.htm
7.2
DETAILED LIST OF CONTRIBUTORS
Addis Ababa University Dr Kumelachew Yeshitela (Leader CLUVA, Task 2.2 lead) Mr Alemu Nebebe (GIS expert) Mr Tekle Woldegerima (Task 2.2 PhD student) 16 Bachelor students Mr Eyob Tenkir (AA EPA) Ardhi University Dr Riziki Shemdoe (Task 2.2 lead) Mr Deusdedit Kibassa (Task 2.2 PhD student) Ms Bertha Sambo Technical University Munich Prof. Stephan Pauleit (Leader WP2) Dr Andreas Printz Ms Katya Buchta Mr Florian Renner (Task 2.2 PhD student) UFZ (The Helmholtz Centre for Environmental Research) 99
Nathalie Jean Baptiste (Task 2.3) University of Copenhagen Gertrud Jorgensson (WP3) Patrik Karlsson Nyed (Task 3.2) University of Gaston Berger, Saint Louis Adrien Coly (Leader, CLUVA) Fatimatou Sall Ndèye Marème Ndour University of Manchester Sarah Lindley (Leader WP2 Task 2.2) Gina Cavan (Task 2.2 researcher) Karl Hennermann (spatial data officer) Thomas Barker (MSc GIScience student) Joseph McGenn (MSc GIScience student) Frederick Holmes (MSc GIScience student) University of Ouagadougou Youssoufou Ouédraogo Bani Saïdou Samari Bakary T. Sankara University of Yaoundé Prof. Emmanuel Tonye (Leader, CLUVA) Mr Rodrigue Aimé Feumba (Task 2.2 lead) Dr Jean Noel Ngapgue Monique Tatsa Ngoumo Prof. Maurice Tsalefac M. Kandé (appointed) M. Gilles Ambara (appointed)
100
