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Participatory mapping of flood hazardrisk in Munamicua, District of Búzi,MozambiqueStefan Kienbergera

a University of Salzburg, Interfaculty Department ofGeoinformatics - Z_GIS, Salzburg, AustriaPublished online: 19 Feb 2014.

To cite this article: Stefan Kienberger (2014) Participatory mapping of flood hazardrisk in Munamicua, District of Búzi, Mozambique, Journal of Maps, 10:2, 269-275, DOI:10.1080/17445647.2014.891265

To link to this article: http://dx.doi.org/10.1080/17445647.2014.891265

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MAPPING ENVIRONMENTAL RISKS - QUANTITATIVE ANDSPATIAL MODELLING APPROACHES

Participatory mapping of flood hazard risk in Munamicua, District ofBuzi, Mozambique

Stefan Kienberger∗

University of Salzburg, Interfaculty Department of Geoinformatics - Z_GIS, Salzburg, Austria

(Received 13 July 2012; resubmitted 17 January 2014; accepted 28 January 2014)

Detailed maps, appropriate for decision making at the local level are outdated or currently notavailable in Mozambique. The community map presented in this paper is built on participatorymapping and Participatory GIS practices (especially photo mapping) and links to advancedspatial analysis in the context of disaster risk reduction and flood hazard assessment. Basedon a very high-resolution satellite imagery, community members mapped different featuressuch as the community boundary, settlement areas and their names, agricultural areas,important infrastructure and most importantly ‘low’ and ‘high risk’ zones for floods. It hasbeen for the first time that a community was mapped in Mozambique in such a wayintegrating local knowledge. The identification of hazard zones in a participatory mannerwas seen as one way to overcome the bottleneck of limited available data for a proper GIS-based hazard modeling. Next to the digitization of the community mapped features, animage classification on land use, settlement areas (houses), and the integration of GPScollected points (infrastructure, photos) was carried out. In a final step the map was printedand handed-over to the community members. In the applied methodology it has beendemonstrated that the assessment of risks through the integration of community knowledgeand paper-based satellite images is valid. Next to the result of deriving a community-basedhazard map, the process of mapping is understood as essential to sensitize and learn aboutlocal flood hazard risk.

Keywords: participatory GIS; flood hazard mapping; cartography; indigenous knowledge;vulnerability

1. Introduction

The assessment of natural hazard risks and its spatial representation is essential for effectivelytargeting disaster risk reduction measures at the local level. In the District of Buzi, located down-stream at the river Buzi and at the shores of the Indian Ocean in Central Mozambique (Figure 1),a community-based flood early warning system has been established, where local disaster riskcommittees have been formed to support disaster mitigation, response and relief activities intheir community (Ferguson, 2005). This early warning system was established after majorflood events in the years 2000 and 2001, which affected large areas of the district along theriver Buzi and Pungue (see also Christie & Hanlon, 2001).

# 2014 Stefan Kienberger

∗Email: stefan.kienberger@sbg.ac.at

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The major aim of the developed mapping approach is to provide community members, andspecifically the disaster risk committees, with the appropriate decision support and awarenesstools to identify and reduce their own vulnerabilities and exposures to floods, and to assistthem with their responsibilities as disaster risk reduction committees. Additionally, the participa-tory mapping exercise enables communities through learning and sensitization to get a betterunderstanding of their environment. Answering the central questions of ‘where’ and ‘what’, isessential in dealing with challenges in a general community planning context, and especially inthe case of disaster risk reduction. However, a ‘map’ is not a solution on its own, as it also requirescertain structures, commitments and technical expertise. Therefore, the applied methodology cancontribute significantly to the support of community-based disaster risk reduction measures, buthas to be embedded in the context of an integral disaster risk reduction program.

2. Methods

2.1. Assessing risk, hazard and vulnerabilities in Buzi – the larger context

The community mapping exercise presented here integrates participatory mapping (as a tool ofParticipatory Rural Appraisal (PRA), Chambers, 1994) and Participatory GIS (PGIS) practices(especially photo mapping) and links to advanced spatial analysis. Rambaldi, Kyem, McCall,and Weiner (2006) define PGIS ‘as emergent practice in its own right’. They see the practiceas a spontaneous merger of PRA and GIS. The use and application of different tools andmethods are used “to represent peoples’ spatial knowledge [ . . . ] for spatial learning, discussion,information exchange, analysis, decision making and advocacy” (ibid.). Furthermore, a strongfocus is seen on disadvantaged groups which are somehow ‘empowered’ through the capacityto generate, analyze, manage and communicate spatial information.

The above mentioned early warning system specifically targets the community level, inaddition to several policies in Mozambique which empower the district level to implement andcarry out spatial planning and disaster risk reduction activities. Therefore, the communitymapping not only represents the spatial knowledge in terms of flood hazard zonations for

Figure 1. Location map of the Munamicua flood hazard and community map as well as the overview map.

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improved decision making at the very local level, but also integrates a component to identify rel-evant vulnerability factors through group discussions and weighting exercises, which furtherhelped to develop appropriate vulnerability indicators at the district level (for further detailssee Kienberger, 2012, Kienberger, 2013).

In summary, the specific objectives for the community mapping have been (a) the compilationand design of a community hazard map which should assist the community members withintheir disaster risk reduction activities, (b) to map the community according to the needs of thecommunities in a participatory manner, (c) to define, analyze and prioritize the driving forcesof vulnerability according to the perception of the communities and (d) to enhance the ‘maps’through spatial analysis results for different community characteristics related to disaster riskreduction.

2.2. Workflow for a spatial hazard and vulnerability mapping

The overall workflow is shown in Figure 2 and comprises four major steps, whereas thestep 2 – community mapping and vulnerability prioritization – are described in more detailbelow. The workflow itself has also been compiled in a toolbox for field practioners(Kienberger, 2009)

Figure 2. Workflow of the developed approach to assess the vulnerability at the community level(Kienberger, 2013).

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The initial common steps included the definition of project objectives and the identification ofthe case study area, with the subsequent identification of previous work and familiarization withthe general setting.

The ensuing process included two parallel steps where community maps are produced andvulnerability factors identified and weighted. These steps are on the same level with respect totheir importance and are somewhat independent from each other. In the best case the mappingis carried out first, for instance on the first afternoon, and the prioritization exercise subsequently,e.g., on the second day.

The method for the specific hazard mapping consisted of two major steps: First, a very high-resolution satellite imagery (such as a Quickbird image with a resolution of �0.7 m) was acquiredand presented to community members as a ‘blank’ map to facilitate discussion and mapping ofspecific features. In a second step, these results were integrated in a GIS and enhanced throughadditional spatial analysis.

After an orientation phase and the identification of key landmarks, committee members ident-ified and marked different features on the paper map with different colored pens (Figure 3).

This included (a) the community boundary (requires most of the time, as this is an oftenheavily discussed issue, as it has never been documented before), (b) names of neighboring com-munities, (c) flood hazard zones (high-risk/low-risk/safe areas . . . ; past flood affected areas), (d)lower and higher elevated areas and areas close to the river, (e) agricultural zones, (f) special infra-structure of the community (such as wells, markets, schools, assembly points, accommodationcenters etc.; sacred places should only be mapped if community members agree), (g) the settle-ment areas and (h) the naming of areas and natural features. It was recommended that peopledraw directly on the paper as transparencies might be inconvenient (see also Figure 3). In an

Figure 3. Different phases during the community mapping.

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additional step locations of specific community sites were collected through GPS measurementsand/or georeferenced photographs.

After agreeing on the mapped features, a second copy of the final map was made, whereas onewas left with the community as a first reward and the second one was used to integrate the resultsin a GIS environment (digitization) for further analysis. Additional data deriving from NationalSpatial Data Infrastructures (NSDI) or other sources (if available) were collected and integratedinto a database and the map.

Next to the (digital) representation of the features identified and mapped during the commu-nity mapping exercise, a strong emphasis was on the application of spatial analysis tools/methods.These analysis results are seen as reflecting the ‘conditions of vulnerability’ and as an analysis ofexposure to flood hazards.

The following spatial analysis elements were implemented:

. Spatial queries: To estimate the amount of people living within a certain area (e.g. howmany houses/people are within the high-risk zone) spatial queries (e.g. ‘are locatedwithin’) were applied

. Buffer zones/Distance analysis were added to the map (e.g. distance to disaster risk kit/safeareas etc . . . ) and statistics were calculated to provide additional information about dis-tances to specific areas (e.g. the estimated amount of people living in flood hazard zones)

. Density analysis: To estimate the density of the settlement area ‘Kernel’ functions wereapplied. This additionally helped to delineate the settlement area. Kernel density calculatesthe density of features in a neighborhood around those features. It has been originallydescribed by Silverman (1986) and conceptually a smoothly curved surface is fitted overthe different points. The value is higher close to the points (such as houses) and diminisheswith increasing distance.

. Land use/land cover (LULC) classification: Not directly related to spatial analysis, a LULCmap was produced using the Quickbird satellite imagery. This result was used to enhancethe final cartographic product. However, the integration of information needs to be chosencarefully as the LULC are more complex and already represent an abstraction of reality ascompared to a true color satellite image and could then be perceived as not useful for com-munity members in the final map product. However, a quantification of LULC classes wascarried out to characterize the natural environment of the community. The methodologyapplied is not discussed in detail here, but builds on the approaches of object-basedimage analysis (Blaschke, 2010). In this case the classes of agriculture, bare soil,burned-off areas, meadow, trees, shrub, water and shadow have been derived from theQuickbird satellite image.

In a second parallel step (see Figure 2) the issue of vulnerability to natural hazards was addressedtogether with the community members. The main objective of this part was to understand the per-ception of vulnerability as seen from the community perspective and to identify the importance ofdifferent factors. Additionally, the exercise aimed to stimulate a discussion among the communitymembers on their susceptibility to different triggers of vulnerability. First the question of ‘Whatare the factors which define your vulnerability to floods’ was expressed to the committeemembers. Feedback and specific factors where then noted on paper cards for the flood anddrought hazards. Once all factors have been discussed and agreed upon, a pre-defined amountof peanuts (40) – as representative of points and weights – was given to the communitymembers. Committee members then distributed the peanuts according to the importance ofeach factor. As shortly outlined above, this exercise helped to discuss vulnerability issues withthe communities and get a clearer understanding of their perception. Next to this learning

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component, the results were later used for the establishment of an appropriate indicator frame-work for the district vulnerability assessment (Kienberger, 2013; Kienberger, 2012).

2.3. Map elements

The results from the community hazard mapping and vulnerability prioritization were compiled inthe step ‘map composition’ (see Figure 2). As mentioned above, it was assumed that in addition tothe learning process initiated with the mapping and vulnerability prioritization, maps shouldsupport the communities in their activities with respect to disaster risk reduction. Therefore, afinal laminated copy was handed over to the community members.

The community hazard map includes the following elements:

. Land Use and Land Cover classification (based on Quickbird satellite imagery)

. Community infrastructure (houses, school, location of the flood response kit, health facili-ties, water wells, market, accommodation centers, paths). Data were collected throughvarious sources and with additional community input during the mapping exercises, aswell as GPS collections.

. Community boundary (community derived)

. Houses and settlement area (partly community derived, enhanced through digitization andkernel function)

. Flood hazard zones (community derived)

. Additional flooded zones for the overview map (based on a satellite derived water mask ofthe floods 2000)

. Results of vulnerability identification (as a list and as a treemap, photographs of the scoringresults and a short textual interpretation). A treemap is a visualization of hierarchical datawith nested rectangles. The size of the rectangles displays the weight of the representingfactor. This type of visualization allows for easy capturing of the structure and theweight of the different factors (see Slingsby, Dykes, and Wood (2008) for a discussionon the spatio-temporal use of treemaps).

. Buffer zones indicating the distance to the safe areas (GIS analysis)

. Analysis of exposure (spatial analysis and distance queries; number of houses in risk zones,distance to safe areas, statistical data on the community and an estimation on people livingin risk zones)

3. Conclusions

It has been the first time that a community was mapped in such a way in Mozambique and localknowledge was integrated through PGIS practices. The identification of hazard zones in a parti-cipatory manner was seen as one way to overcome the bottleneck of limited data (e.g. appropriateelevation models) to conduct GIS-based hazard delineation. In the applied methodology it hasbeen demonstrated that the assessment of risks through the integration of community knowledgeand paper-based satellite images is valid and can be easily transferred to other natural hazards,preferably with a clear spatial extent (e.g. landslides, forest fires, river bank erosion, . . . ). Forcommunity members it was easy to orientate themselves on the maps and to draw and highlightthe essential features related to hazards in the community. It is clear that such mappingapproaches, which include a representative group of a community, has to be seen as a snapshot.It has to be noted that such assessments of course to some extent reflect fuzzy boundaries whichalso underlie different accuracy levels than a sophisticated risk delineation based on numericaland physical models.

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As it never can be assured that a map is 100% correct, the possibility of updates has to beensured. The decision on the integration of maps as a central decision making tool has toinvolve the possibility for feedback loops, continuous updates and the strengthening of capacitiesat all levels. Therefore a process was started which involves different actors on different levels.The process does actually not end with the handing over of the map. Continuous support,updates and evaluation of the ‘success’ and usability of the maps are necessary. It is essentialthe appropriate integration within a project design which commits itself to the use of maps as adecision support tool in communities, and maintains the transfer and integrity from traditionalsketch mapping toward real-world coordinate based PGIS maps.

Software

Pre-processing of the satellite imagery (pan-sharpening, mosaicking, improved georeferencing)was carried out in ERDAS Imagine 9.3. Final cartography, but also the spatial analysis wasimplemented in ArcGIS 9.3 (with the Spatial Analyst extension). The treemaps were designedwith the software HiDE v1.0 and later improved in Adobe Illustrator CS3. Microsoft Excelwas used to design the different diagrams.

Funding

The research leading to these results has received funding through the Munich Re Foundation,where I would like to express my sincere gratitude to Thomas Loster. Special thanks go toINGC in Mozambique (especially Sr. Gomez for facilitating), CIG-UCM and the communitymembers providing their time to participate in this research. The author would also like tothank the reviewers for providing helpful feedback and comments to the paper and map.

ReferencesBlaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and

Remote Sensing, 65(1), 2–16.Chambers, R. (1994). The origins and practice of participatory rural appraisal. World Development, 22(7),

953–969.Christie, F., & Hanlon, J. (2001). Mozambique & the Great Flood of 2000. African Issues.Ferguson, J. (2005). Disaster risk management along the Rio Buzi – Case study on the background, concept

and implementation of disaster risk management in the context of the GTZ-programme for rural devel-opment (PRODER). GTZ.

Kienberger, S. (2009). Toolbox & Manual: Mapping the vulnerability of communities – Example from Buzi,Mozambique. Retrieved December 21, 2011, from http://projects.stefankienberger.at/vulmoz/wp-content/uploads/2008/08/Toolbox_CommunityVulnerabilityMapping_V1.pdf.

Kienberger, S. (2012). Spatial modelling of social and economic vulnerability to floods at the district level inBuzi, Mozambique. Natural Hazards, 64(3), 2001–2019.

Kienberger, S. (2013). Mapping vulnerability – Integration of GIScience and participatory approaches at thelocal and district levels. In J. Birkmann (Ed.), Measuring Vulnerability to Natural Hazards (2nd ed., pp.401–422). New York: United Nations University.

Rambaldi, G., Kyem, P. A. K., McCall, M., & Weiner, D. (2006a). Participatory spatial information manage-ment and communication in developing countries. Electronic Journal on Information Systems inDeveloping Countries, 25(1), 1–9.

Silverman, B. W. (1986). Density estimation for statistics and data analysis. New York: Chapman and Hall.Slingsby, A., Dykes, J., & Wood, J. (2008). Using treemaps for variable selection in spatio-temporal visu-

alisation. Information Visualization, 7(3–4), 210–224.

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