Master Thesis ______

______

Urban Growth and Environmental Risks – A GIS-Based Analysis of Landslide Susceptibility in Bukavu (Democratic Republic of the Congo)

By: Simon Sebastian Paul (Umu-ID: sipa0016)

Supervisor: Magnus Strömgren

Umeå University Department of Geography and Economic History Programme: Human Geography with Specialization in Geographical Information Systems (1-Year Master)

Submitted: June 2019

Acknowledgements:

I would like to express my gratitude to my supervisor Magnus Strömgren for the generous support and valuable suggestions during the process of writing this thesis and throughout the entire Master programme. I am equally grateful for teaching, assistance and advice provided by Cenk Demiroglu, Roger Marjavaara, Erika Sandow, and Kerstin Westin, who laid the foundations for this thesis with their respective contributions to the programme.

Furthermore, I would like to extent my gratitude to my academic opponent Nils Wilhemsson, whose reflections, recommendations and constructive critique helped revising and finalizing my work. I also thank my classmates for the enriching times of learning, suffering and growing together.

In addition, I owe a very special thanks to Ronia Anacoura for her unrelenting encouragements and generous emotional support. Without her, the burden of getting done all the work that went into this thesis within the short time period of just two months would have been twice as heavy. Thank you for being an infinite source of strength and motivation and for lending your ear in all our conversations about geography-related topics.

I would also like to thank the people who granted me the great opportunity to visit Bukavu for the first time in March 2016. These are especially Prof. Dr. Martin Doevenspeck, Prof. Dr. Cyrus Samimi, and Dr. Claudia Gebauer from the University of Bayreuth, as well as Dr. Nene Morisho Mwanabiningo from Pole Institute in Goma. Likewise, I thank my dear friends in the D.R.C. who were involved in further visits by showing me around, sharing their knowledge about Bukavu or giving me travel advice. Therefore, the following people deserve my particular gratitude: Olivier Ngirabanzi, Gérard Saleh Bitendelo, Aspirine Emana Mwimuka, Emmanuela, Maurine, Maurice, and Shubu Ngesso.

Cover page header picture: Nyamoma peninsula, northern part of Bukavu (Photo taken by the author on 17th February 2018)

Table of Contents:

I. List of Figures ...... p. I II. List of Tables ...... p. II III. List of Abbreviations ...... p. III

Abstract ...... p. 1 1. Introduction ...... p. 2-5 2. Aim of the Thesis ...... p. 7-9 2.1 Purpose and Research Questions ...... p. 7-8 2.2 Limitations ...... p. 8-9 3. Different Landslide Types ...... p. 11-12 4. Literature Review ...... p. 13-19 4.1 General Overview ...... p. 13-14 4.2 Landslides and Anthropogenic Factors ...... p. 14-16 4.3 Environmental Risks in Bukavu ...... p. 16-19 5. Study Area ...... p. 21-24 6. Methods ...... p. 25-35 6.1 General Approach and Considerations ...... p. 25-27 6.2 Workflows Involving the Digital Elevation Model ...... p. 27-30 6.3 Workflows Involving Satellite Imagery and Satellite Data Properties . . . . . p. 30-35 7. Results ...... p. 36-53 7.1 Classification of Land Cover ...... p. 36-38 7.2 Aspect of Hillsides and Mountain Slopes ...... p. 39-40 7.3 Slope Inclination ...... p. 41-42 7.4 Urban Growth ...... p. 43-47 7.4.1 Urban Growth from 1990 to 2019 ...... p. 43-45 7.4.2 Current Urban Growth Tendencies ...... p. 46-47 7.5 Slope and Urban Growth Overlay ...... p. 48-53 8. Discussion ...... p. 54-56 9. Conclusion ...... p. 57

I. List of Figures:

Figure 1: Overview map of the DRC and the study area’s location ...... p. 6

Figure 2: Overview map of the Kivu provinces in the eastern DRC ...... p. 10

Figure 3: Illustration of different landslide types according to Varnes 1978 ...... p. 12

Figure 4: Landslide susceptibility map of Africa ...... p. 14

Figure 5: Historical landslides in the urban area of Bukavu and the slopes to the west . . p. 18

Figure 6: Map of communes and neighbourhoods in Bukavu ...... p. 20

Figure 7: 3D elevation map of the study area ...... p. 22

Figure 8: Aspect of central Bukavu facing eastwards, as seen from Nkafu ...... p. 23

Figure 9: Example of an aspect-slope raster of a larger study area than Bukavu . . . . . p. 29

Figure 10: Example of using panchromatic Band 8 to outline densely built-up areas . . . p. 33

Figure 11: View from western Ndendere neighbourhood towards the southwest . . . . . p. 36

Figure 12: Map showing a classified satellite image of the study area ...... p. 38

Figure 13: Aspect-slope map of the study area ...... p. 40

Figure 14: Slope map of Bukavu ...... p. 42

Figure 15: Graph showing the size of built-up areas ...... p. 44

Figure 16: Map showing the growth of densely built-up areas from 1990 to 2019 . . . . p. 45

Figure 17: Map showing the growth of densely built-up areas from 2016 to 2019 . . . . p. 47

Figure 18: Nkafu neighbourhood as seen from Lake Kivu (Bay of Bukavu) ...... p. 51

Figure 19: Overlay map of slope inclination and urban growth trends ...... p. 53

II. List of Tables:

Table 1: Temperature and precipitation by month in Bukavu ...... p. 24

Table 2: Origin of the satellite images used to map urban growth in Bukavu ...... p. 32

Table 3: Landsat 4 and Landsat 5 spectral bands ...... p. 34

Table 4: Landsat 8 spectral bands ...... p. 34

Table 5: Total size of built-up areas by year ...... p. 44

III. List of Abbreviations:

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

EAR East African Rift

DEM Digital Elevation Model

DInSAR Differential Interferometry Synthetic-Aperture Radar

DRC Democratic Republic of the Congo

GCS Geographic Coordinate System

GDEM Global Digital Elevation Model

GIS Geographical Information System

NASA National Aeronautics and Space Administration

OLI Operational Land Imager

QGIS Quantum Geographical Information System

RGB Red, Green, Blue

TIRS Thermal Infrared Sensor

TM Thematic Mapper

USGS United States Geological Survey

UTM Universal Transverse Mercator

WGS World Geodetic System

WV WorldView (Satellite)

III

Abstract: The city of Bukavu, located at the eastern border of the Democratic Republic of Congo in the province of South Kivu, is a large and densely populated urban agglomeration that has experienced rapid growth during recent years. At the same time, Bukavu has been repeatedly struck by environmental hazards, especially by devastating landslides. The steepness of slopes in the city’s hilly and mountainous terrain is one of the most important factors contributing to landslide susceptibility, but the anthropogenic impact resulting from uncoordinated urban sprawl and land cover change additionally plays a crucial role in exacerbating the vulnerability of neighbourhoods. This thesis utilizes GIS software to provide cartographic material for landslide risk assessment in Bukavu and the city’s surroundings. It examines risk exposure related to slope inclination of densely built-up areas, the spatial development of the city and urban growth tendencies, and complements these aspects with information about land cover and the terrain.

Keywords: Bukavu, Landslides, Environmental Risks, GIS, Urban Growth, Slope Inclination, Land Cover, D. R. Congo, South Kivu, Satellite Data, Digital Elevation Model

1. Introduction:

In the past decades, the topic of urban settlements and their exposure to damage caused by environmental hazards has become a major research subject in both the social sciences and the natural sciences. Its transdisciplinary relevance originates from the fact that in many past scenarios catastrophic events were linked to multiple interrelated factors (see Chang, Slaymaker 2002; Douglas et al. 2008; Satterthwaite, Sverdlik, Brown 2019). While in a worldwide context different types of hazards vary naturally in their origin, scope and impact, it has repeatedly been shown how exacerbated vulnerability of urban communities and higher prevalence of environmental disasters are influenced by anthropogenic activity (Petley 2012; Thomalla et al. 2006, 43-44). This can be observed in relation to human-made climatic change and cases of extreme weather events (Eckstein, Hutfils, Winges 2018, 9-11; Bele, Sonwa, Tiani 2014, 332-334) as well as on regional level where land cover alteration, population growth and settlement in risk- prone areas have resulted in intensification of damage suffered from environmental disasters in urban settings (Usamah 2017, 36-49; Jones 1992).

A wide range of urban hazards pose a potential threat to the security of people living in cities and municipal areas that are located in vulnerable regions, depending on the respective geological and climatic conditions. For example, earthquakes and volcanic eruptions are reoccurring phenomena in zones of high tectonic activity, putting urban centres and smaller settlements alike at risk of being affected by destructive natural disasters (Thouret 1999). Many such incidents occur in hot-spot regions like the East African Rift (EAR) (D’Oreye et al. 2011), the subduction zone west of Indonesia (Marfai et al. 2008; Hadi 2008) or the fault systems in New Zealand (Dionisio, Pawson 2016). In coastal regions they can furthermore be exposed to tsuna- mis, as is the case along the Circum-Pacific Belt (see Liu et al. 2007; Dionisio, Pawson 2016), or suffer from increased damage by hurricanes, as for example recent incidents on the southern coast of the United States of America have shown (Curtis, Fussell, DeWaard 2015; Zhang, Peacock 2010; Olshansky 2006; Olshansky et al. 2008). Moreover, human settlements in many places of the world can experience flooding during times of high seasonal precipitation or extreme weather events (Ozkan, Tarhan 2016, 374; Chan 2011). The context of urban hazards is therefore a broad field that has to deal with multidimensional scenarios where humans may or may not play a significant role in causing a specific type of hazardous event. Yet, their exposure to such risks and the impact disasters have on urban spaces depend on the adaptive and preventive capabilities of societies as well as their ability and create hazard-resilient spaces of living. This thesis focusses on one particular type of hazard in urban

settings that is arguably one of those where natural factors and human-induced influences on the environment are strongly interrelated – landslides.

When it comes to ongoing developments within urbanized landscapes in countries of the Global South, the connection between human activities and the environment remains an exceedingly problematic issue. This is due to the stress people exert on their surroundings by construction, removal of natural land cover or pollution, which often results in negative repercussions on communities (see Usamah 2017; Gurung et al. 2013). Therefore, diligent monitoring, reliable analyses, and effective prevention strategies are urgent necessities. Human geography and the geosciences play an essential role in this matter, as they can collect, process, and provide spatial information in order to be utilized for the protection of people’s lives, property and local infrastructure. Today, this can be achieved in various new and more accurate ways than in the past, thanks to latest technological innovations, advanced mapping methods, and improvements in remotely sensed data gathering (Guzzetti et al. 2012; Shahabi, Hashim 2015). Such endeavours are often formidably challenging in data-poor contexts, which are frequently encountered in countries of sub-Saharan Africa (Monsieurs et al. 2018; Igwe 2018, 2509). Hence, scientific approaches to risk assessment require case-specific adaptation that depends substantially on the availability of data. In regions where ‘on-the-ground’ data about the development of urban spaces and the impact of anthropogenic activity on the environment is scarce, difficult to access or inconsistent with regard to its systematic collection over time, modern GIS software can play a vital role to bypass such disadvantages to a significant extent. This particularly holds true when examining people’s susceptibility to landslides – events that pose a con- stant threat to both densely populated locations and rural villages, for instance in East Africa (Guha-Sapir et al. 2017, 41-45). In countries where state authorities exercise very little control in terms of urban development or spatial planning, the lack of capacious governance that would ensure the implementation of risk reduction measures adds another layer of difficulty (Maes et al. 2017). This is the context encountered in the east of the Democratic Republic of the Congo (DRC) and the city of Bukavu (Hoffmann, Pouliot, Muzalia 2019) (see figure 1), which is one of the most dynamic places with respect to political, economic, social or spatial developments, while at the same time being very dynamic in the inglorious category of environmental hazards, especially concerning landslides (Maki Mateso, Dewitte 2014). The study area of Bukavu that will be analysed in this thesis is thus a very good example to demonstrate the high usefulness of GIS methods in data-poor tropical settings with weak control by the state.

After latest landslide disasters in several African countries that have resulted in unprecedented numbers of casualties, destruction of property, and damage to living environments, the issue has created increased apprehensiveness about future fatal incidents in regions that show corresponding characteristics (Igwe 2018, 2509; Shahabi, Hashim 2015, 2). This is where special attention has to be payed to tropical environments “with a combination of high relief, intense rainfall, and a high population density, [even more so in zones with] high rates of tectonic processes” (Petley 2012, 3). Cases like the tragic mudslide in Freetown (Sierra Leone) in August 2017, with more than 1100 people reported dead (Cui et al. 2019), vast rock-debris mass wasting at the Nigeria-Cameroon border in 2010 (Igwe 2018) or the devastating landslide that occurred on the slopes of Mount Elgon in Uganda during the same year, razing three villages to the ground, killing more than 300 people and causing massive displacement (Atuyambe et al. 2011; Nakileza et al. 2017, 1), underscore the strong need for comprehensive scientific analyses and multifarious preventive actions.

Turning towards the East African Rift, which forms the physical environment that Bukavu is located in, not only the aforementioned high tectonic activity can be encountered, but also geomorphological, geological and climatic characteristics that make it one of the geographical macroregions in sub-Saharan Africa that naturally possesses predispositions leading to high landslide proneness (Butara et al. 2015, 130-133). Cities, towns and villages in mountainous rift zones are often located on topographic landscapes with steep slopes that make gravitational mass movements and slope failure frequent phenomena. Bukavu is located in the eastern centre of the Kivu provinces within the DRC (see figure 2) where in this context numerous disastrous landslides have occurred over the past years, resulting in high death tolls and massive damage to buildings and infrastructure (Michellier et al. 2019, 21-22). Different circumstances in this region, that has experienced long-lasting armed conflicts, multiple migration waves, and political instability, have led to rapid growth rates of cities and towns, which commonly take place without effective spatial planning by any super- ordinate centralized institution (Pech, Lakes 2017; Van Overbeek, Tamás 2018, 290-291; Hoff- mann, Pouliot, Muzalia 2019). Uncontrolled urban sprawl has degraded the ground stability and in densely populated urban centres located in mountainous or hilly environments, people continue to settle on steep slopes and risky locations (Balegamire et al. 2017, 264). Especially poorer social groups are constructing their homes in dangerously destabilized places, since they often arrive looking for economic opportunities or security, while neglecting or accepting en-

vironmental risks because they lack alternative options (Mugaruka et al. 2017, 249-250). De- forestation and vegetation loss – major factors of ground stability deterioration – generally go hand in hand with such growth processes (Karamage et al 2016).

Amongst the cities in the two Kivu provinces (South Kivu and North Kivu) that are built within a very landslide-prone landscape, Bukavu is the most populous. However, other large cities in the region have been affected by landslide disasters as well, such as Uvira (Moeyersons et al. 2009) or the Burundian capital Bujumbura (Michellier et al. 2019, 22) (see figure 2). The rapid, haphazard growth and unplanned urban sprawl that Bukavu has experienced in its recent history has caused the city to expand in some areas on very steep hillsides and the foothills of surrounding mountains (Balegamire et al. 2017). Not only has human activity destabilized rock and soil mass in the city and its surrounding that resulted in sudden slope failures, but it is also linked to slow ground movements that cause gradual damage to buildings and infrastructure (Kalikone et al. 2017; Balegamire et al. 2017, 270-275). High precipitation rates during most months of the year aggravate the city’s vulnerability, raising the risk of mudslides and accelerating soil erosion (Ndyanabo et al. 2010, 124-126). In the past, strategies to mitigate environmental risks and raise inhabitants’ resilience against future environmental disasters have suffered from not being well implemented, proved to be ineffective or did not even existed at all due to of a lack of information about risk exposure (Michellier et al. 2019, 131-133). This thesis will react to these shortcomings by providing a GIS-based analysis and maps that help identifying areas susceptible to future landslides. It will include topic-related contextual information about the study area and outline the current state of previous studies in order to enhance an effective usage of the findings.

______

Figure 1: Overview map of the Democratic Republic of the Congo and the study area’s location

2. Aim of the Thesis:

2.1 Purpose and Research Questions:

This thesis intends to offer an up-to-date analysis of landslide risk in Bukavu that takes into account the city’s current extent and latest spatial development trends, complemented by aspects related to land cover and the terrain. Rather than looking at historical landslides and studying previous disastrous events as it has been done in the majority of previous studies about landslides in Bukavu (see chapter 4) and within the research field in general (Maldonado, Chio Cho 2012, 24), meaning that most attention was payed to explaining the causes and impact of disasters after they had struck people and their homes, the main objective is to illustrate landslide risk in cartographic material for prediction, mitigation and prevention of potential future incidents. By improving the understanding of past and present-day urban growth dynamics, the final maps allow for a considerably more detailed and refined risk assessment in all of Bukavu’s densely built-up areas that was thus far not available from existing publications.

The research questions that are going to be answered by the analysis are the following:

- How much have anthropogenic activities impacted the study area in regard to land cover (especially vegetation, open soil and human-made structures)? - In which direction do slopes face? - How steep are the slopes in the study area? - How did Bukavu grow over the past decades? - In which areas can the city be expected to grow in the future?

To examine these aspects in a way that makes the best possible use of the source data incorporated throughout the analysis, the thesis is based on the following substructure: It will first separately deal with the factors of land cover, slope aspect, slope inclination, urban spatial development since 1990, and current growth tendencies. In the final section, these factors will be combined in order to assess the degree of landslide risk for each specific neighbourhood and its respective characteristics.

The general approach involved the use of a time series of satellite images and a Digital Eleva- tion Model (DEM) of the study area. These datasets were analysed with a specific selection of GIS tools and functions that were most suitable for each research question. By harnessing the potential of latest technological advancements in GIS software, primarily through the utilization

of new powerful functionalities and improved tools in ArcGIS Pro, datasets could not only be processed considerably faster, but it was also possible to provide outputs that are of higher quality than other currently accessible materials. At the same time, it was also possible to include the entire study area, which has not been done in this manner before. In all steps except for the supervised image classification where ArcGIS for Desktop was used, workflows were performed in ArcGIS Pro.

To improve the understanding of the phenomenon of landslides in general, the thesis will begin by giving a brief overview over the most common types of landslides in Bukavu. In the literature review that follows, the main findings of scientific publications on landslides in sub-Sa- haran Africa and the interrelation between natural environments and anthropogenic influences will be presented. In the same chapter, previous studies on different kinds of environmental risks and landslides in Bukavu will be summarized. Then, the study area will be described, including relevant characteristics of the city’s location and key figures. Next, the methods applied in the process of creating the maps in the result section will be explained, subdivided into the overall approach and important considerations, the workflows involving the DEM, and the usage and properties of satellite images. After that, the results will be presented in the aforementioned order. They will subsequently be discussed before concluding the thesis.

2.2 Limitations:

Due to restrictions coming along with this format, especially concerning data accessibility and intended scope, this thesis will not deal in-depth with aspects of mass movements that involve high water saturation and liquified material, such as mudslides, debris flows or shallow landslides, though they are specific landslide types that have been reported in Bukavu (Kulimushi et al. 2017, 236-240; Michellier et al. 2019, 78-85). It can also not include an extensive elabo- ration of the historic events and influencing factors that have led to Bukavu’s rapid growth over the past decades, although this broad topic is certainly of relevance for a better understanding of the city’s dynamics and urban development processes in the politically unstable and conflict- stricken Kivu provinces. Further reading on this matter is therefore highly recommended (see Schmidl 1997; Solhjell 2015, 85-120; Van Overbeek 2014; Van Overbeek, Tamás 2018; Vlas- senroot, Raeymaekers 2004, 123-156; Marysse, Tshonda 2015; Pech, Lakes 2017, 167-170; Hoffmann, Pouliot, Muzalia 2019).

In addition, since this thesis focusses primarily on the non-natural dimensions of landslide risk, it has to be pointed out that an emphasis is put on aspects related to urban geography. All relevant aspects closely linked to physical geography are included as well, but they will not be elaborated beyond what is necessary to explain the examined issues. The existing scientific literature provides ample material on these facets of the topic – particularly the geological processes behind landslide hazards and soil saturation through water infiltration – and should be consulted for further information (see Trefois et al. 2007; Wafula et al. 2007; Miščević, Vlasdel- ica 2014; Gariano, Guzzetti 2016; Hidalgo, Vega, Obando 2018)

______Figure 2: Overview map of the Kivu provinces (North and South Kivu) in the eastern DRC

3. Different Landslide Types:

The term ‘landslide’ can be understood as a collective term that “describes a wide variety of processes that result in the downward and outward movement of slope-forming materials including rock, soil, artificial fill, or a combination of these” (USGS 2004, 1). Landslides can therefore be sub-categorized into different types, depending on the causal factors leading to the movement of mass as well as the physical parameters and ground characteristics that underlie such a process. Especially the material composition of a landslide influences its spatial and temporal dimension, hence it can occur as a very sudden event that causes immediate damage within a specific delimitable local range or it can take place as gradual, slow-moving mass wasting that affects a larger area. Slopes of different gradients can hence be affected, and shallow landslides can also pose a significant problem in tropical urban areas (Michellier et al. 2019, 78-85), aside from abrupt slope collapses. The most commonly used typological classification model for slope movements was developed by Varnes (1978) (illustrated in figure 3).

In accordance with this classification, the landslide types that have been reported the most in Bukavu are the following:

- Type A – Rotational landslides: Rotational landslides are characterized by a surface of rupture that has a concavely curved shape. The affected scarps are usually relatively large and their impact remains visible on the topographic surface, since the gravitational slope failure usually transports a big main body of ground mass. In relation to a parallel axis to the surface, the slide describes a more or less rotational movement when triggered.

- Type B – Translational landslides: Translational landslides differ from rotational landslides in that the movement takes place on a planar surface, leaving a roughly straight rupture scarp and a trench-like gap on its upper activation zone.

- Type F – Debris flows: Debris flows are caused by high precipitation or other causes of strong water flow, such as heavy snowmelt or sudden, human-made leakage. They are often triggered by slope saturation and liquification of the ground, carrying a mixture of water, loose soil, rocks and other material.

(Sources: Nobile et al. 2018, 5-14; Kulimushi et al. 2017, 236-240; USGS 2004, 1-2; Usamah 2017, 6-9)

Figure 3: Illustration of different landslide types according to Varnes 1978 (Source: USGS 2004)

4. Literature Review:

4.1 General Overview:

On a global level, the literature on environmental hazards comprises a quite large body of publications about mass movements and landslides in different settings. Given their worldwide occurrence and “owing to their major role in slope evolution in mountainous areas [as well as] the considerable economic, social and geomorphological impacts” (Mugagga et al. 2011, 40), they constitute a research field that concerns societies in numerous places. The most affected areas worldwide are those where a strongly developed relief, heavy precipitations, and high population density can be found, in particular in regions with significant seismic and tectonic activity (Petley 2012).

However, literature about landslide hazards in Africa is less extensive and remains strongly underrepresented in comparison to other continents (Reichenbach et al. 2018; Maes et al. 2017). Most publications focus predominantly on confined study areas or a small number of selected countries. Multiple regions that have been identified as being generally landslide-prone on a macro level still lack comprehensive local examinations, landslide inventories or risk monitoring (Gariano, Guzzetti 2016, 228-231; Jacobs et al. 2014).

In terms of mapping landslide risk in Africa, Broeckx et al. (2018) were the first to create a continent-wide susceptibility map (see figure 4). Here, the regions falling into the EAR system stand out as being some of the most vulnerable areas in Africa. Amongst these moderate to very high susceptibility environments, the Great Lakes region is considered one of the most affected hot spots for landslide incidents. Despite this fact, the majority of publications about disasters in the mountainous highlands along the western branch of the EAR have appeared after such events had already occurred (see Knapen et al. 2006; Wafula et al. 2007; Moeyersons et al. 2009; Mugagga 2011; Atuyambe et al. 2011; Maki Mateso, Dewitte 2014). All things considered, this can be interpreted as a worrying sign that this scientific field still has a long way to go to become more application-oriented and help prevent future tragic events in African regions.

Figure 4: Landslide susceptibility map of Africa (Source: Broeckx 2018; marker added by the author)

4.2 Landslides and Anthropogenic Factors:

Gradual or sudden mass movements are widespread natural phenomena in the sense that they appear relatively frequently in landscapes with steep slopes, strong weathering impact, low vegetation cover or susceptible soil and rock compositions. In sparsely populated areas or rural regions they rarely cause large-scale harm to humans. Yet, they can severely affect single livelihoods and building structures or render cultivated land unusable (Nakileza et al. 2017, 3; Mu- gagga, Kakembo, Buyinza 43-45).

The majority of cities in the Global South show very fast population growth (Di Ruocco, Gas- parini, Weets 2015), which is one of the main drivers behind people settling in dangerous areas that are prone to landslides. Spatial limitations, insecure land tenure conditions or socioeconomic circumstance often force newcomers to construct their homes in such unfavourable locations (Douglas et al. 2008; Van Overbeek, Tamás 2018). In cases where people possess limited financial means and where there are no institutions that effectively plan and direct urban growth, building structures are often too weak to resist environmental hazards and local communities may underestimate the threat they pose (Michellier et al. 2016, S32; Balegamire et al. 2017). A widespread problem of human-made landcover change in the tropics are gradual de- stabilisation processes, as Igwe (2018, 2518) describes:

“[…] In most parts of Africa, the stability of slopes is readily compromised in part because the weak residual soils and the underlying rocks have been subjected to continuous but differential cycles of seasonal degradation. Increase in pore pressure can breach the stability of steep slopes and trigger failure by reducing the effective stress along a potential slip surface which decreases the factors of safety along the surface.”

After recent landslides in sub-Saharan Africa that caused severe damage, assessing the causes leading to these incidents and understanding their course of events has been a major concern of the scientific community. A prominent example for urban disasters linked to anthropogenic impacts on the environment is the catastrophe that occurred in Sierra Leone’s capital Freetown in August 2017, when a side of Sugar Loaf Mountain collapsed after several consecutive days of heavy rainfall and floods. The thereby triggered mudslide turned into a devastating debris flow that was of unprecedented intensity, unrecorded in the history of Freetown Peninsula. This revealed not only the dangerous effects of urban sprawl and haphazard settlement growth in destabilized locations, but also the new dimensions urban hazards can have in such settings (Cui et al. 2019; Usamah 2017; Nelson et al. 2019; Jackson 2018). Though the main causative factor triggering the mudslide was attributed to the continuous rainfall saturating the soil and making the heavy ground mass slip off its steep source area (The World Bank 2017, 16-17), the event would not have appeared in this way without human alteration of the land cover and the destruction of its natural ecological and hydrological structure (Usamah 2017, 24-54; Jackson 2018). It has occurred that similar damage to the environment was dealt through infrastructural improvements or developmental projects that did not take environmental protection measures into

account, resulting in unanticipated ground movements. One such case happened for example in Yaoundé (Cameroon) after improvements to the sanitary infrastructure (Ngounou Ngatcha, Ekodeck, Ntana 2003).

Urban agglomerations with comparable impacts on their surroundings can be found in many places of tropical Africa, including the environment of Lake Kivu lies at Bukavu’s northern boundary. The Lake Kivu basin comprises multiple cities, towns, and smaller settlements where population growth and rapid expansion of informal settlements have led to deforestation and vegetation loss (mainly due to cropland expansion), which leads to the removal of root systems that hold soil near the surface together (Karamage et al. 2016; Basnet, Vodacek 2015).

To summarize, the main direct anthropogenic factors leading to higher landslide susceptibility and weakened ground stability of slopes that are broadly discussed in scientific literature are the following:

. Deforestation and harmful logging practices . Removal of shrub and other dense vegetation . Irrigation and over-cultivation . Excavation or loading with additional material . Construction of buildings or infrastructure

(Sources: Chang, Slaymaker 2002; USGS 2004, 2; Knapen et al. 2006, 158-163)

In more case-specific scenarios landslides can also be caused by mining activities, water leak- ages or artificially caused vibration (Ngounou Ngatcha, Ekodeck, Ntana 2003; USGS 2004, 2).

4.3 Environmental Risks in Bukavu:

Bukavu’s high vulnerability to hazards is strongly linked to the aforementioned anthropogenic factors, particularly in the case of landslides. Yet, beyond that also the conditions of its natural environment and the geographical location are responsible for a very high prevalence of disasters on a wider spectrum, including other types of urban hazards. The Kivu provinces experience high seismic activity due to tectonic movements in the EAR, frequently causing earthquakes that affect urban spaces (D’Oreye et al. 2011; Geirsson et al. 2016; Ilunga 1991). Not only can these earthquakes cause direct damage to buildings and infrastructure, but they may also be an immediate triggering cause of landslides in vulnerable locations and an additional

factor for the destabilization of ground structure. The frequency of historical landslide occurrence has been attributed to this high activity rate, despite the fact that it is difficult to provide a direct proof in most individual cases (Moeyersons et al. 2004). Though mostly unrelated related to landslides, there is an additional and rather exceptional environmental threat that Bukavu is exposed to in the form of a potential limnic eruption in Lake Kivu. In a worst-case scenario high gas concentrations in the lake could erupt from deep waters, destroy buildings or asphyxiate people and animals in its vicinity, though it has to be empha- sized, that such incidents are exceedingly rare (Michellier et al. 2019, 54-55).

As already indicated, there is a small number of GIS-based landslide-related scientific studies that have been conducted on Bukavu so far. They predominantly focus on historical landslides, specific areas of individual neighbourhoods or slow-moving ground deformations. Some of them include cartographic material that is based on similar methods like the ones applied in this thesis, but either only include a particular section of Bukavu (see Butara et al. 2015; Trefois et al. 2007) or are used as low-detail complementary information (see Ndyanabo et al. 2010). Some other publications involve maps of the growth of Bukavu’s densely built-up areas dating back to the 1950s, though their outlines are very vague and do not allow for a sufficient understanding of current growth processes and do not demonstrate a neighbourhood’s landslide susceptibility (see Hoffmann, Pouliot, Muzalia 2019, 8; Trefois et al. 2007, 73; Michellier et al. 2019, 103). Besides, there is no available information about the city’s current state, since the most up-to-date map only includes 2013 as a last time step (Michellier et al. 2019, 103). These studies have widely relied on using remotely sensed data to analyse both historical incidents and gradual mass movements. Nobile et al. (2018) have utilized multi-temporal DInSAR methods to identify ground deformations and generate displacement rate maps, proving that especially on the central western slopes of the city (eastern neighbourhoods of Kadutu; see figure 6) large landslides have occurred, some of them predating the present built-up land cover, whereas others took place within the last decades. They also state that the measurements have shown active ground movements that are current, gradually ongoing processes. The largest one they discovered takes place in the neighbourhood of Buholo (see figure 6), an extremely densely populated place with a high rate of inhabitants living under the poverty limit (Balegamire et al. 2017). Another significant deformation was identified by Nobile et al. (2018) in the northeastern districts of Bukavu, adding that “this area remained cultivated until the early 2000s, while the neighbour zones were urbanized in the 1950s, [which had] the effect of overloading (compaction) potentially combined with drainage / water pumping”, therefore possibly causing slow

ground movement in the relatively flat area, though the recorded signals do not provide sufficient proof to fully confirm this theory (ibid. 13-14).

The best currently available inventory of historical landslides in Bukavu has been compiled by Michellier et al. 2019 (83) (illustrated in figure 5). They collected records of past landslides and identified their age. Whereas the largest historical landslides were located in the commune of Kadutu (see figure 6) and were classified as being old or very old, the more recent activity appeared to be mostly affecting the southern side of the river bend to the eastern margins of the city. Other medium-sized slides were indicated in the communes of Kadutu and Bagira in the western and northwestern areas of the city, as well as outside the boundaries of the urban area. They furthermore cartographically displayed the risk of reactivation of past slides, the hazard risk related to the current dynamics of recorded landslides, and the risk of occurrence of shallow superficial ground movements.

Figure 5: Historical landslides in the urban area of Bukavu and the mountain slopes to the west A: Relative age, B: Shallow landslides, C: Reactivation, D: Landslide Dynamics (Michellier et al. 2019, 83)

In a study that involved the use of QGIS, Butara et al. (2015) have examined the commune of Ibanda that comprises the eastern part of Bukavu (see figure 6). They found that the construction of buildings at unstable locations is in fact the strongest factor when it comes to landslide activation, proving that the majority of past landslides – most of which appeared close to the Ruzizi River bend – had happened in medium to high and very high risk areas. They had identified these by creating a ‘Fuzzy Overlay’ map that included the four parameters slope, slope orientation, distance to drainage and distance to slope. However, landslide occurrence in areas that they had categorized as very low risk was also registered, meaning that they cannot be generally excluded from zones of potential landslide activation. Most importantly, based on their statistical findings, Butara et al. state that the factors chosen in their analysis do not have a strong impact on triggering landslides when compared to the role of the anthropological activity and the construction of buildings (ibid. 146-147).

Furthermore, it has been shown that ground deformations in Bukavu affect the water and elec- tricity distribution systems and cause damage to corresponding infrastructure (Kalikone et al. 2017). The vulnerability of buildings in selected geomorphological constraints areas has been studied by Balegamire et al. (2017) and Kulimushi et al. (2017) have examined the impact of human-induced land use change and the consequential landslide exposure of buildings and infrastructure within the Wesha watershed, an area within the commune of Bagira. Similarly, Mugaruka et al. (2017) have investigated the interaction between natural and anthropological factors in the northwestern neighbourhood of Nyakavogo, stating that rotational landslides have reoccurred over the past decades and that they have been reactivated mainly due to the strong impact of human-caused exploitation of building materials and erosion. One of the earlier geological studies dealing with landslide origin due to tectonic movements has been conducted by Moeyersons et al. (2004), a subject that has then been further elaborated by Trefois et al. (2007).

______

Figure 6: Map of communes and neighbourhoods in Bukavu

5. Study Area:

The city of Bukavu is located in the very east of the Democratic Republic of the Congo. In terms of geographic coordinates, the city stretches approximately from 2° 28’ S to 2° 34’ S in latitude and from 28° 50’ E to 28° 53’ E in longitude. To its eastern edge Bukavu is directly adjacent to the border with Rwanda and towards its northern limits, comprising the oldest quar- ter and historical centre (Michellier et al. 2019, 107), the city lies at the shores of Lake Kivu. The lake’s surface is at a height of 1452 m a.s.l., meaning that the lowest areas of the city are at a similar level, while the highest points of the urbanized area currently reach up to a height of over 2100 m a.s.l. (see figure 7). This makes Bukavu one of the highest-located cities in the DRC (Butara et al. 2015, 130).

The Ruzizi River, representing the border to neighbouring Rwanda and flowing from Lake Kivu to Lake Tanganyika in the south, delimits the cities extent towards the east and forces further expansion on the Congolese side to continue mainly towards the south, alongside the wide bend that the river takes in its upper reaches. On the other side of the border, the Rwandan city of Cyangugu represents a geographical ‘sister city’ to Bukavu, though it is significantly smaller. This bears similarities to the northern shores of Lake Kivu, where Goma on the Congolese side and Gisenyi on the Rwandan side are contiguous cities of different sizes (see figure 2). Towards the west there are the foothills of mountains whose peaks reach heights of nearly 3000 m a.s.l. and which are part of the Mitumba Mountain range. Bukavu is the capital city of South Kivu province and houses most of the province’s administrative institutions. Bukavu’s history, growth, and economic ties have always been strongly linked to the capital city of North Kivu, Goma, which is located on the opposite side of the lake (Vlassenroot, Büscher 2009; Stearns 2012; Michellier et al. 2016).

In terms of administrative divisions, Bukavu is divided in three communes which are again subdivided into neighbourhoods (‘quartiers’) (see figure 6). The three communes are Ibanda, which stretches from the northeastern parts of the city to the south, Kadutu, which comprises most of the central western neighbourhoods (see figure 8), and Bagira to the northwest, officially also including large areas of scattered rural settlements within its boundaries. These communes will serve as an important reference throughout the analysis. It has to be pointed out that there are further urbanized areas that belong to Bukavu but are officially within the boundaries of the territory (‘territoire’) of Kabare, a sub-division of South Kivu province. This includes the dense settlement south of Ibanda called ‘Nyantende’ that is connected to the urban fabric and will be incorporated in all results.

______

Figure 7: 3D elevation map of the study area

Bukavu has an estimated population of between 800˙000 and 1˙000˙000 people (Michellier et al. 2019, 13; CAID 2016; Populationdata 2016). Unfortunately, it is difficult to obtain reliable numbers on the exact amount of people in the city, particularly because of a lack of verified census data. Therefore, caution has to be exercised when relying on estimations about population or density, which for example Michellier et al. (2019, 106) have vaguely described as being more than 85˙000 inhabitants per km2 in some places. However, based on the analysis of satellite images and the unquestionable rapid growth of the city, it can be assumed that the population has grown significantly ever since the number of 800˙000 people has been circulating, making a total population of nearly 1˙000˙000 not unlikely.

Figure 8: Aspect of central Bukavu facing eastwards, as seen from Nkafu neighbourhood; On the top right: Cathédrale Notre-Dame-de-la-Paix de Bukavu (Photo taken by the author on 3rd March 2016)

As figure 7 illustrates, the city is constructed in an extremely hilly environment where barely any part of the city is built upon an entirely flat surface. Up-hill growth trends have been observed since the 1940s (Michellier et al. 2019, 107). Bukavu’s recent growth has been strongly influenced by historical events in the Kivu provinces, particularly since the First Congolese War of 1996-1997. Large numbers of people fleeing violent conflict have sought safety in the city, trying to escape fights between rebel groups, government forces and local militias. They also fled from looting, attacks on village communities, rape and other crimes. Until today,

armed rebel groups remain active in many places where the Congolese state exercises little to no control and corruption and impunity continue to be widespread phenomena (Solhjell 2015; 108-111; Van Overbeek 2014, 29; Van Overbeek, Tamás 2018, p. 290-291). Yet, other than for example the city of Goma at the northern shore of Lake Kivu, Bukavu has not been a direct site of active fighting and did not witness an occupation by rebel groups (Pech, Lakes 2017, 165- 170; Van Overbeek, Tamás 2018). This has led to the perception that “from a context of rural violence, Bukavu is seen as a safe haven […]” (Van Overbeek, Tamás 2018, 290).

In regard to climatic conditions, the city shows the typical characteristics of tropical environments close to the equator. Precipitation in Bukavu is high during most months of the year and reaches peaks in March and November (see table 1). A dry season lasts roughly from June to August, with July being the month with lowest rainfalls during the year. Temperatures vary little throughout the year and remain at an average of about 20°C (Climate-Data 2019). Before the emergence of strong human-induced land cover alteration and settlement growth, the area south of Lake Kivu was covered by tropical forest (Butara et al. 2015, 131). Today however, many surfaces within or close to urban areas are unsealed. This makes it so that water can easily ingress into the soil and material gets carried off when rainwater flows downhill into topographic sinks, particularly during the rainy season (Ndyanabo et al. 2010, 124).

January Feb. March April May June July August Sept. October Nov. Dec.

Average Temp. 19,8 19,9 19,9 19,6 19,9 19,6 19,5 20,4 20,5 20,1 19,8 19,7 (in °C)

Min. Temp. 14,7 14,7 14,7 14,7 15,1 14,2 13,4 14 14,5 14,7 14,6 14,6 (in °C)

Max. Temp. 25 25,1 25,1 24,6 24,7 25 25,7 26,8 26,6 25,6 25 24,8 (in °C)

Precipitation 135 137 170 165 103 34 17 52 110 151 172 145 (in mm/month)

Table 1: Temperature and precipitation by month in Bukavu (Source: Climate-Data 2019)

6. Methods:

6.1 General Approach and Considerations:

To analyse the land cover, terrain and urban growth processes in the study area and to create the maps illustrating additional contextual information as seen in the previous chapters, a selection of suitable GIS software tools and functions used. They were chosen in a way that would allow for the best possible utilisation of the datasets acquired for the study area. The first part involved preparing the data and adjusting the geographic accuracy and spatial extent of specific datasets, since they had been obtained from multiple different sources in a study area spanning two national territories. This was performed within a file geodatabase, making use of feature datasets to organize most of all non-raster-based data. In the steps following the creation of the overview maps of the DRC and the Kivu provinces, a Digital Eleva- tion Model (DEM) was used to examine and analyse the terrain of the study area and its geomorphological properties, resulting in the 3D elevation map, the aspect-slope map and the slope inclination map. In subsequent workflows satellite images were utilized to classify land cover and to vectorise Bukavu’s urban growth, represented in an image classification map, a detailed map about the growth of densely built-up areas from July 1990 to March 2019, as well as a map indicating urban growth processes during recent years and the corresponding expansion tendencies. In order to facilitate a more accurate landslide risk assessment, results from previous steps were then combined in a final overlay map that includes slope inclination, growth tendencies, neighbourhood divisions, and the current urban area extent. This was meant to be an intention- ally more complex illustration, merging the factors that had been presented separately before- hand. All maps are displayed in the coordinate system ‘WGS 1984 UTM Zone 35S’, except for the 3-dimensional elevation map, which uses the 3D compatible coordinate system ‘GCS WGS 1984’.

A very short note on the first two overview maps as presented in figure 1 and figure 2 should be made: In both cases a design technique was applied that uses different transparency settings to emphasize the relief and geomorphological contours of mountain ridges, plateaus or fault lines by underlaying a hillshade base map. This was not only meant to extend the informational content concerning the study area’s environments, but also to give the map reader an idea of other areas in the region where settlements are potentially experiencing similar exposure to environmental risks.

The commune and neighbourhood divisions included in the description of administrative units (figure 6), in the depiction of urban growth trends (figure 17), and in the final overlay map (figure 19) were not taken from an external database but were instead manually drawn by the author. It may appear surprising for a city at the size of Bukavu, but the available information and maps about administrative divisions do not entirely match each other when it comes to naming neighbourhoods, subdividing groups or outlining communes. There are apparently some ambiguities when comparing different sources that display these administrative divisions; as for example in Hoffmann, Pouliot and Muzalia (2019, 7), Babe et al. (2018, 3), Van Overbeek and Tamás (2018, 295; indicating the Communal Office of Bagira as source) and in Kalikone et al. (2017, 281). Even though most of these are very recent publications, the mismatches in some areas may be explainable by varying local opinions on correct neighbourhood names, the current political situation in the DRC, governance deficits or potential inconsistency in the administrative apparatus (see Van Overbeek 2014; Van Overbeek, Tamás 2018; Solhjell 2015). Since mapping and labelling the communes and neighbourhoods of Bukavu appeared to be a crucial part of being able to properly present the results, new feature classes were created for both levels of divisions based on comparisons of the abovementioned sources. The pre-existing maps were georeferenced using the latest available satellite image and natural features such as Lake Kivu and Ruzizi River as reference layers. Making use of suitable transparency settings, the border lines were then checked for congruency and manually drawn with maximum possible coincidence. In many cases, rivers, sinks, the limits of settlement agglomerations or streets helped identifying the division lines. The urban growth of the last years has also been taken into account (especially in southern Ibanda where no clear limit of the commune was indicated in any of the original sources). Hence, the maps showing these commune boundaries can be considered updated representa- tions of Bukavu if compared to other sources that ignore the current extent of the city. Given the initial ambiguities, some uncertainties remained and the following remarks have to be made about the divisions presented in the maps:

- There is a densely urbanized area west of Cahi neighbourhood that does not fall inside one of the three communes but is instead officially part of the territory of Kabare. This area undoubtedly represents a part of the continuous urban fabric and is accordingly seen as such throughout the result chapter, though in terms of administrative affiliation it is officially part of Kabare.

- Similarly, the settlement of Nyantende to the very south has merged with the urbanized areas towards its northwest during the last years and is therefore considered a part of Bukavu, though it is not an official neighbourhood within a commune of the city.

- In some sources Cahi is a neighbourhood of the commune of Ibanda whereas elsewhere it is presented as a part of Bagira. Adhering to the majority, it was here incorporated into Ibanda.

- The areas of Buholo and Mosala sometimes form a single, combined neighbourhood. Elsewhere they are subdivided into three neighbourhoods or are named either only Buholo or only Mosala. It is also unclear whether they belong to Kadutu or Bagira. In accordance with Kalikone et al. (2017, 281), they have been divided into two separate neighbourhoods, with Buholo in the west (Bagira) and Mosala in the east (Kadutu).

The data used in this study does not involve information that can be linked to individual residents in the study area or infringe somebody’s privacy. The primary intention of this thesis is to provide inhabitants of Bukavu and decision-makers of the city’s administrative institutions with means to protect people against landslide-related hazards and to prevent them from being severely exposed to environmental risks. However, it has to be ensured by every user that the presented materials are not abused to justify arbitrary spatial planning practices. They also must not serve as a pretext to remove people from certain locations or prohibit construction of buildings when the true intentions are not related to protecting peoples’ lives or property but instead harm the affected.

6.2 Workflows Involving the Digital Elevation Model:

An ‘Advanced Spaceborne Thermal Emission and Reflection Radiometer’ (ASTER) Global DEM (GDEM) served as the main source data for the contextual 3D elevation map (figure 7), the aspect-slope map (figure 13), the slope inclination map (figure 14), and accordingly also for the final overlay map (figure 19). This ASTER GDEM (Version 2) of the study area was obtained from a dataset provided by the United States Geological Survey (USGS). It was collected by NASA’s Terra satellite and released for free public use in October 2011. The cell size is

based on a 1 arc-second grid – which corresponds to approximately 28 m in the study area – with an accuracy of about 17 m at a 95% confidence level (ERSDAC, USGS 2011, 4-5).

Using options to represent elevation data in a 3D scene of ArcGIS Pro, the DEM was assigned as an elevation surface in combination with a vertical exaggeration factor of 2, thereby creating an enhanced 3-dimensional visualization of the study area. The main additional steps resulting in the elevation map depicted in figure 7 were the adjustment of its colour scheme in the symbology settings and the inclusion of the boundaries of Bukavu’s current urban extent (derived from the latest satellite image) with an 80 m extrusion added to the base height of all features’ vertices. Finally, an appropriate perspective towards south-southwest was chosen that made most of the relief’s features well visible.

When creating both the aspect-slope map (figure 13) and the slope inclination map (figure 14), taking advantage of the ‘Surface Functions’ subcategory within ArcGIS Pro’s raster functions proved very useful. In the former case the DEM was used as an input of the aspect-slope function to create an output raster that simultaneously shows the direction and the steepness of each raster cell. Once again, an outline of the urban area was added that had been derived from the latest satellite image of March 2019. Since this way of illustrating both aspect and slope at the same time is a rather novel method within the community of GIS users and may therefore be difficult to be interpreted by map readers who are unfamiliar with the concept, some elements of the map creation process have to be illuminated: The surface direction is classified in eight classes of different colour hues, whereas the steepness is represented by three different classes of saturation. As with generic aspect maps, the colour hues indicate the cardinal directions (e.g. orange = west, blue = east), while the changes in saturation intend to simulate the shading of the relief created by a hypothetical light source shining from the northwest of the map (for an additional example see figure 9). The lowest saturation represents the class of lowest slope inclinations. The most saturated colour values represent the steepest slopes. It is important to note that the slope classes to not show absolute values in degree but are instead based on relative values depending on the range of minimum to maximum degree derived from the input DEM (Buckley 2017). For example, the steepest slopes in the aspect-slope map about Bukavu constitute 60% of the steepest slopes in a range from 0º to 68º. The lowest 5% are considered ‘flat’ and are shown in grey, though they do not appear in figure 13 since all such cells are part of Lake Kivu’s surface.

Figure 9: Example of an aspect-slope raster of a larger study area than Bukavu, creating a less pixelated image (Crater Lake in Oregon, USA) (Source: Buckley 2017)

Due to minor spatial inaccuracies, the northern fringe of the city had to be realigned to the lake shorelines in both the aspect-slope map and the slope inclination map, since some areas from vector-based layers were slightly displaced.

To create the slope inclination map as depicted in figure 14, the DEM was used as an input for the ‘Slope’ function, which is yet again found within the raster functions of ArcGIS Pro. Like this, the change rate of elevation in degree was calculated for each raster cell, resulting in values that reached up to a maximum of 68º. By classifying the output layer’s symbology into 5º steps, seven classes were created that allowed for good distinguishability of cell colours while preserving enough detail and informative value. Cells indicating a steepness of more than 30º were grouped together to avoid isolated cells or heterogenous areas and because any slope steeper than 30º could be considered highly susceptible to landslides in any case. Another reason to choose 5º steps was to enable the map reader to easily identify areas above a threshold of 15º, which Nakileza et al. (2017, 2) mention as a crucial threshold for elevated landslide risk exposure. After adding the boundaries of the urban area’s current extent, a transparent mask was generated that was used to overlay all areas outside the densely urbanized agglomerations. This was to create a slight figure-ground effect, which is a common technique to help map readers distinguish specific areas or features from each other (Peterson 2015, 104-105). In this case it

made the urban area emerge from the surroundings in more saturated colours while retaining the possibility to identify the slope gradient of areas outside of Bukavu.

The same technique was applied in the process of creating the final overlay map (figure 19). Again, the map had to include the continuous raster surface indicating slope inclination, which can appear to look somewhat cluttered at the study area’s scale, especially once the other overlay layers and labels were added. The main purpose of this map was to provide a single, combined product that represents the major aspects of an urban-growth-related landslide risk study. This naturally comes at the expense of intuitive map readability but in return allows for a more detailed examination of specific neighbourhoods. Aside from the slope inclination raster, the overlay map includes all vector-based neighbourhood divisions and outlines showing densely built-up areas. Arrows indicating ongoing growth trends, which had been derived in the workflows involving satellite imagery, were added as well. The symbology of the underlying slope raster was adjusted and reclassified into four classes of 10º steps in order to decrease cluttering and remain with three classes that can be interpreted as medium (10º - 20º), high (20º - 30º), and very high (> 30º) risk areas. It was avoided to include this as visible notes in the map’s legend, since there is no scientific consensus on this issue, but this categorisation will serve as reference in the presentation of the results. It also allowed for ‘low risk’ areas of ≤ 10º to be coloured in white, thus in a sense removing them from the map – at least in terms of their visual illustration. Special attention was payed to using colours that are commonly perceived as different levels of danger (Conger 2004) when selecting new colouration for the three slope-related risk classes. Furthermore, some small polygons inside the urbanized area of Bukavu, showing gaps in the urban fabric with vegetation cover, were removed if they were so small that they did not significantly affect the informative value of the map and obstructed map readability.

6.3 Workflows Involving Satellite Imagery and Satellite Data Properties:

Satellite images from three different databases were used to create the land cover classification map (figure 12) and the maps related to urban growth (figure 16 and figure 17), which eventually also became part of the overlay map (figure 19).

The image classification was performed on a satellite image from 22nd March 2019 in order to be as up-to-date as possible. The same image was used to outline the current extent of densely built-up areas in the study area, serving as the last time step in the urban growth maps and representing the polygon that indicates the urban area in all aforementioned maps. To achieve

a higher precision than with older satellite data, this image was obtained from Digital Globe’s image gallery that provides imagery collected by the satellite WorldView 2 (WV2). WV2 is operating since October 2009 and collects high-resolution 8-band multispectral data. It is a commercial satellite whose sensors have a resolution of 1,85 m (DigitalGlobe 2019). While single band datasets with the highest resolution require the payment of a fee, multispectral com- posite images with a reduced resolution are freely available for public use. Since the images with lowered quality still proved to be a more suitable option than data acquired from the other sources while providing enough detail at the study area’s scale, this option was chosen for the analysis.

The classified land cover map as portrayed in figure 12 was created by carrying out a ‘Super- vised Maximum Likelihood Classification’ in ArcGIS for Desktop, using the ‘Spatial Analyst’ extension. GIS-based classification methods that group pixel depending on user-defined settings and typological characteristics are common ways of illustrating the spatial distribution of different land cover types. Similarities are generally detected based on the colour value properties of the pixels in a raster image. Many other scientific studies have used classification techniques to evaluate and monitor land cover and its change over time, also within tropical environments (see Mundia, Aniya 2005; Basnet, Vodacek 2015; Karamage et al. 2016; Mansaray, Huang, Kamara 2016; Reiche et al. 2018; Cui et al. 2019). In the case of Bukavu it was important to be able to differentiate buildings and other human- made structures from open soil and trees. Additionally, an extra class for all other kinds of vegetation and a class for water surfaces were created when performing the ‘Supervised Maxi- mum Likelihood Classification’. Accordingly, a set of more than 100 training samples were manually drawn on areas in the satellite image that could undoubtedly be identified as belonging to one of the five classes. Inspecting the histograms and scatterplots, which indicate colour value overlaps from the input training samples that may result in imprecisions or assignment of some areas to the wrong class and can be generated in the training sample manager, helped to make adjustments and improve the result. After refining the output with the ‘Majority Filter’ tool and comparing it to the original satellite image, the resulting map showed high precision in nearly all areas. Just minor isolated, scattered red pixel remained in some open soil environments that may potentially not be human-made structures in reality. The only larger problematic areas where surface reflections led to inaccuracies in the classified output can be found along-

side the Ruzizi River bend. Here, the relatively steep slopes of the river basin appeared in colours that resembled those of buildings and where therefore classified as such, while they are actually predominantly covered with open soil or vegetation in reality.

In order to map the growth of Bukavu from July 1990 to March 2019 (figure 16) and latest development trends (figure 17), the following satellite images were used:

Date of Capture Satellite 30.07.1990 Landsat 4-5 TM 25.07.1994 Landsat 4-5 TM 18.08.1997 Landsat 4-5 TM 22.08.2010 Landsat 4-5 TM 22.08.2016 Landsat 8 OLI / TIRS 11.07.2018 Landsat 8 OLI / TIRS 22.03.2019 WorldView 2

Table 2: Origin of the satellite images used to map urban growth in Bukavu

When selecting the images, special attention was payed to keep the cloud cover as low as possible and to have an unobstructed view on the study area. Since Bukavu is situated close to the equator and therefore affected by the intertropical convergence zone, many satellite images show relatively large cloud cover or patches of clouds that may hinder visual interpretation. This is a common challenge for scientists and analysts working with optical satellite imagery in tropical environments (Reiche et al. 2016) and particularly the Kivu region (Basnet, Vodacek 2015, 6703). Aside from minor exceptions in 2018 (see figure 10), it was possible to find images with barely any interfering clouds over Bukavu in the months of July or August for each year listed in table 2. Even though the time steps are obviously irregular as a result of not being able to obtain clear and usable for other years, the idea was to at least have roughly full-year steps in between that make it easier to interpret growth speed.

Mapping urban growth can be performed with different techniques that utilize satellite data. GIS-based methods have been used on multiple urban centres in Africa and other continents and they constitute one of the most important approaches to understand growth and development processes (see El Garouani 2017; Linard, Tatem, Gilbert 2013; Mundia, Aniya 2005; Almazroui et al. 2017). Depending on cell resolution and the spectral bands collected by the sensor, either algorithm-based processing or visual interpretation of specific colour bands can

be applied to group and separate pixel cells with similar values. The latter approach has been successfully used on the city of Goma by Pech and Lakes (2017), who stacked selected wavelength layers in order to outline the extent of the built-up area. This was repeated for multiple years with data taken from different Landsat satellites, allowing the vectorised built environment to be compared with each other and urban development processes to be analysed over a long time span. In the same fashion, visual interpretation was applied in this thesis to produce detailed polygonal shapefiles that show areas of densely built-up agglomerations in Bukavu and around the city.

During the visual interpretation of the selected datasets in ArcGIS Pro, different colour bands from the Landsat images were used depending on their resolution and clarity in particular areas. For the most part, this was either done by creating RGB composites or by selecting a single band (see table 3 and 4) and adjusting the symbology, if this proved a better option with enhanced distinguishability. In unclear scenarios where potential vegetation, open soil or built

Figure 10: Example of using panchromatic Band 8 (11th July 2018, Landsat 8 OLI/TIRS) to outline the extent of densely built-up areas with adjustment of the symbology

environment could not be easily associated, also the infrared bands were used as aid. Especially when working with Landsat 8 OLI / TIRS datasets, the panchromatic band 8 proved very useful due to its high resolution and wavelength properties (see example in figure 10 and table 4).

The satellites of Landsat 4 and Landsat 5 carried the ‘Landsat Thematic Mapper’ (TM) sensor that collected six spectral bands during its time of operation from mid-1982 until mid-2001 and was launched by NASA (Kramer 2012). The spatial resolution of data collected by all bands except for the thermal band (Band 6) is 30 m. As table 3 shows, the sensor provided imagery for each wavelength spectrum of blue, green and red as well as one infrared band, two shortwave infrared bands and a thermal band (USGS N.D.).

Landsat 4-5 TM

Band Wavelength (micrometres μm) Resolution Band 1: Blue 0,45 – 0,52 30 m Band 2: Green 0,52 – 0,60 30 m Band 3: Red 0,63 – 0,69 30 m Band 4: Near Infrared 0,76 – 0,90 30 m Band 5: Shortwave Infrared 1 1,55 – 1,75 30 m Band 6: Thermal 10,40 – 12,50 120 m Band 7: Shortwave Infrared 2 2,08 – 2,35 30 m

Table 3: Landsat 4 and Landsat 5 spectral bands (Source: USGS N.D.)

Landsat 8 OLI / TIRS

Band Wavelength (micrometres μm) Resolution Band 1: Ultra Blue (Coastal/Aerosols) 0,435 – 0,451 30 m Band 2: Blue 0,452 – 0,512 30 m Band 3: Green 0,533 – 0,590 30 m Band 4: Red 0,636 – 0,673 30 m Band 5: Near Infrared 0,851 – 0,879 30 m Band 6: Shortwave Infrared 1 1,566 – 1,651 30 m Band 7: Shortwave Infrared 2 2,107 – 2,294 30 m Band 8: Panchromatic 0,503 – 0,676 15 m Band 9: Cirrus 1,363 – 1,384 30 m Band 10: Thermal Infrared 1 10,60 – 11,19 100 m Band 11: Thermal Infrared 2 11,50 – 12,51 100 m

Table 4: Landsat 8 spectral bands (Source: USGS N.D.)

The still operational Landsat 8 satellite carries the two sensors ‘Operational Land Imager’ (OLI) and ‘Thermal Infrared Sensor’ (TIRS). It was launched by NASA in February 2013. The nine OLI spectral bands collect data at a resolution of 30 m with the exception of Band 8 (panchromatic) that provides images at a maximum resolution of 15 m (Barsi et al. 2014).

This band variety and the respective resolutions allowed for a relatively clear distinction of buildings and other environments, though this approach can of course not entirely rule out mi- nor imprecisions. The results from performing the visual interpretation on all time steps were partially used to create the map showing urban growth tendencies (figure 17). By only including the years 2016, 2018, and 2019, a harmonized illustration was created that allows for the iden- tification of the most recent spatial expansion processes and future-oriented predictions.

On a last note, it has to be pointed out that there are different concepts about what defines an ‘urban space’ worldwide and the perception about their typical characteristics may differ from between countries and contexts (Jabareen 2006; McFarlane 2011). For the analysis, it was therefore decided to map ‘densely built-up areas’ rather than working with the more complex and sometimes vague term of ‘urban areas’ in the maps. This helped to prevent ambiguity and facilitate the inclusion of densification processes outside of the contiguous central body of the city. Areas were deemed densely built-up if they showed roughly more than 10 buildings per hectare. In addition, they had to be part of at least 30 building structures in a distinct group of buildings in order to be mapped.

7. Results:

7.1 Classification of Land Cover:

The classified image of the study area indicates five different land cover types: ‘Water’, ‘Build- ings and Human-Made Structures’, ‘Open Soil and Bare Land’, ‘Trees’ and ‘Other Vegetation’. It shows that very little vegetation cover can be found along the fringes of the city and in the vicinity of building agglomerations. This concerns especially the northwestern parts, where mainly open soil and bare land dominate the city’s surroundings. If human-made structures and water are excluded, it can be stated that open soil and bare land cover overall more surface in the study area than tress or other vegetation. Residual tree cover patches can mainly be found to the south of Bukavu, in a longer distance to the southwestern urban boundary, in the unin- habited open space within the commune of Kadutu, and on the Rwandan side (particularly along the Ruzizi River bend). Additionally, some isolated dark green spots can still be found within densely built-up areas, though they represent little more than single trees on compounds that are large enough to contain them. The tree cover areas that show an elongated or curved shape are predominantly remnants on steep slopes which are unsuitable for agriculture and not pre- ferred for primary settlement (see example of a group of residual trees in figure 11). It can be assumed that at a certain threshold of very high demographic pressure, remaining trees in fast growing zones will eventually be removed in order to make way for new construction spaces. In the past, such processes have already occurred in other locations, particularly where gaps within the urban fabric have progressively been filled up with buildings. This was for example observable in the process of vectorising the satellite image time series (see figure 16).

Figure 11: View from western Ndendere neigbourhood (Ibanda) towards the southwest (Photo taken by the author on 18th February 2018)

The classification was based on an image collected in March, meaning that the precipitation rate at this time was averagely very high and preceding months had already brought a lot of rain. Most of the areas classified as ‘Other Vegetation’, such as those that can be found to the southwest of Bukavu, are fields used for agriculture, as the original satellite images showed. These fields were covered by cultivated plants that appeared predominantly in similar light green shades, either due to the rainfall at this time of the year or artificial irrigation. They should therefore be interpreted as vegetation cover related to agricultural crops, meaning that they do not represent natural vegetation cover. Furthermore, areas classified as ‘Open Soil, Bare Land’ can also potentially indicate areas used for cultivation. This is especially the case in the vicinity of rural settlements outside the central urban agglomerations, where fields are often directly adjacent to smaller housing units and supply households with domestic needs. Regardless of that, all the areas illustrated in beige showed low vegetation and predominantly brown soil cover during the time of late March when the source image was captured.

To some extent the classification also portrays a visual impression of the density of buildings and other human-made structures. In the oldest parts of the city only few spots belong to one of the other land cover categories. This is most notable in northern Kadutu and Ibanda, as well as in the neighbourhood of Buholo (central west). Nevertheless, this should not automatically be mistaken to signify highest population density. It has to be kept in mind that the oldest parts of the city in the north and northeast include bigger buildings and more asphalted roads, while newer areas that have only grown over the recent years tend to show very dense settlement structures consisting of smaller houses with narrow in-between gaps and weakly developed infrastructure.

With regard to potential landslide triggering, the described characteristics of the study area demonstrate elevated susceptibility in sections that lack significant tree coverage, since this is widely regarded as one of the most important factors to ensure ground stability and to prevent erosion (Karamage et al. 2016; Kulimushi et al. 2017). Given the fact that nearly all western and northwestern fringes of Bukavu are dominated by open soil and bare land, there is alarm- ingly little natural protection from strong, deep-rooted vegetation remaining at the current state. Landslide risk is exacerbated by the potential effects of agriculture-related soil loosening and irrigation, which lead to accelerated soil liquification during times of strong precipitation and higher erosion rates (Mugagga, Kakembo, Buyinza 2011).

Based on a Satellite Image taken on 22. March 2019

______

Figure 12: Map showing a classified satellite image of the study area

7.2 Aspect of Hillsides and Mountain Slopes:

As the colorized aspect-slope map shows, the majority of slopes within the boundaries of the urban area face towards the east and northeast. Most slopes in Bukavu with southern or southeastern orientation can be found in Lumumba neighbourhood (northwestern Bagira) and in the city’s northeast (Ibanda), where they run somewhat parallel to the basin of the Ruzizi River. The respective opposite hillsides are mainly directed towards the north and northwest. The few westwards oriented slopes found in the study area are mainly located along two stretches in the centre and the west (western edge of Kadutu). In the southern parts of the urban area there is a clear tendency of northeast orientation of slopes. This reflects the fact that the southern neighbourhood of Panzi is situated on the foothills of mountains to the southwest of Bukavu, declining towards the river basin. The slightly separated settlement of Nyantende that is connected to Panzi in the southwest expands on a hilltop with all aspect directions found around its centre. In the inner northwest of Bukavu’s urban area, eastward facing slopes dominate the commune of Kadutu. Further to the northwest and adjacent to Lake Kivu’s shores, there is a sink that marks the delineation between the east and southeast facing neighbourhood of Mulambula and the mainly north and northeast facing neighbourhood of Chikonyi.

When taking a look at the entire study area, including the slopes outside the urban boundaries, the map conveys a visual impression that in the centre of Bukavu there is wide-ranging sink starting from nearby the central peninsula in Lake Kivu and spanning the core of the city. More- over, a tendency towards a general north-south orientation of ridges, which is typical for locations within the EAR (Nobile et al. 2018, 2), can be vaguely identified on the Congolese side.

Knowing the orientation of slopes in the study area is important for understanding gravitational mass movements, because it determines the direction of material slipping downhill in the event of a landslide. Since the terrain is significantly more elevated towards the west and the southwest (see figure 7), it can also be predicted how water, mud or debris flows would move over a longer distance.

______

Figure 13: Aspect-slope map of the study area

7.3 Slope Inclination:

As it was already possible to tell from the previous elevation and aspect-slope maps, the study area is characterized by a hilly and mountainous terrain with a heterogeneous relief. This be- comes apparent in more detail in the slope inclination map. The study area comprises many steep zones that fall within Bukavu’s boundaries as well as defining the city’s surroundings. High slope gradients can be found in all directions, except for the northern parts that are obviously covered by the lake. The original DEM showed that even within the densely built-up areas there are zones that reach an inclination of up to 68º. The steepest and therefore exceedingly landslide-prone areas can especially be found along Bukavu’s eastern limits, within the central western neighbourhoods, at the north-south oriented ridges and sinks in Kadutu, and in some parts of Bagira in the northwest.

On a wider spectrum that takes a look at all areas steeper than 15º – which has been considered a critical threshold for accelerated mass movement by Nakileza et al. (2017, 2) – many smaller, additional areas can be identified as being susceptible to landslides. The following fields consisting of multiple continuous cells above 15º can thus be determined (depicted in the map from yellow to red hues):

. Alongside the Ruzizi River bend, starting from Lake Kivu’s outlet and following the entire western side of the river course towards the south, even beyond the urban area’s boundaries

. At the slopes facing southeast and northwest in northeastern Ibanda, though interrupted by flatter surfaces

. Several small spots in the south of Bukavu, being part of Panzi neighbourhood

. The vast majority of slopes in the central western zones of the urban area

. A sequence of slopes in Kadutu, forming risk zones elongated from north to south that are interrupted by strips of flat intermediate zones

. Most areas in the northwestern parts of the city, except for relatively flat surfaces bordering the lake and at the outer parts of the urban area farthest to the northwest

These zones are at particular risk of sudden slope failure, especially concerning rotational and translational landslides, and are examined in further detail in the overlay map (figure 19).

______

Figure 14: Slope map of Bukavu

7.4 Urban Growth:

7.4.1 Urban Growth from 1990 to 2019:

The map showing Bukavu’s growth from July 1990 to March 2019 provides valuable insight into the city’s history over the past three decades. Digitizing the densely built-up areas of a series of seven time steps resulted in a map with detailed outlines that demonstrate the considerable changes and expansion of the city. Population growth of resident families and the influx of newcomers have led to densification of building structures in many areas. In recent years, such processes have especially been taking place along the peripheries, ranging from the entire western to the southern side. Mapping these areas, consisting of roughly more than 10 buildings per hectare in a group of at least 30 houses, revealed not only dynamic, fast-growing zones, but also some margins that have barely changed in specific historical time periods or are currently nearly stagnant. This hints at geomorphological obstacles that impede expansion.

At its earliest visible state in July 1990, Bukavu covered mainly the lower areas that represent the historical ‘heart’ of the city adjacent to Lake Kivu’s shoreline. At this time, the commune of Ibanda was far developed in its northern and central parts, though rapid southward growth did not set in up until 1994. Besides that, the urban area did not yet come very close to the northern section of the Ruzizi River bend. Several unoccupied locations and elongated gaps were still present in the core of the city, most of them retaining patches of tree cover. These gaps coincide with some of the major sinks in the landscape, which were not the most suitable locations for primary settlement. However, they were eventually filled up with building structures and had disappeared for the most part by 1997. Nearly all parts of the commune of Kadutu in the west that are urbanized today were already densely built-up in 1990. Only minor changes to the western margins occurred over the following seven years.

One remarkable ‘open’ space stands out in the inner western part of Bukavu (right in the centre of Kadutu). Up to today, this area shows a lot of tree and vegetation cover and has seen only small changes during the last years. In this location, the compounds and plots of land belonging to religious facilities and educational institutions (e.g. Université Catholique de Bukavu, ISTM Bukavu, Lycée WIMA) have prevented the expansion of residential settlements. By August 1997, significant growth processes had taken place in the southern and southwestern parts of the city. In the northwest, Bukavu developed further towards the upper course of Ruzizi River. Also the disconnected settlement in the very northwest had experienced some growth.

In the year 2010, the urbanized area reached even further southwards and the building structures in Nyantende – the somewhat separated settlement in the very south – had begun to densify. A lot of new dwellings had appeared at the western edge of Bukavu. At this point, the neighbourhoods to the very northwest had grown to about half of their current size. While only few new groups of houses occurred in the northeast of the city after 2010, growth rates remained very high in the other directions that were not blocked by natural features like the river or the lake.

Until August 2016, the scattered southeastern agglomerations had witnessed the construction of numerous new buildings, beginning to merge them gradually into the urban fabric. The same process took place in the very south (neighbourhood of Nyantende), while a sink in the topo- graphical relief still separates these rapidly developing areas for the most part. Furthermore, most gaps in the older parts of Bukavu had disappeared in 2016 and the westwards growth trend had continued to move the city’s margins further up the higher mountainsides. At its current extent, Bukavu is more than twice as large as compared to its size in 1990. People have continued to settle especially on the more elevated, deforested locations in the west and in the remaining free spaces in the south, preferring flat surfaces where possible. These processes will be further examined in figure 17.

The total size of all polygons representing densely built-up areas on the Congolese side is presented in the table 5 and figure 15 below. They show that the total area of high building dense- ness has nearly doubled in the time period from 1990 to 2016. Today it is above 34 km2 and can be estimated to reach 40 km2 in 3 – 4 years from now, if growth rates remain steady.

Month & Size of Densely Built-Up Areas 40 Year (Congolese Side) 35 July 1990 15,02 km2 30 2 July 1994 17,54 km 2 25 August 1997 20,66 km2 20

August 2010 24,77 km2 Size in km 15 August 2016 29,31 km2

......

July 2018 31,29 km2 :

1990 1994 1997 2010 2016 2018 2019 March 2019 34,25 km2 Year

Table 5: Total size of built-up areas by year Figure 15: Graph showing the size of built-up areas

______

Figure 16: Map showing the growth of densely built-up areas in the study area from 1990 to 2019

7.4.2 Current Urban Growth Tendencies:

The map indicating the latest growth tendencies visualizes the development of densely built-up areas from August 2016 until March 2019. Directional arrows were added based on visual interpretation of growth rate and size of connected areas where significant expansion and densification took place. They have to be understood as means to emphasize general spatial development trends and as a reference for the final overlay map.

In the most recent years, densely built-up areas of Bukavu have grown in many places that are not limited by Lake Kivu, the Ruzizi River or geomorphological obstacles. The compaction of formerly loose settlements beyond the margins of the urban area has made them become larger and finally get incorporated in the city’s continuous fabric – an unplanned, haphazard process that has occurred since the 1990s. The most notable development trends are the following:

- The southeastern end of the commune of Ibanda proceeds to experience very rapid growth, particularly further south and towards the lower course of the Ruzizi River. This young part of Bukavu is located on top of the relatively flat surface between steep scarps along the river basin and a sink in the terrain on the opposite side.

- The neighbourhood of Nyantende in the far south, which is marginally connected to the outskirts of Ibanda, also appears to be one of the fastest expanding areas where people continue to settle and construct new houses. Compaction of buildings has taken place at a very high rate and expansion is especially significant towards the southwest.

- The western extension of Ibanda shows very rapid up-hill growth as well. In between Nyantende and this area there are steep slopes that currently impede additional construction.

- Furthermore, the area officially belonging to none of the communes (part of the territory of Kabare) is growing westwards at a very rapid rate. The same holds true for areas of southern and central Bagira.

- The neighbourhood farthest to the northwest of Bagira (Nyakavogo) is expanding further in northwestern direction, likewise at a very rapid rate.

- A dense settlement agglomeration north of Bagira has shown notable growth over recent years and keeps expanding. It is likely that it will merge with the outer neighbourhoods of Bagira in the future. Additionally, significant growth processes are taking place in smaller offshoots in central Bagira.

______

Figure 17: Map showing the growth of densely built-up areas from August 2016 to March 2019 with growth tendency indicators

7.5 Slope and Urban Growth Overlay:

The final, more complex map shows the reclassified slope inclination raster with the outlines of all current densely built-up areas (including the Rwandan side), growth tendency indicator arrows and the labelled neighbourhood subdivisions. It allows for a detailed inspection of each neighbourhood. There is no clear scientific consensus on how to categorize areas’ landslide susceptibility based on slope inclination (see Butara et al. 2015; Ndyanabo et al. 2010; The World Bank 2017). To facilitate the presentation of risk assessment that can be derived from the overlay map and the previous chapters (including contextual information), slope inclination of 0º - 10º will be regarded as ‘low risk’, 10º - 20º will be considered ‘medium risk’, 20º - 30º will be referred to as ‘high risk’, and values of > 30º will be seen as ‘very high risk’ areas. This evaluation concerns primarily rotational and translational landslides and is less appropriate for debris flows. Accordingly, the neighbourhoods will undergo a stepwise examination:

Nyalukemba: The neighbourhood of Nyalukemba, representing one of the oldest parts of Bukavu whose history dates back to the founding of the city (Michellier et al. 2019, 107), is predominantly situated on low to medium risk areas. At the present, growth has halted before the dangerous slopes of the river basin. The strongly developed infrastructure and lack of reported rotational or translational landslides suggest that Nyakulemba is generally not prone to landslides.

Ndendere: Apart from some buildings on the steep slopes of the river basin in the southeast, Ndendere also shows mainly low and medium risk areas. The last remaining free spaces are unlikely to experience significant building expansion, partly because they belong to compounds of educational institutions. Except for the southwestern fringe, the neighbourhood’s age, comparatively well- developed infrastructure, and a lack of past incidents indicate generally low landslide susceptibility.

Panzi: The large neighbourhood of Panzi is characterized by formidable high and very high risk areas along its western limits, following the Ruzizi River basin. This long-stretched strip of steep eastwards oriented slopes has not only experienced multiple landslides in the past (Butara et al.

2015, 139), but it is also low-lying land where water flow accumulates in times of high precipitation. The fact that there is a high number of buildings located in this zone amidst the high risk exposure is alarming. The relatively extensive flat areas in the centre and the very southeast are significantly safer housing locations. However, Panzi has experienced one of the fastest growth rates at its southeastern margins that tends to continue towards more high and very high risk areas nearby the river. Preserving the remaining tree and vegetation cover in this increasingly human-impacted environment should be paramount in order to avoid similarly destabilized grounds like they can be found further north. Medium risk areas in the southwest of Panzi should be monitored as well, since these parts are rather young and urbanization processes have only approximately taken place since the beginning of the 2000s. Here, building stability and infrastructure are weaker than elsewhere and could affect a wide area if landslides are triggered in the high-elevation zones to the southwest of the settlement. Furthermore, the rapidly growing western extension of Panzi is located on many medium to high risk areas and they keep expanding on high and very high risk slopes, raising the concern that residual tree cover nearby the city’s outskirts might be removed in the near future and additionally destabilize the northeast and southeast oriented hillsides.

Nyantende: Currently, the settlement of Nyantende is at low risk of landslides in its centre, but medium risk can be seen in its northern and partially southern parts. Future rapid growth is likely to fill in the remaining flat free spaces before population pressure is high enough to force settlement on more dangerous slopes. The surroundings of Nyantende still show several compact groups of trees and other vegetation that help stabilizing the ground, but increasing anthropogenic activity and new construction threatens to reduce this effect if it results in deforestation.

Cahi and urban areas in the territory of Kabare: Cahi and the densely built-up areas west of it are predominately at medium to high risk of being affected by landslides. The relief is very heterogeneous and further up-hill growth towards the west proceeds to integrate more medium to high risk areas. Patches of trees exist on high-altitude slopes, but in the vicinity of dense settlements they have been replaced with agricultural fields. Landslides would move in a general northeastern direction, potentially affecting a large number of houses in this environment of high building density.

Cimpunda, Kasali, Buholo, Mosala and Nyamugo: This group is part of the oldest and densest neighbourhoods in Bukavu (Michellier et al. 2019, 112-113). The eastern neighbourhoods of Nyamugo, Kasali and Mosala have relatively compact infrastructure on mostly flat and low gradient surfaces that represent low to medium risk. However, there is a very high risk zone between the neighbourhoods of Cimpunda and Kasali that indicates particularly high landslide susceptibility. Landslide activation on these slopes would affect buildings in their vicinity to the east. Apart from this, there are more medium to high risk areas in Cimpunda. At the western side of Buholo neighbourhood, another very high risk zone can be identified. Buholo is one of the densest settlements in Bukavu and has experienced landslides in the past (ibid., 78-85). The orange to red cells in the map cover an eastwards oriented scarp with barely any tree cover. Especially the closest, eastern dwellings in Buholo are therefore at very high risk of being struck by future landslides.

Kajangu, Ciriri and Mulwa: These neighbourhoods comprise the urban areas with highest altitude in Bukavu, reaching above 2000 m above sea level. To their northwestern and eastern limits there are high and very high risk areas, whereas the inner parts are dominated by medium risk areas with a plateau-like surface in the centre that shows low landslide risk. Most buildings are less than 20 years old and are mainly the result of unplanned sprawl, suggesting comparatively weaker building stability. Especially along the neighbourhoods’ edges that show scarce tree cover and along the southwestern offshoot growing towards medium and high risk slopes, inhabitants’ dwellings are more exposed to potential landslides.

Nkafu and Nyakaliba: The neighbourhoods of Nkafu and Nyakaliba constitute some of the oldest parts of the historical centre of Bukavu. Though building density is higher and infrastructure less developed than for example in the eastern neighbourhoods of Ibanda (Michellier et al. 2019, 112), Nkafu and Nya- kaliba show at least some smaller patches of trees close to the lake (see figure 18). Also several bigger groups of tree cover in the central area that is unoccupied by residential settlements due to the educational institutions and religious facilities located within Nkafu and Nyakaliba can be found. Nevertheless, historical landslides have been recorded that date back far into the past as well as to more recent decades (Nobile et al. 2018). In particular the central slopes stretching north to south, which form a continuation of the steep slopes between Cimpunda and Kasali, and steep slopes in the northwest indicate high to very high risk areas, surrounded by medium risk zones.

Figure 18: Nkafu neighbourhood as seen from Lake Kivu (Bay of Bukavu), facing towards southwest (Photo taken by the author on 17th February 2018)

Kanoshe: The neighbourhood of Kanoshe is characterized by significant settlement density in its northeastern parts and more scattered building structures towards the rapidly growing southwestern margins, but the overlay map displays multiple high to very high risk areas at exactly these densely built-up locations. In fact, the majority of buildings in Kanoshe can currently be found on these dangerous steep slopes, while flatter ridges and sinks are less occupied by houses. Only isolated or small groups of trees are left due to anthropogenic activities, mostly replaced by open soil and fields for cultivation, therefore raising the risk of landslide activation.

Mulambula, Chikonyi and Chikera: Bordering the lake, there is a relatively even surface with low risk of landslide occurrence. Further inland, Chikonyi is dominated by medium to high risk areas with a quite young offshoot of densely built-up spaces in the southwest, following a low risk ridge. Similarly, Mulambula shows predominantly medium to high risk areas and small groups of cells that show very high landslide risk. The thin strip of land belonging to Chikera that declines in the direction of a creek basin dividing Mulambula and Chikonyi is at very high risk of future landslides. Small spots of rapid growth are developing towards medium and high risk areas in a strongly deforested environment.

Nyakavogo and Lumumba: The northwestern neighbourhoods of Nyakavogo and Lumumba are located on an elongated hill that shows low landslide risk on its central top, but for the most part medium and high risk areas on both hillsides. This is especially the case on the slopes facing southeast, where very high risk areas can be spotted. Very rapid expansion has taken place in northwestern direction and shows a tendency of potentially incorporating further medium and high risk areas in the future. There have reportedly been small landslides in the recent past (Michellier et al. 2019, 80-83), urging the preservation of remaining isolated trees within residential spaces and of the few tree groups in the southwest and the north.

______

Figure 19: Overlay map of slope inclination and urban growth trends in the study area

8. Discussion

The results presented in this thesis provide essential, multifaceted information about landslide susceptibility in Bukavu. They give profound insight into the city’s spatial development and illuminate how natural, geomorphological risk exposure is intertwined with the consequences of anthropogenic alteration of living environments. For optimal use however, this information should be combined with the findings of other researchers who have studied historical landslides, gradual ground movements or specific aspects of human settlement in the study area (see Moeyersons et al. 2004; Trefois et al. 2007; Ndyanabo et al. 2010; Butara et al. 2015; Balegamire et al. 2017; Kalikone et al. 2017; Kulimushi et al. 2017; Mugaruka et al. 2017; Nobile et al. 2018; Michellier et al. 2019, 78-85). This will help to gain an in-depth understanding of the causative factors that activate different types of landslide and of the underlying geological processes.

Furthermore, the cartographic material is supposed to serve as a basis that supports risk mitigation and prevention strategies. In order to effectively decrease inhabitants’ vulnerability to future landslide disasters, it has to be complemented by field visits and case-specific examination of a location’s additional characteristics. This should particularly include a survey of the quality of buildings and their stability, residents’ socioeconomic conditions, and the state of the infrastructure. Also existing structures in low-vegetation areas that may potentially improve ground stability or reduce erosion, such as walls or well-constructed roads, should be registered. It is imperative that local authorities, state institutions or external decision-makers take residents’ interests and rights into consideration before developing risk reduction strategies.

While it is very difficult or even impossible to actually predict the moment of slope failure or sudden gravitational mass movements in most cases, concerning rotational or translational landslides as well as frequently occurring shallow slides (Gariano, Guzzetti 2016, 139; Michellier et al. 2019, 73-85; Moeyersons et al. 2004, 86), there are some indicators that can hint at an elevated likelihood of imminent landslide triggering. Such typical signs of reduced ground stability can be superficial discontinuities, notable fault lines or ruptures, lack of sufficient lateral support, strong erosive runoff, heavy surcharge load and artificial pressure, cracks resulting from earthquakes, and strongly weathered soft rock mass on slope surfaces (Varnes 1978; Miščević, Vlasdelica 2014). Raised alertness is urgently required in times of multiple consecutive days of heavy rainfall and if there are signs of potential earthquakes or increased volcanic activity in the Kivu region.

There is a wide range of measures that are globally applied in landslide-prone locations to mitigate susceptibility, to strengthen communities’ resilience and to prevent disasters (see USGS 2004). In the aforementioned medium, high, and very high risk areas they should be considered and customised according to case-specific conditions, adequacy, and feasibility. Such measures should particularly imply the preservation of remaining tree and vegetation cover. In addition, planting of new trees or other deep-rooted plants can help re-establishing ground stability on open and bare surfaces. Most obviously, people should avoid to further settle on steep slopes. Especially in the areas indicated as experiencing rapid urban growth, it should be ensured that new construction is constrained to locations below a 15º slope inclination threshold as far as possible. Thoughtful spatial planning practices or early interventions to ‘channel’ settlement expansion along flat surfaces can be useful in this matter. Also restriction or conditional access to highly landslide-prone or historically active zones may be implemented.

In every respect, it has to be made sure that newcomers and residents alike are aware of their exposure to environmental risks, for example by means of information campaigns or provision of educational materials. In particular, simplified and user-friendly inventory maps showing the landslide history of affected areas or maps such as the ones presented in this thesis can serve as convincing evidence. People have to be sensitised to detect land deformations, irregularities and the previously described indicators, so that suspicious anomalies can be reported to local authorities and monitoring institutions. Naturally, such endeavours have to be implemented in full awareness of the socio-political circumstances in South Kivu and the challenges possibly arising in neighbourhoods with high poverty rates. People who lack options to settle elsewhere or who do not possess over the financial means to construct landslide-resistant houses with stronger materials may readily ne- glect or even ignore their vulnerability. In such scenarios the main objective should be to reduce risk exposure by all available means that are seen as favourable without harming residents’ living environments or threatening their livelihoods.

Apart from the methods applied in this thesis, modern GIS software offers further powerful tools and possibilities to perform analyses related to landslide risk. For example, the ‘Hydrology Toolset’ offers means to create water flow networks that indicate the paths of water accumula- tion. This information can be utilized to identify areas that are at higher risk of potential mudslides. Moreover, the ‘Overlay Toolset’ provides means to create overlay outputs commonly

used for suitability analysis (especially by performing a ‘Fuzzy Overlay’ or ‘Weighted Over- lay’). When working with multiple raster datasets, these options could be used to extend the scope of this analysis. Furthermore, the maps in this thesis had to be illustrated with deliberate use of colours to express the information they were supposed to convey. In order to be used as educational material that supports raising awareness, it may help to first simplify them for map readers who are unfamiliar with geography and to adjust them for potential colour-blind users, though this would of course come at the expense of their informative value.

Apart from that, the technologically advanced sensors of modern commercial satellites collect high-resolution images that would enable researchers with the required financial means to further improve the precision of GIS-based mapping and analysis. In a study area like Bukavu, this would have for example been possible with access to data from the IKONOS earth obser- vation satellite or the original high-resolution WorldView 2 images.

9. Conclusion:

This thesis has examined landslide susceptibility in Bukavu, a large city located in the eastern Democratic Republic of the Congo. Based on different GIS methods, satellite images and elevation data were used to create maps related to the study area’s geomorphological properties, land cover, and urban growth. The results were presented in separate steps that provide information about classified land cover types, slope orientation, slope inclination, the growth of densely built-up areas from 1990 to 2019, and current growth tendencies. Merging these factors in a final overlay map and including neighbourhoods’ characteristics allowed for a detailed landslide risk assessment of all affected areas. The findings serve as a profound basis for preventive measures and risk mitigation strategies. They also offer up-to-date information about urban development processes in Bukavu and facilitate the prediction of future spatial dynamics.

This study improved the scope and detail of GIS-based analysis of Bukavu and the city’s surroundings. It has filled important gaps in existing scientific literature about landslide risk, urban growth, and the impact of anthropogenic activity in the study area. It serves as a good example of how GIS methods can be utilized to assess exposure to environmental hazards such as landslides in data-poor contexts of sub-Saharan Africa. Given the present deficits in this field of research, more studies should focus on application-oriented analyses in highly landslide-prone regions that have not been sufficiently covered yet. More emphasis has to be placed on the prevention of future incidents rather than examining past disasters.

References:

ALMAZROUI, MANSOUR; MASHAT, ABDULWAHAB; ASSIRI, MAZEN EBRAHEEM; BUTT, MOHSIN JAMIL (2017): Ap- plication of Landsat Data for Urban Growth Monitoring in Jeddah. In: Earth Systems and Environment. Vol. 1, No. 2, Article 25, pp: 1-11, Springer.

ATUYAMBE, LYNN M.; EDIAU, MICHAEL; ORACH, CHRISTOPHER G.; MUSENERO, MONICA; BAZEYO, WILLIAM (2011): Land slide disaster in eastern Uganda: Rapid assessment of water, sanitation and hygiene situation in Bulucheke camp, Bududa district. In: Environmental Health. Vol. 10, No. 38, pp: 1-13, BioMed Central.

BABE, THEOPHILINE; MUNYULI, THEODORE; OMBENI, JUSTIN; KASHOSI, THEOPHILE; MWANGI, TRESOR (2018): Hygienic Quality Assessment of Fresh Beef Meat in Bukavu Urban Slaughterhouses, South Kivu Province of the Long Sale Chain: Potential Health Risks for Consumers Eastern D.R. Congo. In: Bacterial Empire. Vol. 1, No. 1, pp: 1-9, SciCell.

BALEGAMIRE, CLARISSE; MICHELLIER, CAROLINE; MUHIGWA, JEAN BERCKMANS; DELVAUX, DAMIEN; GÉRARD, IMANI; DEWITTE, OLIVIER (2017): Vulnérabilité du bâti face aux glissements de terrain: Analyse spatio- temporelle à Bukavu (RD Congo). In: Geo-Eco-Trop. Vol. 41, No. 2, pp. 263-278.

BARSI, JULIA A.; LEE, KENTON; KVARAN, GEIR; MARKHAM, BRIAN L.; PEDELTY, JEFFREY A. (2014): The Spectral Response of the Landsat-8 Operational Land Imager. In: Remote Sensing. Vol. 6, pp: 10232-10251, MDPI.

BASNET, BIKASH; VODACEK, ANTHONY (2015): Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa. In: Remote Sensing. Vol. 7, pp: 6683-6709, MDPI.

BELE, MEKOU Y.; SONWA, DENIS J.; TIANI, ANNE M. (2014): Local Communities Vulnerability to Climate Change and Adaptation Strategies in Bukavu in DR Congo. In: Journal of Environment & Development. Vol. 23, No. 3, pp: 331-357, Sagepub.

BROECKX, JENTE; VANMAERCKE, MATTHIAS; DUCHATEAU, RICA; POESEN, JEAN (2018): A data-based landslide susceptibility map of Africa. In: Earth-Science Reviews. Vol. 185, pp: 102-121, Elsevier.

BUCKLEY, AILEEN (2017): New Aspect-Slope Raster Function Now Available. Accessed: 28.05.2019, URL: https://www.esri.com/arcgis-blog/products/imagery/imagery/new-aspect-slope-raster-function-now-available/.

BUTARA, S.; FIAMA, S.; MUGISHO, B. ESPOIR; MONGANE, A. (2015): Susceptibilité aux glissements de terrain: cas de la Commune d’Ibanda, Bukavu, République Démocratique du Congo. In: International Journal of Inno- vation and Applied Studies. Volume 11, No. 1, pp: 129-147, Innovative Space of Scientific Research Jour- nals.

CAID (CELLULE D’ANALYSES DES INDICATEURS DE DÉVELOPPEMENT) (2016): Ville de Bukavu. Accessed: 02.05.2019, URL: https://www.caid.cd/index.php/donnees-par-villes/ville-de-bukavu/?domaine=fiche.

CHAN, NGAI WENG (2011): Addressing Flood Hazards via Environmental Humanities in Malaysia. In: Malaysian Journal of Environmental Management. Vol. 12, No. 2, pp: 11-22, Penang.

CHANG, JUI-CHIN; SLAYMAKER, OLAV (2002): Frequency and spatial distribution of landslides in a mountainous drainage basin: Western Foothills, Taiwan. In: Catena. Vol. 46, pp: 285-307, Elsevier.

CLIMATE-DATA (2019): Climate Bukavu. Accessed: 02.05.2019, URL: https://en.climate-data.org/africa/congo- kinshasa/sud-kivu/bukavu-4604/.

CONGER, STEVEN (2004): A Review of Colour and Cartography in Avalanche Danger Visualization. pp: 477-482, Vancouver: University of British Columbia.

CUI, YIFEI; CHENG, DEQIANG; CHOI, CLARENCE E.; JIN, WEN; LEI, YU; KARGEL, JEFFREY S. (2019): The cost of rapid and haphazard urbanization: Lessons learned from the Freetown landslide disaster. In: Landslides. Journal of the International Consortium on Landslides. Vol. 16, Issue 6, pp: 1167-1176, Springer.

CURTIS, KATHERINE J.; FUSSELL, ELIZABETH; DEWAARD, JACK (2015): Recovery Migration After Hurricanes Katrina and Rita: Spatial Concentration and Intensification in the Migration System. In: Demography. Vol. 52, No. 4, pp: 1269-1293, Springer.

DIGITALGLOBE (2019): WorldView-2. Data Sheet. Singapore.

DIONISIO, MARIA RITA; PAWSON, ERIC (2016): Building Resilience Through Post-Disaster Community Projects: Responses to the 2010 and 2011 Christchurch Earthquakes and 2011 Tōhoku Tsunami. In: Australasian Journal of Disaster and Trauma Studies. People in Disasters Special Issue, Vol. 20, pp: 107-116, Massey University.

DI RUOCCO, ANGELA; GASPARINI, PAOLO; WEETS, GUY (2015): Urbanisation and Climate Change in Africa: Set- ting the Scene. In: Pauleit, S.; Coly, A.; Fohlmeister, S.; Gasparini, P.; Jorgensen, G.; Kabisch, S.; Kombe, W.J.; Lindley, S.; Simonis, I.; Yeshitela, K. (eds.): Urban vulnerability and climate change in Africa. Vol. 4, pp. 1-35, Springer International Publishing.

D’OREYE, NICOLAS; GONZÁLEZ, PABLO J.; SHULER, ASHLEY; OTH, ADRIEN; BAGALWA, LOUIS; EKSTRÖM, GÖRAN; KAVOTHA, DÉOGRATIAS; KERVYN, FRANÇOIS; LUCAS, CELIA; LUKAYA, FRANÇOIS; OSODUNDU, ETOY; WAUTHIER, CHRISTELLE; FERNÁNDEZ, JOSÉ (2011): Source parameters of the 2008 Bukavu-Cyangugu earthquake estimated from InSAR and teleseismic data. In: Geophysical Journal International. Vol. 184, pp: 934-948.

DOUGLAS, IAN; ALAM, KURSHID; MAGHENDA, MARYANNE; MCDONNELL, YASMIN; MCLEAN, LOUISE; CAMP- BELL, JACK (2008): Unjust waters: Climate change, flooding and the urban poor in Africa. In: Environment & Urbanization. Vol. 20, No. 1, pp: 187-205, Sage Publications.

ECKSTEIN, DAVID; HUTFILS, MARIE-LENA; WINGES, MAIK (2018): Global Climate Risk Index 2019. Who Suffers Most From Extreme Weather Events? Weather-related Loss Events in 2017 and 1998 to 2017. Briefing Paper, Germanwatch: Bonn.

EL GAROUANI, ABDELKADER; MULLA, DAVID J.; GAROUANI, SAID EL; KNIGHT, JOSEPH (2017): Analysis of urban growth and sprawl from remote sensing data: Case of Fez, Morocco. In: International Journal of Sustainable Built Environment. Vol. 6, pp: 160-169, Elsevier.

ERSDAC; USGS (2011): ASTER GDEM 2 Readme. Advanced Spaceborne Thermal Emission and Reflection Ra- diometer (ASTER). Global Digital Elevation Model (GDEM) Version 2. Earth Remote Sensing Data Anal- ysis Center.

GARIANO, STEFANO LUIGI; GUZZETTI, FAUSTO (2016): Landslides in a changing climate. In: Earth-Science Re- views. Vol. 162, pp: 227-252, Elsevier.

GEIRSSON, HALLDOR; D’OREYE, NICOLAS; MASHAGIRO, NICHE; SYAUSWA, MUHINDO; CELLI, GILLES; KADUFU, BENJAMIN; SMETS, BENOÎT; KERVN, FRANCOIS (2016): Volcano-tectonic deformation in the Kivu Region, Central Africa: Results from six years of continuous GNSS observations of the Kivu Geodetic Network (KivuGNet). In: Journal of African Earth Sciences. Vol. 134, pp: 809-823, Elsevier.

GUHA-SAPIR, DEBARATI; HOYOIS, PHILIPPE; WALLEMACQ, PASCALINE; BELOW, REGINA (2016): Annual Disaster Statistical Review 2016. The numbers and trends. Centre for Research on the Epidemiology of Disasters (CRED), Université Catholique de Louvain.

GURUNG, ANUP; GURUNG, OM PRAKASH; KARKI, RAHUL; OH, SANG EUN (2013): Improper agricultural practices lead to landslide and mass movement disasters: A case study based on upper Madi watershed, Nepal. In: Emirates Journal of Food & Agriculture. Vol. 25, No. 25, Issue 1, pp: 30-38, Al-Maqam: College of Food and Agriculture.

GUZZETTI, FAUSTO; MONDINI, ALESSANDRO CESARE; CARDINALI, MAURO; FIORUCCI, FEDERICA; SANTANGELO, MICHELE; CHANG, KANG-TSUNG (2012): Landslide inventory maps: New tools for an old problem. In: Earth-Science Reviews. Vol. 112, pp: 42-66, Elsevier.

HADI, SUPRAYOGA (2008): Improving Vulnerable Urban Space in Post-disaster in Yogyakarta and Central Java, Indonesia: Participatory and Comprehensive Approach. In: Kidokoro, T.; Okata, J.; Matsumura, S.; Shima, N. (eds.): Vulnerable Cities: Realities, Innovations and Strategies. cSUR-UT Series, Library for Sustainable Urban Regeneration, Vol. 8, Tokyo: Springer.

HIDALGO, CESAR AUGUSTO; VEGA, JOHNNY ALEXANDER; OBANDO, MELISSA PARRA (2018): Effect of the Rainfall Infiltration Processes on the Landslide Hazard Assessent of Unsaturated Soils in Tropical Mountainous Regions. In: HROMADKA II, THEODORE V (ed.): Engineering and Mathematical Topics in Rainfall. Chapter 10, pp: 163-185. Intech Open.

HOFFMANN, KASPER; POULIOT, MARIÈVE; MUZALIA, GODEFROID (2019): Constructed Anarchy: Governance, Con- flict, and Precarious Property Rights in Bukavu, Democratic Republic of the Congo. Congo Research Briefs. Issue 1, pp: 1-11, CRG, SSRC, GEC-SH, CERUKI, GiC.

IGWE, OGBONNAYA (2018): The characteristics and mechanisms of the recent catastrophic landslides in Africa under IPL and WCoE projects. In: Landslides. Journal of the International Consortium on Landslides. Vol. 15, Issue 6, pp: 2509-2519, Springer.

ILUNGA, LUTUMBA (1991): Morphologie, volcanisme et sédimentation dans le rift du Sud-Kivu. In: Bulletin de la Société géographique de Liège. Vol. 27, pp: 209-228, Liège.

JABAREEN, YOSEF RAFEQ (2006): Sustainable Urban Forms. Their Typologies, Models, and Concepts. In: Journal of Planning Education and Research. Vol. 26, pp: 38-52, Association of Collegiate Schools of Planning.

JACKSON, EMERSON ABRAHAM (2018): Political Economy of Forest Ecology in Sierra Leone: A focus on the West- ern Area Peninsular Forest (WAPFoR). In: Postmodern Openings. Vol. 9, No. 1, pp: 63-90, Lumen Inter- national Publishing.

JACOBS, LIESBET; DEWITTE, OLIVIER; POESEN, JEAN; DELVAUX, DAMIEN; THIERY, WIM; KERVYN, MATTHIEU (2014): The Rwenzori Mountains, a landslide-prone region? In: Landslides. pp: 1-18, Berlin & Heidelberg: Springer.

JONES, D. K. C. (1992): Landslide hazard assessment in the context of development. In: MCCALL, G. J. H.; LAMING, D. J. C.; SCOTT, S. C. (eds.): Geohazards: Natural and man-made. pp: 117-141, London: Chapman & Hall.

KALIKONE, CHRISTIAN BUZERA; TEGANYI, FRANCOISE; DEWITTE, OLIVIER; MICHELLIER, CAROLINE (2017): Im- pact des déformations du sol sur la vulnérabilité des réseaux de distribution d’eau et d’électricité à Bukavu (RD Congo). In: Geo-Eco-Trop. Vol. 41, No. 2, pp: 279-292.

KARAMAGE, FIDELE; SHAO, HUA; CHEN, XI; NDAYISABA, FELIX; NAHAYO, LAMEK; KAYIRANGA, ALPHONSE; OMI- FOLAJI, JAMES KEHINDE; LIU, TONG; ZHANG, CHI (2016): Deforestation Effects on Soil Erosion in the Lake Kivu Basin, D.R. Congo-Rwanda. In: Forests. Vol. 7, No. 281, pp: 1-17, MDPI.

KIRSCHBAUM, DALIA; STANLEY, THOMAS; ZHOU, YAPING (2015): Spatial and temporal analysis of a global landslide catalog. In: Geomorphology. Vol. 249, Elsevier.

KNAPEN, A.; KITUTU, M. G.; POESEN, J.; BREGELMANS, W.; DECKERS, J.; MUWANGA, A. (2006): Landslides in a densely populated county at the footslopes of Mount Elgon (Uganda): Characteristics and causal factors. In: Geomorphology. Vol. 73, pp: 149-165, Elsevier.

KULIMUSHI, MATABARO SYLVAIN; MUGARUKA, BIBENTYO TOUSSAINT; MUHINDO, SAHANI WALÈRE; MICHEL- LIER, CAROLINE; DEWITTE, OLIVIER (2017): Glissements de terrain et éléments à risqué dans le bassin ver- sant de la Wecha (Bukavu, RD Congo). In: Geo-Eco-Trop. Vol. 41, No. 2, pp: 233-248.

LINARD, CATHERINE; TATEM, ANDREW J.; GILBERT, MARIUS (2013): Modelling spatial patterns of urban growth in Africa. In: Applied Geography. Vol. 44, pp: 23-32, Elsevier.

LIU, YINGCHUN; SANTOS, ANGELA; WANG, SHUO M.; SHI, YAOLIN; LIU, HAILING; YUEN, DAVID A. (2007): Tsu- nami hazards along Chinese coast from potential earthquakes in South China Sea. In: Physics of the Earth and Planetary Interiors, Vol. 163, pp: 233-244, Elsevier.

MAES, JAN; KERVYN, MATTHIEU; DE HONTHEIM, ASTRID; DEWITTE, OLIVIER; JACOBS, LIESBET; MERTENS, KEWAN; VANMAERCKE, MATTHIAS; VRANKEN, LIESBET, POESEN, JEAN (2017): Landslide risk reduction measures: A review of practices and challenges for the tropics. In: Progress in Physical Geography. Vol. 42, No. 2, pp: 191-221, Sage.

MAKI MATESO, JEAN-CLAUDE; DEWITTE, OLIVIER (2014): Vers un inventaire des glissements de terrain et des éléments à risque sur les versants du Rift à l’ouest du lac Kivu (RDC). In: Geo-Eco-Trop. Vol. 38, No. 1, pp: 137-154.

MALDONADO, ESPERANZA RONDÓN; CHIO, GUSTAVO CHO (2012): Índice de vulnerabilidad structural ante los efec- tos de remoción en masa en edificaciones de mampostería basado en conjunto difusos. In: Revista Inge- niería de Construcción. Vol. 27, No. 1, pp: 23-39, Ricuc.

MANSARAY, LAMIN R.; HUANG, JINGFENG; KAMARA, ALIMAMY A. (2016): Mapping deforestation and urban expansion in Freetown, Sierra Leone, from pre- to post-war economic recovery. In: Environmental Monitor- ing and Assessment. Vol. 188: 470, pp: 1-16, Springer.

MARFAI, MUH ARIS; KING, LORENZ; SINGH, LALAN PRASAD; MARDIATNO, DJATI; SARTOHADI, JUNUM; HAD- MOKO, DANANG SRI; DEWI, ANGGRAINI (2008): Natural hazards in Central Java Province, Indonesia: An overview. In: Environmental Geology. Vol. 56, pp: 335-351, Springer.

MARYSSE, STEFAAN; TSHONDA, JEAN OMASOMBO (eds.) (2015): Conjonctures congolaises 2014. Politiques, terri- toires et ressources naturelles: changements et continuités. No. 86, Tervuren & Paris.

MCFARLANE, COLIN (2011): Encountering, describing and transforming urbanism. Concluding reflections on as- semblage and urban criticality. In: City. Vol. 15, No. 6, pp: 731-739, Routledge.

MICHELLIER, CAROLINE; PIGEON, PATRICK; KERVYN, FRANCOIS; WOLFF, ELEONORE (2016): Contextualizing vulnerability assessment: A support to geo-risk management in central Africa. In: Natural Hazards. Vol. 82, pp: S27-S42, Springer.

MICHELLIER, C.; DELVAUX, D.; DEWITTE, O.; D’OREYE, N.; HAVENITH, H.-B.; KERVYN, M.; POPPE, S.; SMETS, B.; TREFON, T.; WOLFF, E. KERVYN, F. (2019): Geo-Risk in Central Africa: Integrating Multi-Hazards and Vulnerability to Support Risk Management. GeoRisCa. Science for a Sustainable Development. Brussels: Belgian Science Policy.

MIŠČEVIĆ, PREDRAG; VLASDELICA, GORAN (2014): Impact of weathering on slope stability in soft rock mass. In: Journal of Rock Mechanics and Geotechnical Engineering. Vol. 6, pp: 240-250, CSRME.

MOEYERSONS, J.; TRÉFOIS, PH.; LAVREAU, J.; ALIMASI, D.; BADRIYO; MITIMA, B.; MUNDALA, M.; MUNGANGA, D.O.; NAHIMANA, L. (2004): A geomorphological assessment of landslide origin at Bukavu, Democratic Republic of the Congo. In: Engineering Geology. Vol. 72, pp: 73-87, Elsevier.

MONSIEURS, ELISE; JACOBS, LIESBET; MICHELLIER, CAROLINE; TCHANGABOBA, JOSEPH BASIMIKE; GANZA, GLOIRE BAMULEZI; KERVYN, FRANCOIS; MATESO, JEAN-CLAUDE MAKI; BIBENTYO, TOUSSAINT MUGA- RUKA; BUZERA, CHRISTIAN KALIKONE; NAHIMANA, LOUIS; NDAYISENGA, ALOYS; NKURUNZIZA, PASCAL; THIERY, WIM; DEMOULIN, ALAIN; KERVYN, MATTHIEU; DEWITTE, OLIVIER (2018): Landslide inventory for hazard assessment in a data-poor context: A regional-scale approach in a tropical African environement. In: Landslides. Journal of the International Consortium on Landslides, Vol. 15, pp: 2195-2209, Springer.

MUGAGGA, F.; KAKEMBO, V.; BUYINZA, M. (2011): Land use changes on the slopes of Mount Elgon and the im- plications for the occurrence of landslides. In: Catena. Vol. 90, pp: 39-46, Elsevier.

MUGARUKA, BIBENTYO TOUSSAINT; KULIMUSHI, MATABARO SYLVAIN; MUHINDO, SAHANI WALÈRE; DEWITTE, OLIVIER (2017): Glissement de terrain de Nyakavogo (Bukavu, RD Congo): Interaction entre facteurs na- turels et anthropiques. In: Geo-Eco-Trop. Vol. 41, No. 2, pp: 249-262.

MUNDIA, C. N.; ANIYA, M. (2005): Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. In: International Journal of Remote Sensing. Vol. 26, No. 13, pp: 2831-2849, Taylor & Francis.

NAKILEZA, BOB R.; MAJALIWA, MWAJALOLO J.; WANDERA, ABU; NANTUMBWE, CLARE M. (2017): Enhancing resilience to landslide disaster risks through rehabilitation of slide scars by local communities in Mt. Elgon, Uganda. In: Jàmbá – Journal of Disaster Risk Studies. Vol. 9, No. 1, pp: 1-11, AOSIS.

NDYANABO, SADIKI; VANDECASTEELE, INE; MOEYERSONS, JAN; OZER, ANDRÉ; OZER, PIERRE; DUNIA, KALEGAMIRE; CISHUGI, BAHATI (2010): Développement de la ville de Bukavu et cartographie des vulnéra- bilités, R. D. Congo. In: Annales Sci. Vol. 2, pp: 120-127.

NELSON, ANDREW; LINDBERGH, SARAH; STEPHENSON, LUCY; HALPERN, JEREMY; ARROYO, FATIMA ARROYO; ESPINET, XAVIER; GONZÁLEZ, MARTA C. (2019): Coupling Natural Hazard Estimates with Road Network Analysis to Assess Vulnerability and Risk: Case Study of Freetown, Building Disruption Simulations in Hydrometeorological Risk Areas in Data-Scarce Sierra Leone. In: Transportation Research Record. Vol. 0, pp: 1-14, Sage.

NGOUNOU NGATCHA, BENJAMIN; EKODECK, GEORGES EMMANUEL; NTANA, PERPÉTUE ESPÉRENCE (2003): Urban development and natural hazards. Case of ground movement in SIC estate at Mendong (Yaoundé – Came- roon). In: Materials and Geoenvironment. Vol. 50, No. 1, pp: 269-272, RMZ.

NOBILE, ADRIANO; DILLE, ANTOINE; MONSIEURS, ELISE; BASIMIKE, JOSEPH; MUGARUKA, BIBENTYO TOUSSAINT; D’OREYE, NICOLAS; KERVYN, FRANCOIS; DEWITTE, OLIVIER (2018): Multi-Temporal DInSAR to Charac- terise Landslide Ground Deformations in a Tropical Urban Environment: Focus on Bukavu (DR Congo). In: Remote Sensing. Vol. 10, No. 626, pp: 1-18, MDPI.

OLSHANSKY, ROBERT B. (2006): Planning After Hurricane Katrina. In: Journal of the American Planning Associ- ation. Vol. 72, No. 2, pp: 147-153, American Planning Association.

OLSHANSKY, ROBERT B.; JOHNSON, LAURIE A.; HORNE, JEDIDIAH; NEE, BRENDAN (2008): Planning for the Re- building of New Orleans. In: Journal of the American Planning Association. Vol. 74, No. 3, pp: 273-287, American Planning Association.

OZKAN, SEVIM PELIN; TARHAN, CIGDEM (2016): Detection of Flood Hazard in Urban Areas Using GIS: Izmir Case. In: Procedia Technology. 9th International Conference Interdisciplinarity in Engineering, INTER- ENG 2015, 8-9 October 2015, Tirgu-Mures, Romania. Vol. 22, pp: 373-381, Elsevier.

PECH, L.; LAKES, T. (2017): The impact of armed conflict and forced migration on urban expansion in Goma: Introduction to a simple method of satellite-imagery analysis as a complement to field research. In: Applied Geography. Vol. 88, pp: 161-173, Elsevier.

PETERSON, GRETCHEN N. (2015): GIS Cartography. A Guide to Effective Map Design. Second Edition, Boca Ra- ton, London, New York: CRC Press.

PETLEY, DAVID (2012): Global patterns of loss of life from landslides. In: Geology. G33217 1st pages, Geological Society of America.

POPULATIONDATA (2016): République démocratique du Congo. Accessed: 02.05.2019, URL: https://www.populationdata.net/pays/republique-democratique-du-congo/.

REICHE, JOHANNES; VERHOEVEN, ROB; VERBESSELT, JAN; HAMUNYELA, ELIAKIM; WIELAARD, NIELS; HEROLD, MARTIN (2018): Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts. In: Remote Sensing. Vol. 10: 777, pp: 1-18, MDPI.

RIECHENBACH, PAOLA; ROSSI, MAURO; MALAMUD, BRUCE D.; MIHIR, MONIKA; GUZZETTI, FAUSTO (2018): A review of statistically-based landslide susceptibility models. In: Earth-Science Reviews. Vol. 180, pp: 60-91, Elsevier.

SATTERTHWAITE, DAVID; SVERDLIK, ALICE; BROWN, DONALD (2019): Revealing and Responding to Multiple Health Risks in Informal Settlements in Sub-Saharan African Cities. In: Journal of Urban Health. Vol. 96, pp: 112-122, Springer.

SCHMIDL, ERWIN A. (1997): The ‘battle’ of Bukavu, Congo 1960: Peacekeepers under fire. In: Small Wars & In- surgencies. Vol. 8, No. 3, pp: 25-40, Routledge.

SHAHABI, HIMAN; HASHIM, MAZLAN (2015): Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. In: Scientific Reports. Vol. 5, Article No. 9899, pp: 1- 15, Springer Nature.

SOLHJELL, RANDI (2015): Dimensions of statehood: A study of public goods in Bukavu, the Democratic Republic of Congo. Ph.D. thesis, London School of Economics and Political Science.

STEARNS, JASON (2012): North Kivu. The background to conflict in North Kivu province of eastern Congo. Nai- robi: Rift Valley Institute.

THE WORLD BANK (2017): Sierra Leone. Rapid Damage and Loss Assessment of August 14th, 2017 Landslides and Floods in the Western Area. Washington: International Bank for Reconstruction and Development & International Development Association.

THOMALLA, FRANK; DOWNING, TOM; SPANGER-SIEGFRIED, ERIKA; HAN, GUOYI; ROCKSTRÖM, JOHAN (2006): Re- ducing hazard vulnerability: Towards a common approach between disaster risk reduction and climate adaptation. In: Disasters. Vol. 30, No. 1, pp: 39-48, Oxford & Malden: Blackwell Publishing.

THOURET, JEAN-CLAUDE (1999): Urban hazards and risks; consequences of earthquakes and volcanic eruptions: An introduction. In: GeoJournal. Vol. 49, pp: 131-135, Kluwer Academic Publishers.

TREFOIS, P.; MOEYERSONS, J.; LAVREAU, J.; ALIMASI, D.; BADRYIO, I.; MITIMA, B.; MUNDALA, M.; MUNGANGA, D. O.; NAHIMANA, L. (2007): Geomorphology and urban geology of Bukavu (R. D. Congo): Interaction between slope instability and human settlement. In: TEEUW, R. M. (ed.): Mapping Hazardous Terrain using Remote Sensing. Special Publications, Vol. 283, pp: 65-75, Special Publications, London: Geological So- ciety.

USAMAH, MUHIBUDDIN (2017): Analysis of the Causal and Trigger Factors of the August 2017 Landslide in Free- town: Towards a Sustainable Landslide Risk Management in Sierra Leone. Technical publication, UNDP Sierra Leone and Environmental Protection Agency, Freetown. USGS (N.D.): Landsat 4. Accessed: 16.05.2019, URL: https://www.usgs.gov/land-resources/nli/landsat/landsat- 4?qt-science_support_page_related_con=0#qt-science_support_page_related_con.

USGS (N.D.): Landsat 5. Accessed: 16.05.2019, URL: https://www.usgs.gov/land-resources/nli/landsat/landsat- 5?qt-science_support_page_related_con=0#qt-science_support_page_related_con. USGS (N.D.): Landsat 8. Accessed: 17.05.2019, URL: https://www.usgs.gov/land-resources/nli/landsat/landsat- 8?qt-science_support_page_related_con=0#qt-science_support_page_related_con. USGS (2004): Landslide Types and Processes. Fact Sheet 2004-3072. U.S. Department of the Interior & U.S. Geological Survey.

VAN OVERBEEK, FONS (2014): Studying ‘the state’ in Bukavu. A system, an idea, and a process. In: Human Secu- rity in Fragile States, Occasional Paper #12. IS Academy, Wageningen.

VAN OVERBEEK, FONS; TAMÁS, PETER A. (2018): Autochthony and insecure land tenure: The spatiality of ethni- cized hybridity in the periphery of post-conflict Bukavu, DRC. In: Journal of Eastern African Studies, Vol. 12, No. 2, pp: 290-309, Routledge.

VARNES, DAVID J. (1978): Slope Movement Types and Processes. In: Landslides: Analysis and Control. Issue 176, Chapter 2, pp: 11-33, Transportation Research Board Special Report.

VLASSENROOT, KOEN; RAEYMAEKERS, TIMOTHY (2004): Conflict and Artisan Mining in Kamituga (South Kivu). In: VLASSENROOT, KOEN; RAEYMAEKERS, TIMOTHY (eds.): Conflict and Social Transformation in Eastern DR Congo. Chapter 6, pp: 123-156, Gent: Academia Press.

VLASSENROOT, KOEN; BÜSCHER, KAREN (2009): The City as Frontier: Urban Development and Identity Processes in Goma. Working Paper No. 61, Cities and Fragile States, Crisis States Research Centre.

WAFULA, D. M.; YALIRE, M.; KASEREKA, M.; CIRABA, M.; KWETUENDA, M.; HAMAGUCHI, H. (2007): Natural Dis- asters and Hazards in the Lake Kivu Basin, Western Rift Valley of Africa.

ZHANG, YANG; PEACOCK, WALTER GILLIS (2010): Planning for Housing Recovery? Lessons Learned From Hur- ricane Andrew. In: Journal of the American Planning Association, Vol. 76, No. 1, pp: 5-24, Taylor & Fran- cis.

Source Data:

DIGITAL GLOBE (WV2): Accessed: 16.04.2019 https://discover.digitalglobe.com/7f1719ee-61d8-11e9-bb27-7a6f622195bd

HUMANITARIAN DATA EXCHANGE: Accessed: 11.04.2019 https://data.humdata.org/group/cod?res_format=zipped+shapefile&q=&ext_page_size=25 https://data.humdata.org/group/rwa?res_format=zipped+shapefile&q=&ext_page_size=25

NATURAL EARTH: Accessed: 05.04.2019 https://www.naturalearthdata.com/downloads/

REFÉRENTIEL GEOGRAPHIQUE COMMUN: Accessed: 08.04.2019 https://www.rgc.cd/index.php?option=com_content&view=category&layout=blog&id=60&Itemid=76

USGS EARTH EXPLORER (ASTER GDEM, LANDSAT 4-5 TM, LANDSAT 8 OLI / TIRS): Accessed: 14.04.2019; Note: ASTER GDEM is a product of METI and NASA. https://earthexplorer.usgs.gov/