Cameroon is exposed to geohazards (flood, landslide, volcanic eruption; ). The International Disaster Database (http://www.emdat.be/www.emdat.be/ see “Database / Country Profile”) points out four catastrophic recent (2007-2012) floods that affected between 10 000 and more than 30 000 inhabitants.
The Ndop Plain (35 × 20km) is situated at a mean altitude of 1150 m along the Cameroon Volcanic Line (CVL). It is surrounded by a series of escarpments which climb up to 2151 m, corresponding to the peripheries of the Mounts Oku and Bamenda, the Mbam and Nkogam Massifs (Fig. 1). The CVL is interpreted as a mega shear zone that seems to be structurally subdivided into an important network of faults associated with a system of alternating horsts and grabens [2–7]. The morphological formations of numerous plains all along the CVL have been discussed by [8, 9] and .
Floods are the major hazard in the Ndop Plain due to its flat nature between a combination of both high and low rise morphologies. Notwithstanding, the Ndop Plain is well inhabited due to the presence of fertile alluvial soils resulting from erosion, transport and deposition of materials from the surrounding hills by streams and rivers. More generally, different geohazards have occurred in the Western Cameroon Highlands (WCH), such as landslides, rock falls and floods [11–16].
In this paper, we shall discuss the topographical and morphological aspects of the Ndop Plain, using Digital Elevation Model (DEM), laboratory analyses, field observations, satellite imagery and computer modeling. The geohazards associated to this plain will also be assessed in terms of typology, and mapped using the Geographic Information System (GIS) approach (see ). The findings in this research work will assist to mitigate the effects of geohazards in the Ndop Plain and other plains in Cameroon.
2 Description of the study area
The Ndop Plain belongs to the Western Cameroon Highlands (WCH). This sector of the CVL comprises Mounts Bambouto, Bamenda and Oku. The Ndop Plain is partly surrounded by Mount Bamenda to the W-SW and Mount Oku to the N-NW (Fig. 1). The basement is made up of granites and gneisses.
The Ndop area is characterised by two major relief features: a mountainous sector with steep hills and a flat plain ( Fig. 1). This flat relief opens up to Lake Bamendjing (a dam lake) in the south. The soils are lateritic, andosol or alluvial types. These soils are hydromorphic owing to the presence of clays which tend to hold water especially during floods. The drainage pattern is of two types: radial and dendritic (Fig. 2). The climate is subtropical: Annual rainfall and annual average temperature stand at 1700 to 2000 mm and ≈ 21.3°C respectively . Upper Noun Valley Development Authority (UNVDA) annual average rainfall data (2007-2012) is presented in Fig. 3 and substantiates the high rainfall in July and August (up to 600 mm each month). The vegetation is of the Sudan Savannah type  and has been greatly modified by anthropogenic activities such as bush fire, intensive farming and overgrazing.
2.2 Geological outlines, tectonic and morphological evolution
The Ndop Plain was formed during a series of metamorphic, tectonic, volcanic, and sedimentation phases, which succeeded one another . Structural tectonic activity started with plutonic and metamorphic phases linked to the Pan African Orogeny during the Cambrian period (540 Ma) when gneisses and granites were emplaced [21–23]. The CVL, that was emplaced later, starting at 52 Ma, consists of a series of volcanoes which are separated by plains or low lying areas corresponding to collapsed grabens such as: Tombel, Mbo, Noun, and Tikar [3, 5–10]. Ndop (this paper) is somewhat different as it is a low lying plain.
2.2.1 Pan African plutonic and metamorphic phases
The Pan African Orogeny resulted from the collision of the Congo Craton and the East-Saharan Metacraton during the Neoproterozoic (Ediacarian, 635 to 540 Ma) with its climax near 600 Ma. During this long period, gneisses formed from the protolith and emplaced. During the Cambrian (540–485 Ma) post-collision phase, late granites emplaced, associated with ignimbritic volcanic formations. Then, an intense erosion phase built a peneplain, and this finished during the Late Ordovician (480 Ma) (see  and references therein).
2.2.2 Cenozoic magmatic phases
The Ndop Plain is surrounded by a series of volcanic districts such as Mount Bamenda, Mount Oku, and the Nkogam and Mbam Massifs. The formation of the Ndop Plain can thus be correlated to the episodes of formation of these mountains. This magmatic phase is related to the formation of the Cameroon Volcanic Line which has been active since 52 Ma until Present [25, 26].
The radiometric analyses of Mount Bamenda lavas indicate two episodes of felsic volcanism during the Oligocene and Miocene: a first volcanic phase from 27.4 ± 0.5 Ma to 18.7 ± 0.3 Ma and a second volcanic phase from 13.2 ± 0.3 Ma to 12.74 ± 0.25 Ma. The Mount Bamenda felsic volcanism is the oldest of the whole WCH . A mafic volcanic phase emplaced from the Lower Miocene (17.6 Ma) to 0 Ma and shows that mafic volcanism has existed over a long period of time and is partly coeval to the felsic volcanism (between 17.6 and 12.7 Ma) [28, 29].
During the Quaternary up to the Present, weathering and erosion have been intensive, resulting in the formation of Quaternary alluvial deposits on basement rocks. As these materials were transported by streams and rivers, they were deposited into the plain, forming alluvial deposits. These alluvia are rich in the soil nutrients essential for plant growth. This is the reason why the plain is very fertile, evidenced by the presence of the Upper Noun Valley Development Agency (UNVDA) rice plantation.
Our cartography work involved the use of appropriate software such as Surfer 9, MapInfo 8.5, ArcGIS 10.1, Global Mapper 13 and 3DEM, to realize the various maps required for this study. The base maps used during this phase were topographic maps. The procedure employed was as follows:
Topographic maps (1:50,000) of Nkambe 1b and Foumban 3d from the National Institute of Cartography (NIC) Cameroon were georeferenced, and vectors such as contour lines, rivers, localities and roads were digitized.
The GeoTiff DEM of the Ndop was downloaded from NASA’s Shuttle Radar Topography Mission (SRTM V2), with a resolution of 90 m and introduced and imported into the ArcGIS software.
Surface parameters such as contours, reliefs, slope dippings, and slope orientations were realised using the 3D spatial analyst tool in the ArcGIS 10.1 software.
With the Global Mapper software, the DEM of the Ndop Plain was first introduced. The x, y and z data were exported to the Surfer software for further modelling, using the elevation grid and surfer grid in the American Standard Code for Information Interchange (ASCII) format.
The landslide and flood hazard maps for the Ndop Plain were realised with combining or weighting the various parameters in the ArcGIS software. The model employed in mapping the hazards was a data-driven bivariate hazard mapping model (see [16, 30]). The various parameters (predisposition factors) involved in the realization of the hazard map are rock type, soil type, land cover, slope dipping, slope orientation, nature (size) of river and proximity to river (see [31–34], and references therein).
3.2 Characterization of the hazard parameters
The parameters selected for this study were based on field data and on site analysis (Table 1). The choice of variables that affect landslides is an important step in susceptibility assessment and the prediction of new events [35, 36]. Geohazards are complex natural processes which are difficult to model with a few parameters due to variations in instability over space and time and are conditioned by several factors [35, 37–40].
The factors chosen here were operational, non-uniform, non-redundant, measurable, and represented over the entire area . They include: rock type, soil type, land cover, slope dipping, slope orientation, distance from river channel and nature of rivers. Rainfall which is an important parameter related to the occurrence of landslides and floods is not used here because it is uniformly distributed in the Ndop Plain. Thematic maps were prepared for each of these factors following the methods here described.
3.2.1 Slope orientation and slope dipping
Very steep slopes surround the flat Ndop Plain (Fig. 4). As pointed out in many regions worldwide, landslides are linked to slopes [16, 36, 42–47]. Two characteristics are classically considered: slope orientation and slope dipping.
These slope parameters were realised with the ArcGIS 10.1 software using the DEM of the area in the 3D spatial analyst tool.
An aspect map (slope orientation) is essential in slope failure analysis because of the varying exposure to sunlight and rainfall. Here, the slope orientations range from 0 to 360°, and are grouped into 10 classes: flat (-1), N (0-22.5), NE (22.5-67.5), E (67.5-112.5), SE (112.5-157.5), S (157.5-202.5), SW (202.5-247.5), W (247.5-292.5), NW (292.5-337.5) and N once more (337.5-360) (Fig. 5a). NW facing slopes receive higher precipitation more frequently than SW facing slopes. This is because rainfall is influenced by the effects of the moist southwest monsoon winds originating from the Atlantic Ocean, and the Harmattan trade winds originating from the Sahara Desert in the North .
The slope dipping was evaluated in degree and grouped into six classes: 0–5°, 5–10°, 10–15°, 15–20°, 20–35°, and > 35° (up to almost vertical) (Fig. 5b). Generally the steeper a slope the higher the hazard of a landslide; however when it becomes too steep, this hazard drops since soil cannot accumulate on very steep slopes ) Two zones of the studied area (one in western part and one in north-eastern part) are exposed (Fig. 5b).
3.2.2 3-D representation
Precise knowledge of the relief of an area is vital when carrying out landslide studies.
The role of relief in slope instability has been disputed with some authors [49–52] arguing that altitude is a good indicator conditioning slope movements, while others  do not see any changes in slope movements between low altitudes and the high altitudes. The three dimensional representation of the relief of the area was done using the DEM, from which vertices of points with spatial references were generated with the Global Mapper 13 software. These vertices were then exported to the Surfer 9 software to generate a 3D representation of the area (Fig. 5c). It is confirmed that the northern part of the studied area is very uneven and hilly compared to the central and southern parts of the area, corresponding to the plain.
3.2.3 Land cover
Land use practice may considerably affect the occurrence of landslides in an area [38, 54]. The land cover map was obtained from a SPOT image, map data © 2013, extracted from Google Earth. This image was imported into ArcGis 10.1; the different land uses were manually digitized based on the textures of objects and then calibrated. This was later converted to a raster map and a hazard index was attributed to each class. Five main land use patterns were considered, namely: built up area (25%) close to the town of Ndop (30 000 inhabitants) and five villages, forest (10%) scattered in the plain, subsistence farm area (25%), plantation area (20%) and unused land (20%) corresponding more or less to the principal slopes (Fig. 5d).
3.2.4 Soil type
The occurrence of landslides within a particular area depends noticeably on the soil type . The soil type depends on the rock type and its morphology, but different soil types may result from the same parent rock, following differential weathering and drainage. Soils were not mapped in this research; the soil map produced by ISRIC (International Soil Reference and Information Centre) Library (65.0), PO Box 353 6700 A.J. Wageningen, The Netherlands, was digitized and used for our study.
Five soil type patterns were adopted which are: soils formed from recent lava flows (12%), andosol (8%), laterite (5%) close to Babungo, colluvium (35%) and alluvium (40%) (Fig. 5e). Andosols are located on steep slopes and are less stable than colluvium and alluvium which are formed on gentle to flat slopes. Hence andosols have a higher hazard index . These different soil types were manually digitized in the ArcGis 10.1 software and converted into a raster map and index values attributed to it.
3.2.5 Rock type (material)
The studied area is covered by the following rock types: plutonic rock (20% granite and gneiss), volcanic rock (15% basalt, 4% trachyte, 5% rhyolite and 5% ignimbrite) (Fig. 5f). The rest of the area is covered by alluvial materials. Two representative samples of each rock were collected in the study area for the preparation of thin sections.
Granites show a porphyritic granular texture with angular phenocrysts interlocked. It is made up of quartz, orthoclase, biotite, microcline, and opaque minerals (Fig. 6a). Basalts present a microlitic texture; they contain minerals such as olivine, pyroxene, plagioclase and opaque Fe-Ti oxides which occur as phenocrysts. These mineral phases also constitute the groundmass (Fig. 6b). Trachytes present a microlitic porpyhritic texture with phenocrysts of sanidine and oxides which are automorphic and well developed; chlorite is also present in these rocks (Fig. 6c). The crystalline phases are embedded in a groundmass made up of microlites of sanidine, biotite and oxides. The groundmass displays a preferred orientation of alkali feldspars. This rock evidences re-crystallization of calcite indicating that weathering is taking place. Rhyolites show a microlitic porpyhritic texture, with minerals such as alkali feldspar, pyroxene, quartz, and oxides in a glassy groundmass (Fig. 6d). The groundmass shows a fluidal structure with preferred orientation of the feldspars and devitrification of the quartz. Ignimbrites have a vitroclastic texture made up of rock fragments, broken pieces of feldspars (sanidine and plagioclase), quartz and fiammes embedded in a glassy groundmass (Fig. 6e). Locally, groundmass is devitrified into small quartz and feldspar crystals.
The rock type distribution in an area may affect landslides at different scales. A lithological sketch map was realized from field observation data, thin section analysis (Fig. 6), reading and interpretation of satellite images (i.e. vegetation cover helped in characterizing unexposed outcrops as vegetation cover is scarce when growing on thin layers of soil lying on rock) and DEM (i.e. 3D view helped to locate domes in the area, regardless if they were covered by vegetation or not). The difficulty to observe the contact and extension of different rock units reduced the accuracy of this map.
3.2.6 Nature and proximity to river
The nature of the rivers was determined from the dimension of the river channels. This was obtained by digitizing the 1:50,000 topographic maps of Nkambe 1b and Foumban 3d using the ArcGIS 10.1 software and also from field work. Rivers were grouped into five classes, from the largest to the smallest: major river, main river, river, stream and temporal stream. The dimensions of the rivers increase as the rivers flow into lowlands and coalesce together. Generally, the wider and shallower a river channel, the higher the degree of flood hazard is. Moreover, some streams may cause severe floods when they receive an abnormal influx of water. In case of a flood, the areas close to a river channel are highly affected compared to distant areas. A ring buffer was realised at a distance of 600 m from the river at intervals of 100 m, 200 m, 400 m and 600 m and grouped into four classes (Fig. 5g). Proximity to rivers was implemented by applying the Euclidean distance function in ArcGIS using the multiple buffer tool.
3.2.7 Combining hazard parameters
Many different types of landslide hazard zonation techniques have been developed over the last decades, and the difficulty lies in the weighting the factors [32, 56–60]. In this paper, the parameters were weighted as follows: the model builder was used in the ArcGIS 10.1 software and all the environmental settings were checked such as processing extent, raster analysis and cell size. The parameters were then introduced into the model builder and reclassified to realize floating points, continuous datasets, categorize datasets into ranges, and assign each range of values a discrete integer value. Each parameter was classified based on its influence on landslides and floods. Dipping slopes, for example, were reclassified by assigning new input field values to them. Consequently, steeper slopes were assigned higher values and less steep slopes lower values. This was done for the other parameters used in the model. Using the connection tools, the parameters were connected with their input data, their corresponding tools and their resultant outputs raster. The model was then run to ensure functionality.
Using the weighted overlay tool, the values of each dataset were then weighted [44, 60], and all input parameters were assigned each a percentage of influence or hazard index. The higher the percentage of influence, the greater impact a particular input parameter will have on landslides or floods. The percentage of influence for both landslides and floods are presented in Tables 2 and 3. Accordingly, the weighted overlay operation was done as follows; 1, 10, and 1 were typed in the From, To, and By fields in the weighted overlay tool box to avoid having to update the scale values after adding the input datasets. At this level, some field values were restricted to give them a minimum value in the evaluation process, as, for example, steep dipping slopes > 35° cannot be exposed to flood hazards while flat areas cannot be exposed to landslides.
4 Discussions and conclusions
4.1 Causes of the geohazards and mapping
The causes of floods in the area include natural causes (siltation, peculiar geomorphology, the nature of soils, and rainfall) as well as anthropogenic causes (subsistence farming, plantation agriculture, the dam-backing effect of the Bamendjing Dam, and dumping of refuse into rivers). For landslides, natural causes include rainfall, slope steepness, groundwater, gravity, and the erosion of the toe of slopes by rivers, while anthropogenic causes consist of deforestation, excavation, and anarchical construction as pointed out by Che et al. (2010). Among all the mentioned causes, slope steepness is the most important in this area. Note that anthropogenic factors have a minor effect compared to natural factors.
In hazard mapping, the main problem resides in the combination of factors. These factors are not standardized nor regulated by any international norm. In this study, the selected factors are based on physical data obtained from the field. They do not affect flood and landslide hazards to the same degree. It is a combination of these parameters acting together which cause the hazard. The realization of the hazard map (Fig. 7) consists of computational weighting all these parameters. The percentage of influence (hazard index) for each parameter (listed in Table 1) on the flood and landslide event has been recapitulated in Tables 2 and 3. It should be noted that hazard indices vary from one area to another.
From the results obtained it is observed that flood hazard may affect about 25% of the studied area, along a north-south stripe: 13% are exposed to high flood hazard and 12% to moderate (Fig. 7). Landside hazard impacts about 5% of the area, along the borders: 2% are exposed to high landslide hazard and 3% to moderate. None of the hazard areas overlap. Thus, about 30% of the area is exposed to a natural hazard (Fig. 7).Although floods are destructive, they are also beneficial in the agricultural plain: flooded areas which are swampy are crucial for the growth of Ndop rice.
More generally, geohazards are common in Cameroon especially along the CVL [11, 12, 14], with landslides [13, 30, 61] and floods  having devastating effects on man and the environment. Landslides impact areas with slopes of more than 35° (see Fig. 5b and 7, 35–80° according to ) while floods are the most widespread hazard in the plain and affects all localities when it does occur .
This is true in all rainy tropical volcanic (active or extinct) regions with contrasting relief, i.e. on oceanic islands [i.e. Tahiti 31]; [Cape Verde 63] as well as on continents (Uganda, ), with various magnitudes.
Ref.  point out the increase of human risks in CVL as evidenced by the loss of about 30 lives within the last 20 years because of numerous landslides in the Limbe area on the foot slope of Mount Cameroon. Twenty-four people died during the 2001 Limbe landslide (2800 people homeless) and five others during the 2003 Bambouto-Magha landslide [13, 62].
Ref. used similar parameters, such as slope, rock type and soil type, to map the landslide, rock fall and flood hazards in the environs of Bamenda that have severe environmental and socioeconomic impacts on the population. In the same way,  described natural hazards in the Mount Bambouto caldera, where landslides are most frequent.
A retrospective analysis of data from the last three decades clearly indicates an upward trend in the number of landslides in Cameroon . A proper hazard monitoring and assessment committee needs to set up to manage these hazards better as attention is only paid in cases where there are casualties or severe destruction. A proper understanding of geohazards is vital for the management and understanding of landscape evolution for sustainable development and a better arrangement of the national territory.
4.2 Suggestions for the mitigation and management of geohazards
To render these hazards less severe or less devastating, some mitigation and management measures are proposed to be implemented in the Ndop Plain. Mitigation involves emplacing measures in the geological context, while management involves measures in the way that people can manage themselves and respond to geohazards successfully to ultimately survive. These suggestions are presented in Tables 4 and 5.
B. Bonin is thanked for useful remarks. Careful reviews by C. Principe and an anonymous reviewer greatly helped to improve the manuscript.
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About the article
Published Online: 2016-07-08
Published in Print: 2016-07-01
Citation Information: Open Geosciences, Volume 8, Issue 1, Pages 429–449, ISSN (Online) 2391-5447, DOI: https://doi.org/10.1515/geo-2016-0030.
© 2016 P. Wotchoko et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0