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The Bamboutos Mountains experience a persistent deterioration of their natural environment, which is evidenced by the ongoing loss of vegetation and growing instability of the ecosystem. As such, several soil restoration projects have been put in place to restore this mountain ecosystem and maintain its agricultural potential. This article goes in-line with this premise by studying the sensitivity of soils to water erosion in a watershed where agriculture is the main form of land use. The objective of the study is to examine various aspects of the study area, including its topography, lithology, hydrology, climate, and land use, in order to adopt a multi-criteria approach that involves intersecting these factors related to soil vulnerability to erosion using GIS. Results showed that the Menoua watershed is characterized by very steep slope classes (60% of the area occupied by slopes greater than 50°), with agricultural land alone covering approximately 49% of the watershed or almost half of the available space. The map of soil sensitivity to erosion shows that areas most sensitive to erosion (42%) generally coincide with the sloping land cultivated on lateritic soils in the northern part of the basin. Very strong and strong sensitivity to erosion represents 8.82%. The basin is therefore a geographical area at risk of erosion. Adopting no-tillage farming technique and the agroforestry can reduce sensitivity to erosion and ensure sustainable management of mountains.
*Address all correspondence to: gabriel.nanfack@yahoo.fr
1. Introduction
The Menoua watershed is a hydrosystem where agriculture is the main form of land use [1]. The combined effect of ancestral farming practices, which often do not take soil conservation into account (slash-and-burn method, absence of fallows, ridging along steep slope) [2], high rainfall (2000 mm/year), relief, soil type (ferralitic soils), and the regression of the plant cover promotes water erosion. This leads to soil degradation and a drop in agricultural production potential. This phenomenon creates imbalances and damage in the production basins: degradation of the topsoil with loss of fertilizing elements upstream and excessive deposits of alluvium downstream [3, 4].
As such, the need to achieve risk reduction by identifying potential sites with high erosive sensitivity is the major objective of this study. It will be a question of researching for this intra-mountain space, the natural and/or anthropogenic predispositions that characterize the sites prone to this phenomenon of soil erosion. It is therefore important to identify these areas by taking into account the biophysical and environmental realities, which predominate in the watershed in order to improve the sustainability of production systems. Therefore, in order to map the sensitivity of soils to erosion in the Menoua watershed, an approach based on modeling, which makes it possible to intersect factors taken into account for the mapping of soil sensitivity to erosion using GIS was chosen. This chapter mainly deals with the methodology for mapping the sensitivity of soils to erosion. It is divided into four main parts. The first part presents the geographical framework of the study area, the second briefly details the methodology used while results and discussion are presented in the third and fourth parts, respectively.
With a superficial area of 68,200 hectares, the Menoua watershed is part of the Western Highlands of Cameroon. It is located on the southern slope of the Bamboutos Mountains between latitude 5° and 6° North of the equator and 9° and 10° of the Greenwich Meridian. Its average altitude varies between 700 m downstream and 2270 m upstream (Figure 1). It has a compactness index of around 2.107 [5], which reduces the response time of the basin to precipitation and promotes erosion. It receives an average annual rainfall amount of 1900 mm [6], characterized by both spatiotemporal irregularity and high intensity [7]. It has an irregular slope system that exceeds 15° on nearly 70% of the basin [8] and a reduced vegetation cover that now represents 28.35% of the catchment area [1].
Figure 1.
Presentation and localization of the study zone.
Like in all of the Western Highlands, geological formations are mainly made up of products of volcanic eruptions (rhyolitic, trachytic, basaltic flows, and ignimbritic projections) of varying ages ranging from the tertiary to the current period [9]. These rocks consist of minerals with a fairly high degree of alterability [10]. The drainage density is high showing an impermeable lithology that favors surface runoff. The hydrographic network is of order 5, and therefore very active during intense and concentrated rains. However, it is a densely populated environment where agriculture is the main economic activity [11, 12]. Anthropogenic interventions remain the major process that directly influences the acceleration of water erosion processes on the slopes.
2.2 Data and materials
The analysis of soil sensitivity of the Menoua watershed was carried out on the basis of a set of data such as the Landsat 8 satellite image of 30 m spatial resolution https://www.earthexplorer.org; the Bafoussam 1c and 1d topographic maps of 1/50,000 provided by the National Institute of Cartography; the soil map extracted from the morpho-pedological map of West Cameroon at 1/100,000, and data collected using a navigation GPS during fieldwork on the southern slopes of the Bamboutos Mountains in December 2021. This data collection have enabled the establishment of a Spatial Reference database. ArcGIS 10.3 and Envi 5.2 software were used for data processing.
2.3 Methods
The hierarchical multi-criteria analysis applied in a Geographic Information System for the assessment of soil erosion hazard is increasingly used nowadays. Literature is abundant and has revealed that anthropogenic factors contribute to soil erosion [13, 14, 15]. The evaluation of soil sensitivity to erosion in the Menoua intra-mountain watershed made use of the Hierarchical Analytical Process (PAH) developed by Saaty [16]. Multi-criteria spatial evaluation methods are generally made up of six stages: the entry criteria, the hierarchical structuring of the criteria, the development of binary combinations, the determination of the value and proper vector, the study of consistency of judgment, and finally an aggregation structure, leading to a final relevance map [17].
2.3.1 Elaboration of the erosion sensitivity
The entry criteria are factors considered included in the decision-making process as having a major influence on soil erosion, and which can be characterized by their respective attributes. This stage aims to characterize the drainage basin from the topographical, lithological, land use, and climatic perspectives. In this study, the sensitivity of soils to erosion is assessed by applying GIS techniques that take into account the operations of extraction, reclassification, geo-referencing, vectorization, and rasterization based on multi-criteria analysis. These factors are evaluated using the spatial analysis capabilities of ArcMap 10.3. Each evaluation leads to a map representing the entire basin, and its suitability for the factor considered.
2.3.1.1 Topography
The topographic factor that reveals the slope is one of the first criteria for conditioning runoff. The slope greatly influences the importance of erosion by its gravitational action and provides its erosive energy to water. Thus, the action of erosion increases strongly with slope. Slope steepness acts directly on runoff velocity. When it increases, the kinetic energy of runoff increases and accelerates the transportation of solid objects downwards, increasing the impact of the ablation. According to Refs. [18, 19], the transportation of particles increases exponentially with a percentage inclination of the slope. Topography shows the inclination of relief and the length of the slope. These criteria are very important and directly influence the phenomenon of erosion because it conditions the speed of runoff. In the context of this study, we will retain only the inclination of slope, through its preponderance in the topographic parameter. To this effect, a topographic map of Bafoussam 1c at 1/50,000 provided by the National Institute of Cartography made it possible to digitalize the contour lines. ArcGIS 10.3 made it possible to establish in a precise manner, the interpolation of side points resulting from the contour lines on the slopes map of the basin. These slopes were ordered into five classes (Table 1).
Pente (°)
Surface (%)
Influence on erosion
≤ 5
26.68
1
5–10
35.67
2
10–20
20.82
3
20–30
14.01
4
> 30
2.83
5
Table 1.
Slope classification.
2.3.1.2 Land use
The erosion process is closely linked to the mode of land use, which largely contributes either to its aggravation or attenuation. Land use determines the degree of soil protection. This factor is a measure of the relative effectiveness of soil and crop management systems in preventing or reducing soil loss [20]. In terms of soil protection, vegetation is essential because foliage, tree trunks, and roots constitute obstacles that slow down the speed of runoff through the phenomenon of “stem-flow.” It also protects against the phenomenon of rain splashing, prolongs the permeability of soils, and reduces the volume of runoff. The degradation of woodlands leads to a profound disturbance in the hydrological regime of the basin and exposes soils, leading to topographic instability conditions [7]. To characterize land use in this study, the Landsat 8 OLI satellite image sensor acquired on December 18, 2021 in our study area (WRS_PATH = 186 and WRS_ROW = 56) is the main data used. It was analyzed using Envi 5.3 image processing software, taking field observations into account. The classification of the different land use units was carried out depending on whether or not it promotes erosion (Table 2).
Land use
Surface (%)
Influence on erosion
Agricultural zone
47.12
4
Built up area
4.81
3
Herbaceous savannah
32.21
2
Shrubs
15.86
1
Table 2.
Landuse distribution.
2.3.1.3 Soils
Soil data are a control variable for the erosion process. Its participation in the erosion phenomenon depends on its permeability and the ability to detach and transport its particles. Each soil type will react differently to the attack of rain and shear of runoff, depending on its texture, structure, porosity, and level of organic matter. The Menoua watershed is made up of a mosaic of soils linked to the geological history of the region. They come from basalts, sandy soils, and alluviums. To assess erodibility, we took into account not only the infiltration capacity which, according to Ref. [21], allows us to know the soil runoff potential, but also texture and organic matter content which, according to Ref. [22], have a considerable influence on the sensitivity of soils to erosion. These factors condition the permeability and cohesion of aggregates. According to these criteria, four major classes of soil are thus defined for the Menoua basin. Ferralitic soils, which benefit from good internal drainage form the first class and have a high infiltration capacity. In the second class, we have soils rich in humus and poorly developed soils. They generally have lower percolation and infiltration rates. The third class consists of moderately organic soils, such as poorly drained fine sands, loamy soils, and thin permeable soils. Hydromorphic soils consist of poorly structured and poorly drained heavy clays, which are found in the fourth class. According to these criteria, two major soil classes are defined for the Menoua basin. The first class is made up of ferralitic soils with good internal drainage and very high infiltration capacity. The second class includes hydromorphic soils consisting of moderately organic, poorly drained, and permeable thin soils (Table 3).
Soil type
Class
Surface (%)
Influence on erosion
Humic ferralitic soil on basalt
Ferralitic soil
6.53
5
Typical indurated ferralitic soil on basalt
1.93
4
Ferralitic soil is typically red on basalt
52.08
3
Typical ferralitic soil on gneiss
32.72
2
Moderately organic hydromorphic soil
Hydromorphic soil
6.73
1
Table 3.
Soil classification according to their contribution to erosion.
2.3.1.4 Drainage density
Drainage density is an input parameter into the various soil water erosion models, which represent tools to help implement future soil conservation plans. It is indicative of the infiltration and permeability of the basin. A high drainage density reflects the impermeable lithological nature that favors surface runoff. The hydrographic network of the Menoua basin is of order 6; therefore, very active during the rainfall.
2.3.1.5 Climate
Climate is an important factor that directly or indirectly influences soil erosion [23]. Rainfall in the humid tropics is the most important climatic variable that affects soil erosion. The action of rainfall amplifies the driving forces necessary for the uprooting of soil particles. Rainfall intensity and energy trigger soil erosion. The precipitation map of the Menoua watershed was produced using rainfall data from two meteorological stations (IRAD-Dschang, Djutitsa). These data have undergone interpolation operations (spline interpolation).
Establishing the hierarchical structure consists of classifying the various factors selected according to their degree of influence on soil erosion. To facilitate the task, [16] set up a scale of numerical values (Table 4).
Values
Numerical scale for a comparative judgment of the indicators
After taking the advice of some researchers and experts on the study of water erosion of soils, the binary combinations, which consist of comparing the factors of erosion with each other within a matrix and assigning to each pair a comparison coefficient were made (Table 5).
Factors
Numerical scale for a comparative judgment of indicators
Factors
Slope
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Land use
Slope
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Land use
Slope
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
D density
Slope
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Rainfall
Land use
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Soil Type
Land use
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
D density
Land use
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Rainfall
Soil type
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
D density
Soil type
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Rainfall
D density
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
rainfall
Table 5.
Comparison of factors by the expert.
NB: D density = Drainage density.
The values in red are those checked by the expert to materialize the existing links between the factors to be compared. For example, considering the fourth line, the “Slope” indicator is really more important than the “rainfall” factor in the evaluation of the sensitivity of soils of the Menoua watershed to erosion. From this comparison between the different factors, a reciprocal comparison matrix was produced (Table 7) by applying the following relationship Eq. (1):
2.3.4 Determination of the value and the proper vector for each indicator
The weighting of the criteria makes it possible to reflect the relative importance given to each criterion by the experts. Once the comparison matrix has been obtained, the proper value of each combination and the proper vector corresponding to it are determined. The proper value of each pair comparison is obtained by dividing the numerical importance assigned to the pair by the sum of the numerical degrees of importance of the column. The proper vector indicates the order of priority or the hierarchy of the vulnerability indicators studied. It indicates the relative importance of the indicators. It is estimated by first calculating the sum of the proper values contained in each row of the matrix, then dividing this value by the number of indicators contained in the matrix. The proper vector associated with each factor is the weight assigned to each factor. Calculating the weight of each factor normalizes the comparison matrix so that the sum of all the weights equals 1.
2.3.5 Study of coherence of judgment
Computed priorities make sense only if the matrix of comparison by pairs is coherent. The evaluation of the coherence of judgments can be made using a Coherence Index (CI). This index measures the logical coherence of judgments of the people consulted. It provides information on consistency in terms of the ordinal importance of indicators to be compared. The estimation of this index is based on the calculation of the proper values of the comparison matrix using the mathematical procedure Eq. (2);
IC=λmax−n/n−1E2
such that λmax is the maximum proper value of the comparison matrix, obtained by multiplying the total of each column of the comparison matrix by pairs with the relative weight of the indicator of this column, and by adding the results obtained for each column. n is the number of indicators compared in the matrix. The consistency ratio (CR) is then calculated, such as Eq. (3);
CR=ICIAE3
where IA is the random index fixed according to the number of factors (4 in the case of this study). The value of AI was given by Ref. [16], and it is a function of the number of elements compared (Table 6).
If CR is less than or equal to 0.1 or 10%, then it is accepted that the weights assigned to the indicators are acceptable and, therefore consistent. If this threshold is exceeded, we are in a situation of inconsistency, then the matrix resulting from the comparisons will have to be reevaluated.
2.3.6 Aggregation of weighted data
This final stage of the Hierarchical Multi-criteria Analysis (HPA) occurs once the weighting of the landslide assessment factors has been carried out. At this point, it is easy to combine them to obtain an assessment of the sensitivity of the watershed to erosion. The most common and well-known technique of this approach is the weighted linear combination, which integrates all the considered factors into one [24, 25, 26]. It consists of multiplying each layer factor by its respective weighting coefficient, and then adding these results to produce a sensitivity index. The mathematical transcription of this combination is expressed as follows Eq. (4);
Vi=∑j=15ωj.aijE4
where Vi is the summary index of susceptibility, ωj is the weight attributed to each indicator, and aij is the weighting coefficient evaluating the relative importance of the factors.
This methodology for modeling and mapping soil sensitivity to erosion, which takes into account not only the functioning of the entire system but also the interrelationships between its various factors, is shown diagrammatically in Figure 2.
Figure 2.
Methodology for mapping soil sensitivity to erosion using the multi-criteria assessment.
The production of five analytical maps (Figure 3) was necessary to obtain the erosion sensitivity map.
Figure 3.
Predisposition parameters of the cartographic model.
Once the maps were produced, we used experts’ assessment to compare in a reciprocal matrix, the different parameters of soil predisposition to erosion. The weights expressed by the comparison matrix (Table 7) were validated by the consistency of judgment in accordance with the equation …. and appended to the various maps produced.
Factors
Slope
Land use
Soil Type
D.d
Rainfall
Weights (ω)
Weights (ω)%
Land use
1
2
4
5
7
0.44
44
Slope
1/2
1
3
4
5
0.28
28
Soil type
1/4
1/3
1
3
4
0.15
15
D.d
1/5
¼
1/3
1
2
0.08
8
Rainfall
1/7
1/5
1/4
1/2
1
0.05
5
RC = 0,041
Table 7.
Comparison of weights relative to factors of sensitivity.
According to the judgments made at the level of the comparison matrix, it is noted that the soil cover plays the most important role in the occurrence of erosion. According to experts consulted, the effectiveness of a downpour depends on the degree of protection that the plant cover can provide to preserve the soil. This led them to consider the role of the land use factor with a score of 44%. Secondly, the slope factor contributes about 28% to the occurrence of erosion. Indeed, runoff acts when the slope becomes steeper on soft soils.
However, on the steep slopes of Bamboutos Mountains, the technique of cultivation by plowing, which is the most widespread, helps to crumble the soil into fine particles that are easily transported by runoff water along steep slopes. For these experts, the structural stability that determines the soil’s susceptibility to erosion depends on organic matter, aggregation, and texture. The result of the weighting obtained displays a score of 15% for the soil type factor. In general, the sensitivity to erosion in the Nkam watershed from the pedological point of view increases from moderately organic hydromorphic soils to humic ferralitic soils on basalt and shows on the whole that these soils are less resistant to erosion. The triggering factor precipitation (5%), which is the sinequanone condition for water erosion precedes that of drainage density (8%) that conditions diffuse erosion and generates the formation of capping crusts by reducing soil infiltration capacity.
3.2 Mapping of soil sensitivity to erosion
Once the final matrix was produced, the weights (ω) expressed by the matrix were associated with each thematic map in GIS using reclassification functions. Then, the cartographic algebra operations were carried out by applying Eq. (4). The usual formats for these calculations are rasters. As such, the resultant is a soil erosion sensitivity map for the Menoua watershed (Figure 4).
Figure 4.
Potential sensitivity of the Menoua watershed to erosion.
The analysis of Figure 2 and the statistical distribution of the zoning of soil sensitivity to erosion in the Menoua watershed shows that levels of very strong and strong erosion sensitivity represent 8.82% or 5592.26 hectares of the total area. Field observations show that these classes of erosion are characterized by constantly plowed bare ferralitic soils on steep slopes, highly perceptible in the northern (toward the summit of the Bamboutos Mountains) and southern (on the Foréké escarpment which separates the Bamileke plateau from the Mbo plain) parts of the watershed. The moderate erosion sensitivity level covers 4781.31 hectares or 7.55% of the basin and is explained by the coincidence of medium slope classes with areas with a reduced vegetation cover, especially in the north, west, and southeastern parts of the watershed. Finally, the class of low and very low erosion status of 52981.91 hectares (i.e., 7% of the study area) occupies the center of the basin. Here, erosion is less advanced due to a fairly large vegetation cover and limited influence of the slope gradient.
3.3 Model validation
Any model must be validated so that the mapping of risks does not lead to risks for the mapping system [27]. Since no previous survey of the study area has relied on quantitative methods, this makes confirmation of results difficult. The model presented (Figure 3) was validated thanks to a reconnaissance mission of the mapped sites. Therefore, the validation of this model required field measurements and observations.
GPS surveys of certain bare slopes were carried out. It appears from this mission that all the sites mapped do not fully reflect the reality on the ground. For each test value, the two information plans (GPS surveys/model) were superimposed using GIS. Some sites identified as prone to erosion represent a rate of 73.87% coincidence with the model. Moreover, for about 26.13% of the results obtained, the model did not agree and sometimes very largely with field measurements (Figure 5). Sites classified as very sensitive are those on which we can either carry out reforestation schemes or no-till farming techniques.
Figure 5.
Overlay of the different information planes (model & field data).
The assessment of soil sensitivity to water erosion using a multi-criteria approach calls on several criteria which, when put together, makes it possible to establish a balance sheet and a diagnosis of the physical degradation of soils in a given area. This study employed five criteria (slope, land use, lithology, drainage density, and precipitation) whose weighting was ensured by the AHP method. This method envisages a good understanding of the criteria involved in the erosion process, based on the pair-wise comparison of criteria at the same hierarchical level [28]. Land use was the most determining criterion in the process. Results obtained from the weighting of the criteria corroborate those of Refs. [29, 30, 31], in which plant cover protects the soil against ablation and reduces risk of erosion. Vegetation, therefore, acts as a protective screen against aggressive climatic conditions by intercepting the energy released by raindrops [32]. The topographic factor through the rigidity of the slope accentuates the erosive force of sheets’ flowing water. But its behavior with respect to infiltration depends on the type of soils [33]. However, the model produced on the basis of these five criteria after validation gave satisfaction at a rate of 73.87%. The non-satisfaction of the model (23.13%) stems from the input data used in this study. They are mostly medium resolutions. One of the parameters whose imprecision impacted the final result was land use. Indeed, the satellite image used to establish the land cover map has a spatial resolution of 30 m x 30 m. This spatial resolution certainly did not allow more detailed observation of the earth’s surface. The consequence of this imprecision on the result of this study is that the soils which could have been identified as less sensitive to water erosion were not, and vice versa. Forests with small dimensions may not have been taken into account in the analyses. The topographic data used to produce the slope and drainage density maps were generated with a scale of 1/50,000, which does not provide excellent precision. These inaccuracies in the model’s input data can therefore influence the final result obtained in this study.
Moreover, the AHP method used for the weighting of the criteria, although relatively efficient, presents difficulties. One of the difficulties of this method concerns the subjectivity in the choice of the scale of scores ranging from 1 to 9 with their reciprocal correspondence [34]. The choice of the score corresponding to a criterion is arbitrary and may influence the calculation of the weight of the criterion considered. However, this method (AHP) is appropriate for this study because it performs well when the number of actions is reduced [35]. Nevertheless, despite these imprecisions on the input data, the quality of the model proves to be satisfactory for simulating the sensitivity of soils to erosion, which is necessary for any spatial planning study. It could be improved through the acquisition of high spatial resolution data.
Associating multi-criteria analysis with GIS offers possibilities for risk management that integrates, in a systemic vision, a set of parameters relating to its comprehension. These techniques have been applied to the Menoua watershed for modeling soil sensitivity to erosion. The final map obtained is intended to guide actors on the sustainable management of potential erodible zones. It highlights five categories of sites: very low, low, moderate, high, and very high sensitivity to erosion. There are two sites whose sensitivity to soil erosion is high. The first to the north, is around the summit of Mount Bamboutos, where a combination of ferralitic soils, agricultural activities, and steep slopes are decisive factors of erosion. The second in the south, is limited to the major tectonic zones of the area (foréké escarpment). It has a high drainage density in areas whose outcropping lithology corresponds to ferralitic soil formations on gneiss that stand out on the sloping ground. As for areas with low erosive sensitivity, they are found in isolated points throughout the basin. The study reveals that the level of very strong and strong erosion sensitivity accounts for 8.82%. The basin is therefore a geographical area at risk of erosion. In order to optimize the reliability of the map produced, a field mission made it possible to validate the sites prone to erosion. Based on these findings, it is recommended that erosion sensitivity should be taken into account when carrying out agricultural development projects. Adopting no-tillage farming technique and the agroforestry can reduce sensitivity to erosion and ensure sustainable management of mountains.
We express sincere thanks to late Professor TSALEFAC Maurice (Former-Dean of the Faculty of Letters and Human Sciences of the University of Dschang), who created favorable and essential conditions for the realization of this research work. He took upon himself the responsibility to supervise our academic works from the Masters to the Doctorate level. May his soul finds in this chapter, the expression of our deepest gratitude and infinite recognition.
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Written By
Gabriel Nanfack and Moye Eric Kongso
Submitted: 16 April 2023Reviewed: 03 May 2023Published: 10 January 2024
Open access peer-reviewed chapter
