Analysis of the Spatio-Temporal Dynamics of Land Use in the Bamboutos Mountains of the West Region of Cameroon

The current level of knowledge of the biophysical situation, human activity and governance in the Bamboutos Mountains does not shed enough light on the dynamics of the vegetation, the socio-economic aspects and ecological op-portunities that are essential for a successful restoration initiative in this degraded landscape of the Bamboutos mountain ecosystems. The objective of the study was to map and analyze the dynamics of land use from Landsat images of 1980, 2000 and 2021. Supervised classification by maximum likelihood was applied and the dynamics were analyzed using area curves and calculations. The cartographic results were used to produce land use maps. The analysis of the land cover dynamics shows that the evolutionary trend of the vegetation formations is essentially regressive for agro-forests and dense forests at −21.20% and −3.62% respectively. The classes that showed a clear progression were bare soil (9.78%), crop land (8.03%), built-up areas (5.19%) and shrubby savannahs and grassland (1.84%). Agriculture, livestock grazing and demographic pres-sure are the main causes of land degradation and mutation of the landscape. The results of this study provide an understanding of the land-use history of this landscape, and a solid basis for planning a restoration initiative. They provide guidance on priority areas and types of restoration intervention from a social, economic and ecological perspective.


Introduction
The Bamboutos Mountains landscapes located in the agro-ecological zone of the Western Highlands constitute precious natural potential conditioning the survival of many human communities because of their stakes as the water tower of the west region of Cameroon, climatic regulator of this region, the site of great biological richness with multiple socio-cultural and economic virtues, the site with enormous tourism potential, food hoop for many Cameroonians and Central African cities (Ngoufo, 2014). According to Messinger & Winterbottom (2016), this ecosystem, like those of the Western highlands, is subject to severe montane forest degradation and exacerbated by climate change and poor agricultural land management techniques.
In the past decade, the town of Mbouda in the Bamboutos Highlands has been experiencing an unprecedented water crisis (Yemmafouo et al., 2009). As time goes by, the phenomenon continues to grow, to the point of worrying the collective conscience. The populations of this city have quickly set their sights on Cameroon water, which is responsible for supplying Cameroonian cities with drinking water (Ngoufo, 2014). However, Cameroon water, which is at the centre of all the controversies, is experiencing a deep-seated problem in Mbouda, as in many other Cameroonian towns: the gradual reduction in the quantity and quality of water in the catchment areas. Pioneering studies on this subject (Yemmafouo et al., 2009;Matsaguim et al., 2019;Fogaing & Tsalefac, 2020) showed that water scarcity in Mbouda is exacerbated by changes in land use in recent years in the Tsedeng catchment area of the Bamboutos Mountains, upstream of the dam. The area of permanent vegetation has decreased by 36% and the area of bare soil has increased by 66% between 1988 and 2007 (Yemmafouo et al., 2009). The current urgency of a multi-stakeholder intervention in this area stems from the fact that for several decades, numerous human actions have been erected at a dizzying pace to weaken this potential (Ngoufo, 2014). The human and ecological risks involved are multiple.
In view of this situation, it is important to carry out specific studies that will provide a sustainable basis for the successful restoration of these landscapes. Remote sensing and mapping offer an immense source of data to study. The spatial and temporal dynamics of environmental parameters can provide timely synoptic information for the identification and monitoring of local territories (Smith, 2012). In addition, they play a vital role in applications such as environmental damage assessment, land use monitoring, urban planning, as well as soil and crop yield assessment (Avakoudjo et al., 2014). The objective of this study is to assess land use changes in the Bamboutos Mountains based on mapping data of the locality. It is based on the hypothesis that the expansion of crop farms in the area combined with the effects of unsustainable management practices intervenes in the spatio-temporal dynamics of the landscape. The communities that exploit the slopes of the Bamboutos Mountains are spread over 3 regions (West, North-West and South-West), 4 divisions (Bamboutos, Menoua, Mezam and Lebialem), and 7 subdivisions (Babadjou, Batcham, Nkong-Ni, Fongo-Tongo, Santa, Alou and Wabane) with 30 villages.

Village Sampling
Within a 5 km area of influence of the Bamboutos Mountains, 30 villages were identified, using the open street map 2019 platform, which provides a spatial data set of the world's landmass. They are presented in Figure 2.
Indeed, the choice of villages is based on the potential influence that these populations may have on the Mountain ecosystem, due to their proximity and accessibility. Thus, on the basis of the spatial stratification established from the boundaries of the Bamboutos Mountains, according to a zone of influence of 5 km, 33% which represented 10 villages were selected for data collection. This gradation is based on the assumption that the closer a community is to the Mountains, the more interactions it has with the landscape. Also, taking into account the reasons for insecurity in the North West and South West regions, only  communities located in the districts of the Western region were selected.

Data Collection
Landsat satellite images from 1980, 2000 and 2021 were uploaded from the website https://earthexplorer.usgs.gov/ platform into GEOTIF format using Path 184 and Row 057. The Digital Globe image of 2021 was used for the finalization of the land cover map. The final validation of the different land cover maps was done using the pixel confusion matrix and data from field observations with a Global Positioning System (GPS) handheld receiver to locate the different land cover units.
A camera was also used to film important sites (dense forest, agro-forest, crop fields, shrub savannah, etc). A data collection sheet was used to record all the information useful for the evaluation of the different changes.
10 Focus Group Discussions were organized in the 10 selected communities, i.e., 1 per community with 4 participants per exchange for the categorization of the drivers of deforestation and landscape degradation.

Image Processing
Satellite image processing was carried out in two stages: image pre-processing and Image pre-processing This phase is the set of techniques (radiometric improvements and geographical recalibrations) aiming at standardizing the data format to allow their comparison at different dates. It was carried out in several stages, namely: • Unzipping and importing image strips: this operation allows the extraction of the downloaded file into several image strips in order to exploit the information contained in each strip. • Combining bands: in order to obtain a single multi-spectral image. Landsat images are made up of several bands. All bands were combined to obtain a master file containing all the information to be highlighted in the study area. Open Journal of Forestry the probability of a pixel belonging to a given class rather than another. Pixels were assigned to the class with the highest probability.

Post classification and validation
After the image classification, the post classification was done. This consists of the validation of the treatments from the field observations and the visualisation on Google Earth. Once the classification was completed, processing was carried out to refine, evaluate the accuracy and validate the results as shown in  FAO (1996). S 1 is the area occupied by a land cover class at date t 1 and S 2 is the area of that same class at date t 2 . If the rate of change is positive, then this will represent an increase in the area of the class during the period analyzed, while negative values indicate the loss of area of this class between the two dates. Values close to zero express a relative stability of the class in both periods. The overall rate of change was expressed by Equation (1) and the annual rate of change by Equation (2) (Bernier, in 1992). However, the rate of deforestation corresponds to the overall rate of change of the forest classes in the study area. The annual deforestation rate was obtained by dividing the overall deforestation rate by the number of years between the two periods studied. Tg: Overall rate of change; S 1 is the area occupied by a land use class in 1980 for the period  and in 2000 for the period (2000-2021); S 2 is the area occupied by a land use class in 2000 for the period  and in 2021 for the period (2000-2021); S is the area occupied by a land use class in 1980 for the period  and in 2000 for the period (2000-2021). With: T c : Annual rate of change; ln: the neperian logarithm; e: the base for neperian logarithms (e = 2.71828); t 1 : Period 1; t 2 : Period 2.

Drivers of Land Use Change
The content analysis technique was used to describe and understand the drivers of degradation and deforestation as expressed by the different stakeholders. Table 1 shows the statistics of the land use classes in 1980.  Figure 4 shows the distribution of land use in 1980.   new land for construction but also for mountain farming and grazing. Figure 5 shows the distribution of land use in 2000. Table 3 shows the statistics of the land use classes in 2021.  Figure 6 shows the distribution of land use in 2021. Figure 7 shows the summary of land use class statistics in the Bamboutos Mountains      2) Analysis of the evolution of land use between 2000 and 2021 Table 5 shows the evolution of land use between 2000 and 2021.

Summary of Land Use between 1980 and 2021
The analysis of Table 5   3) Evolution of land transition forms between 1980 and 2021 Table 6 shows the transition matrix of land use classes between 1980 and 2021. in general, used to practice coffee-based agroforestry system as their main activity, but since 1990, with the fall in the price of Arabica coffee many farmers have turned to subsistence farming by transforming these coffee-based agroforestry areas into Crop land.
In addition to the ever-increasing population, poverty and the need for food, has led to the conversion of forest lands into agricultural lands in order to feed the hungry months in the study area.   Temgoua et al., 2018).

Conclusion
The study of the spatio-temporal dynamics of land use in the Bamboutos Mountains showed that two main classes have experienced a significant regression over time: agro-forests (−21.20%) and montane forests (−3.62%). In general, the classes that have shown a clear progression are bare soil (9.78%), crop land (8.03%), built-up areas (5.19%) as well as shrubby savannahs and lawns (1.84%).
The regression of these particularly critical plant formations is due to several factors, the most important of which is the extension of agricultural land. This destruction of the vegetation cover leads to soil degradation and, above all, to the loss of biodiversity. This study highlights the need to establish effective restoration mechanisms for the Bamboutos Mountains. Based on the diachronic data obtained, a series of transformations can be carried out on these mountains to restore the functionality of the ecosystem considered indigenous and historical and to improve human well-being within the degraded landscape.