CHAPTER 4 MODELLING LAND USE CHANGES IN THE BONSA CATCHMENT,
4.6 Discussion
92 Figure 4.8: Total projected population of Bonsa catchment from 1986 to 2070, land use areas from 1986 to 2011 (observed) and simulated land use areas from 2020 to 2070 for the BAU scenario
93 demand, the change matrix (transition sequences) and the elasticity of the land cover (Verburg and Overmars, 2009). Hence using Dyna-CLUE ensured that multiple land use types, as well as dynamic simulation were executed successfully.
The Dyna-CLUE model predicted the trend in land use changes, observed on the actual land cover maps for 2002 and 2011(Figure 4.5and Table 4.5). When the simulated land cover of 2011 was compared with the observed land cover of 2011, secondary forest and settlements were overestimated, while evergreen forest, shrubs/farms and mining areas, were underestimated (Figure 4.6 and Table 4.5). However, the model was able to allocate the demand correctly most of the time. Therefore, the major hindrance to the overall model performance is the accuracy of the initial land use maps, demand calculations as well as the driving factors. According to Verburg et al. (2002), Verburg and Veldkamp (2004) and Verburg and Overmars (2009), the CLUE family of models are very sensitive to the demand.
It is therefore not surprising that the model validation achieved only a Kappa of 54%, representing a moderate agreement in simulation of the observed land cover, due to inadequate input data which include lack of information on how individuals and communities/the Government make land use decisions. However, since the overall trend in land cover changes is well simulated and the purpose of the modelling was to provide plausible pathways of land cover changes, the generated model statistics were assumed to be valid for the future land cover scenarios. This approach is consistent with previous studies (Chu et al., 2010; Park et al., 2011a; Park et al., 2011b).
Furthermore, the results of this study provide evidence of the existence of the Sahel Syndrome (Petschel-Held et al., 1999) in the southern part of Ghana, where the ever increasing poor population of Bonsa catchment, who have no other livelihoods, converted forested lands into agriculture, thereby contributing significantly to degradation of the environment. With regards to influence of population size on deforestation, results of this study are similar to those for the Eastern province of Cameroon (Mertens and Lambin, 2000) and the West Biosphere reserve in Northern Benin (Houessou et al., 2013), but contradict that of Braimoh and Vlek (2005), where population did not contribute significantly to deforestation in the northern Savannah of Ghana. Braimoh and Vlek (2005) noted that during the early stages of the economic recovery programme in Ghana, large parcels of land in the north were converted into irrigated rice fields, which explains why population size did not contribute significantly to deforestation. However, in terms of the influence of accessibility
94 factors on deforestation, the results of this study are similar those of Mertens and Lambin (2000), Braimoh and Vlek (2005) and Houessou et al. (2013).
The future scenarios of land cover changes, consisting of the BAU, the EG, as well as the EGR scenarios, indicate that in both the near and far future, there may be extensive land cover/land use changes in Bonsa catchment, through population growth and increased surface mining activities (Figure 4.7 and 4.8 and Table 4.6, 4.7 and 4.8). In future, settlement area is projected to increase, with all three scenarios projected to have relatively the same area. The settlement expansion may occur mainly in the Tarkwa area, as well as in towns and villages along the major roads. It is important to note that higher settlement expansion rates will exacerbate impacts, such as increased stormflows, runoff, soil erosion, sedimentation of rivers and the pollution of water bodies. For evergreen forests, all scenarios show substantial changes. In the BAU scenario, although the forest reserves were strictly protected, after 2002 (Sutton and Kpentey, 2012) (Figure 4.8), the authorization of mining near forest reserves for the existing mines, as well as expansion of agriculture, mainly rubber and cocoa cultivation, may inevitably lead to encroaching into the evergreen forests, located mostly in forest reserves. In the EG and the EGR scenarios, the decrease in evergreen forest is due to increased mining activities, as new mining leases (Figure 4.4) may be approved for mining to start in various parts of the catchment, including forest reserves. It is also expected that once mining is allowed in the forest reserves, timber logging will likely take place in those mining concessions. Moreover, increases in mining areas, resulting in a decrease in evergreen forests, will also negatively impact the environment. The biodiversity of the ecosystem will reduce;
runoff volumes and speed may increase leading to soil erosion, sedimentation and pollution of water bodies. The increased mining activities also have the potential to pollute both surface and groundwater, with heavy metals, such as mercury and arsenic, resulting from processing gold ores.
For the shrubs/farms, the scenarios show contrasting futures. While there is an increase in the BAU scenario, as a result of population growth and expansion in agriculture, there is a decrease in the EG and the EGR scenarios, which is a result of the combined expansion in settlements, mining areas and secondary forests. Increased shrubs/farms areas under the BAU scenario, at the expense of evergreen and secondary forest, call for implementation of farming technologies that maintain soil fertility, farming practices that encourage less encroachment of forested areas, as well as protect the water sources from pollution. The reduction of
95 shrubs/farm areas under EG and the EGR scenarios, calls for measures to intensify production for food security, by using modern agricultural technologies, as well as diversifying the local economy, in order to provide the local communities with sources of livelihood. Hence the extensification of rubber and indigenous tree plantations, which is represented by an increase in secondary forests in the EG and the EGR scenarios, can contribute to providing the local communities with a source of livelihood, as a larger portion of their farm lands may be taken for settlements and mining operations. Under the BAU scenario, secondary forest is projected to decrease, which means lands currently (2011) being used as secondary forests, are not protected, unlike the EG and the EGR scenarios. In the EG and the EGR scenarios, it is anticipated that even if new mining leases are granted and settlement expansion is allowed to occur at the current rate, the protection of the secondary forests or encouragement of rubber cultivation will partly offset the potential negative effects that reduction in evergreen forest and shrubs/farms areas will have on both the local communities and the environment.
It is important to state that the maps generated under both historical and future scenarios of land cover/land use changes in this study do not represent real land cover changes. The maps are only a projection of different development pathways for the catchment. The predicted maps are intended to be used to support the formulation and revision of land use policies in the medium to long-term time slices. It is also acknowledged that even though the land use modelling, using Dyna-CLUE was moderately successful in terms of the Kappa statistic in simulating the 2011 land cover, there were some limitations. These include the inability of the Dyna-CLUE model to incorporate actor decisions, such as preference of individuals and communities, government policy, globalization of economics, as well as inadequate input data. Thus, the set of driving factors used were local; distant and underlying factors were excluded. Due to lack of data, it was also difficult to separate correlation from causality of land cover changes during the modelling process, as noted by Mertens and Lambin (2000) and Tran Van et al. (2012) in previous studies. Consequently, the regression statistics generated for the different land cover types were assumed to be valid for the future time slices, used for the two scenarios, as was the case in previous studies elsewhere (Sohl et al., 2007; Chu et al., 2010; Park et al., 2011a; Tran Van et al., 2012).
It must also be mentioned that Dyna-CLUE model cannot simulate changes such as introduction of new land use types, due to lack of historical precedence (Verburg et al., 1999).
However, the land cover maps generated in this study provide an avenue to explore the
96 implications of the different development scenarios on the environment. The study has demonstrated that spatial modelling of land use change is the best way to provide data to infill data gaps as a result of lack of satellite or aerial imagery to generate land cover maps, especially in data scarce regions. In West Africa for example, the historical record of satellite or aerial imagery is limited mainly due to cloud cover, hence spatial land use modelling adopted in this study is vital to provide the best estimate of the distribution of land use for the periods where there are no satellite or aerial imagery to derive land cover. In future, further studies should improve the modelling results, by acquiring more data and identifying techniques to incorporate human actor information. It is also recommended for future studies to refine the scenarios used in this study, as land use planning information in the catchment becomes available. Further studies should also be conducted to quantify the impacts of the predicted land cover/land use changes on environmental goods and services.