Patterns of land cover change (LCC) are often integrative indicators of climatic, environmental, and anthropogenic effects across the globe. While factors influencing LCC include changes in environmental conditions, anthropogenic forcings often promote unstable ecological regimes and disruptions in natural processes that exacerbate LCC (Lambin et al., 2003). Drivers of LCC may therefore be difficult to measure and assess as they represent complex interactions of many different forcings. There is therefore considerable interest in studying the intersections between environmental, socioeconomic, and climatic data as causes or consequences of LCC. For example, changes in land cover (LC) have been found to be correlated to measurable indicators such as erosion, population expansion, industrial growth, surface heating, and soil quality (Teuling et al., 2010; Kuenzer et al., 2014; Rahaman et al., 2020). Direct human activity, such as increased agricultural land use, has been a significant component of change throughout history in natural LC dynamics (Malhi et al., 2013; Lewis et al., 2015; Addo-Fordjour and Ankomah, 2017). In turn, shifting land dynamics are influential in altering anthropogenic characteristics such as culture, economics, and urban expansion (Owusu, 2009; Edusah, 2011; Frondoni et al., 2011). Although governments have often created protective policies in an attempt to preserve environmental stability, a lack of enforcement and the continuation of illegal activity strains these boundaries (Nang, 2016; Kyere-Boateng and Marek, 2021). Ecologically detrimental practices have been a complicated problem to address, in part due to difficulties in identifying definitive long-term consequences of these interferences (Frondoni et al., 2011; Malhi et al., 2013).

Technological and methodological advances in remote sensing techniques have helped close this knowledge gap. Using satellite data, LC trends have been examined to investigate how regions have undergone environmental modifications and been influenced by human activities (Enaruvbe and Atafo, 2016) (Kleemann et al., 2017a). As population growth and anthropogenic developments have escalated over time, many studies have focused on identifying ecological changes, such as within climate and vegetation, that occur with the expansion of urban LC classes (Kleemann et al., 2017b; Asabere et al., 2020; Nyamekye et al., 2020). The large amounts of natural resources required by rapid industrial growth has placed significant demands on the environment that advances LCC beyond natural shifts (Yiran et al., 2012; Kuenzer et al., 2014; Mensah et al., 2019; Sedano et al., 2020). The methods used to acquire these materials also create disturbances within LC dynamics (Alo and Pontius, 2008; Kusimi, 2008; Tutu Benefoh et al., 2018).

Remote sensing techniques have also been used to capture specific environmental indicators and investigate how they follow LC patterns (Jamali et al., 2015; Liu et al., 2017; Rahaman et al., 2020; Chowdhury et al., 2021). Time-series data on vegetative growth has been used to demonstrate the importance of LC conditions on plant productivity (Haile et al., 2019). Rahaman et al., 2020 used remotely sensed measures including land surface temperature (LST), leaf area index (LAI), and normalized difference vegetation index (NDVI) to demonstrate the susceptibility of the environment to these variables involved in LCC. Environmental indicators such as NDVI, rainfall, and temperature gathered through remote sensing were used to predict future land degradation risk zones in Ghana (Mensah et al., 2015). Remote sensing methods have also been utilized to generate LC maps of various regions and time periods, furthering our knowledge of what drives landscape dynamics at large scales (Hackman et al., 2017; Boryan et al., 2011; Masiliūnas et al., 2021). Historical maps of previous LC classes provide base knowledge of past environmental dynamics (Bodart et al., 2013; Spruce et al., 2020). Maps of LCC can also be generated for the purpose of investigating temporal patterns occurring in various LC classes (Fry et al., 2011; Vittek et al., 2013; Bicudo da Silva et al., 2020; Calderón-Loor et al., 2021).

Detecting LCC is especially important in environmentally sensitive nations whose policy-making has the potential to significantly impact their future ecological state and the well being of their populations. This is especially true for regions where regular cloud cover limits the availability of viable satellite data (Hackman et al., 2017). Gaps in satellite coverage often result in information misinterpretations or incomplete characterization of ecological variables. However, modern developments have incorporated algorithms that remove or reduce cloud cover interference from the satellite images to provide clearer data (Lossou et al., 2019).

While previous studies have provided vital information on relationships between the environment, human activity, and LCC, most research has focused largely on correlative analyses of remote sensing-based proxies with measured changes. A lack of the understanding of causal connections of LCC is still a major gap. This study aims to combine socio-ecological and remotely sensed data to determine potential causal linkages between these variables, which are measurable data used in the study, and LC trends. Specific objectives include: 1) assessing spatial patterns of long-term regional-scale environmental and socio-ecological changes using satellite imagery, climate data, and socio-demographic datasets; 2) identifying underlying structural characteristics that explain the linkages between socio-ecological and environmental variables; and 3) utilizing a structural modeling approach to determine causal relationships between regional-scale patterns of landscape, demographic, and environmental change. The methodology is implemented in Ghana, which has undergone significant LCC in recent decades, especially with respect to anthropogenic agents such as urbanization and deforestation (Hackman et al., 2017).

The overall intent of this research was to test if widely-used satellite-data based proxies of urban growth can help disentangle regional-scale changes in bioclimatic and land cover patterns across Ghana. We postulated that losses of forest cover tracked by available multi-temporal tree cover estimates would be related to urban and agricultural expansion, and that these changes would cause changes in local bioclimatology in nonrandom patterns across Ghana. These assumption are supported by studies in the region that have correlated reductions in NDVI measures with forest losses (Ghartey-Tagoe et al., 2020). However, this relationship may not be straightforward as declines in NDVI may be confounded by changes in agriclutural practices (Liu et al., 2017). Although NDVI can track forest density, increases in NDVI can also indicate increased agricultural intensification that might mask forest losses. As expected, forested areas generally corresponded with lower albedo levels, high measures of NDVI, and low air temperatures. Areas with dense forest cover also corresponded with lower air temperature and PET trends. This observation supports the role of forests in regional cooling and suggests that losses in forest cover may be leading temperatures increases in certain regions (Mildrexler et al., 2011). We also found that declines in forest density corresponded with declining precipitation trends. While we do not have the data to corroborate this finding, low rainfall levels have been predicted to result in increased desertification and evapotranspiration (Asante and Amuakwa-Mensah, 2015). Research on droughts in Ghana suggests that this trend may lead to reduced forest productivity (Amissah et al., 2018). Declining precipitation was also related to increasing temperatures, but this observation may be an artifact of the latitudinal gradient from the coastal regions northwards towards the Sahel.

One of the apparent contradictions in our findings indicates that population expansion has a negative effect on land temperatures. According to previous studies conducted on impacts of urbanization on local environments, increases in population density should result in elevated temperatures in and around highly urbanized regions. This is largely attributed to the fact that urban areas tend to be less effective at dissipating heat via evapotranspiration than rural regions (Zhao et al., 2014; Deilami et al., 2016). We speculate that the patterns we find arise out of a latitudinal climatological gradient across Ghana: coastal regions in Ghana tend to be more populated and urbanized and this spatial patterns corresponds strongly with the N-S temperature and precipitation gradients from the coast to the southern Sahel. Trends in urbanization may also indicate rural-urban migration patterns following environmental stress and soil fertility patterns that drive people south (Van der Geest, 2011). The relationship between factor C and B is likely dominated by this socio-environmental gradient. That said, the model does suggest that urban expansion may indirectly affect local climatology, albeit through an indirect link via an effect through vegetation losses. The model suggests strong causal linkages between urban expansion (Factor C) and low green cover (Factor A), which in turn has a strong feedback on Factor B. This observation suggests a scale-dependent relationship between local urban heat island effects, and the proximal drivers of these observations: while the model confounds effects of urban expansion with national-scale gradients in climatology, it captures the fine-scale effects of vegetation losses on elevated temperatures caused in part by reductions in local vegetation cover (Deilami et al., 2016; Simwanda et al., 2019). Areas with high population scores correspond with areas of low forest, most likely due to the agricultural expansion that accompanies migration within Ghana (Damnyag et al., 2013). Due to the substantial role it plays in Ghana’s economy and its continual expansion, agriculture has previousy been identified as a significant influence on deforestation (Kyere-Boateng and Marek, 2021).

One of the limitations of this study is that we were unable to incorporate specific measures of agricultural cover into the model due to a lack of available data. While we can avail multiple sources for estimating forest cover trends, a lack of accurate crop cover data hampers the ability of our model to directly address causality of losses in green cover in Ghana (Hackman et al., 2017). We have attempted to deduce trends in agricultural land use by controlling for forest cover against trends in generic indicators such as NDVI. Locations where forest degradation are superimposed with agricultural expansion may therefore be missed. Similarly, some amount of population expansion (largely encoded as urban growth in our model) may be masked by increasing agricultural land use (Braimoh and Vlek, 2005; Bicudo da Silva et al., 2020). Results therefore indicate that improvements in accurate agricultural LC mapping would likely improve model fits further as agricultural activities support a sizeable and growing Ghanaian population (Braimoh and Vlek, 2005). Further, the difficulty in distinguishing plantations (such as oil palm) from forests may also obscure model interpretation when functional land uses are considered (Chong et al., 2017). The two forest types are relatively easily distinguishable in high spatial resolution but not at coarser resolutions. While many of these issues are gradually being resolved with ongoing improvements in remote sensing instrumentation and algorithms, better access to accurate agricultural data will be needed to resolve several of these issues.

Figure 8 provides a visualization of spatial patterns of the latent vector scores produced by the model. Note that scores for Factors A, D, and E have been inverted to make interpretation clearer (i.e., darker colors indicate higher forest density, PPT levels, and vegetation increases, respectively). Taken together, these maps provide insights into the spatio-temporal variation in drivers of deforestation, urbanization, and bioclimatic changes across Ghana. We hope that these maps can form a starting point for further developing methods for informing regional-scale land use planning policies for sustainably managing Ghana’s landscapes.

Environmental systems naturally undergo transitions over time, but in recent decades, these shifts have become amplified, leading to largely unknown long-term effects. While location and ecological characteristics are the major determinants of LCC, human modification of the land surface can play a large role in altering natural dynamics. In this regard, remotely-sensed indicators of environmental change can be immensely helpful in measuring and characterizing dynamics of the Earth’s surface that might otherwise be difficult to assess. In this study of decadal LCC and environmental dynamics across Ghana, we found that RS-estimated measurements of surface reflectance, greenness, temperature, precipitation, and population change simultaneously impact, and are impacted by, complex interactions between location, socio-economics of migration and urbanization, and the biophysical impacts of such interactions. We find strong evidence of the impacts of spatially disaggregated and non-uniform urbanization and population growth, especially on forest cover, and indirectly on modulating the meso-scale climate of the region. The establishment of these causal-phenomenological relationships may allow predictions to be made about the future ecological state of Ghana using observable and future data. Understanding the nature of LCC in Ghana, and generating information on interconnections between putative determining factors may be important in guiding policy decisions at regional-to-national scales for moving forward with the nation’s development in a sustainable manner.