## 1 Introduction  
Global ecosystem services are finite resources stressed by global environmental change with pressures from both natural processes and human activities [1]. According to the United Nations World Population Report, nearly 55% of the world population lives in urban areas, while a significant percentage (90%) of Sub-Saharan Africans (SSA) live in rural areas [2]. Most of Africa's population is dependent on the land for subsistence farming, small human settlements, and raw materials from forests [3]. Malawi's 9.4 million hectares (ha) of land exemplifies these concerns and national environmental resources face increasing pressures [4]. In Malawi, 61.4% of all land is in agricultural production [5] accounting for over 38% of Malawi's Gross Domestic Product (GDP) and providing livelihoods for 85% of Malawi's population, which is largely smallholder farmers [1]. More than 1.6 million smallholder farmers in Malawi grow maize as the primary crop, cultivating an average of 0.4 hectares of land [1]. Regular droughts, as well as limited availability of input resources for soil amendments exacerbated by over-cultivation of soils, threaten crop yields [6, 7, 8, 9]. Moreover, the high demand for forest products and lack of energy alternatives to firewood and charcoal have contributed to deforestation, and forest and land degradation [10]. Land-tenure insecurity also remains a challenge along with the slow implementation of land reforms [11]. The government of Malawi has applied short- and long-term localized-national strategies (labeled the Malawi Growth and Development Strategy (MGDS)) to advance agricultural development aimed at food and nutrition security, as well as improving economic growth and infrastructure development [12; 13]. Recently, the Malawi government launched the Vision 2063 plan, the end of which corresponds to 100 years of self-governance in Malawi. Vision 2063 commits to addressing the interconnected challenges through improved governance in land tenure systems and sustainable land management, emphasizing the role of public-private partnerships and transformations through investments in community engagement [12; 13]. Land cover and land use are inherently coupled. To capture these related systems, indicators of anthropogenic changes are often quantified in the form of land use land cover (LULC) change trajectories using remote sensing imagery as well as models of driving factors and predicted land-cover changes [14; 15; 16; 17]. Methodological advances enable the integration of imagery and underlying drivers. For example, Markov Chains embedded within diverse statistical models are good predictors of the probability of land cover change [18; 19; 20]. Advanced computation, such as Cellular Automata-Markov chain spatial modeling, predicts changes using a set of replicable rules, and are robust approaches when applied together more so than individually [21; 22; 23]. Previous studies that have utilized robust statistical techniques and algorithms in land use retrieval include the use of the support vector machine (SVM) algorithm to identify non-linear relationships in land use/land cover change for rural to urban transition in Hyderabad, Pakistan [24]; the use of integrated approaches, including landscape fragmentation, machine learning algorithm and fuzzy logic model to determine built-up expansion probability in English Bazar Block, West Bengal, India [25]; as well as the use of multiple classifiers (SVM, Maximum Likelihood (ML) and Random Forest (RF) with multi-modal imageries (Sentinel-1 and Sentinel-2) to understand unique spatial landscape features such as natural resource distribution, human settlements and cultural landscapes in the Sahel region of Niamey, Niger [26]. Recently, studies using Multilayer Perceptron (MLP), a neural networking model together with Markov Chain (MC) to model spatiotemporal land-cover change based on driving factors such as socio-environmental variables across landscapes have emerged.