Our research questions and analytical approaches are used to examine coupled human-natural systems in the Northern Ecuadorian Amazon. They are based on complexity theory and extend from our earlier work in Cellular Automata (CA) in which land use/land cover (LULC) change patterns were spatially simulated to examine deforestation and agricultural extensification on household farms. The basic intent is to understand linkages between people and the environment by explicitly considering pattern-process relationships and the nature of feedback mechanisms among social, biophysical, and geographical factors that influence LULC dynamics within the study area. In this research, we describe how our CA modeling approach emphasizes the human dimensions of LULC change by including socio-economic and demographic characteristics at the household-level along with biophysical data that describe the resource endowments of farms, geographic accessibility of farms to roads and communities, and the evolving nature of human-environment interactions over time and space in response to exogenous and endogenous factors. A LULC change scenario is examined by comparing model outcomes generated for a base CA model and an alternative CA model to explore the effects of increases in household income on land use change patterns at the farm level, achieved as a consequence of improved geographic accessibility to roads and communities and increased off-farm employment as a household livelihood strategy. Growth or transitions rules in our CA model, as well as neighborhood associations are sensitive to socio-economic and demographic factors of households, resource endowments of farms, geographic accessibility, and the uncertainty associated with peasant farming in a frontier setting. Model outcomes indicate that increases in household income are associated with more land in pasture and more land being cultivated for crops as a result of greater access to agricultural markets. In addition, more land in secondary forest succession occurs as a consequence of greater access to roads and communities, thereby, affording a better opportunity for off-farm employment and greater levels of household income.