Disease transmitted by mosquitoes present some of the most pressing challenges facing human health today. Land-use change is a key driver of disease emergence, however the mechanisms linking environmental covariates of change, such as temperature, to the transmission potential of mosquitoes is poorly understood. Studies exploring these relationships have largely been correlative in nature, and thus have limited capacity to predict dynamics through space and time. Mechanistic approaches provide a valuable framework for understanding the processes underlying transmission, however they suffer from a dearth of field data on fundamental mosquito ecology. In both approaches, the environmental data used is typically coarse in scale and interpolated from weather stations located in open areas. In reality, local climatic conditions can vary considerably over fine spatial and temporal scales, particularly in dynamic working landscapes. Wild mosquitoes experience and respond to this highly dynamic environment, and failing to account for this variation may have significant implications for the accuracy of epidemiological models. This thesis uses an established epidemiological framework to explore the effects of tropical forest conversion to oil palm plantation on the potential for Ae. albopictus mosquitoes to transmit disease. Using field-derived microclimate data and published thermal responses of mosquito traits, I first examine how the scale of environmental data affects predictions of mosquito demography under land-use change. Next, I conduct field experiments to investigate whether microclimate heterogeneity across a land-use gradient drives variation in the rates of larval development. By pairing fine-scale microclimate data with temperature-dependent trait estimates, I find that forest conversion significantly increases the potential of Ae. albopictus to transmit disease. Together, these findings advance our understanding of Ae. albopictus ecology, and highlight the importance of incorporating fine-scale environmental and mosquito data into epidemiological frameworks to better understand disease risk in changing landscapes.