Mental health disorders have become a growing challenge globally. As research continues to emphasize the restorative properties of the environment, natural landscapes are increasingly recognized as an effective means to reduce disorders. Research on healthy landscapes may be enhanced and, in some cases, uniquely informed by human response data; however, the existing literature provides limited and insufficient synthesis on this topic. To address the gap, this study first proposes a four-stage classification framework for new measurement technologies based on the intrinsic processing phase through which individuals respond to environmental stimuli—neural processing, physiological adjustment, behavioral expression, and subjective representation—each aligned with its corresponding phase of the body’s response. Within each stage, a narrative review then synthesizes current technologies, their key indicators, applications, and potential mechanisms in healthy landscape research. Finally, we identify three emerging healthy landscape research directions based on the current research gaps, following: 1) applying multimodal measures for mechanistic insights, 2) clarifying affective subtype variability in environmental response, and 3) identifying multilevel mechanisms through embodied big data modeling. Overall, this work provides a theoretical lens and methodological foundation for probing complex human–environment interactions and for designing precision interventions in the digitally enhanced healthy landscapes.
Wang et al. (Thu,) studied this question.