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Around the world today, the magnitude and rates of environmental, social, and economic change are undermining the sustainability of many rural societies that rely directly on natural resources for their livelihoods. Sustainable development efforts seek to promote livelihood adaptations that enhance food security and reduce social-ecological vulnerability, but these efforts are hampered by the difficulty of understanding the complexity and dynamism of rural livelihood systems. Disparate research avenues are strengthening our ability to grapple with complexity. But we are only just beginning to find ways to simultaneously account for problematic complexities, including multiscalar feedbacks in the ecosystems that that support livelihoods, the heterogeneous benefits garnered by different segments of society, and the complex contingencies that constrain people’s decisions and capacities to adapt. To provide a more nuanced analysis of the dynamics of transformation in rural livelihood systems, we identified key complementarities between four different research approaches, enabling us to integrate them in a novel research framework that can guide empirical and modeling research on livelihood adaptation. The framework capitalizes upon parallel concepts of sequentiality in (1) ecosystem services and (2) livelihood adaptation scholarship, then incorporates principles from (3) adaptation in social-ecological systems research to account for the dynamism inherent in these often rapidly-transforming systems. Lastly, we include advances in (4) agent-based modeling, which couples human decisions and land use change and provides tools to incorporate complex social-ecological feedbacks in simulation studies of livelihood adaptation. Here we describe the new Ecosystem Services—Livelihood Adaptation (ESLA) framework, explain how it links the contributing approaches, and illustrate its application with two case studies. We offer guidance for its implementation in empirical and modeling research, and conclude with a discussion of current challenges in sustainability science and the contributions that could be gained through research guided by the ESLA framework.

During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi‐resolution information for analysis. Climate change, land‐use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high‐density, and high‐resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio‐Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross‐site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio‐environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.