With the rapid development of urbanization and industrialization, China’s metropolitan areas have experienced dramatic transitions of land use, which has had a profound impact on the eco-environment. Accordingly, the contradictions of regional production, living, and ecological spaces have intensified. In this context, analysis of the dynamics of regional production–living–ecological (PLE) spaces has become an important entry point for studying land use transition and its eco-environmental effects, by constructing a classification system of PLE land functions. Using remote sensing data from four periods from 1985 to 2018, this paper explores the spatiotemporal evolution characteristics of PLE spaces and their eco-environmental effects in the Beijing–Tianjin–Hebei (BTH) urban agglomeration, based on GIS and the InVEST model. The results revealed that from 1985 to 2018, the living space of the BTH region expanded rapidly, the production space gradually shrank, and the ecological land remained relatively stable. The eco-environmental quality index within the study area shows obvious regional differences, demonstrating the spatial distribution of “high in the northwest and low in the southeast”, and an overall deteriorating trend in the past 33 years. Moreover, the carbon density decreased gradually from northwest to southeast, and the transformations from production land into living land and from ecological land into production land were the major types of eco-environment deterioration. Our findings will provide guidelines for land use management, and offer references for the functional division of PLE spaces and ecological civilization construction, especially in terms of the coordinated development of the BTH region.

Vegetation cover plays a key role in terrestrial ecosystem; therefore, it is important for researchers to investigate the variation and influencing factors of vegetation cover. China has experienced a large-scale vegetation cover change in recent years. We summarized the literature of vegetation cover change and revealed how large-scale anthropogenic activities influence vegetation cover change in China. Afforestation and intensification of cropland played a key role in large-scale greening. Urbanization showed a “U” shape to influence vegetation cover change. Mining and reclamation, land abandonment and land consolidation, and regional natural protection all had a unique influence on the change of vegetation cover. Indeed, the large-scale vegetation cover change was caused by interaction of anthropogenic factors and part human-driven climate change. Anthropogenic factors influenced climate change to indirectly alter the condition of plant growth. Interaction between climate change and human activities influence on vegetation cover still needs to be further investigated in the future.

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

Social-ecological system (SES) archetype has been an emerging research frontier in landscape and sustainability science which acts as a key tool in understanding complex human-environmental interactions in different social-ecological contexts, diagnosing context-specific sustainability challenges and developing transferrable solutions that can be adapted effectively to multiple locations. However, state-of-art mapping of SES archetypes are mostly bounded to administrative borders, andthe lack of spatial details in SES maps hinders us from capturing the panorama of spatial heterogeneity and undergoing challenges of SESs. Here, we propose a conceptual framework to characterize SES archetypes from the perspective of human- environmental interactions, and develop a novel data-driven approach to operationalise it for mapping grid-scale SES archetypes at an unprecedented spatial resolution by combining multiple open-access data streams (remote sensing, model simulations and geospatial big data). By applying this approach to the Beijing-Tianjin-Hebei (BTH) urban agglomeration,our analyses present a two-tier system of SES archetypes consisting of 5 typical SES archetypes, i.e. urban systems, urban-rural transition systems, agricultural systems, ecological transition systems and natural systems, and 11 regional SES subarchetypes. These findings are capable of retaining both general patterns and local details of SESs, which can contribute to identifying location-specific sustainability challenges faced by different SESs, and therefore to provide spatial templates for targeting policy responses to common challenges such as climate change impacts, air pollution, biodiversity loss, urban-rural disparity and cropland abandonment, etc.Besides, we reveal an interesting social-ecological gradient across SESs about trade-offs in the presence and influence between the human part and natural part of human-environmental interactions, which can further improve our understanding of their inherent mechanisms.Generally, our approach demonstrates great potentials for being generalized to other locations to facilitate the recognition of local needs and gaps in existing policies and promote sustainable landscape management, and also provides opportunities in scaling up case-based knowledge to advance SESs theories for addressing global challenges in the Anthropocene.