In their recent paper, Liu and co-authors proposed a novel and highly efficient mapping model to address this shortcoming. The model is explicitly designed to regularly generate accurate geospatial information relating to rural settlements, demonstrated in their case through application to the rapidly urbanizing region of Beijing-Tianjin-Hebei of northern China. This innovative approach enables various strategies for optimizing spatial patterns within rural areas to be evaluated. With the aim of developing a deeper understanding of spatiotemporal changes in rural settlements, the novel mapping method utilizes a combination of multi-source data-fusion techniques, spatial statistics, and topological analysis. Specifically, the model leverages high-resolution satellite imagery and spatial autocorrelation metrics to capture the complex relationships between various land-use classes and evolving socio-economic conditions in the rural context. In mining remote sensing data to identify the rural land-use patterns, the mapping approach facilitates the extraction of detailed, pertinent information from extensive datasets, thereby significantly improving our understanding of rural settlement dynamics and generating knowledge that enables more appropriate management interventions. Utilizing spatial analysis techniques and geo-statistical modeling, it involves the analysis of multi-temporal satellite imagery, topographical maps, and land-use data. Coupled with participatory mapping, the incorporation of community perspectives is also assured. Besides, Liu and co-authors identified a typology of four temporal trend patterns, revealing the evolutionary paths of rural settlements in different counties. Again, such patterns and their details are of great value to regional planners in their efforts to foster economic growth while conserving the resource potential of rural areas.
In summary, the scientific contribution of the study lies in its extending both the theoretical foundation and methodological practices that underpin research on urban-rural transformation and in enabling quantification that offers a more intelligent and nuanced under- standing of the associated processes. This in turn adds value through highlighting and promoting the interdisciplinary integration of geography in the new era that is needed to support research on global sustainable development. Several future research directions are possible: (1) Using higher spatial resolution impervious surface area data and applying deep learning algorithms to capture edge features would enable even more accurate delineation of rural settlement boundaries; (2) Conducting a thorough analysis of the historical mechanisms driving rural settlement changes assists in forecasting potential future land use patterns and in exploring suitably sustainable rural development pathways; (3) Combining multi-source data and methodologies to further quantify the impact of land policies on rural settlement changes and, accordingly, developing more appropriate policy scenarios for future land use planning; (4) Considering the impact of climate change and rapid urbanization on the environment, construct a coordinated nature-society development index from the perspective of human-earth system science that quantitatively evaluates rural sustainable development effectiveness in the context of multiple objectives. This innovative modelling approach yields unique insights into the changing characteristics of rural areas, addresses the rural–urban divide, and may help in supporting targeted policy interventions and coordinated urban– rural planning strategies towards sustainable development.
【Author】Michael E. Meadows is Professor in the School of Geography and Ocean Sciences at Nanjing University, China, and Senior Research Scholar in the Department of Environmental & Geographical Science at the University of Cape Town, South Africa. His research interests lie broadly in the field of physical geography, especially human impact on the environment, including land degradation and desertification. He is President of the International Geographical Union 2020-2024.
【Link】https://doi.org/10.1016/j.scib.2024.04.028