The theoretical cognition and research paradigm of land system science - LONG Hualou, CHEN Kunqiu, ZHENG Yuhan, ZHANG Yingnan (Ziyuan Kexue)
The needs of national strategies, the dilemmas of urban-rural development and the shortcomings in disciplinary development put urgent requirement for the establishment of land system science. On the basis of clarifying the connotation and characteristics of land system science, this article analyzes the development history and constructs the theoretical and technological system, research paradigm and discourse system of land system science. As the interface between human society and the natural environmental system and the site of sustainable land use transitions, the land system is a complex mega-system characterized by spatio-temporal heterogeneity, sensitivity in scales, and the systematic feedback effects. Land system science is committed to a profound understanding of the human-environment interactions occurring on land, focusing on cross-temporal and spatial spillover effects and non-linear transformations of the land system, ultimately serving the formulation of sustainable land use solutions. The “human-land-right” trinity theoretical system of land system science involves four modules, including the domains of land resources, ownership, space and function, with land use as the key process and land ownership as the bond. The research paradigm of land system science has gradually transformed from knowledge and phenomenon description to the simulation and prediction of complex land systems, which emphasizes the integration of multidisciplinary theories and methods as well as understanding the local-distant coupled interactions of land systems based on the integrated multiscale analysis of expanding boundaries, and focuses on the construction of large-scale quantitative land system models to provide comprehensive and dynamic integrated analysis, prediction and simulation of its dynamic mechanisms, integrated effects and future scenarios and risks, and appeals for an opening scientific collaboration in the era of big data.