Changes in the “Production-Living-Ecological Space” Pattern in the Interlocking Mountain and River Zones of the Yellow River Basin—Taking Xinxiang City as an Example

Abstract

Based on the land use data of Xinxiang City from 2010 to 2020, this study integrates the methods of dynamic degreetransfer matrix, landscape pattern index and geographical detector to explore the quantitative structural changes, mutual transformations and landscape pattern characteristics of the “production-living- ecological space” (PLES) in Xinxiang City, and also analyzes the driving factors that affect the characteristic changes to reveal the laws governing the changes in the PLES and the current land use process. The results of this study reveal four important aspects of this system. (1) The changes in the PLES in Xinxiang City have accelerated significantly, and the overall performance shows that the production space continues to decrease, the living space keeps increasing, and the ecological space changes are more stable. (2) Regarding the spatial transfer characteristics, the ecological space has mainly transformed to production space, and the production space has mainly transformed to living space. (3) The landscape pattern characteristics show that the landscape types in Xinxiang City are diversified in terms of their components. (4) The spatial differentiation of the PLES is influenced by the combined effect of socio-economic and natural factors. Based on the empirical research results, we can not only propose corresponding optimization strategies for the better utilization of the PLES in Xinxiang City, but also provide important scientific references for the high-quality development of the prefecture-level cities in the Yellow River Basin.

Study on the Ecological Environment quality and its driving factors of the Spatial Transformation of Production-Living-Ecological Space in Baishan City

“…The land use data come from the Resources and Environment Science data Center of the Chinese Academy of Sciences, selects LandsatTM/OLI remote sensing images with cloud coverage of less than 10%, with a spatial resolution of 30m, through geometric correction, mosaic, image enhancement and other preprocessing [19][20] , using supervised classification, artificial visual interpretation and field investigation. The accuracy of the classification results is tested (kappa coefficients are all greater than 0.88), and the land use status data of 2000, 2010 and 2020 are finally generated.…”

Influence of Natural and Social Economic Factors on Landscape Pattern Indices—The Case of the Yellow River Basin in Henan Province

“…The pivotal role of human-induced land use change in shaping landscape heterogeneity cannot be ignored. It significantly impacts ecological processes within the layout of the landscape by reshaping the interaction between human activities and the surrounding ecosystem [47,48]. Bivariate spatial autocorrelation has been used to study the spatial correlation of influencing factors.…”