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

Abstract

The Yellow River Basin holds significance as a vital ecological shield and economic hub within China. Adapting land utilization practices and optimizing landscape patterns are of paramount significance in preserving the ecological equilibrium of the Yellow River Basin while fostering high-quality economic development. In this study, we selected the Yellow River Basin in Henan Province as our research area. We use a land use transition matrix and FRAGSTATS 4.2 software to analyze changes in land use and landscape patterns within the study area from 1990 to 2020. Furthermore, Geographical Detector is employed to explore the impact of different natural and social economic factors that have influenced the progress of the landscape surface pattern in the study area. Finally, to identify the zonal aggregation effects of primary components in connection with landscaping feature indices at the city dimension, we use bivariate local spatial autocorrelation. The results are as follows: (1) In terms of land use change characteristics, the area of cultivated land, grassland, shrubs, and bare land shows a decreasing tendency, the area of construction land and forest land shows an increasing tendency, and the water area fluctuates and changes. Most of the cultivated land is shifted to construction land, followed by forest land, construction land, and cultivated land mainly transferred from grassland. (2) At the level of type in terms of shifting landscape patterns, cultivated land, forest land, water, and construction land have a more complex landscape shape, reduced fragmentation, and better natural connectivity. At the overall level, the overall landscape pattern indices are relatively stable, with more patch types and a more balanced distribution. (3) The findings regarding influencing factors reveal that the primary industry output value, population, secondary industry output value, and temperature are the principal driving forces behind the progress of the landscape surface pattern. The main drivers have changed over time in different regions. As indicated by the findings from bivariate local spatial autocorrelation analysis, at the city scale, the leading cause of landscape fragmentation in Luoyang is the primary industry output value, while in Xinxiang, landscape fragmentation is primarily driven by the secondary industry output value and temperature. In this study, we introduce the bivariate local spatial autocorrelation method to analyze the clustering effects of key influencing factors and landscape patterns at the city scale. This is crucial for the harmonized growth of land use planning and the urban economy in the Yellow River Basin.