Impact of landscape patterns on ecosystem services in China: a case study of the central plains urban agglomeration

Abstract

Urban agglomeration is the highest stage of urban development, which reasonable planning will be conducive to the rapid and healthy development of the regional economy. However, in recent years, unreasonable urban agglomeration planning has changed landscape patterns and brought huge challenges to ecosystem services. Moreover, there is currently a lack of understanding of the relationship between landscape patterns and ecosystem services, especially in the process of urban agglomeration construction. In this study, we attempt to reveal the impact of landscape patterns on ecosystem services value (ESV) based on many years of remote sensing data in the Central Plains Urban Agglomeration (CPUA). The results show that landscape patterns have significant spatiotemporal evolution characteristics, and there are significant differences between different landscape patterns in the CPUA. Moreover, most of landscape patterns show significant east-west differences. Total ESV has decreased from USD 838.53 million in 2000 to USD 792.32 million in 2018. From a spatial perspective, the areas with high ESV are mostly distributed in the western mountainous and southern hilly areas, but low ESV area are mainly concentrated in the central and northern plains. In addition, the results of model analysis reveal that there is a remarkable positive correlation between ESV and patch density, interspersion juxtaposition index, cohesion index, and splitting index, but a remarkable negative correlation between ESV and total edge. These results provide critical guidance and suggestions for the improvement of future urban agglomeration construction.

Analyzing Spatial–Temporal Characteristics and Influencing Mechanisms of Landscape Changes in the Context of Comprehensive Urban Expansion Using Remote Sensing

“…Therefore, various types of remote sensing data are widely used in the study of urban problems [47,48]. Since remote sensing data can provide continuous land cover/use data, a large number of studies have been conducted to analyze urban landscape change based on remote sensing data [42,[49][50][51]. In this study, landscape index and its driving factors were extracted from multi-source remote sensing data to help realize long-term and large-scale landscape pattern research.…”