Spatio-Temporal Variations in Ecological Quality and Its Response to Topography and Road Network Based on GEE: Taking the Minjiang River Basin as a Case

Urbanization has rapidly increased, leading to a wide range of significant disruptions to the global ecosystem. Road construction has emerged as the primary catalyst for such ecological degradation. As a result, it is imperative to develop efficient technological approaches for quantifying and tracking alterations in the ecological environment. Additionally, it is crucial to delve deeper into the spatial correlation between the quality of the ecosystem and the urban road network. This is of crucial importance in promoting sustainable development within the region. In this study, the research area selected was the Minjiang River Basin (MRB). We made optimal use of the Google Earth Engine (GEE) cloud platform to create a long-term series of remote sensing ecological index (RSEI) data in order to assess the quality of the ecological environment in the area. Additionally, we integrated digital elevation data (DEM) and OpenStreetMap (OSM) road network data to investigate the response mechanisms of RSEI with regard to elevation, slope, and the road network. The findings were as follows: (1) There were two distinct phases observed in the average value of RSEI: a slow-rising phase (2000–2010) with a growth rate of 1.09% and a rapidly rising phase (2010–2020) with a growth rate of 5.36%; the overall 20-year variation range fell between 0.575 and 0.808. (2) During the period of 2000 to 2010, approximately 41.6% of the area exhibited enhanced ecological quality, whereas 17.9% experienced degradation. Subsequently, from 2010 to 2020, the proportion of the region with improved ecological quality rose to 54.0%, while the percentage of degraded areas declined to 3.8%. (3) With increasing elevation and slope, the average value of RSEI initially rose and then declined. Specifically, the regions with the highest ecological quality were found in the areas with elevations ranging from 1200 to 1500 m and slopes ranging from 40 to 50°. In contrast, areas with an elevation below 300 meters or a slope of less than 10° had the poorest ecological quality. (4) The RSEI values exhibited a rapid ascent within the 1200 m buffer along the road network, while beyond this threshold, the increase in RSEI values became more subdued. (5) The bivariate analysis found a negative correlation between road network kernel density estimation (KDE) and RSEI, which grew stronger with larger scales. Spatial distribution patterns primarily comprised High–Low and Low–High clusters, in addition to non-significant clusters. The southeastern region contained concentrated High–Low clusters which covered approximately 10% of the study area, while Low–High clusters accounted for around 20% and were predominantly found in the western region. Analyzing the annual changes from 2000 to 2020, the southeastern region experienced a decrease in the number of High–Low clusters and an increase in the number of High–High clusters, whereas the northwestern region showed a decline in the number of Low–High clusters and an increase in the number of non-significant clusters. This study addresses a research gap by investigating the spatial correlation between road distribution and RSEI, which is vital for comprehending the interplay between human activities and ecosystem services within the basin system

The Spatial Structure and Driving Mechanisms of Multi-Source Networks in the Chengdu–Chongqing Economic Circle of China

The phenomenon of polarized development among regional cities has sparked extensive contemplation and indicated a need for research on multi-source regional networks. However, such research faces two obstacles: the absence of quantitative measurement of differences in network structures and the lack of a thorough examination of the degree of city clustering and the dynamics of community composition in hierarchical networks. Thus, we identified 16 cities in the Chengdu–Chongqing Economic Circle (CCEC) as the spatial units to examine the spatial network structures of population, resources, and transportation and the integrated spatial network structure. Using social network analysis, this paper describes the structural characteristics of the three networks (population, resource, and transportation), followed by an analysis of their collective and hierarchical network clustering characteristics, and explores the driving mechanisms and factors that make up each network model. Our results show the following: (1) All three networks exhibit an “east dense, west sparse” characteristic, but there are differences in the layouts of the core cities in terms of the three networks. (2) The clustering characteristics of the hierarchical networks are more pronounced than those of the overall network. The results of the analysis combined with the network formation mechanisms can help effectively plan the future coordinated development of the CCEC

Spatiotemporal Changes in Ecological Quality and Its Response to Forest Landscape Connectivity—A Study from the Perspective of Landscape Structural and Functional Connectivity

Understanding the response of ecological quality (EQ) to forest landscape connectivity is essential to global biodiversity conservation and national ecological security. However, quantitatively measuring the properties and intensities within these relationships from a spatial heterogeneity perspective remains challenging. This study takes the Fujian Delta region as its case study. The Google Earth Engine platform was employed to compute the remote sensing ecological index (RSEI), the landscape metrics were applied to represent the structural connectivity of the forest landscape, and the minimum cumulative resistance model was adopted to measure the cost distance index representing the functional connectivity of the forest landscape. Then, the spatial correlation and heterogeneity between the EQ and forest landscape connectivity were analyzed based on spatial autocorrelation and geographical weighted regression at three scales (3, 4, and 5 km). The results showed the following: (1) from 2000 to 2020, the overall EQ increased, improving in 37.5% of the region and deteriorating in 13.8% of the region; (2) the forest landscape structural and functional connectivity showed a small decreasing trend from 2000 to 2020, decreasing by 1.3% and 0.9%, respectively; (3) eight forest landscape structural and functional connectivity change modes were detected under the conditions of an improving or degrading EQ based on the change in RSEI and forest landscape structural and functional connectivity; (4) the geographical weighted regression results showed that compared with the forest landscape structural connectivity index, the cost distance index had the highest explanatory power to RSEI in different scales. The effect of forest landscape functional connectivity on EQ is greater than that of structural connectivity. It provides a scientific reference for ecological environmental monitoring and the ecological conservation decision-making of managers