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

Xueman Zuo, Jiazheng Li, Ludan Zhang, Zhilong Wu, Sen Lin and Xisheng Hu ()
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Xueman Zuo: College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Jiazheng Li: College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Ludan Zhang: College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Zhilong Wu: College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Sen Lin: College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Xisheng Hu: College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Land, 2023, vol. 12, issue 9, 1-25

Abstract: 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.

Keywords: Minjiang River Basin; remote sensing ecological index; kernel density estimation; Google Earth Engine (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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