Analysis of Spatiotemporal Variation and Drivers of Ecological Quality in Fuzhou Based on RSEI

Abstract

Background: High-speed urbanization has brought about a number of ecological and environmental problems, as well as the use of remote sensing to monitor the urban ecological environment and explore the main factors affecting its changes. It is important to promote the sustainable development of cities. Methods: In this study, we quantify the ecological quality of the study area from 2000 to 2020 based on the remote sensing ecological index (RSEI) and analyze its drivers through Geodetector and geographically weighted regression. Results: The RSEI of Fuzhou City from 2000 to 2020 showed an increasing followed by a decreasing trend, with obvious spatial autocorrelation. The main driving factors causing the spatial divergence of the RSEI were elevation (q = 0.48–0.63), slope (0.42–0.59), and GDP (0.3–0.42), and the driving effect and range of each factor changed with time. Conclusion: In this paper, we explore changes in the ecological environment in Fuzhou City over the past 20 years, as well as the scope and magnitude of the drivers, providing an important reference basis to improve the ecological environment quality of the city.

A regional assessment of ecological environment quality in Thailand special economic zone: Spatial heterogeneous influences and future prediction

“…The natural factors include topographic features, annual precipitation, mean temperature, and distance to major rivers. Topographic features such as elevation and slope can regulate the EEQ through related factors, for example, it is better in higher elevations with fewer disturbances from human activities (Geng et al, 2022; Yan et al, 2021). The topographic features are expected to substantially differentiate the EEQ in the EEC as it is an area with diverse and complex topography (Figure 1).…”

“…We also endeavor to include socioeconomic factors in the analyzes to obtain a diversely representative set of factors. Population density is a widely adopted variable to reveal the contributions of social aspects to EEQ because urbanization is always associated with population growth and adverse impacts on environment (Cui et al, 2022; Geng et al, 2022).…”

“…The RSEI is able to monitor the EEQ under various conditions, such as urban agglomeration, LULCC, land consolidation, mining activities, and geohazards (Airiken et al, 2022; Nie et al, 2021; Shan et al, 2019; Xu, Zhao, et al, 2021; Yan et al, 2021; Zhao et al, 2021). The EEQ depends on disparate factors at different extents, such as climate, terrain, soil properties, landscape patterns, and socioeconomic and anthropogenic elements (Geng et al, 2022; Tang et al, 2022; Wang, Ding, et al, 2022; Zhang, Feng, et al, 2022; Zhang, She, et al, 2022). However, the impacts of a certain factor on the EEQ in different regions and subregions may not be the same.…”

“…The current approaches to analyzing EEQ (e.g., Pearson correlation, linear regression, and geodetection) may not adequately depict the spatial heterogeneity effects leading to underestimation, particularly over a large area (Geng et al, 2022; Pan et al, 2022; Tang et al, 2022). Geographically weighted regression (GWR) can take spatial heterogeneity into account to better reveal the relationship variations and local influence mechanisms compared to overall regression models (Wang, Wang, & Li, 2019; Zhi et al, 2020).…”

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

“…Morphology-based filtering separates above-ground and bare-earth points using an opening operation [39][40][41], and it is robust in steep areas while removing smaller non-ground features. Slope-based strategies [42][43][44] aim to distinguish bare-earth points from above-ground points by detecting inconsistent slope changes. Hence, they are more effective in flat areas but less so in areas with drastic terrain changes.…”