Dynamic monitoring and analysis of factors influencing ecological environment quality in northern Anhui, China, based on the Google Earth Engine

Abstract

Monitoring the ecological environment quality is an important task that is often connected to achieving sustainable development. Timely and accurate monitoring can provide a scientific basis for regional land use planning and environmental protection. Based on the Google Earth Engine platform coupled with the greenness, humidity, heat, and dryness identified in remote sensing imagery, this paper constructed a remote sensing ecological index (RSEI) covering northern Anhui and quantitatively analyzed the characteristics of the spatiotemporal changes in the ecological environment quality from 2001 to 2020. Geodetector software was used to explore the mechanism driving the characteristics of spatial differentiation in the ecological environment quality. The main conclusions were as follows. First, the ecological environment quality in northern Anhui declined rapidly from 2001 to 2005, but the rate of decline slowed from 2005 to 2020 and a trend of improvement gradually emerged. The ecological environment quality of Huainan from 2001 to 2020 was better and more stable compared with other regional cities. Bengbu and Suzhou showed a trend of initially declining and then improving. Huaibei, Fuyang, and Bozhou demonstrated a trend of a fluctuating decline over time. Second, vegetation coverage was the main influencing factor of the RSEI, while rainfall was a secondary factor in northern Anhui from 2001 to 2020. Finally, interactions were observed between the factors, and the explanatory power of these factors increased significantly after the interaction. The most apparent interaction was between vegetation coverage and rainfall (q = 0.404). In addition, we found that vegetation abundance had a positive impact on ecological environment quality, while population density and urbanization had negative impacts, and the ecological environment quality of wetlands was the highest. Our research will provide a theoretical basis for environmental protection and support the high-quality development of northern Anhui.

The Dynamic Monitoring and Driving Forces Analysis of Ecological Environment Quality in the Tibetan Plateau Based on the Google Earth Engine

“…Moreover, the Google Earth Engine (GEE) platform could invoke the medium-resolution imaging spectroradiometer (MODIS), Landsat, and other massive remote sensing (RS) data products. With its robust cloud computing and storage capabilities, GEE has become a valuable tool for evaluating large-scale, long-term dynamics to calculate the RSEI [24][25][26][27][28].…”

Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)

“…It replaces the traditional desktop processing platforms that require significant time and hardware resources for pre-processing such as acquisition, stitching, atmospheric correction, and cloud and shadow removal of massive remote sensing data [40,41]. Researchers have combined GEE with the RSEI for EQ assessment and monitoring in regions such as the Jianghan Plain (Yi et al, 2023), the Loess Plateau (Gong et al, 2023), northern Anhui, China (Wang et al, 2022), the Erhai Lake Basin in Yunnan Province, China (Xiong et al, 2021), and the Yellow River Basin (Yang et al, 2022) [42][43][44][45][46]. This also demonstrates the convenience and efficiency of using the GEE platform for the RSEI assessment on a large scale and over extended time series of imagery.…”

“…This also demonstrates the convenience and efficiency of using the GEE platform for the RSEI assessment on a large scale and over extended time series of imagery. In previous research, the application of the RSEI has typically involved performing analyses based on the synthesis of images from two or more discrete time periods, but it cannot appropriately reflect the continuous long-term EQ changes [26,43,44,47]. Detailed changes in EQ over time scales can be better captured by analyzing time series trajectories [48].…”