Evaluation of Urban Ecological Environment Quality Based on Google Earth Engine: A Case Study in Xi’an, China

Abstract

The rapid expansion of cities has accelerated the impact of human activities on the ecological environment. Existing studies conducted quantitative evaluations of ecological environment quality through complex remote sensing image screening and processing. Dynamic monitoring and modeling based on cloud platform programming are still lacking at present. This paper evaluated the quality of the ecological environment in Xi'an in 2000Xi'an in , 2005Xi'an in , 2010Xi'an in , 2015Xi'an in , and 2020 using the remote Sensing Ecological index (rSEi) model. We selected high-quality remote sensing images based on the Google Earth Engine platform (GEE). The calculation of four indicators and the Principal Component Analysis (PCA) were performed on the platform using JavaScript. The geodetector model was used to detect factors affecting the spatial differentiation of rSEi. The results showed that: (1) The ecological environment quality of Xi'an city during 2000-2020 showed a temporal trend of decreasing and then increasing, and a spatial trend of north-low and south-high. The rSEi value for Xi'an was 0.665 in 2000, 0.653 in 2005, 0.623 in 2010, 0.644 in 2015, and 0.651 in 2020. (2) The Bad level and Fair level of rSEi were mainly distributed in the city's built-up area, and rSEi values were better in the Qinling Mountains. From 2000 to 2020, we found the deterioration areas of rSEi mainly distributed in the Weiyang, Baqiao, Yanliang, Chang'an, and Huyi districts. The improvement areas were mainly distributed in the southeastern mountains.(3) From the geodetector results, elevation (DEM), slope (SLO), precipitation (PrE), temperature (TEM), distance to main roads (rOA), distance to settlements (SET), land use/land cover (LUC), GDP, and population (POP) significantly influenced the regional RSEI spatial differentiation. The rankings of the explanatory power of the single factors were mainly: PrE>TEM>DEM> rOA>LUC>SLO>GDP> SET>POP. PrE has the strongest explanatory power of the nine factors. (4) Positive spatial auto correlation existed for the rSEi values in Xi'an.

Assessment and Simulation of Urban Ecological Environment Quality Based on Geographic Information System Ecological Index

“…The ecological environment quality in Xi'an will be seriously deteriorated by 2030 under the natural development scenario. However, Yang [9] dynamically monitored the ecological quality of Xi'an over the past 20 years and showed that the overall ecological quality of Xi'an will first decrease and then increase. The reason for this is that the theory that lucid waters and lush mountains are invaluable assets was proposed in 2005 and implemented nationwide in 2017, which has led to increased protection of the ecosystem.…”

“…At present, studies on ecological environment quality assessment can be categorized into two types: calculating an RSEI based on remote sensing (RS) and calculating an ecological index (EI) based on a geographic information system (GIS) [7]. Among them, the RSEI is widely used due to the advantages of easy data acquisition, simple calculation methods, and objective and reliable assessment results [8][9][10], especially in the dynamic monitoring of ecological changes. Jing [11] used the RSEI to assess the ecological quality of the Ebi Lake wetland in Xinjiang and used the results to investigate the factors influencing the ecological quality, which provided an important basis for the ecological protection of the wetland.…”

Does City Honor Drive Companies to Remove the Mask of Greenwashing? Evidence from the National Civilized City Program in China

“…Yang pointed out that there is a spatial spillover effect of urban environmental quality improvement. In order to avoid being in a disadvantageous position in urban competition, neighboring cities will learn from each other and follow each other's behavior [18]. As a new urban governance tool, the selection of civilized cities will undoubtedly stimulate the participation of local governments, which will emulate the excellent results already achieved by neighboring cities.…”

Research on the Resilience Evaluation of Rural Ecological Landscapes in the Context of Desertification Prevention and Control: a Case Study of Yueyaquan Village in Gansu Province

“…Rural landscape ecology encompasses ecological, technical, and socio-cultural aspects, aiming to provide a suitable living environment and promote sustainable and diverse development. However, villages developed in desertification control areas pose a significant threat to the landscape pattern of rural human-land systems, as they are susceptible to external influences [13]. Hence, it is crucial to study how to enhance the rural landscape in cold and arid regions, considering ecological, social, and environmental factors.…”