Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

A two-dimensional four-quadrant assessment method to explore the spatiotemporal coupling and coordination relationship of human activities and ecological environment

Human activities that involve diverse behaviors and feature a variety of participations and collaborations usually lead to varying and dynamic impacts on the ecological environment. Quantitative analysis of the dynamic changes and complex relationships between human activities and the ecological environment (eco-environment) can provide crucial insights for ecological protecting and balance maintaining. We proposed a two-dimensional four-quadrant assessment method based on the dynamic changes in Human Activity Index (HAI) - Environmental Ecological Condition Index (EECI) to analyze the dynamic trends and coupling coordination degree (CCD) between HAI and EECI. This approach was applied in an empirical study of Hainan Province. A comprehensive HAI at a resolution of 1 km × 1 km is established to measure human activities, while an EECI is developed to evaluate ecological environment quality. The eco-environment showed continuous improvement, with the HAI initially rising and then declining. Analysis of coupling coordination revealed a ratio of 6:1 between coordinated development regions and conflict regions, indicating a gradual improvement in overall coupling coordination. The interaction between the HAI and EECI is strengthening, though variations exist across different locations. Using the geodetector method, we identified Net Primary Productivity (NPP), Land use and land cover (LULC), and Particulate Matter (PM) as the primary factors influencing changes in coupling coordination between HAI and EECI. These factors indirectly affect the stability and carrying capacity of the ecological environment. This method facilitates a quantitative examination of the dynamic relationship between HAI and EECI in different regions, offering insights into ecosystem functionality, biodiversity maintenance, and the effect of HAI on the region.

Exploring dynamics relationship between carbon emissions and eco-environmental quality in Samarinda Metropolitan Area: A spatiotemporal approach

Carbon emissions have a negative impact on climate change. Environmental quality has faced significant challenges in the last decades. Eco-environmental quality helps assess the condition of the ecological environment to support humans' civilization and development. By using emissions raster dataset, remote sensing images, and LULC data, this study explores the status of carbon emissions (CE), eco-environmental quality (RSEICs), and the dynamic relationship between both variables in Samarinda Metropolitan Area, Indonesia. This study uses the spatiotemporal approach to deepen the understanding of CE-RSEICs during 2000-2021. The methods include the analysis of CE and the principal component of RSEICs. To understand the CE-RSEICs spatial features, the directional distribution ellipse method is used. Also, this study performs CE-RSEICs coupling analysis and identifies its LULC type composition. The findings show that CE status is still on an increasing trend, concentrating in the eastern region and keeping expanding during the period. The location of the low-emission ellipse is in the southwest, while the high-emission ellipse is in the east and intersects with the core cities. The mean RSEICs value is between 0.2878 to 0.4223, which indicates that the eco-environmental quality is categorized as fairly poor to inferior. Greenness, wetness, and Csink have a positive impact on RSEICs. The very poor-class ellipse is located in the inland region, and the very good-class ellipse is in the coastal area. The CE-RSEICs coupling status shows that the majority of the area has a weaker coupling degree. However, the higher coupling degree is concentrated in the population center and built-up region, which is the settlement area. The dominance composition of settlement area in higher coupling degree shows that settlement area has an impact on increasing CE-RSEICs coupling degree. So, sustainable low carbon development in coastal metropolitan area must continue to be carried out by considering CE-RSEICs and its spatial aspects.

Study on coupling coordination degree of urbanization and ecological environment in Chengdu-Chongqing economic circle from 2002 to 2018

Under the western development strategy of China, the urbanization process of Chengdu-Chongqing economic circle (CCEC) develops rapidly, but also brings a series of ecological and environmental problems. Understanding the coordination mechanism that links urbanization and the ecological environment (UE) is crucial for promoting environmental conservation and sustainable development. Using the comprehensive nighttime light index (CNLI) and the remote sensing ecological index (RSEI), this study objectively evaluated the urbanization level and ecological environment quality and used the modified coupling coordination degree model to quantify the coupling coordination of UE for the two indexes. The results show that (1) the urbanization level of each city in the CCEC presents an increasing trend year by year, showing a circle distribution pattern with the twin cities as the core. (2) The overall eco-environmental quality presents a fluctuating upward trend, and in recent years, it has been significantly improved, showing a spatial pattern of higher in the surrounding areas and lower in the middle. (3) The overall coupling coordination of the study area is developing, and the upward trend is larger in recent years. The spatial distribution is centered on the "double nucleus," and the distribution of circles is characterized by a gradual decrease from the inside to the outside; the coupling coordination of cities in the northwest and east of the CCEC is decreasing, and that of cities in the northeast and southwest is increasing. During the study period, a total of 5 cities have reached the level of coordinated development, while most other cities are in the stage of uncoordinated development, mainly due to the lagging development of urbanization. CCEC still needs to strengthen the construction of urbanization, in order to improve the degree of coordination between economic development and ecological environment.

Urban Ecological Environment Quality Evaluation and Territorial Spatial Planning Response: Application to Changsha, Central China

Scientific territorial spatial planning is of great significance in the realization of the sustainable development goals in China, especially in the context of China's ecological civilization construction and territorial spatial planning. However, limited research has been carried out to understand the spatio-temporal change in EEQ and territorial spatial planning. In this study, Changsha County and six districts of Changsha City were selected as the research objects. Based on the remote sensing ecological index (RSEI) model, the spatio-temporal changes in the EEQ and spatial planning response in the study area during 2003-2018 were analyzed. The results reveal that (1) the EEQ of Changsha declined and then rose between 2003 and 2018, showing an overall decreasing trend. The average RSEI declined from 0.532 in 2003 to 0.500 in 2014 and then increased to 0.523 in 2018, with an overall decrease of 1.7%. (2) In terms of spatial pattern changes, the Xingma Group, the Airport Group and the Huangli Group in the east of the Xiangjiang River had the most serious EEQ degradation. The EEQ degradation of Changsha showed an expanding and polycentric decentralized grouping pattern. (3) Massive construction land expansion during rapid urbanization caused significant EEQ degradation in Changsha. Particularly, the areas with low EEQ were concentrated in the areas with concentrated industrial land. Scientific territorial spatial planning and strict control were conducive to regional EEQ improvement. (4) The prediction using the urban ecological model demonstrates that every 0.549 unit increase in NDVI or 0.2 unit decrease in NDBSI can improve the RSEI of the study area by 0.1 unit, thus improving EEQ. In the future territorial spatial planning and construction of Changsha, it is necessary to promote the transformation and upgrading of low-end industries into high-end manufacturing industries and control the scale of inefficient industrial land. The EEQ degradation caused by industrial land expansion needs to be noted. All of these findings can provide valuable information for relevant decision-makers to formulate ecological environment protection strategies and conduct future territorial spatial planning.

Assessment of Ecological Environment Quality for Urban Sustainable Development Based on AHP

Cities are gradually developed on the basis of adapting and transforming the natural environment. In a certain urban area, human activities, natural environment, and other factors and their mutual influence constitute the urban ecological environment. Therefore, the evaluation of urban ecological environment quality is of great significance to the analysis of urban development. This paper takes a city in Western China as the evaluation object, uses AHP to determine the index weight, reasonably analyzes the current situation of the urban ecological environment, and further comprehensively evaluates the quality of the urban ecological environment. The study shows that from 2013 to 2018, the comprehensive capacity of the city's ecological environment quality showed a steady upward trend, except that the natural disasters of floods and mudslides in 2014 had a certain degree of fluctuation. The comprehensive index of ecological environment quality has increased from 0.337 in 2013 to 0.412 in 2018. The overall level is still low, but the development speed is relatively stable. The urban ecological environment has been gradually improved, and society, economy, and nature have maintained a certain degree of sustainable development.

Study on the Spatial Interaction between Urban Economic and Ecological Environment-A Case Study of Wuhan City

In order to study the interactive relationship between urban economic and ecological environment, taking Wuhan as an example, Landsat and MODIS remote sensing satellite data and social and economic data were fused with multisource data, and multidimensional indicators were selected to construct the comprehensive evaluation index system of urban economic and ecological environment. The weights were determined by combining subjective and objective methods. Then, the decoupling elasticity coefficient method and spatial autocorrelation model were used to evaluate the dynamic relationship and spatial relationship between economic development and ecological environment in Wuhan from 2014 to 2020. The results showed that there was an interaction between the urban economic and the ecological environment in Wuhan. The ecological level index had a spatial effect, the adjustment of industrial structure had a positive effect on the improvement of the ecological level, and the improvement of the ecological level was also helpful to promote economic development. The typical districts of Huangpi District, Xinzhou District, Jiangxia District, Hannan District, Caidian District, and Hongshan District had superior location and ecological advantages, as well as high development potential. Lastly, on the basis of the empirical analysis results, policy suggestions are made from four aspects: regional differentiated construction, green development, energy consumption, and wetland construction.

Land Use and Climate Change Altered the Ecological Quality in the Luanhe River Basin

Monitoring and assessing ecological quality (EQ) can help to understand the status and dynamics of the local ecosystem. Moreover, land use and climate change increase uncertainty in the ecosystem. The Luanhe River Basin (LHRB) is critical to the ecological security of the Beijing–Tianjin–Hebei region. To support ecosystem protection in the LHRB, we evaluated the EQ from 2001 to 2020 based on the Remote Sensing Ecological Index (RSEI) with the Google Earth Engine (GEE). Then, we introduced the coefficient of variation, Theil–Sen analysis, and Mann–Kendall test to quantify the variation and trend of the EQ. The results showed that the EQ in LHRB was relatively good, with 61.08% of the basin rated as ‘good’ or ‘excellent’. The spatial distribution of EQ was low in the north and high in the middle, with strong improvement in the north and serious degradation in the south. The average EQ ranged from 0.58 to 0.64, showing a significant increasing trend. Furthermore, we found that the expansion of construction land has caused degradation of the EQ, whereas climate change likely improved the EQ in the upper and middle reaches of the LHRB. The results could help in understanding the state and trend of the eco-environment in the LHRB and support decision-making in land-use management and climate change.

Spatiotemporal and Multiscale Analysis of the Coupling Coordination Degree between Economic Development Equality and Eco-Environmental Quality in China from 2001 to 2020

Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a−1), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a−1), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China’s MCCD indicated an upwards trend (0.0220 a−1), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran’s I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R2 of the fitted curve at different scales ranged from 0.8183 to 0.9915.

A Multi-Criteria Evaluation of the Urban Ecological Environment in Shanghai Based on Remote Sensing

The urban ecological environment is related to human health and is one of the most concerned issues nowadays. Hence, it is essential to detect and then evaluate the urban ecological environment. However, the conventional manual detection methods have many limitations, such as the high cost of labor, time, and capital. The aim of this paper is to evaluate the urban ecological environment more conveniently and reasonably, thus this paper proposed an ecological environment evaluation method based on remote sensing and a projection pursuit model. Firstly, a series of criteria for the urban ecological environment in Shanghai City are obtained through remote sensing technology. Then, the ecological environment is comprehensively evaluated using the projection pursuit model. Lastly, the ecological environment changes of Shanghai City are analyzed. The results show that the average remote sensing ecological index of Shanghai in 2020 increased obviously compared with that in 2016. In addition, Jinshan District, Songjiang District, and Qingpu District have higher ecological environment quality, while Hongkou District, Jingan District, and Huangpu District have lower ecological environment quality. In addition, the ecological environment of all districts has a significant positive spatial autocorrelation. These findings suggest that the ecological environment of Shanghai has improved overall in the past five years. In addition, Hongkou District, Jingan District, and Huangpu District should put more effort into improving the ecological environment in future, and the improvement of ecological environment should consider the impact of surrounding districts. Moreover, the proposed weight setting method is more reasonable, and the proposed evaluation method is convenient and practical.