Eco-environmental quality assessment based on pressure-state-response framework by remote sensing and GIS

Feng

et al. 2022

IJERPH

Using the Google Earth Engine (GEE) platform, Moderate-resolution image spectroradiometer (MODIS) data of the Weihe River Basin from 2001 to 2021 were acquired, four ecological indicators, namely, greenness, wetness, heat, and dryness, were extracted, and the remote sensing ecological index (RSEI) was constructed through principal component analysis. In addition, the geographic detectors and a multi-scale geographic weighted regression model (MGWR) were used to identify the main driving factors of RSEI changes and capture the differences in spatial changes from different perspectives using multiple indicators. The results show that (1) the quality of the eco-environment in the Weihe River basin improved as a whole from 2001 to 2021, and the RSEI increased from 0.376 to 0.414. In terms of the RSEI grade, the medium RSEI and high RSEI areas increased significantly and the growth rate increased significantly, reaching 26.42% and 27.70%, respectively. (2) Spatially, the quality of the eco-environment in the Weihe River Basin exhibited a spatial distribution pattern that was high in the south and low in the north, among which the quality of the eco-environment in the Weihe River Basin in northern Shaanxi and northwestern Ningxia and Gansu was relatively low. In addition, during the study period, the RSEI of the Qinling Mountains in the southern part of the Weihe River Basin and the Jinghe River and Luohe River areas improved significantly. The urban area on the Guanzhong Plain in the Weihe River Basin experienced rapid economic growth, and urban expansion led to a significant decrease in the quality of the eco-environment. (3) The eco-environment quality in the Weihe River Basin is the result of the interaction of natural, anthropogenic, and landscape pattern factors. All of the interactions between the influencing factors had a stronger influence than those of the individual factors. There were significant differences between the individual drivers and the spatial variation in RSEI, suggesting that different factors dominate the variation in RSEI in different regions, and zonal management is crucial to achieving sustainable management of RSEI. The study shows that to improve the eco-environment quality of the Weihe River Basin, it is necessary to further strengthen ecological protection projects, reasonably allocate landscape elements, and strengthen the resistance and resilience of the ecosystem.

Section: Introductionmentioning

confidence: 99%

Exploring the Driving Factors of Remote Sensing Ecological Index Changes from the Perspective of Geospatial Differentiation: A Case Study of the Weihe River Basin, China

Feng

et al. 2022

IJERPH

“…The evaluation of the eco-environment is based on the development characteristics of the ecosystem, mainly based on ecological carrying capacity model 6 , landscape pattern model 7 and remote sensing ecological index model (RSEI) 8 , to build different eco-environmental assessment systems to evaluate specific regions. Ecological carrying capacity refers to the support and capacity of regional resources and environment to social economy and human activities, the most representative methods are energy value analysis, ecological footprint method and life cycle assessment (LCA).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Jiang

Fan

et al. 2022

Sci Rep

The contradiction between the exploitation of coal resources and the protection of the ecological environment in western China is becoming increasingly prominent. Reasonable ecological environment evaluation is the premise for alleviating this contradiction. First, this paper evaluates the eco-environment of Ibei coalfield by combining the genetic projection pursuit model and geographic information system (GIS) and using remote sensing image data and other statistical data of this area. The powerful spatial analysis function of GIS and the advantages of the genetic projection pursuit model in weight calculation have been fully used to improve the reliability of the evaluation results. Furthermore, spatial autocorrelation is used to analyze the spatial characteristics of ecological environment quality in the mining area and plan the specific governance scope. The geographic detector is used to determine the driving factors of the eco-environment of the mining area. The results show that Ibei Coalfield presents a spatially heterogeneous eco-environment pattern. The high-intensity mining area (previously mined area of Ili No.4 Coal Mine) has the worst ecological environment quality, followed by the coal reserve area of Ili No.4 Coal Mine and the planned survey area of Ili No.5 Coal Mine. The eco-environment quality (EEQ) of the study area is affected by both human and natural factors. Mining intensity and surface subsidence are the main human factors affecting the ecological environment in the study area. The main natural factors affecting the ecological environment in the study area are annual average precipitation, elevation, annual average evaporation, NDVI and land use type. Meanwhile, the interaction effect of any two indicators is greater than that of a single indicator. It is also indicated that the eco-environment of the mining area is nonlinearly correlated to impact indicators. The spatial autocorrelation analysis shows three areas that should be treated strategically that are the management area, close attention area and protective area. Corresponding management measures are put forward for different regions. This paper can provide scientific references for mining area eco-environmental protection, which is significant for the sustainability of coal mine projects.

“…One method is to use biomass to characterize the level of regional EEQ, such as by using single indicators such as vegetation net primary productivity [22], normalized vegetation index [23], and other single indicators, or by using the ecological status index [24], remote sensing ecological index [25], and other comprehensive indicators. The second is a complete quantitative measurement method based on land use data, such as landscape ecological risk index [26], eco-environment quality index [27], etc. Few advancements have been achieved in the PLES perspective of analyzing regional land use change and its impact on the environment.…”

Section: Introductionmentioning

confidence: 99%

Land Use Transformation Based on Production−Living−Ecological Space and Associated Eco-Environment Effects: A Case Study in the Yangtze River Delta Urban Agglomeration

Xiao

et al. 2022

Land

We investigate the eco-environmental effects and the driving factors of transforming the production–living–ecological space (PLES) land use function and offer a scientific foundation for developing regional territorial area and environmental preservation. Eco-environment quality index and ecological contribution ratio are used to analyze the spatial–temporal evolution characteristics and eco-environment effects of land use transformation in the Yangtze River Delta Urban Agglomeration (YRDUA) over the three time periods of 2000, 2010, and 2020, and the geographic detectors are used to analyze the factors that influence the spatial difference of eco-environment quality (EEQ). The findings indicate the following: (1) The land use transformation of YRDUA is primarily shown in the shrinkage of the production land area, the stability of ecological land, and the rapid increase of living land. The area of ecological land, such as water, forest, and pasture, has remained relatively steady from the perspective of secondary land types. In contrast, the area of urban and rural living land has significantly increased. (2) Most land use environment comprises the lower-value zone, accounting for about 50%. The area of the low-value zone has continued to rise owing to the rapid urban and rural living land development, tending to continuous growth. (3) Both the ecological improvement and degradation trends are present simultaneously, although the ecological improvement trend is less prominent than the environmental degradation trend. The primary factor is improving the eco-environment by transforming agricultural production land into forest, water, and ecological pasture land. The degradation of the regional EEQ is mostly due to the occupation of agricultural production land by urban and rural living land. (4) Considering natural elements such as altitude, precipitation, and slope, the extent of land use impacts the EEQ. The combination of several factors has changed the EEQ of the YRDUA. The effect of any two elements is stronger than that of a single factor.