Dynamic Analysis of Ecological Environment Quality Combined with Water Conservation Changes in National Key Ecological Function Areas in China

Xu, Jie; Xie, Gaogang; Xiao, Yu; Li, Na; Yu, Fuqin; Pei, Sha; Jiang, Yuan

doi:10.3390/su10041202

Cited by 23 publications

(11 citation statements)

References 6 publications

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“…Var(S) = n(n − 1)(2n + 5) 18 (13) where x j and x i represent the EQ value of each pixel in year j and year i, and n is the length of the time series, which is 20 in this research. S is an intermediate variable.…”

Section: Theil-sen Median Trend Analysis and Mann-kendall Testmentioning

confidence: 99%

“…Previous research has established excellent methods to evaluate ecological quality. These include single indexes such as the normalized difference vegetation index (NDVI) [12] and fractional vegetation coverage (FVC) [13] that are usually used to assess the condition of vegetation, and synthetic models like the eco-environmental quality (EEQ) [14] and the Ecological Index (EI) [15], which have shown great potential in assessing ecological environmental quality precisely and comprehensively. However, there are still some limitations.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Song

Fan

et al. 2022

IJERPH

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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.

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Section: Theil-sen Median Trend Analysis and Mann-kendall Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Song

Fan

et al. 2022

IJERPH

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“…It has been reported that the eco-environment of Beijing is being threatened by various disturbances, and land resources play a key role in environmental ecosystem provision and eco-environment protection. However, with economic development and rapid urbanization, Beijing is experiencing increased resource pressures [25], [47].…”

Section: A Study Areamentioning

confidence: 99%

Eco-Environmental Quality Monitoring in Beijing, China, Using an RSEI-Based Approach Combined With Random Forest Algorithms

Gou

Zhao²

2020

IEEE Access

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The assessment of ecological environmental quality (EEQ) has provided an important knowledge base for protecting human health and realizing sustainable development. Previous studies have often used only principal component analysis (PCA) to perform the EEQ evaluation by determining the remote sensing based ecological index (RSEI) in a single year, and the assessment results are not comparable between years. Thus, a comparable and accurate method needs to be found and applied. In this paper, we applied the PCA combined with a random forest algorithm (a machine learning algorithm) to quantify the EEQ of Beijing, China, in 2014 and 2020 and analysed the relationship between the RSEI and four ecological indicators (greenness, wetness, dryness and heat). The results suggested that the RSEI and the ecological indicators of Beijing all changed substantially from 2014 to 2020, and the method of combining PCA and random forest was suitable for calculating the time-series data of RSEI in the study period. Specifically, the RSEI in Beijing increased slightly from 0.31 to 0.33 overall, the greenness of Beijing increased drastically (26.09%), the wetness decreased by 10.00%, and the dryness and heat increased by 8.62% and 2.00%, respectively. The Pearson correlation coefficient test showed that both the greenness and wetness had positive effects on the RSEI, while the dryness and heat had negative effects. Of the four ecological indicators in Beijing, the greenness contributed greatly as the main positive factor, and dryness was the most negative factor during the six years. This paper developed an improved framework for continuous EEQ monitoring, and these results provide a scientific basis for the sustainable development and ecological environmental monitoring of Beijing and other megacities. INDEX TERMSRemote sensing based ecological index (RSEI); ecological environmental quality (EEQ); principal component analysis (PCA); random forest; dynamic monitoring

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“…NKEFAs bear important ecological functions such as water conservation, soil conservation, windbreak sand fixation and biodiversity maintenance. Currently, China established the first batch of NKEFAs covering 436 counties at the end of 2010 (Xu et al, 2018), to which 240 new counties were added in 2016 (Figure 1). Since 2011, NKEFAs have become the largest regional ecological policy in China (Zhu and Chen, 2020).…”

Section: Introductionmentioning

confidence: 99%

Co-benefits of the National Key Ecological Function Areas in China for carbon sequestration and environmental quality

Chen

Hou

et al. 2023

Front. Ecol. Evol.

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IntroductionThe National Key Ecological Functional Areas (NKEFAs) are location-oriented ecological engineering of China, which rely on the main functional area planning. The co-benefits of ecological product supply and ecological environment improvement of NKEFAs has not been fully assessed in the literature.MethodsNKEFAs is considered a quasi-natural experiment, and the time-varying difference-in-differences (DID) model is used to assess the impact of NKEFAs on carbon sequestration (CS) and environmental quality (EQ) based on the panel data of 330 cities in China from 2001 to 2019. Then, we explore whether the co-benefits of ecological product supply and eco-environment protection can be achieved.Results and discussionNKEFAs can enhance CS and EQ and thus achieve co-benefits for both. NKEFAs can achieve the co-benefits of CS and EQ through territory spatial allocation and labor force aggregation, but industrial structure upgrading only positively mediates the impact of NKEFAs on CS. The co-benefits of CS and EQ are heterogeneous across functional area types, geospatial locations, and quantiles, while only CS at windbreak-sand fixation area, northwestern region, and low quantile regions is enhanced. This study makes a theoretical and methodological contribution to the existing literature on the policy effect assessment of ecological engineering. It also provides a comprehensive framework for evaluating the ecological effects of relevant policies in other countries by integrating the co-benefits of ecological products and eco-environment, analyzing regional heterogeneity, and exploring the underlying mechanisms.

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Dynamic Analysis of Ecological Environment Quality Combined with Water Conservation Changes in National Key Ecological Function Areas in China

Cited by 23 publications

References 6 publications

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

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

Eco-Environmental Quality Monitoring in Beijing, China, Using an RSEI-Based Approach Combined With Random Forest Algorithms

Co-benefits of the National Key Ecological Function Areas in China for carbon sequestration and environmental quality

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