Spatiotemporal evolution of island ecological quality under different urban densities: A comparative analysis of Xiamen and Kinmen Islands, southeast China

Liu, Chao; Yang, Mei; Hou, Yuting; Zhao, Yuning; Xue, Xiongzhi

doi:10.1016/j.ecolind.2021.107438

Cited by 44 publications

(17 citation statements)

References 91 publications

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“…The heat index is represented by the inversion results of land surface temperature (LST). The Radiative Transfer Equation is used to invert the surface temperature to obtain the surface temperature [39]. We employed the normalization in calculating these indexes for weakening the influence of spectral sensitivity between Landsat TM and OLI data.…”

Section: Evaluation Index Calculationmentioning

confidence: 99%

Evaluation of Eco-Environmental Quality and Analysis on Spatio-Temporal Variation in Jinan, China

Cui¹,

Zhu²,

Mi³

et al. 2022

Pol. J. Environ. Stud.

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Rapid and accurate evaluation of ecological environmental quality and analysis of spatio-temporal changes has important practical significance for Jinan, China, which is in a critical period of rapid economic and social development, leading to the contradiction between ecological environment and urban development increasingly prominent. In this study, we utilized Landsat 5/TM and Landsat 8/ OLI images to construct the remote sensing ecological index (RSEI) for evaluating and analyzing the ecological quality of Jinan from 2006 to 2018. The results indicate that the ecological environmental quality of Jinan is in a satisfied order. The RSEI of Jinan increased slightly from 2006 to 2018 and the area of improving environmental quality accounted for 32.78%. By contrast, the area of decreasing ecological environmental quality only accounted for 18.40%, indicating that the overall ecological environmental quality of Jinan was in an improving trend in recent years. The ecological quality of the southern mountainous area changed greatly and the ecological environment improved obviously, while the ecological environmental quality of the central urban area was relatively stable. The results provide a guidance for local government and data support for ecological protection in the context of rapid urbanization.

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Section: Evaluation Index Calculationmentioning

confidence: 99%

Evaluation of Eco-Environmental Quality and Analysis on Spatio-Temporal Variation in Jinan, China

Cui¹,

Zhu²,

Mi³

et al. 2022

Pol. J. Environ. Stud.

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“…results of single factor detector. We used the Matlab 2020a to project the sample points to detect the relationship between rSEi and the four indicators of 2020 [60]. The 3D scatterplots are shown in Fig.…”

Section: Spatial Autocorrelation Analysis Of Rseimentioning

confidence: 99%

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

Yang¹,

Su²

2022

Pol. J. Environ. Stud.

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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.

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“…Xu (2013) proposed the remote sensing ecological index (RSEI) based on remote sensing technology, which integrated four indicators (greenness, wetness, heat, and dryness) and was used to monitor and evaluate the EEQ of Changting county in western Fujian Province. Hereafter, due to its characteristics of efficient data acquisition and objective reflection of EEQ, the RSEI has been widely used in the assessment of EEQ in various scenarios, such as in cities (Ji et al, 2020a;Maity et al, 2022;Zhang et al, 2021b), islands (Han et al, 2022;Liu et al, 2021), basins (Wu et al, 2020;Xiong et al, 2021;Zhang et al, 2022;Zhou and Liu, 2022), oases (Gao et al, 2020), plateaus (Cao et al, 2022;Sun et al, 2020), and others (Hui et al, 2021;Zhu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Gou and Zhao (2020) used RSEI combined with Random Forest (RF) based on Landsat-8 images to monitor the EEQ of Beijing, China. Liu et al (2021) employed RSEI based on Landsat-TM/OLI/TIRS to evaluate the spatiotemporal change of EEQ of Xiamen and Kinmen islands, China. MODIS data, on the other hand, offered a cost-effective way to monitor EEQ on a large scale and at regular intervals.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of optimum season selection for construction RSEI: A case study of ecological environment quality assess-ment in the Beijing-Tianjin-Hebei region from 2001 to 2020

Huang

et al. 2023

Preprint

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Timely and objective assessment of the optimal season for the construction of remote sensing ecological index (RSEI) is of great significance for accurate and effective assessment of ecological environment quality. We manipulated RSEI in different seasons to monitor and evaluate seasonal ecological environment quality (EEQ) variations in the Beijing-Tianjin-Hebei （JJJ） region from 2001 to 2020. First, we evaluated the image quality, and the bad observations were interpolated. Second, the seasonal RSEI was constructed by MODIS, and we compared the eigenvalues contribution rate of PC1 in seasons. Third, we assessed the temporal and spatial variations in EEQ across the same season within distinct years. Third, Moran’s I was employed to evaluate the spatial autocorrelation of EEQ and the stability of mean and standard deviation of correlation between RSEI and four indicators of seasons was compared. The results showed that: 1)the PC1 component concentrates most of the characteristics of the four indicators, especially in summer (over 71%); 2) the Moran’ I in the summer of 2001, 2006, 2011, 2016, and 2020 are 0.909, 0.898, 0.917, 0.921, and 0.892, respectively, which indicated that the EEQ has a strong positive spatial correlation. 3) the correlation between the four indicators and summer RSEI showed high correlation in different years, and the standard deviation of the correlation between the four indicators and RSEI fluctuated most slightly in summer, which the std of NDVI, WET, LST, and, NDBSI were 0.005, 0.052, 0.026, and 0.017, respectively. 4) Only the RSEI in summer and the VCF show spatial distribution consistency in the long time series RSEI spatial distribution of four seasons. This study explored the spatiotemporal variations of EEQ in the JJJ region at a seasonal scale, which can provide a reference for selecting the optimum season for the ecological quality monitoring of urban agglomeration in the future.

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Spatiotemporal evolution of island ecological quality under different urban densities: A comparative analysis of Xiamen and Kinmen Islands, southeast China

Cited by 44 publications

References 91 publications

Evaluation of Eco-Environmental Quality and Analysis on Spatio-Temporal Variation in Jinan, China

Evaluation of Eco-Environmental Quality and Analysis on Spatio-Temporal Variation in Jinan, China

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

Evaluation of optimum season selection for construction RSEI: A case study of ecological environment quality assess-ment in the Beijing-Tianjin-Hebei region from 2001 to 2020

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