Dynamic monitoring and analysis of factors influencing ecological quality in rapidly urbanizing areas based on the Google Earth Engine DOI Creative Commons

Fuxianmei Zhang,

Zhongfa Zhou, Denghong Huang

et al.

Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)

Published: Sept. 7, 2023

Rapid urbanization poses significant challenges to ecological preservation in karst ecologically fragile regions. Systematically monitor and evaluate of its urban pattern change driving factors are the basis for achieving regional sustainable development. Taking Gui'an New Area(GNA) China as object, using Google Earth Engine(GEE) cloud platform, Remote Sensing Ecological Index(RSEI) method Geodetector study quality(EQ) changes between 2010 2020. The results show that: (1) An overall increase RSEI (0.12), with concentrated +1 0 range, revealing spatial autocorrelation. (2) Comparing LU/LC types, forest showed highest RSEI, followed by shrub, cropland, impervious, grassland, barren areas. (3) Among considered, interaction greenness had most influence, was primary external factor affecting EQ. result provides a reference decision makers formulate protection policies implement coordinated development strategies.

Language: Английский

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China DOI Open Access
Meng Luo, Shengwei Zhang, Lei Huang

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(20), P. 13232 - 13232

Published: Oct. 14, 2022

The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With acceleration industrialization urbanization, human activities have caused enormous challenges to local environment. To achieve region’s economic sustainability make development plans more objective, it evaluate basin’s environment quality over period time. First, Landsat historical images, we selected 5 years data investigate changes this time-period (2000–2020). Second, based on opened remote sensing database Google Earth Engine, calculated remote-sensing index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, dryness. Thirdly, assessed ecological-environmental change characteristics Basin. Finally, analyzed spatial auto-correlation study area. mean values 2000, 2005, 2010, 2015, 2020 were 0.418, 0.421, 0.443, 0.456, 0.507, respectively, which indicated that had gradually improved. from 2000 2005 biggest change, as area with drastically changed water levels accounted for 78.98% total basin. It showed downward trend central western regions. upward eastern region. For 20 years, deterioration decreased by 24.37%, slight increased 45.84%. Global Moran’s I value ranged 0.324 0.568. results demonstrated was positively correlated, clustering degree gradually. Local high-high(H-H) mainly distributed southern regions, where population density low vegetation good condition. Low-low(L-L) regions high, industrial mining enterprises concentrated. This provided theoretical basis sustainable Basin, crucial development.

Language: Английский

Citations

10

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

Miao Tian,

Qibing Ding

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 31(2), P. 3134 - 3151

Published: Dec. 12, 2023

Language: Английский

Citations

5

Effects of climate change and human activities on environment and area variations of the Aral Sea in Central Asia DOI
Zhijuan Duan, Muhammad Mannan Afzal, Xiaomang Liu

et al.

International Journal of Environmental Science and Technology, Journal Year: 2023, Volume and Issue: 21(2), P. 1715 - 1728

Published: July 10, 2023

Language: Английский

Citations

4

Spatial-Temporal Coupling Coordination Relationship between Well-Being and Technological Innovation: Panel Evidence from China DOI Creative Commons

Lingmei Han,

Yulong Fu,

Hongtao Shen

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e37759 - e37759

Published: Sept. 1, 2024

Language: Английский

Citations

1

Dynamic monitoring and analysis of factors influencing ecological quality in rapidly urbanizing areas based on the Google Earth Engine DOI Creative Commons

Fuxianmei Zhang,

Zhongfa Zhou, Denghong Huang

et al.

Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)

Published: Sept. 7, 2023

Rapid urbanization poses significant challenges to ecological preservation in karst ecologically fragile regions. Systematically monitor and evaluate of its urban pattern change driving factors are the basis for achieving regional sustainable development. Taking Gui'an New Area(GNA) China as object, using Google Earth Engine(GEE) cloud platform, Remote Sensing Ecological Index(RSEI) method Geodetector study quality(EQ) changes between 2010 2020. The results show that: (1) An overall increase RSEI (0.12), with concentrated +1 0 range, revealing spatial autocorrelation. (2) Comparing LU/LC types, forest showed highest RSEI, followed by shrub, cropland, impervious, grassland, barren areas. (3) Among considered, interaction greenness had most influence, was primary external factor affecting EQ. result provides a reference decision makers formulate protection policies implement coordinated development strategies.

Language: Английский

Citations

2