Investigating the urban eco-environmental quality utilizing remote sensing based approach: evidence from an industrial city of Eastern India DOI Creative Commons
Sharmistha Mondal, Kapil Kumar Gavsker

Deleted Journal, Год журнала: 2024, Номер 6(12)

Опубликована: Дек. 4, 2024

Urbanization, coupled with industrialization, leads to both economic growth and exponential urban growth, resulting in deteriorating environmental quality areas, which poses a significant threat the sustainability of cities. Hence, restore biodiversity ensure regional sustainability, it is necessary immediately evaluate eco-environmental areas. The present research investigates spatio-temporal changes Asansol industrial city using an integrated 'Urban Eco-Environmental Index' (UEEQI) developed utilizing Google Earth Engine platform remote sensing-based approach. study used four spectral indices, including Normalized Difference Vegetation Index (NDVI), Modified Water (MNDWI), Built-up (NDBI), Bareness (NDBaI), along Land Surface Temperature (LST) (as thermal index), derived from sensing data measure quality. Global Moran's I LISA were quantify spatial autocorrelation, showing clustering similar values or outliers UEEQ within geographic space. findings showed that high mean NDBI NDBaI contributed lower UEEQI value 0.38 2021 compared previous decades. distribution 'Very Poor' category had grown 0.06% 1991 2% 2021, while 'Poor', 'Good', 'Excellent' categories declined. Over 30 years, rising trend 'Highly Degraded' 'Degraded' areas decreasing 'Improved' Improved' city. scatter plot illustrated highly positive clustered pattern across Hotspots mainly found urbanized "Average" eco-environment Conversely, covered bare surfaces, fallow lands, brickfields recognized as Coldspots. This crucial for determining specific regions declining encourages local authorities decision-makers integrate conservation zones into planning foster healthier, more resilient, sustainable

Язык: Английский

Spatiotemporal evolution and multi-scale coupling effects of land-use carbon emissions and ecological environmental quality DOI
Xinmin Zhang, Houbao Fan, Hao Hou

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 922, С. 171149 - 171149

Опубликована: Фев. 24, 2024

Язык: Английский

Процитировано

21
Coupling coordination between the ecological environment and urbanization in the middle reaches of the Yangtze River urban agglomeration DOI
Xinmin Zhang, Houbao Fan, Fei Liu

и другие.

Urban Climate, Год журнала: 2023, Номер 52, С. 101698 - 101698

Опубликована: Сен. 27, 2023

Язык: Английский

Процитировано

40
Identifying regional eco-environment quality and its influencing factors: A case study of an ecological civilization pilot zone in China DOI
Xinmin Zhang, Houbao Fan, Lu Sun

и другие.

Journal of Cleaner Production, Год журнала: 2023, Номер 435, С. 140308 - 140308

Опубликована: Дек. 19, 2023

Язык: Английский

Процитировано

25
Analysis of Changes in Ecological Environment Quality and Influencing Factors in Chongqing Based on a Remote-Sensing Ecological Index Mode DOI Creative Commons
Yizhuo Liu,

Zhou Tinggang,

Wenping Yu

и другие.

Land, Год журнала: 2024, Номер 13(2), С. 227 - 227

Опубликована: Фев. 12, 2024

Chongqing is a large municipality in southwestern China, having the characteristics of vast jurisdiction, complex topography, and prominent dual urban–rural structure. It vitally important to optimize spatial layout land efficiency natural resource allocation, achieve sustainable development, conduct influence assessment causation analysis this region. Here, using Google Earth Engine platform, we selected Landsat remote-sensing (RS) images from period 2000–2020 constructed ecological index (RSEI) model. Considering urban pattern division Chongqing, Sen + Mann–Kendall analytical approach was employed assess fluctuating quality environment different sectors Chongqing. Subsequently, single-factor interaction detectors Geodetector software tool were used on RSEI, with use eight elements: elevation, slope, aspect, precipitation, temperature, population, use, nighttime lighting. Findings indicate that, over course investigation period, eco-quality displayed degradation, succeeded by amelioration. The RSEI decreased 0.700 2000 0.590 2007, then gradually recovered 0.716 2018. Overall, eco-environment improved. Spatially, changes consistent planning positioning pattern. main new area periphery central showed slight deterioration, while other regions marked improvement. combined effect any two elements enhanced explanatory power single factor, being strongest most influential factor explaining variation determined be impact elevation use. At temporal scale, related human activities evident trend power.

Язык: Английский

Процитировано

13
Ecological assessment and driver analysis of high vegetation cover areas based on new remote sensing index DOI Creative Commons
Xiaoyong Zhang, Weiwei Jia,

Shixin Lu

и другие.

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102786 - 102786

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

11
Revealing the Eco-Environmental Quality of the Yellow River Basin: Trends and Drivers DOI Creative Commons
Meiling Zhou,

Zhenhong Li,

Meiling Gao

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(11), С. 2018 - 2018

Опубликована: Июнь 4, 2024

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There growing demand assess the status order promote sustainable of YB. eco-environmental quality (EEQ) YB was assessed at both regional provincial scales utilizing remote sensing-based index (RSEI) with Landsat images from 2000 2020. Then, Theil–Sen (T-S) estimator Mann–Kendall (M-K) test were utilized evaluate its variation trend. Next, optimal parameter-based geodetector (OPGD) model used examine drivers influencing EEQ Finally, geographically weighted regression (GWR) further explore responses RSEI changes. results suggest that (1) lower value found north, while higher south Sichuan (SC) Inner Mongolia (IM) had highest lowest EEQ, respectively, among provinces. (2) Throughout research period, improved, whereas it deteriorated Henan (HA) Shandong (SD) (3) soil-available water content (AWC), annual precipitation (PRE), distance impervious surfaces (IMD) main factors affecting spatial differentiation (4) influence meteorological (PRE TMP) on changes greater than IMD, IMD showed significant increasing provide valuable information application local construction planning.

Язык: Английский

Процитировано

7
Analysis of the spatiotemporal dynamics and driving factors of ecosystem quality in Inner Mongolia from 2005 to 2020 DOI Creative Commons
Mengyuan Li, Xiaobing Li, Siyu Liu

и другие.

Environmental Technology & Innovation, Год журнала: 2024, Номер 35, С. 103686 - 103686

Опубликована: Май 27, 2024

Enhancing ecosystem quality is a necessary part of establishing ecological civilization. The study requires the development comprehensive evaluation indices. existing simple remote sensing indices are insufficient for achieving and systematic quality. Therefore, we constructed an index based on landscape pattern, stability, services. spatiotemporal dynamics in Inner Mongolia were analyzed over period from 2005 to 2020. Moreover, geographically weighted regression model was used investigate factors influencing variations main results as follows. (1) spatial distribution exhibited gradual decline northeast southwest. In contrast, temporal analysis revealed general increase (2) Climate significant factor heterogeneity variability Socioeconomic factors, population density livestock numbers had notable impacts quality, respectively. (3) Mongolia, temperature showed positive correlation northeastern region, but negative central western regions. findings offer valuable support local policymakers making informed decisions regarding targeted management. Furthermore, our provide governments with insights enhancing regional ecosystems by taking into account specific conditions area.

Язык: Английский

Процитировано

5
Climate change and human activities have resulted in substantial alterations to ecosystem quality within the Yarlung Zangbo River basin DOI

Zhangxi Ye,

Jie Gong, Teng Wang

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Spatial Heterogeneity of Driving Factors in Multi-Vegetation Indices RSEI Based on the XGBoost-SHAP Model: A Case Study of the Jinsha River Basin, Yunnan DOI Creative Commons
Jisheng Xia, Guoyou Zhang,

Shiping Ma

и другие.

Land, Год журнала: 2025, Номер 14(5), С. 925 - 925

Опубликована: Апрель 24, 2025

The Jinsha River Basin in Yunnan serves as a crucial ecological barrier southwestern China. Objective assessment and identification of key driving factors are essential for the region’s sustainable development. Remote Sensing Ecological Index (RSEI) has been widely applied assessments. In recent years, interpretable machine learning (IML) introduced novel approaches understanding complex mechanisms. This study employed Google Earth Engine (GEE) to calculate three vegetation indices—NDVI, SAVI, kNDVI—for area from 2000 2022, along with their corresponding RSEI models (NDVI-RSEI, SAVI-RSEI, kNDVI-RSEI). Additionally, it analyzed spatiotemporal variations these relationship indices. Furthermore, an IML model (XGBoost-SHAP) was interpret RSEI. results indicate that (1) levels 2022 were primarily moderate; (2) compared NDVI-RSEI, SAVI-RSEI is more susceptible soil factors, while kNDVI-RSEI exhibits lower saturation tendency; (3) potential evapotranspiration, land cover, elevation drivers variations, affecting environment western, southeastern, northeastern parts area. XGBoost-SHAP approach provides valuable insights promoting regional

Язык: Английский

Процитировано

0
Evaluation and Prediction of Ecological Restoration Effect of Beijing Wangping Coal Mine Based on Modified Remote Sensing Ecological Index DOI Creative Commons

Anya Zhong,

Chunming Hu, Li You

и другие.

Land, Год журнала: 2023, Номер 12(11), С. 2059 - 2059

Опубликована: Ноя. 13, 2023

As the construction of ecological civilization has become more and important in recent years, restoration its effect assessment have also received increasing attention. Taking Wangping coal mine Beijing as an example, based on Landsat TM/OLI series remote sensing data, we chose five metrics, i.e., fraction vegetation coverage, humidity, heat, dryness, black particulates, to construct model for modified index (MRSEI). It was combined with Hurst conduct dynamic monitoring, spatiotemporal analysis, prediction studies environment quality study area. The results showed that: (1) Compared RSEI, first principal component MRSEI better integrates information each indicator, a average correlation reflects habitat condition (2) mean value area increased from 0.433 1990 0.722 2021, increase 40.03%. (3) From 2001, poor fair MRSEI-grade areas were concentrated northeastern southwestern parts After project carried out, environmental improved year by year, small number border (4) predicted that future would show general trend continuous improvement, but certain percentage northeast had weak antisustainability trend. could provide reference planning, sustainable development, management mining areas.

Язык: Английский

Процитировано

8