Spatiotemporal Dynamics and Driving Forces of Ecological Environment Quality in Coastal Cities: A Remote Sensing and Land Use Perspective in Changle District, Fuzhou DOI Creative Commons

Tianxiang Long,

Zhuhui Bai,

Bohong Zheng

и другие.

Land, Год журнала: 2024, Номер 13(9), С. 1393 - 1393

Опубликована: Авг. 29, 2024

In the face of persistent global environmental challenges, evaluating ecological environment quality and understanding its driving forces are crucial for maintaining balance achieving sustainable development. Based on a case study Changle District in Fuzhou, China, this research employed Remote Sensing Ecological Index (RSEI) method to comprehensively assess analyze impact various factors from 2000 2020. GeoSOS-FLUS model, simulated predicted land use classifications if RSEI factors. The results reveal an overall improvement southern southwestern regions, while northwest eastern areas localized degradation. index increased 0.6333 0.6625 2022, indicating significant shifts over years. key identified include vegetation coverage, leaf area index, aerosol levels. Industrial emissions transportation activities notably affect air quality, changes, particularly expansion construction land, play critical role altering conditions. If current RESI without any improvement, will experience continued urbanization development, leading increase built-up 32.93% by 2030 at expense grasslands. This offers valuable insights policymakers managers formulate targeted strategies aimed reducing industrial traffic emissions, optimizing planning, enhancing sustainability. methodology findings provide robust framework similar assessments other rapidly urbanizing contributing broader discourse conservation. By advancing forces, supports development informed protection coastal regions developing countries globally.

Язык: Английский

Pathways for Hydrological Resilience: Strategies for Adaptation in a Changing Climate DOI
Francesco Granata, Fabio Di Nunno

Earth Systems and Environment, Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Язык: Английский

Процитировано

1
Mapping groundwater-related flooding in urban coastal regions DOI
Montana Marshall, Emmanuel Dubois,

Saleck Moulaye Ahmed Cherif

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132907 - 132907

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Advancing flood risk assessment: Multitemporal SAR-based flood inventory generation using transfer learning and hybrid fuzzy-AHP-machine learning for flood susceptibility mapping in the Mahananda River Basin DOI Creative Commons
Chiranjit Singha, Satiprasad Sahoo,

Alireza Bahrami Mahtaj

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 380, С. 124972 - 124972

Опубликована: Март 23, 2025

The Mahananda River basin, located in Eastern India, faces escalating flood risks due to its complex hydrology and geomorphology, threatening socioeconomic environmental stability. This study presents a novel approach susceptibility (FS) mapping updates the region's inventory. Multitemporal Sentinel-1 (S1) SAR images (2020-2022) were processed using U-Net transfer learning model generate water body frequency map, which was integrated with Global Flood Dataset (2000-2018) refined through grid-based classification create an updated Eleven geospatial layers, including elevation, slope, soil moisture, precipitation, type, NDVI, Land Use Cover (LULC), wind speed, drainage density, runoff, used as conditioning factors (FCFs) develop hybrid FS approach. integrates Fuzzy Analytic Hierarchy Process (FuzzyAHP) six machine (ML) algorithms models FuzzyAHP-RF, FuzzyAHP-XGB, FuzzyAHP-GBM, FuzzyAHP-avNNet, FuzzyAHP-AdaBoost, FuzzyAHP-PLS. Future trends (1990-2030) projected CMIP6 data under SSP2-4.5 SSP5-8.5 scenarios MIROC6 EC-Earth3 ensembles. SHAP algorithm identified LULC, type most influential FCFs, contributing over 60 % susceptibility. Results show that 31.10 of basin is highly susceptible flooding, western regions at greatest risk low elevation high density. projections indicate 30.69 area will remain vulnerable, slight increase SSP5-8.5. Among models, FuzzyAHP-XGB achieved highest accuracy (AUC = 0.970), outperforming FuzzyAHP-GBM 0.968) FuzzyAHP-RF 0.965). experimental results showed proposed can provide spatially well-distributed inventory derived from freely available remote sensing (RS) datasets robust framework for long-term assessment ML techniques. These findings offer critical insights improving management mitigation strategies basin.

Язык: Английский

Процитировано

0
Spatiotemporal Dynamics and Driving Forces of Ecological Environment Quality in Coastal Cities: A Remote Sensing and Land Use Perspective in Changle District, Fuzhou DOI Creative Commons

Tianxiang Long,

Zhuhui Bai,

Bohong Zheng

и другие.

Land, Год журнала: 2024, Номер 13(9), С. 1393 - 1393

Опубликована: Авг. 29, 2024

In the face of persistent global environmental challenges, evaluating ecological environment quality and understanding its driving forces are crucial for maintaining balance achieving sustainable development. Based on a case study Changle District in Fuzhou, China, this research employed Remote Sensing Ecological Index (RSEI) method to comprehensively assess analyze impact various factors from 2000 2020. GeoSOS-FLUS model, simulated predicted land use classifications if RSEI factors. The results reveal an overall improvement southern southwestern regions, while northwest eastern areas localized degradation. index increased 0.6333 0.6625 2022, indicating significant shifts over years. key identified include vegetation coverage, leaf area index, aerosol levels. Industrial emissions transportation activities notably affect air quality, changes, particularly expansion construction land, play critical role altering conditions. If current RESI without any improvement, will experience continued urbanization development, leading increase built-up 32.93% by 2030 at expense grasslands. This offers valuable insights policymakers managers formulate targeted strategies aimed reducing industrial traffic emissions, optimizing planning, enhancing sustainability. methodology findings provide robust framework similar assessments other rapidly urbanizing contributing broader discourse conservation. By advancing forces, supports development informed protection coastal regions developing countries globally.

Язык: Английский

Процитировано

1