Optimizing Urban Green Space Configurations for Enhanced Heat Island Mitigation: A Geographically Weighted Machine Learning Approach

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106087 - 106087

Published: Dec. 1, 2024

Language: Английский

---

Long-term trends in urban vegetation greenness and its causal relationship with built-up land expansion: A case study of the Yangtze River Delta, China

Habitat International, Journal Year: 2025, Volume and Issue: 156, P. 103286 - 103286

Published: Jan. 8, 2025

Language: Английский

---

Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing

Building and Environment, Journal Year: 2024, Volume and Issue: 262, P. 111790 - 111790

Published: June 27, 2024

Language: Английский

---

Impact of urban spatial dynamics and blue-green infrastructure on urban heat islands: A case study of Guangzhou using Local Climate Zones and predictive modeling

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105819 - 105819

Published: Sept. 1, 2024

Language: Английский

---

Exploring the impacts of urbanization on vegetation growth from the perspective of urban expansion patterns and maturity: A case study on 40 large cities in China

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105841 - 105841

Published: Sept. 1, 2024

Language: Английский

---

A cross-scale indicator framework for the study of annual stability of land surface temperature in different land uses

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105936 - 105936

Published: Oct. 1, 2024

Language: Английский

---

Land Use Change in the Russian Far East and Its Driving Factors

Land, Journal Year: 2025, Volume and Issue: 14(4), P. 804 - 804

Published: April 8, 2025

This study systematically analyzes land use changes in the Russian Far East from 2000 to 2020, identifying key transformations and their driving factors. Using multi-temporal remote sensing images combined with dynamics analysis, transition matrices, gray relational this research comprehensively evaluates evolution its influencing The purpose of is elucidate how patterns shift under influence natural conditions, demographic trends, cross-border cooperation a particular emphasis on border areas adjacent northeast China. findings reveal that during observed period, underwent substantial expanses arable built-up areas, while forest decline. Grassland demonstrated relative stability, water bodies continued decrease, unused exhibited fluctuating initially increasing then decreasing. In three regions (Amur Oblast, Jewish Autonomous Region, Primorsky Krai), these were more pronounced compared overall, reflecting intensified agricultural development urban growth strategic zones. Gray analysis shows climate change local population are principal drivers change, regional trade—particularly China–Russia trade industrial raw materials, agriculture, food exports—plays moderate role. evolving carry significant implications for resource acquisition, ecological security, cooperation. underscores necessity formulating scientifically sound management policies balance economic protection, thus fostering sustainable stability.

Language: Английский

---

Daytime Surface Urban Heat Island Variation in Response to Future Urban Expansion: An Assessment of Different Climate Regimes

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(10), P. 1730 - 1730

Published: May 15, 2025

This study focuses on assessing the physical growth of cities and land-cover changes resulting from it, which play a crucial role in understanding environmental impacts managing phenomena such as Daytime Urban Surface Heat Island Intensity (DSUHII). Predicting trends these for future provides valuable insights urban planning mitigating thermal effects arid environments. research aims to evaluate spatial temporal intensity surface heat islands under different climatic conditions, past, predict trends. For this purpose, Landsat satellite data products, including Reflectance with 30-m resolution Land Temperature (LST) originally at 100 (120)-meter 8 (Landsat 5) (resampled 30 m compatibility), along database underlying criteria affecting growth, were used analyze LST changes. The classification was carried out using Support Vector Machine (SVM) algorithm, its accuracy assessed. Spatial classes quantified cross-tabulation models subtraction operators. Subsequently, impact climates analyzed, DUSHII simulated CA–Markov model. results showed that humid climate (Babol Rasht), built-up areas increased by over 100% 1990 2023 are projected grow further 2055, while green spaces significantly decreased. In cold–dry (Mashhad), development dramatically, nearly halved. hot–dry (Yazd Kerman), tripled, reduction will continue. Additionally, hot dry climates, significant area barren land converted into areas, trend is predicted continue future. DSUHII Babol 2.5 °C 5.4 rise 7.8 2055. Rasht, value 2.9 5.5 °C, expected reach 7.6 °C. Mashhad, negative, decreasing −1.1 −1.5 2023, decline −1.9 Yazd, also remained −2.5 −3.3 an drop −6.4 Similarly, Kerman, decreased −2.8 −5.1 it −7.1 Overall, conclusions highlight has increased, have moderate, cold, gradual observed. climate, most substantial decrease evident, indicating varying across regions.

Language: Английский

---

The Spatio-Temporal Impact of Land Use Changes on Runoff in the Yiluo River Basin Based on the SWAT and PLUS Model

Water, Journal Year: 2025, Volume and Issue: 17(10), P. 1516 - 1516

Published: May 17, 2025

As a major tributary of the Yellow River, Yiluo River holds vital importance for regional water resource management and ecological sustainability. In this study, SWAT (version 2012) PLUS models were used in combination to simulate hydrological responses basin analyze how land use changes have influenced runoff dynamics over time. During calibration validation periods, Nash–Sutcliffe efficiency coefficient (NS) determination (R2) model both exceeded 0.8, while Kappa indicated an overall accuracy 0.91, confirming applicability Basin. However, despite strong annual performance, potential monthly or seasonal simulation uncertainties should be acknowledged warrant further analysis. From 2000 2020, areas forest land, water, urban unused Basin increased by 795.15 km2, 29.33 573.67 0.25 respectively, cultivated grassland decreased 814.50 km2 583.89 km2. The spatial distribution average depth generally exhibited pattern “higher upstream lower downstream”. An increase forestland was found suppress generation, whereas expansion promoted production. Implementing water-sensitive strategies—such as expanding cover conserving grasslands—is crucial reducing negative impacts expansion. Such measures can improve regulation, enhance groundwater recharge, support sustainable resources within basin. Assuming climate conditions remain constant, 2025 2030 is expected dominated forestland. Under scenario, projected 0.42% 0.51% compared respectively.

Language: Английский

---

Analysis of Urban Heat Island Effect in Wuhan Urban Area Based on Prediction of Urban Underlying Surface Coverage Type Change

Advances in Meteorology, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 19

Published: April 22, 2024

The rapid development of urbanization makes the phenomenon urban heat islands even more serious. Predicting impact land cover change on island has become one research hotspots. Taking Wuhan, China, as an example, this study simulated type in 2020 through Cellular Automata-Markov-Chain (CA-Markov) model. was and analyzed conjunction with Weather Research & Forecasting Model (WRF), simulation results wind velocity temperature were confirmed using weather station observation data. Based this, Wuhan 2030 predicted. found to be well-fit by CA-Markov use data, average inaccuracy about 2.5°C for stations. Wind speed had a poor fitting effect; error roughly 2 m/s. built-up area center high both before after prediction, water low area, peak happened at night. According forecast results, there will 2030, greater intensity than 2020.

Language: Английский

---