Environmental and Sustainability Indicators, Год журнала: 2024, Номер unknown, С. 100519 - 100519
Опубликована: Окт. 1, 2024
Язык: Английский
Sustainable Cities and Society, Год журнала: 2024, Номер 114, С. 105763 - 105763
Опубликована: Авг. 23, 2024
Язык: Английский
Процитировано
23
Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 35, С. 101206 - 101206
Опубликована: Апрель 25, 2024
Язык: Английский
Процитировано
13
Remote Sensing, Год журнала: 2024, Номер 16(9), С. 1637 - 1637
Опубликована: Май 3, 2024
Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), use cover (LULC) changes, identification urban heat island (UHI) (SUHI) phenomena. This research focuses on nexus between LULC alterations variations in LST air (Tair), with a specific emphasis intensified SUHI effect Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat MODIS satellites, alongside Tair records, utilizing machine learning techniques linear regression analysis. Key findings indicate statistically significant upward trend during summer months 1984 to 2023, notable positive correlation across both datasets. exhibit stronger (R2 = 0.879) compared 0.663). The application supervised classification through Random Forest algorithms vegetation indices reveals alterations: 70.3% increase decrement vegetative comprising 15.5% reduction dense 62.9% decrease sparse vegetation. Change detection analysis elucidates 24.6% conversion land, underscoring pronounced trajectory towards urbanization. Temporal seasonal different classes were analyzed using kernel density estimation (KDE) boxplot Urban areas had smallest average fluctuations, at 2.09 °C 2.16 °C, respectively, but recorded most extreme values. Water exhibited slightly larger fluctuations 2.30 2.24 bare class showing highest fluctuation 2.46 fewer extremes. Quantitative Kolmogorov-Smirnov tests various substantiated normality distributions p > 0.05 monthly annual Conversely, Shapiro-Wilk test validated normal distribution hypothesis exclusively data, indicating deviations data. Thresholded classifies lands as warmest 39.51 38.20 water 35.96 35.52 37.71 coldest, which is that consistent annually monthly. effects demonstrates UHI intensity, statistical trends growth values over time. comprehensive underscores role remote understanding addressing urbanization local climates, emphasizing need sustainable planning green infrastructure mitigate effects.
Язык: Английский
Процитировано
12
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Март 15, 2024
Abstract Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading a phenomenon known as urban overheating. Understanding spatial distribution land surface temperature (LST) its driving factors is crucial for mitigation adaptation So far, there has been an absence investigations into spatiotemporal patterns explanatory LST in city Addis Ababa. The study aims determine temperature, analyze how relationships between vary across space, compare effectiveness using ordinary least squares geographically weighted regression model these connections. findings showed that show statistically significant hot spot zones north-central parts area (Moran’s I = 0.172). relationship variables were modelled square thereby tested if dependence Koenker (BP) Statistic.The result revealed non-stationarity (p 0.000) consequently was employed performance with OLS. research that, GWR (R 2 0.57, AIC 1052.1) more effective technique than OLS 0.42, 2162.0) studying selected variables. use improved accuracy by capturing heterogeneity Statistic. ((p Consequently, Localized understanding formulated.
Язык: Английский
Процитировано
11
Journal of Environmental Management, Год журнала: 2024, Номер 357, С. 120705 - 120705
Опубликована: Апрель 1, 2024
Язык: Английский
Процитировано
10
Ecological Indicators, Год журнала: 2023, Номер 155, С. 111026 - 111026
Опубликована: Окт. 9, 2023
Developing urban agglomerations and establishing metropolitan areas have led to heat islands breaking through the original single scale form regional (RHIs) with a large influencing range in recent decades. However, spatial temporal patterns of their evolution are still poorly understood, which limit decisionmakers make more informed decisions improve environment. We selected Zhengzhou Metropolitan Area as case study MOD11A2 surface temperature products from 2001 2020 describe intensity RHIs by calculating relative temperature. Then multi-scale spatiotemporal characteristics RHI were identified help Sen + MK trend analysis, contribution index analysis standard deviation ellipse main driving factors internal relations discussed correlation Random Forest (RF) regression analysis. found that (1) increased 2970 km2 5776 2020, growth rate about 0.46% per year. The contributions each county-level region differ significantly. positive include City, Kaifeng Zhongmu County, Xinzheng while negative Lankao Fengqiu Huixian City. (2) There two aggregated within area. Xinxiang City Jiaozuo formed complete, cross-city, large-area 2005, accounting for 63% total Zhengzhou, Kaifeng, Xuchang Cities developed an all-round, cross-regional, rapid manner 2010 78% (3) Different land cover types varying effects on RHIs. cooling effect forests water is better than grasslands. Human social positively affect RHI, vegetation strongly inhibits meteorological less influence. methods results this play significant role analyzing construction status, optimizing layouts, improving thermal environments Area. Insights into environment research practice also provided other agglomerations.
Язык: Английский
Процитировано
20
Remote Sensing, Год журнала: 2025, Номер 17(3), С. 403 - 403
Опубликована: Янв. 24, 2025
The Urban Heat Island (UHI) phenomenon, combined with reduced vegetation and heat generated by human activities, presents a major environmental challenge for many European urban areas. UHI effect is especially concerning in hot temperate climates, like the Mediterranean region, during summer months as it intensifies discomfort raises risk of heat-related health issues. As result, assessing dynamics steering sustainable land management practices becoming increasingly crucial. Analyzing relationship between cover Land Surface Temperature (LST) can significantly contribute to achieving this objective. This study evaluates spatial correlations various types LST trends Thessaloniki, Greece, using data from Coordination Information on Environment (CORINE) program advanced index techniques within Google Earth Engine (GEE). Our analysis revealed that there has been gradual increase average surface temperature over past five years, more pronounced observed last two years (2022 2023) mean annual values reaching 26.07 °C 27.09 °C, respectively. By employing indices such Normalized Difference Vegetation Index (NDVI) performing correlation analysis, we further analyzed influence diverse landscapes distribution across different use categories area, contributing deeper understanding effects.
Язык: Английский
Процитировано
1
Sustainable Cities and Society, Год журнала: 2024, Номер 116, С. 105893 - 105893
Опубликована: Окт. 11, 2024
Язык: Английский
Процитировано
5
Earth Systems and Environment, Год журнала: 2025, Номер unknown
Опубликована: Май 17, 2025
Язык: Английский
Процитировано
0
Опубликована: Март 19, 2024
Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), use cover (LULC) changes, identification urban heat island (UHI) (SUHI) phenomena. This research focuses on nexus between LULC alterations variations in LST air (Tair), with a specific emphasis intensified SUHI effect Kharkiv, Ukraine. Employing an integrated approach, study analyzes time-series data from Landsat MODIS satellites, alongside Tair records, utilizing machine learning techniques linear regression analysis. Key findings indicate statistically significant upward trend during summer months 1984 to 2023, notable positive correlation across both datasets. exhibit stronger R² = 0.879, compared 0.663. The application supervised classification through Random Forest algorithms vegetation indices reveals alterations, manifested as 70.3% increase land, concurrently decrement vegetative cover, especially 15.5% reduction dense 62.9% decrease sparse vegetation. Change detection analysis elucidates 24.6% conversion underscoring pronounced trajectory towards urbanization. Temporal seasonal different classes were analyzed using kernel density estimation (KDE) boxplot Urban areas had smallest average fluctuations, at 2.09°C 2.16°C, respectively, but recorded most extreme values. Water exhibited slightly larger fluctuations 2.30°C 2.24°C, bare class showing highest fluctuation 2.46°C, fewer extremes. Quantitative Kolmogorov-Smirnov tests various substantiated normality distributions p > 0.05 monthly annual sets. Conversely, Shapiro-Wilk test validated normal distribution hypothesis exclusively data, indicating deviations data. Thresholded classifies lands warmest 39.51°C, 38.20°C water by 35.96°C 35.52°C, 37.71°C coldest, consistent annually monthly. effects demonstrates UHI intensity, statistical trends growth values over time. comprehensive underscores role remote understanding addressing urbanization local climates, emphasizing need sustainable planning green infrastructure mitigate effects.
Язык: Английский
Процитировано
2