Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(5)
Опубликована: Апрель 2, 2024
Язык: Английский
Sustainable Cities and Society, Год журнала: 2024, Номер 115, С. 105875 - 105875
Опубликована: Окт. 2, 2024
Язык: Английский
Процитировано
12
The Science of The Total Environment, Год журнала: 2024, Номер 923, С. 171203 - 171203
Опубликована: Фев. 28, 2024
Язык: Английский
Процитировано
11
Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 35, С. 101206 - 101206
Опубликована: Апрель 25, 2024
Язык: Английский
Процитировано
11
Urban Climate, Год журнала: 2024, Номер 56, С. 102046 - 102046
Опубликована: Июнь 28, 2024
Язык: Английский
Процитировано
9
Environmental Research Letters, Год журнала: 2024, Номер 19(4), С. 044037 - 044037
Опубликована: Март 6, 2024
Abstract Accurately capturing the impact of urban trees on temperature can help optimize heat mitigation strategies. Recently, there has been widespread use remotely sensed land surface ( T s ) to quantify cooling efficiency (CE) trees. However, reflects emitted radiation from an object seen point view thermal sensor, which is not a good proxy for air perceived by humans. The extent CEs derived reflect true experiences residents debatable. Therefore, this study systematically compared -based CE (CE with in 392 European clusters. and were defined as reductions , respectively, every 1% increase fractional tree cover (FTC). results show that FTC substantial reducing most cities during daytime. at night, response increased appears be much weaker ambiguous. On average, cities, daytime reaches 0.075 °C % −1 significantly higher (by order magnitude) than corresponding 0.006 . In contrast, average nighttime are similar, both approximating zero. Overall, lower temperatures, but magnitude their effect notably amplified when using estimates situ measurements, important consider accurately constraining public health benefits. Our findings provide critical insights into realistic efficiencies alleviating through planting.
Язык: Английский
Процитировано
8
Atmosphere, Год журнала: 2025, Номер 16(1), С. 40 - 40
Опубликована: Янв. 2, 2025
In order to assess the spatial and temporal characteristics of urban thermal environment in Zhengzhou City supplement climate adaptation design work, based on Landsat 8–9 OLI/TIRS C2 L2 data for 12 periods from 2019–2023, combined with lLocal zone (LCZ) classification subsurface classification, this study, we used statistical mono-window (SMW) algorithm invert land surface temperature (LST) classify heat island (UHI) effect, analyze differences distribution environments areas aggregation characteristics, explore influence LCZ landscape pattern temperature. The results show that proportions built natural types Zhengzhou’s main metropolitan area are 79.23% 21.77%, respectively. most common landscapes wide mid-rise (LCZ 5) structures large-ground-floor 8) structures, which make up 21.92% 20.04% study area’s total area, varies seasons, pooling during summer peaking winter, strong or extremely islands centered suburbs a hot cold spots aggregated observable features. As building heights increase, UHI 1–6) increases then reduces spring, summer, autumn decreases winter as increase. Water bodies G) dense woods A) have lowest effects among settings. Building size is no longer primary element affecting LST buildings become taller; instead, connectivity clustering take center stage. Seasonal variations, variations types, responsible area. should see an increase vegetation cover, gaps must be appropriately increased.
Язык: Английский
Процитировано
1
Remote Sensing, Год журнала: 2025, Номер 17(2), С. 318 - 318
Опубликована: Янв. 17, 2025
Addressing global warming and adapting to the impacts of climate change is a primary focus adaptation strategies at both European national levels. Land surface temperature (LST) widely used proxy for investigating climate-change-induced phenomena, providing insights into radiative properties different land cover types impact urbanization on local characteristics. Accurate continuous estimation across large spatial regions crucial implementation LST as an essential parameter in mitigation strategies. Here, we propose deep-learning-based methodology using multi-source data including Sentinel-2 imagery, cover, meteorological data. Our approach addresses common challenges satellite-derived data, such gaps caused by cloud image border limitations, grid-pattern sensor artifacts, temporal discontinuities due infrequent overpasses. We develop regression-based convolutional neural network model, trained ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment Space Station) mission which performs pixelwise predictions 5 × patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills enhances coverage. In non-gap areas validated against ground truth model achieves with least 80% all pixel errors falling within ±3 °C range. Unlike traditional satellite-based techniques, our leverages high-temporal-resolution capture diurnal variations, allowing more robust time periods. The model’s performance demonstrates potential integrating urban planning, resilience strategies, near-real-time heat stress monitoring, valuable resource assess visualize development use changes.
Язык: Английский
Процитировано
1
The Science of The Total Environment, Год журнала: 2024, Номер 927, С. 172168 - 172168
Опубликована: Апрель 4, 2024
Язык: Английский
Процитировано
5
Advances in Space Research, Год журнала: 2024, Номер 74(10), С. 4598 - 4615
Опубликована: Июль 14, 2024
Язык: Английский
Процитировано
5
Sustainable Cities and Society, Год журнала: 2024, Номер unknown, С. 105936 - 105936
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
5