scholarly journals Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yuanzheng Li ◽  
Lan Wang ◽  
Min Liu ◽  
Guosong Zhao ◽  
Tian He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Southern China ◽  
Well Being ◽  
Urban Heat Islands ◽  
Western China ◽  
Machine Learning Method ◽  
Learning Method ◽  
Heat Islands ◽  
Urban Heat

The thermal environment is closely related to human well-being. Determinants of surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, some research fields remain blank or have conflicting findings, which need to be further addressed. Particularly, few studies focus on drivers of SUHIs in massive cities with different sizes under various contexts at large scales. Using multisource data, we explored 11 determinants of surface urban heat island intensity (SUHII) for 1449 cities in different ecological contexts throughout China in 2010, adopting the Spearman and partial correlation analysis and machine learning method. The main results were as follows: (1) Significant positive partial correlations existed between daytime SUHII and the differences in nighttime light intensity and built-up intensity between cities and their corresponding villages except in arid or semiarid western China. The differences in the enhanced vegetation index were generally partially negatively correlated with daytime and nighttime SUHII. The differences in white sky albedo were usually partially negatively correlated with nighttime SUHII. The mean air temperature was partially positively correlated with nighttime SUHII in 40% of cases. Only a few significant partial relationships existed between SUHII and urban area, total population, and differences in aerosol optical depth. The explanation rates during daytime were larger than during nighttime in 72% of cases. The largest and smallest rates occurred during summer days in humid cold northeastern China (63.84%) and in southern China (10.44%), respectively. (2) Both the daytime and nighttime SUHII could be well determined by drivers using the machine learning method. The RMSE ranged from 0.49°C to 1.54°C at a national scale. The simulation SUHII values were always significantly correlated with the actual SUHII values. The simulation accuracies were always higher during nighttime than daytime. The highest accuracies occurred in central-northern China and were lowest in western China during both daytime and nighttime.

scholarly journals SATELLITE-DRIVEN ASSESSMENT OF SURFACE URBAN HEAT ISLANDS IN THE CITY OF ZAGREB, CROATIA

2020 ◽  
Vol V-3-2020 ◽  
pp. 757-764
Author(s):  
A. Krtalić ◽  
A. Kuveždić Divjak ◽  
K. Čmrlec
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Principal Component ◽  
Urban Heat Islands ◽  
City Centre ◽  
Landsat 8 ◽  
Heat Islands ◽  
Critical Areas ◽  
Urban Heat ◽  
The City

Abstract. This study aims to assess surface urban heat islands (SUHIs) pattern over the city of Zagreb, Croatia, based on satellite (optical and thermal) remote sensing data. The spatio-temporal identification of SUHIs is analysed using the 12 sets of Landsat 8 imagery acquired during 2017 (in each month of the year). Vegetation cover within the city boundaries is extracted by using Principal Component Analysis (PCA) data fusion method on calculated three vegetation indices (VI): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Ratio Vegetation Index (RVI) for each set of bands. The first principal component was used to compute the land surface temperature (LST) and deductive Environmental Criticality Index (ECI). As expected, the relationship between LST and all VI scores shows a negative correlation and is most negative with RVI. The environmentally critical areas and the patterns of seasonal variations of the SUHIs in the city of Zagreb were identified based on the LST, ECI and vegetation cover. The city centre, an industrial area in the eastern part and an area with shopping centers and commercial buildings in the western part of the city were identified as the most critical areas.

scholarly journals The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)

2021 ◽  
Vol 13 (22) ◽  
pp. 4697
Author(s):  
Muhammad Amir Siddique ◽  
Yu Wang ◽  
Ninghan Xu ◽  
Nadeem Ullah ◽  
Peng Zeng
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Single Channel ◽  
Urban Ecosystems ◽  
Landscape Composition ◽  
Urban Heat Islands ◽  
Markov Modeling ◽  
Heat Islands ◽  
Urban Heat ◽  
Vegetation Water

The rapid increase in infrastructural development in populated areas has had numerous adverse impacts. The rise in land surface temperature (LST) and its associated damage to urban ecological systems result from urban development. Understanding the current and future LST phenomenon and its relationship to landscape composition and land use/cover (LUC) changes is critical to developing policies to mitigate the disastrous impacts of urban heat islands (UHIs) on urban ecosystems. Using remote sensing and GIS data, this study assessed the multi-scale relationship of LUCC and LST of the cosmopolitan exponentially growing area of Beijing, China. We investigated the impacts of LUC on LST in urban agglomeration for a time series (2004–2019) of Landsat data using Classification and Regression Trees (CART) and a single channel algorithm (SCA), respectively. We built a CA–Markov model to forecast future (2025 and 2050) LUCC and LST spatial patterns. Our results indicate that the cumulative changes in an urban area (UA) increased by about 908.15 km2 (5%), and 11% of vegetation area (VA) decreased from 2004 to 2019. The correlation coefficient of LUCC including vegetation, water bodies, and built-up areas with LST had values of r = −0.155 (p > 0.419), −0.809 (p = 0.000), and 0.526 (p = 0.003), respectively. The results surrounding future forecasts revealed an estimated 2309.55 km2 (14%) decrease in vegetation (urban and forest), while an expansion of 1194.78 km2 (8%) was predicted for a built-up area from 2019 to 2050. This decrease in vegetation cover and expansion of settlements would likely cause a rise of about ~5.74 °C to ~9.66 °C in temperature. These findings strongly support the hypothesis that LST is directly related to the vegetation index. In conclusion, the estimated overall increase of 7.5 °C in LST was predicted from 2019–2050, which is alarming for the urban community’s environmental health. The present results provide insight into sustainable environmental development through effective urban planning of Beijing and other urban hotspots.

scholarly journals Satellite Image Fusion to Detect Changing Surface Permeability and Emerging Urban Heat Islands in a Fast-Growing City

2018 ◽  
Author(s):  
Rajchandar Padmanaban ◽  
Pedro Cabral ◽  
Avit K Bhowmik
Keyword(s):  
Surface Temperature ◽  
Image Fusion ◽  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Satellite Image ◽  
Urban Heat Islands ◽  
Heat Islands ◽  
Urban Heat ◽  
Surface Permeability

Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of urban heat islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they have mostly involved time consuming and expensive field studies, and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and UHI emergence in Tirunelveli, Tamilnadu, India. Cartosat-2 and Landsat-7 ETM+ imageries from 2007 and 2017 were fused and classified using a Rotation Forest (RF), while surface permeability and temperature were quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Fused images exhibited higher classification accuracies than non-fused images, i.e. overall kappa coefficient values 0.83 and 0.75, respectively. We observed an overall increase of 20 km2 (45%) in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease of 27 km2 (37%) for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall (0.4) and also for almost all LULC zones. The LST values exhibited an associated overall increase (1.30C) of surface temperature in Tirunelveli with the highest increase (2.40C) for urban built-up areas between 2007 and 2017. The SAVI-LST combined metric depicted the Southeastern built-up areas in Tirunelveli as a potential UHI hotspot, while a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.

scholarly journals Bogor Botanic Gardens as a nature-based solution for mitigating urban heat island and microclimate regulation

2021 ◽  
Vol 914 (1) ◽  
pp. 012050
Author(s):  
E M D Rahayu ◽  
S Yusri
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Botanic Gardens ◽  
Heat Islands ◽  
Urban Heat ◽  
Relationship Of

Abstract This paper explores the role of Bogor Botanic Gardens (BBG) as a form of Nature-Based Solution (NBS) to mitigate Urban Heat Islands (UHI). Time series analysis of LANDSAT 8 OLI thermal band and Normalized Difference Vegetation Index (NDVI) was done from 2013 to 2020 using Google Earth Engine. Land Surface Temperature (LST) from Bogor and BBG were calculated, compared, and annual UHI areas were derived. The relationship of LST and NDVI were also explored annually to describe the effect of vegetation towards LST with linear regression. Overall, Bogor experiences a decrease of mean LST from 30.67°C and a maximum of 39.14°C in 2013 to 27.07°C and a maximum of 34.35°C in 2020. However, the inside of BBG is cooler with temperature ranging from 28.41°C and a maximum of 35.62°C in 2013 to 24.25°C and a maximum of 29.41°C in 2020. This is an effect of vegetation inside the BBG that regulate microclimate in its surrounding. It can be seen in the negative correlation between NDVI and LST observed with r2 ranging from 0.27 to 0.82. While UHI areas tended to increase from 8220 ha in 2013 to 8926 ha in 2020, BBG consistently acts as an urban cool island in the middle of UHI. Therefore, heat mitigation is proven to be one of the environmental services provided by BBG.

scholarly journals Cool Materials and the Heat Island Effect: a bibliometric analysis

2021 ◽  
Vol 9 (73) ◽  
Author(s):  
Rodrigo Borges Nascimento Guedes ◽  
Marina Lavorato ◽  
Cláudia Cotrim Pezzuto
Keyword(s):  
Well Being ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Heat Islands ◽  
Island Effect ◽  
Base Study ◽  
Urban Heat ◽  
The Subject ◽  
Health And Well Being ◽  
A Current

A current problem triggered by anthropological factors that many cities in the world face is the increase in temperature in urban centres caused by urban heat islands. There is a constant proposal for measures to reduce these effects, which can be harmful to the health and well-being of the population. The purpose of this work is to carry out a bibliometric mapping to analyse the published documents on cool materials regarding the mitigation effects of heat islands and to understand their origin and possible reasons that led to the study of this theme. From the SCOPUS database, searching for “Cool Material AND (Albedo OR Reflectance) AND Heat Island”, the scientific documents (engineering area) published between 1995 and 2021 were selected and analysed: the types of documents, their origin, the year of publication, the main authors and the journals in which they were published. Energy and Buildings and Building and Environment were the two main journals on the subject. 1995 had the first article published and coincided with the year of the first Conference of the Parties (COP1), on climate change. Through a bibliometric survey, it is possible to understand the importance of the beginning of this line of research and that climatic events can trigger interest and bring the scientific view to a certain area. However, because it is a single-base study, the research, despite bringing good information, still needs constant updates.

scholarly journals Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)

2021 ◽  
Vol 13 (16) ◽  
pp. 3177
Author(s):  
Talha Hassan ◽  
Jiahua Zhang ◽  
Foyez Ahmed Prodhan ◽  
Til Prasad Pangali Sharma ◽  
Barjeece Bashir
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Thermal Environment ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Heat Islands ◽  
Urban Heat

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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