scholarly journals Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City

2018 ◽  
Vol 10 (12) ◽  
pp. 1965 ◽  
Author(s):  
Nguyen Thanh Hoan ◽  
Yuei-An Liou ◽  
Kim-Anh Nguyen ◽  
Ram Sharma ◽  
Duy-Phien Tran ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Heat Islands ◽  
Land Use Types ◽  
Urban Heat ◽  
Negative Impacts ◽  
The Individual

Hanoi City of Vietnam changes quickly, especially after its state implemented its Master Plan 2030 for the city’s sustainable development in 2011. Then, a number of environmental issues are brought up in response to the master plan’s implementation. Among the issues, the Urban Heat Island (UHI) effect that tends to cause negative impacts on people’s heath becomes one major problem for exploitation to seek for mitigation solutions. In this paper, we investigate the land surface thermal signatures among different land-use types in Hanoi. The surface UHI (SUHI) that characterizes the consequences of the UHI effect is also studied and quantified. Note that our SUHI is defined as the magnitude of temperature differentials between any two land-use types (a more general way than that typically proposed in the literature), including urban and suburban. Relationships between main land-use types in terms of composition, percentage coverage, surface temperature, and SUHI in inner Hanoi in the recent two years 2016 and 2017, were proposed and examined. High correlations were found between the percentage coverage of the land-use types and the land surface temperature (LST). Then, a regression model for estimating the intensity of SUHI from the Landsat 8 imagery was derived, through analyzing the correlation between land-use composition and LST for the year 2017. The model was validated successfully for the prediction of the SUHI for another hot day in 2016. For example, the transformation of a chosen area of 161 ha (1.61 km2) from vegetation to built-up between two years, 2016 and 2017, can result in enhanced thermal contrast by 3.3 °C. The function of the vegetation to lower the LST in a hot environment is evident. The results of this study suggest that the newly developed model provides an opportunity for urban planners and designers to develop measures for adjusting the LST, and for mitigating the consequent effects of UHIs by managing the land use composition and percentage coverage of the individual land-use type.

scholarly journals Assessment of Urban Heat Islands Based on the Relationship Between Land Surface Temperature and Land Use/Land Cover in Greater Doha

2020 ◽  
Author(s):  
Simil Amir Siddiqui
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Heat Islands ◽  
Land Use Land Cover ◽  
Heat Islands ◽  
Urban Heat ◽  
The City

Urban heat islands (UHI) are areas with elevated temperatures occurring in cities compared to surrounding rural areas. This study realizes the lack of research regarding the trends of UHIs in desert countries and focuses on Doha. The research includes twelve months of two-time periods; 2000-2019. ArcGIS software was used to compute the land surface temperature (LST) of the city using Landsat images. Land use/land cover (LULC) maps were computed to show how the city has evolved in 19 years. 30 field samples were used to verify the accuracy of the LULC. Results showed UHI in Doha did not display similar pattern to that of cities in subtropical and temperate regions. Higher temperatures were prevalent in out-skirts comprising of barren and built-up areas with high population and no vegetation. Comparatively, the main downtown with artificially planted vegetation and shade from skyscrapers created cooler microclimates. The overall LST of greater Doha has increased by 0.7°C from 2000 to 2019. Furthermore %LULC of built up, vegetation, barren land, marsh land and water body were 29%, 4.5%, 58.6%, 2.8% and 5% in 2000 and 56.5 %, 8.2%, 33.2 %, 0% and 2.1% in 2019 respectively. Overall, there was an increase in built-up and vegetation decrease in water and barren areas and complete loss of marshland. Highest temperatures were recorded for marshland area in year 2000 and barren and built in year 2019. Transect profiles showed positive correlation between NDBI and LST and a negative correlation between NDVI and LST.

scholarly journals Developing land-cover driver model for estimating the intensity of surface urban heat islands using landsat 8 satellite imagery

2019 ◽  
Vol 41 (3) ◽  
pp. 201-215 ◽  
Author(s):  
Nguyen Thanh Hoan ◽  
Nguyen Van Dung ◽  
Ho Le Thu ◽  
Hoa Thuy Quynh
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Moving Window ◽  
Land Cover Types ◽  
Heat Islands ◽  
Urban Heat

It is of utmost importance to understand and monitor the impact of urban heat islands on ecosystems and overall human health in the context of climate change and global warming. This research was conducted in a tropical city, Hanoi, with a major objective of assessing the quantitative relationships between the composition of the main land-cover types and surface urban heat island phenomenon. In this research, we analyzed the correlation between land-cover composition, percentage coverage of the land cover types, and land surface temperature for different moving window sizes or urban land management units. Landsat 8 OLI (Operational Land Imager) satellite data was utilized for preparing land-cover composition datasets in inner Hanoi by employing the unsupervised image clustering method. High-resolution (30m) land surface temperature maps were generated for different days of the years 2016 and 2017 using Landsat 8 TIRS (Thermal Infrared Sensor) images. High correlations were observed between percentage coverage of the land-cover types and land surface temperature considering different window sizes. A new model for estimating the intensity of surface urban heat islands from Landsat 8 imagery is developed, through recursively analyzing the correlation between land-cover composition and land surface temperature at different moving window sizes. This land-cover composition-driven model could predict land surface temperature efficiently not only in the case of different window sizes but also on different days. The newly developed model in this research provides a wonderful opportunity for urban planners and designers to take measures for adjusting land surface temperature and the associated effects of surface urban heat islands by managing the land cover composition and percentage coverage of the individual land-cover types.

MONITORING THERMAL POLLUTION SOURCES IN VORONEZH (RUSSIA) USING REMOTE SENSING DATA

2021 ◽  
pp. 54-65
Author(s):  
Дмитрий Владимирович Сарычев ◽  
Ирина Владимировна Попова ◽  
Семен Александрович Куролап
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Heat Islands ◽  
Land Surface Temperatures ◽  
Treatment Facilities ◽  
Urban Heat ◽  
Industrial Zones

Рассмотрены вопросы мониторинга теплового загрязнения окружающей среды в городах. Представлена методика отбора спектрозональных спутниковых снимков, их обработки и интерпретации полученных результатов. Для оценки городского острова тепла были использованы снимки с космического аппарата Landsat 8 TIRS. На их основе построены карты пространственной структуры острова тепла города Воронежа за летний и зимний периоды. Определены тепловые аномалии и выявлено 11 основных техногенных источников теплового загрязнения в г. Воронеже, установлена их принадлежность к промышленным зонам предприятий, а также к очистным гидротехническим сооружениям. Поверхностные температуры данных источников в среднем были выше фоновых температур приблизительно на 6° зимой и на 15,5° С летом. Синхронно со спутниковой съемкой были проведены наземные контрольные тепловизионные измерения температур основных подстилающих поверхностей в г. Воронеже. Полученные данные показали высокую сходимость космических и наземных измерений, на основании чего сделан вывод о надежности используемых данных дистанционного зондирования Земли в мониторинговых наблюдениях теплового загрязнения городской среды. Результаты работ могут найти применение в городском планировании и медицинской экологии. The study deals with the remote sensing and monitoring of urban heat islands. We present a methodology of multispectral satellite imagery selection and processing. The study bases on the freely available Landsat 8 TIRS data. We used multitemporal thermal band combinations to make maps of the urban heat island of Voronezh (Russia) during summer and winter periods. That let us identify 11 artificial sources of heat in Voronezh. All of them turned out to be allocated within industrial zones of plants and water treatment facilities. Land surface temperatures (LST) of these sources were approximately 6° and 15.5° C above the background temperatures in winter and summer, respectively. To prove the remotely sensed temperatures we conducted ground control measurements of LST of different surface types at the satellite revisit moments. Our results showed a significant correlation between the satellite and ground-based measurements, so the maps we produced in this study should be robust. They are of use in urban planning and medical ecology studies.

scholarly journals Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1449 ◽  
Author(s):  
Carlos Granero-Belinchon ◽  
Aurelie Michel ◽  
Jean-Pierre Lagouarde ◽  
Jose A. Sobrino ◽  
Xavier Briottet
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Image Resolution ◽  
Urban Heat Islands ◽  
Land Uses ◽  
Regression Kriging ◽  
Heat Islands ◽  
Urban Heat ◽  
Land Covers ◽  
The City

Urban Heat Islands (UHIs) at the surface and canopy levels are major issues in urban planification and development. For this reason, the comprehension and quantification of the influence that the different land-uses/land-covers have on UHIs is of particular importance. In order to perform a detailed thermal characterisation of the city, measures covering the whole scenario (city and surroundings) and with a recurrent revisit are needed. In addition, a resolution of tens of meters is needed to characterise the urban heterogeneities. Spaceborne remote sensing meets the first and the second requirements but the Land Surface Temperature (LST) resolutions remain too rough compared to the urban object scale. Thermal unmixing techniques have been developed in recent years, allowing LST images during day at the desired scales. However, while LST gives information of surface urban heat islands (SUHIs), canopy UHIs and SUHIs are more correlated during the night, hence the development of thermal unmixing methods for night LSTs is necessary. This article proposes to adapt four empirical unmixing methods of the literature, Disaggregation of radiometric surface Temperature (DisTrad), High-resolution Urban Thermal Sharpener (HUTS), Area-To-Point Regression Kriging (ATPRK), and Adaptive Area-To-Point Regression Kriging (AATPRK), to unmix night LSTs. These methods are based on given relationships between LST and reflective indices, and on invariance hypotheses of these relationships across resolutions. Then, a comparative study of the performances of the different techniques is carried out on TRISHNA synthesized images of Madrid. Since TRISHNA is a mission in preparation, the synthesis of the images has been done according to the planned specification of the satellite and from initial Aircraft Hyperspectral Scanner (AHS) data of the city obtained during the DESIREX 2008 capaign. Thus, the coarse initial resolution is 60 m and the finer post-unmixing one is 20 m. In this article, we show that: (1) AATPRK is the most performant unmixing technique when applied on night LST, with the other three techniques being undesirable for night applications at TRISHNA resolutions. This can be explained by the local application of AATPRK. (2) ATPRK and DisTrad do not improve significantly the LST image resolution. (3) HUTS, which depends on albedo measures, misestimates the LST, leading to the worst temperature unmixing. (4) The two main factors explaining the obtained performances are the local/global application of the method and the reflective indices used in the LST-index relationship.

scholarly journals GEOSPATIAL ASSESSMENT AND MODELING OF URBAN HEAT ISLANDS IN QUEZON CITY, PHILIPPINES USING OLS AND GEOGRAPHICALLY WEIGHTED REGRESSION

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 85-92
Author(s):  
C. A. Alcantara ◽  
J. D. Escoto ◽  
A. C. Blanco ◽  
A. B. Baloloy ◽  
J. A. Santos ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Land Surface ◽  
Google Earth ◽  
Urban Heat Islands ◽  
Weighted Regression ◽  
View Factor ◽  
Landsat 8 ◽  
Quezon City ◽  
Heat Islands ◽  
Urban Heat

Abstract. Urbanization has played an important part in the development of the society, yet it is accompanied by environmental concerns including the increase of local temperature compared to its immediate surroundings. The latter is known as Urban Heat Islands (UHI). This research aims to model UHI in Quezon City based on Land Surface Temperature (LST) estimated from Landsat 8 data. Geospatial processing and analyses were performed using Google Earth Engine, ArcGIS, GeoDa, and SAGA GIS. Based on Urban Thermal Field Variance Index (UTFVI) and the normalized mean per barangay (village), areas with strong UHI intensities were mapped and characterized. high intensity UHIs are observed mostly in areas with high Normalized Difference Built-up Index (NDBI) like the residential regions while the weak intensity UHIs are noticed in areas with high Normalized Difference Vegetation Index (NDVI) near the La Mesa Reservoir. In the OLS regression model, around 69% of LST variability is explained by Surface Albedo (SA), Sky View Factor (SVF), Surface Area to Volume Ratio (SVR), Solar Radiation (SR), NDBI and NDVI. OLS yield relatively high residuals (RMSE = 1.67) and the residuals are not normally distributed. Since LST is non-stationary, Geographically Weighted Regression (GWR) regression was conducted, proving normally and randomly distributed residuals (average RMSE = 0.26).

scholarly journals A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI

2019 ◽  
Vol 33 (2) ◽  
pp. 162-172
Author(s):  
Iswari Nur Hidayati ◽  
R Suharyadi
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Spectral Characteristics ◽  
Impervious Surface ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Landsat 8 Oli ◽  
Heat Islands ◽  
Urban Heat

Impervious surface is one of the major land cover types of urban and suburban environment. Conversion of rural landscapes and vegetation area to urban and suburban land use is directly related to the increase of the impervious surface area. The impervious surface expansion is straight-lined with decreasing green spaces in urban areas. Impervious surface is one of indicator for detecting urban heat islands. This study compares various indices for mapping impervious surfaces using Landsat 8 OLI imagery by optimizing the different spectral characteristics of Landsat 8 OLI imagery. The research objectives are (1) to apply various indices for impervious surface mapping and (2) identifies impervious surfaces in urban areas based on multiple indices and provide recommendations and find the best index for mapping impervious surface in urban areas. In addition to utilizing the index, land use supervised classification method, maximum likelihood classification used for extracting built-up, and non-built-up areas. Accuracy assessment of this research used field data collection as primary data for calculating kappa coefficient, producer accuracy, and user accuracy. The study can also be extended to find the land surface temperature and correlate the impervious surface extraction data with urban heat islands.

scholarly journals Spatial Temporal Analysis of Urban Heat Hazard on Education Area (University of Indonesia

2017 ◽  
Vol 49 (1) ◽  
pp. 1 ◽  
Author(s):  
Adi Wibowo ◽  
Khairulmaini Osman Salleh ◽  
Adi Wibowo
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Temporal Analysis ◽  
Maximum Temperature ◽  
Climate Index ◽  
Landsat 8 ◽  
Urban Heat ◽  
Heat Hazard

As education area, campus or university is full with various activities which have an impact on the existence of land-use or land-cover. The variation of activities dynamically change the shape of land-use or land-cover within the campus area, thus also create variations in Land Surface Temperature (LST). The LST are impacting the coziness of human activity especially when reaches more than 30 oC. This study used the term Urban Heat Signature (UHS) to explain LST in different land-use or land-cover types. The objective of this study is to examine UHS as an Urban Heat Hazard (UHH) based on Universal Temperature Climate Index (UTCI) and Effective Temperature Index (ETI) in University of Indonesia. Thermal bands of Landsat 8 images (the acquisition year 2013-2015) were used to create LST model. A ground data known as Air Surface Temperature (AST) were used to validate the model. The result showed an increased level of maximum temperature during September-October since 2013 until 2014. The maximum temperature was reduced in October 2014, however it increased again in August 2015. The UTCI showed “moderate” and “strong heat stress”, while EFI showed “uncomfortable” and “very uncomfortable” categories during that period. This research concluded that build up area in UI Campus highest temperature on UI campus based on UHS. Range UHS in Campus UI on 2013 (21.8-31.1oC), 2014 (25.0-36.2oC) and 2015 (24.9-38.2oC). This maximum UHS on September (2014 and 2015) put on levelling UTCI included range temperature 32-35oC, with an explanation of sensation temperature is warm and sensation of comfort is Uncomfortable, Psychology with  Increasing Stress Case by Sweating and Blood Flow and Health category is Cardiovascular Embarrassment. This UHS occurs in September will give impact on psychology and health, that’s become the UHH of the living on education area.

scholarly journals Quantifying the effect of waterways and green areas on the surface temperature

2017 ◽  
Vol 39 (1) ◽  
pp. 89 ◽  
Author(s):  
Elis Dener Lima Alves
Keyword(s):  
Surface Temperature ◽  
Temporal Variability ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Natural Area ◽  
Heat Islands ◽  
Green Areas ◽  
Urban Heat ◽  
Cooling Effects ◽  
The Relationship

The cooling effects of urban parks and green areas, which form the “Park Cool Island” (PCI) can help decrease the surface temperature and mitigate the effects of urban heat islands (UHI). Therefore, the objective of this research was to know the temporal variability of PCI intensity, as well as analyze the factors that determines it and propose an equation to predict the PCI intensity in Iporá, Goiás State, Brazil. To this purpose, the PCI intensity values were obtained using the Landsat-8 satellite (band 10), and then correlated with the NDVI and the LAI, in which proposes equations through multiple linear regression to estimate the PCI intensity. The results indicated that: 1) the greater the distance of the natural area, greater the surface temperature; 2) there is a great seasonality in PCI, in which the intensity of PCI is much higher in the spring (or close to it); 3) the relationship between NDVI and LAI variables, showed good coefficients of determination; 4) the equations for the buffer of 200 and 500 m, had low RMSE with high coefficients of determination (r2 = 0.924 and r2 = 0.957 respectively). 

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