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 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.

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 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 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.

scholarly journals INTRA-URBAN HEAT ISLAND DETECTION AND TREND CHARACTERIZATION IN METRO MANILA USING SURFACE TEMPERATURES DERIVED FROM MULTI-TEMPORAL LANDSAT DATA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 275-282
Author(s):  
K. P. Landicho ◽  
A. C. Blanco
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Google Earth ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Landsat Data ◽  
Heat Islands ◽  
Surface Temperatures ◽  
Metro Manila ◽  
Urban Heat

Abstract. Unprecedented urbanization in Metro Manila has led to the proliferation of the urban heat island (UHI) effect. This is characterized by a prominent difference in the temperatures of the urban and its surrounding rural and less urbanized areas. Temperature differences occur within these UHI’s indicating the existence of intra-urban heat islands (IUHI). UHI’s and IUHI’s are well-documented indicators of urban environmental degradation and therefore puts the population of Metro Manila at risk. In anticipation of these effects, their detection and the characterization of their behaviour through time can contribute to proper urban planning thus mitigating harmful effects. Google Earth Engine was used to retrieve land surface temperatures (LST) from Landsat data from 1997 to 2019 using emissivity estimation. The Local Moran’s I statistic was then used to identify cluster and outlier types (COT). A histogram with 10 bins representing the net COT frequencies per barangay was then used to identify IUHI’s. Annual temperature measurements and COT areas were plotted against time and based on linear-fit trend lines they characterize the study area as to having an annual increase in temperature of roughly 0.18 °C and hotspot area extent of around 0.03 km2, and a decrease in coldspot area extent around 0.01 km2. Hotspots were found to be frequent in the cities of Caloocan, Manila, Pasay, and Quezon while coldspots were found to be frequent in the cities of Caloocan, Las Piñas, Malabon, Navotas, and Valenzuela. In conclusion, IUHI’s were detected with statistical basis, both spatially and temporally.

scholarly journals SEASONS IN STUTTGART: DEVELOPING A GOOGLE EARTH ENGINE TOOL FOR HEAT ISLAND MAPPING

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1123-1130
Author(s):  
R. Zafrir ◽  
S. Ojeda ◽  
H. Singh ◽  
M. Hahn
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Goodness Of Fit ◽  
Polynomial Regression ◽  
Google Earth ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Night Time ◽  
Heat Islands ◽  
Google Earth Engine

Abstract. This study generates a process in GEE (Google Earth Engine) for SUHI (Surface Urban Heat islands) identification derived from TIRS (Thermal Infrared Sensor) and OLI (Operational Land Imager) sensors of Landsat 8 imagery in the area of Stuttgart, Germany. By comparing the temperature images in winter and summer seasons through a regression model, a relation between the Surface Cover (SC), the Terrain Shape (DEM) and the LST (Land Surface Temperature) is established. A Python code is developed for modelling the data and displaying the results linked to GEE. Three different models are used to establish the relationship between different variables (Temperature, Height, Wind etc.). Accuracy/goodness of fit of these models are measured using R-squared and standard error. Results shows that polynomial regression of 3rd order degree fits best to the dataset used in this study. Moreover, it is found that temperature values are not perfect for this study, as Landsat 8 have been acquired at 10’o clock in the morning (local time), whereas night time acquisition (which was not available for Stuttgart, Germany) would be best suited for the study. The results indicate that urban areas and meadow (open areas without vegetation) get the bigger values of temperature. Terrain Shape with respect to height indicates that the bigger the height, the lower the temperature in most of the regions. This project provides insight into the development of applications using a web-based platform and leads to a fast and accurate result for identifying the SUHI effect. It can contribute to the necessity of planning more vegetation areas in order to reduce hot temperature values in Stuttgart.

scholarly journals Urban heat island: Aerodynamics or imperviousness?

2019 ◽  
Vol 5 (4) ◽  
pp. eaau4299 ◽  
Author(s):  
Dan Li ◽  
Weilin Liao ◽  
Angela J. Rigden ◽  
Xiaoping Liu ◽  
Dagang Wang ◽  
...  
Keyword(s):  
Green Infrastructure ◽  
Rural Areas ◽  
Land Surface ◽  
Urban Heat Islands ◽  
Lower Atmosphere ◽  
Geographic Variations ◽  
Heat Islands ◽  
Urban Heat ◽  
The Difference ◽  
Urban And Rural Areas

More than half of the world’s population now live in cities, which are known to be heat islands. While daytime urban heat islands (UHIs) are traditionally thought to be the consequence of less evaporative cooling in cities, recent work sparks new debate, showing that geographic variations of daytime UHI intensity were largely explained by variations in the efficiency with which urban and rural areas convect heat from the land surface to the lower atmosphere. Here, we reconcile this debate by demonstrating that the difference between the recent finding and the traditional paradigm can be explained by the difference in the attribution methods. Using a new attribution method, we find that spatial variations of daytime UHI intensity are more controlled by variations in the capacity of urban and rural areas to evaporate water, suggesting that strategies enhancing the evaporation capability such as green infrastructure are effective ways to mitigate urban heat.

scholarly journals Effect of Land Cover Fractions on Changes in Surface Urban Heat Islands Using Landsat Time-Series Images

2019 ◽  
Vol 16 (6) ◽  
pp. 971 ◽  
Author(s):  
Tao Chen ◽  
Anchang Sun ◽  
Ruiqing Niu
Keyword(s):  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Urban Heat Islands ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Impervious Surfaces ◽  
Heat Islands ◽  
Urban Heat ◽  
Surface Urban Heat Island

Man-made materials now cover a dominant proportion of urban areas, and such conditions not only change the absorption of solar radiation, but also the allocation of the solar radiation and cause the surface urban heat island effect, which is considered a serious problem associated with the deterioration of urban environments. Although numerous studies have been performed on surface urban heat islands, only a few have focused on the effect of land cover changes on surface urban heat islands over a long time period. Using six Landsat image scenes of the Metropolitan Development Area of Wuhan, our experiment (1) applied a mapping method for normalized land surface temperatures with three land cover fractions, which were impervious surfaces, non-chlorophyllous vegetation and soil and vegetation fractions, and (2) performed a fitting analysis of fierce change areas in the surface urban heat island intensity based on a time trajectory. Thematic thermal maps were drawn to analyze the distribution of and variations in the surface urban heat island in the study area. A Multiple Endmember Spectral Mixture Analysis was used to extract the land cover fraction information. Then, six ternary triangle contour graphics were drawn based on the land surface temperature and land cover fraction information. A time trajectory was created to summarize the changing characteristics of the surface urban heat island intensity. A fitting analysis was conducted for areas showing fierce changes in the urban heat intensity. Our results revealed that impervious surfaces had the largest impacts on surface urban heat island intensity, followed by the non-chlorophyllous vegetation and soil fraction. Moreover, the results indicated that the vegetation fraction can alleviate the occurrence of surface urban heat islands. These results reveal the impact of the land cover fractions on surface urban heat islands. Urban expansion generates impervious artificial objects that replace pervious natural objects, which causes an increase in land surface temperature and results in a surface urban heat island.

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