Urban Heat Island Monitoring Research Articles

Environmental Monitoring in Internet of Things (IOT)

Abstract: The Internet of Things (IoT) connects physical objects to the internet, enabling autonomous data collection and analysis. In environmental monitoring, IoT plays a crucial role in gathering real-time data on parameters like air and water quality, weather, wildlife behavior, and urban heat islands. This abstract explores how IoT is used in environmental monitoring, covering standards, device types, and applications. IoT comprises interconnected devices that collect environmental data and transmit it for analysis, facilitating continuous observation and better resource management. Standards like MQTT and communication technologies like Wi-Fi govern IoT implementation. Environmental monitoring involves assessing air and water quality, weather conditions, wildlife behavior, and urban heat islands. Air quality monitoring measures pollutant levels in the atmosphere using IoT-based sensor networks. Water quality monitoring assesses chemical and biological characteristics of water bodies. Weather and climate monitoring observe atmospheric conditions using IoT-based sensor networks. Wildlife monitoring tracks animal movements and behavior using IoT-enabled devices. Urban heat island monitoring assesses temperature distribution in urban areas using IoT-based sensors. IoT revolutionizes environmental monitoring by enabling real-time, remote observation across various domains, contributing to better resource management and sustainability efforts.

Time series analysis of landsat data for urban heat island monitoring in Semarang City

The urban heat island (UHI) condition has received significant attention because it has the potential to negatively impact life quality and comfort, especially livability and level of comfort. Semarang City, the capital of Central Java, has been attracting many people to live and causing land use conversion to built-up areas. This study aims to observe the land surface temperature (LST) trend and to analyse the UHI spatiotemporal pattern in Semarang based on satellite time series data within 10 years. Land cover and LST were extracted from Landsat data of 2008, 2013, and 2019. Based on the results, it was observed that the vegetated area has reduced from 20.347 Ha to 7.241 Ha, while the average LST value has increased from 29.24°C to 31.31°C in 2008 and 2019 respectively. We also found that the UHI pattern, which was initially detected only in a few areas in 2008 and was focused in the city centre, has gradually spread to the east and south of Semarang in 2019. The result also shows the spatiotemporal trend of land cover conversion and UHI areas, which can be beneficial for mitigation planning in the future.

USING REMOTE SENSING TECHNOLOGIES FOR MONITORING URBAN HEAT ISLANDS

Background. The urbanization process is accelerating every day, which entails significant changes in the natural landscape. This leads to microclimatic changes, air pollution, thermal effect, etc. Due to air pollution by man-made emissions in urbanized areas, the thermal regime is changing; the concentration of carbon dioxide and water vapor has now reached 90 % of the total amount of pollutants. As a result, another problem arises that contributes to global warming – the "greenhouse effect". Elevated air temperatures affect human health leading to breathing problems, seizures, heat and sunstroke, heat stress, and increased mortality. Considering the potential danger of elevated air temperatures caused by urban heat islands affecting the lives of residents, an effective and relevant method for surface temperature analysis and heat island location determination should be developed. Methods. During urban heat island monitoring, the main factor of analysis is surface temperature, which was determined in the study using indices such as: Normalized Difference Vegetation Index (NDVI), Urban Thermal Field Variance Index (UTVFI) and Normalized Difference Built-up Index (NDBI). Results. This paper describes a study of the distribution of urban heat islands in three European capitals, including Kyiv, Oslo and Rome, from May 2013 to August 2023. Using the capabilities of the GoogleEarthEngine cloud platform and data from the Landsat 8 OLI/TIRS satellite, the condition of green spaces, the number of impervious surfaces and indices of surface temperatures (LST) were compared, resulting in maps of the distribution of urban heat islands (UHIs) in the areas of selected cities and towns demonstrating quantitative changes. Conclusions. Thus, the study showed a decrease in the number of urban heat islands along with impervious surfaces in the city of Kyiv by 4 %. At the same time, Rome and Oslo experienced an increase in the number of urban heat islands along with impervious surfaces. The data obtained prove the feasibility of using the chosen research method and can be used to assess the environmental condition, identify risk zones, and develop effective measures to further prevent the spread of UHI in megacities.

도시열섬 완화로 시민건강권 확보

A core project of Busan’s healthy city policy is the community health center project. Community health centers are established at the donglevel, the smallest and nearest place to residents’ everyday lives, to address community health issues such as health counseling, support for resident-led health community activities, and revitalization of health networks.
 The climate response activities of the health guardians in the 74 community health centers in Busan were promoted as a key component of Busan Metropolitan Government’s climate response policy. In 2022, Busan’s climate response policy with the community health centers was awarded the Korea Health City Award.
 This case study aims to describe Busan’s efforts on climate change adaptation and greenhouse gas reduction that is organized by an intersectoral effort of Carbon Neutral Policy Division 24 departments that includes the Health Policy Division. Busan City made efforts to create a city that adapts to climate change created with citizens by improving the vulnerability of heat wave mitigation and responding to climate change through citizen participation policies. And greenhouse gas reduction policies for various sectors (building, public, transportation, waste, and absorption sources) were promoted, while data-based efficient responses were promoted with urban heat island monitoring systems.

Editorial: Special Issue on Geographical Analysis and Modeling of Urban Heat Island Formation

This Special Issue focuses on the data, methods, techniques, and empirical outcomes of urban heat island studies from a time and space perspective. We showcase research papers, empirical studies, conceptual or analytic reviews, and policy-related tasks to help achieve urban sustainability. We are interested in target methodologies and datasets capturing urban heat island phenomena, including novel techniques for urban heat island monitoring and forecasting with the integration of remote sensing and GIS, the spatial relationship between urban heat island intensity and land use/cover distribution in metropolitan areas, the geographical patterns and processes of urban heat island phenomena in large cities, spatial differences in urban heat island intensity between developing and developed countries, urban heat island disaster mitigation and adaptation for future urban sustainability, and prediction and scenario analysis of urban heat island formation for policy and planning purposes.

Seasonal Flooding Impact on Land Surface Temperature(LST) in Response to Vegetation Index Using Spectral Radiance Model in Kirkuk, Iraq

Change detection of land use is essential in management and planning decisions. Land Surface Temperature (LST) has a significant importance in many geoscience aspects, such as heat transportation between land and atmosphere, state of corps, heat flux, greenhouse effect both locally and regionally, surface moisture and surface energy budget estimation, the monitoring of urban heat island, evapotranspiration and environmental biogeochemistry process simulation. A geographic information system was efficiently utilized to observe the changes in land use different categories and to map annual and seasonal LST variations. In this study, Land sat 5 and 8 images for the years 2010, 2017 and 2019 were used to study the flooding imact on the LST. The Mono-Window algorithm and sepectral rediance model on Landsat-5 TM and OLI were used to estimate LST. Taking 2010 as a reference, an empirical study shows that the No-vegetation category percentage was 9.28 % and 11.03% in the 2017 and 2019 summer seasons, respectively, while it was -1.48% and -0.46% in winter. Vegetation change was -60.9% and -50.04% in the 2017 and 2019 summer seasons, respectively, while it was 28.28% and -6.11% in winter. Water area change was -65.8% and 84.1% in the 2017 and 2019 summer seasons, respectively, while it was -38.5% and 59.89% in winter. The results indicate that the water area had greatly decreased during 2017 which led to increase the LST and the floods in 2019 led to decrease the LST by 5-8 degrees. The temperature trend analysis reveals a definite negative association between LST and flooded area, representing this study’s significant finding.

Monitoring Land Use/Land Cover Change (LULCC) Using Remote Sensing

Land use/land cover change (LULCC) is crucial for social and economic systems. For detecting LULCC, remote sensing technology has become crucial. This paper illustrated the use of remote sensing to monitor LULCC, with particular emphasis on urban planning and the ecological environment which is currently in the news, and the limitations of remote sensing. Remote sensing performs well in urban land cover change, urban growth analysis and urban heat island monitoring, giving these areas a effective way to solve problems. In ecological environment monitoring, it develops some new methods combined with computer science in vegetation monitoring. Meanwhile, more and more researchers focus on the wetland monitoring and ecosystem service evaluation. Remote sensing is anticipated to support the monitoring and study of LULCC in the future. This paper provides an overview of current study and developments in the application of remote sensing to track LULCC.

A Sensor Placement Strategy for Comprehensive Urban Heat Island Monitoring

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations.

Seasonal monitoring of urban heat island based on the relationship between land surface temperature and land use/cover: a case study of Kabul City, Afghanistan

Seasonal monitoring of urban heat island based on the relationship between land surface temperature and land use/cover: a case study of Kabul City, Afghanistan

Long Time-Series Urban Heat Island Monitoring and Driving Factors Analysis Using Remote Sensing and Geodetector

Urban heat island (UHI) effect decribes significant change due to rapid urbanization development. This study focused on the long time series analysis of UHI during the period 2000-2018, and analyzed the impact of land cover type and landscape metric factors on surface temperature. The results revealed that the UHI had a continuously decreasing trend in 2005–2010, and an increasing trend in 2000–2005 and 2010–2018. Cropland, built-up land, patch density (PD), Shannon Diversity Index (SHDI), and Landscape Shape Index (LSI) had a positive relationship with UHI, whereas forestland, open water, and CONTAG had a negative correlation with the UHI effect. The Geodetector analysis further revealed that PD, SHDI, and LSI had the greatest influences on LST as the three factors had the largest q values (0.287, 0.286, and 0.278). Forestland, cropland, and built-up land had greater impacts on the UHI than other land cover type factors. The explanatory power reached a maximum value of 0.408 when built-up land and cropland variables interacted. The findings of this study provide new understandings of the relationship between urban landscape and UHI, as well as important insights for urban planners to mitigate the UHI effect for the sustainable development of future urban agglomeration.

Monitoring of Urban Heat Island Based on Community Scale: A Case Study of Guangzhou

This paper is purposed to systematically explain the mechanism of community-scale urban heat island effect, thus providing a practical guidance on how to mitigate it in the urban community-scale thermal environment. Herein, a research is conducted into the surface urban heat island (SUHI) intensity on community scale in the five center urban districts of Guangzhou city. the urban heat island intensity on community scale (CUHII) of the five center urban districts is calculated to analyze the correlation between CUHII and six community factors, including plot ratio, building height, building density, the total number of community households and greening rate. The significant influencing factors for CUHII are building height, plot ratio and greening ratio. The areas with higher CUHII are located in the south of Baiyun District, the whole of Liwan District and the southwest of Haizhu District, but low in the north and the east. The CUHII in different stages of the construction concentrates on the second and third levels (4~8℃), showing an upward trend with the age of construction. There is also an increasing trend shown over time by the significance of correlation between the spatial anisotropy of CUHII and the following four factors, building height, plot ratio, the total number of households, and greening rate.

Co-construction of climate services based on a weather stations network: Application in Toulouse agglomeration local authority

This article provides insights into the development of climate services in Toulouse, France, based on an automated weather station network for microclimatic applications, such as urban heat island monitoring. Climate services are often thought of as a unique and complete entity that can be built by providers/sellers for users/buyers using different methods and degrees of involvement and participation. In this paper, the the local authority and its technical departments, helped by the national meteorological agency and researchers, were in charge of both providing and using the climate services. Each component, from network deployment to data production and their operational application, was directed by the local authority. Providers, users, products, and solutions were built from the ground up and developed over the last 5 years using the methodology of action research. This article discusses the possibility of understanding climate services by decomposing them into smaller components organized according to the disciplines, abilities, and challenges of each component to easily identify which actors in the local authority administrative organization can most effectively address them. Each component of the climate services based on a weather station network is discussed. This paper also describes how the governance and organization of climate services are built using action research. Co-construction processes with multiple actors encompassing multi-component projects, such as climate services, mobilize multiple disciplinary fields and require project management and organization. This article shows how the different components of the climate services in Toulouse have been integrated into different urban departments taking into consideration their competencies and their associated disciplinary fields with the goal of providing reproducible methods than can be applied elsewhere. The results indicate the real interest of urban departments in climate services. Some departments assumed responsibility for entire parts of the climate services. Their involvement reveals the complexity of truly integrating climate services as a transdisciplinary department into a public structure, in this case, the local authority. Managing the involvement and participation of all the stakeholders of the climate services implies organizing them using a governing body, even if they belong to the same organization or structure.

Assessment of the relationship between building density and urban heat island using Landsat images in Makassar City

Increasing temperatures in urban areas provide many adverse effects, and such is the potential for fires, air pollution up to the comfort of life in the state capital, and the other problems, with the global warming which occurred in the world, such as the melting of ice on most of the north pole, unpredictable Global warming and the number of flora and fauna habitat damaged. The need for facilities and infrastructure is the main factor that triggers settlements’ development; in general, urban areas often experience densely populated settlements. Therefore, researchers feel the need to monitor the relationship between Urban Heat Island (UHI) in Makassar City. Monitoring of Urban Heat Island needed surface temperature data from Landsat 8 OLI/TIRS with a thermal band resolution of 100m, building density needed NDBI models for data building density. This study analyses the relationship between building density and urban Heat Island using Landsat Imagery Data in Makassar city. The Landsat 8 OLI/TIRS imagery provides to be used to show the Urban Heat Island (UHI) phenomenon in Makassar City. In addition to UHI analysis, Landsat 8, Oil/TIRS imagery provides building density information using the NDBI spectral transformation. The results of the two result models’ determination tests show that Urban Heat Island and Building Density have a very high relationship.

Cooling power of sea breezes and its inland penetration in dry-summer Adelaide, Australia

Extreme high-temperature events pose a threat to human beings on Earth. In coastal cities, the sea breeze is widely known as a prevailing wind that can cool the near-surface air. However, the cumulative cooling effect and its attenuation process during the sea breeze penetration have not been well investigated. In this study, we analyze sea breeze cooling capacity (SBCC) and propose a new method in estimating the penetration distance of sea breeze cooling in metropolitan Adelaide during summer using data from the Adelaide urban heat island monitoring network. The results show that during a sea breeze day, wind direction rapidly changes from southeast to southwest in the morning, and it gradually returns to southeast in the afternoon. It takes 67 min on average for the sea breeze cooling fronts to penetrate inside metropolitan Adelaide. The SBCC value is 21.3 °C h per event averaged spatially in Adelaide summer. During the penetration process, the SBCC values decrease at a rate of 0.7 and 0.9 °C h per kilometer from coast to inland on an average sea breeze day and a hot sea breeze day, respectively. Correspondingly, the mean cooling penetration distances are 42 and 29 km along the prevailing wind path. A multiple linear regression analysis indicates that the distance from the coast and elevation at the onshore point together explain 88% of the spatial variability of the temporally average SBCC in the study area. The spatial pattern and penetration distance of the cumulative sea breeze cooling effect contribute to a better understanding of this common cooling source for heat mitigation in coastal cities where a large number of people reside.

Urban Heat Island Monitoring and Impacts on Citizen's General Health Status in Isfahan Metropolis: A Remote Sensing and Field Survey Approach.

Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens’ health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens’ health in Isfahan metropolis, Iran. For this purpose, the land surface temperature (LST) over a three-year period was monitored by Landsat-8 satellite imagery based on the split window algorithm. Then, the areas where UHI and urban cold island (UCI) phenomena occurred were identified and a general health questionnaire-28 (GHQ-28) was applied to evaluate the health status of 800 citizens in terms of physical health, anxiety and sleep, social function, and depression in UHI and UCI treatments. The average LST during the study period was 45.5 ± 2.3 °C and results showed that the Zayandeh-Rood river and the surrounding greenery had an important role in regulating the ambient temperature and promoting the citizens’ health. Citizens living in the suburban areas were more exposed to the UHIs phenomena, and statistical analysis of the GHQ-28 results indicated that they showed severe significant (P < 0.05) responses in terms of non-physical health sub-scales (i.e., anxiety and sleep, social functioning, and depression). Therefore, it can be concluded that not all citizens in the Isfahan metropolis are in the same environmental conditions and city managers and planners should pay more attention to the citizens living in the UHIs. The most important proceedings in this area would be the creation and development of parks and green belts, as well as the allocation of health-medical facilities and citizen education.

Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers; (2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF; (3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 °C in 2009 to 4.04 °C in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

DIEGO: A Multispectral Thermal Mission for Earth Observation on the International Space Station

ABSTRACT Observations in thermal infrared (IR) contribute substantially to the understanding of the global fluxes of energy and matter between Earth’s surface, ocean and atmosphere. Key parameters derived from such observations are Sea Surface Temperature (SST), Land Surface Temperature (LST) and Land Surface Emissivity (LSE). These variables are important for weather forecasting and climate modelling. However, satellite systems currently in orbit provide only a small number of spectral bands in the thermal region, and consequently cannot be used for temperature emissivity separation (TES) to accurately derive LST and LSE. Hence, capacities to investigate processes or phenomena where LST in high temporal and high spatial resolution (<100 m) is required, such as agricultural applications or urban heat island monitoring, are limited. Additionally, the measurement of radiative energy released from active large and small fires, which contribute significantly to greenhouse gas emissions, is still challenging with current IR systems. Here, we introduce the proposed multispectral sensor system DIEGO (Dynamic Infrared Earth Observation on the ISS Orbit) with 11 spectral bands and a ground sampling distance of less than 60 m, which aims to reduce the observation gap in the thermal infrared significantly.

Sea breeze cooling capacity and its influencing factors in a coastal city

The sea breeze is a common phenomenon in coastal cities, but its cooling effect has not been well investigated. In this research, we firstly propose a metric of sea breeze cooling capacity (SBCC) to quantify the cooling effect of sea breezes in a coastal city, Adelaide, Australia. Based on data from the Adelaide urban heat island monitoring network in 2010–2013, we reveal the temporal and spatial patterns of SBCC in summer days in the Adelaide Central Business District (CBD) and examine their associations with environmental variables. The results show that the inter-event variability of SBCC is explained by specific humidity and wind speed, while the spatial variability of SBCC is explained by distance towards the coast, frontal area index (FAI), terrain ruggedness index (TRI) and temperature prior to the sea breeze onset. Specifically, SBCC is negatively correlated with FAI and positively correlated with TRI. Future development of high-rise buildings in the Adelaide CBD can lead to changes in both FAI and TRI. It is estimated that the projected building development in Adelaide may cause a change of SBCC ranging from −195 to 143 °C h over an average summer. This research has important implications for urban planning, public health and energy consumption in coastal cities.

Characterizing urban redevelopment process by quantifying thermal dynamic and landscape analysis

Urban redevelopment practices have received substantial attention in urban planning. Remotely sensed thermal infrared monitoring of urban heat island (UHI) is a well-documented topic, however, there is a lack of understanding of the influence on UHI change caused by urban redevelopment process. The objectives of this study was to investigate the urban redevelopment-induced efforts incorporating remote sensing of UHI and land use change. Multi-temporal ASTER thermal infrared images were employed to characterize UHI change; and high resolution Worldview images were employed to perform land use classification. UHI dynamic was quantified with urban heat island ratio index. Analysis of urban redevelopment-induced land use change with response to UHI were carried out. Landscape ecology methods were employed to quantitatively identify land use change with landscape metrics. Result demonstrated that UHI effect had a trend of mitigation during urban redevelopment process in the study area. Urban heat island intensity could be significantly eliminated or weakened by changes of land use composition and spatial configuration. These phenomena were closely related to redevelopment practices such as industrial relocation, buildings demolition, and brownfield transformation. This article presented a case study for characterizing urban redevelopment process with remote sensing monitoring, quantifying the change with urban heat island ratio index and landscape ecology analysis, research findings could be utilized as indicators for urban planning.