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The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological condi...
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... used a total of 15 LANDSAT multispectral images. Eight LANDSAT 7ETM+ images, taken between 1999 and 2002, covered different but partially overlapping extents in the southern portion of Quebec Province, Canada (See Figure 1: extents correspond to areas of roughly 200 km × 200 km, for which images provide 30 m cell-sizes for non-IR channels, and 60 m for IR chan- nels). The regions covered, as listed below, included the main inhabited and urban areas of the province of Que- bec: Montreal, Quebec City, Rimouski, Sept-Îles, Sher- brooke, Saguenay, Gatineau, and Rouyn-Noranda, representing some 90.3% of the total province population according to the 2006 Census (See Table 1 and Figure 1). ...Context 2
... LANDSAT 7ETM+ images, taken between 1999 and 2002, covered different but partially overlapping extents in the southern portion of Quebec Province, Canada (See Figure 1: extents correspond to areas of roughly 200 km × 200 km, for which images provide 30 m cell-sizes for non-IR channels, and 60 m for IR chan- nels). The regions covered, as listed below, included the main inhabited and urban areas of the province of Que- bec: Montreal, Quebec City, Rimouski, Sept-Îles, Sher- brooke, Saguenay, Gatineau, and Rouyn-Noranda, representing some 90.3% of the total province population according to the 2006 Census (See Table 1 and Figure 1). In addition to these images, seven LANDSAT 5TM images, taken between 1987 and 1999, and covering part of the extent 014-028 over the greater Montreal region, were also used (cell size of 60 m for non-IR channels, and 120 m for IR channels); this extent selection covered urban, rural and suburban areas. ...Citations
... In the literature, IR satellite images across the city have been used together with individual building characteristics to predict indoor temperature (Smargiassi et al., 2008) and energy performance (Sun and Bardhan, 2023). A statistical approach is proposed to exploit both spatially explicit satellite TIR data and time-varying meteorological data for estimating surface temperature, which can be used to further assess indoor exposure to heat by taking into account building characteristics (Kestens et al., 2011). Moreover, recent advances in highresolution TIR space telescopes can provide a ground sample distance (GSD) of less than 7 m/pixel with a daily revisit rate, introducing the capacity to identify the thermal efficiency and anomalies of individual buildings, even for residential ones (Ben et al., 2021). ...
Energy performance certificate (EPC) and thermal infrared (TIR) images both play a key role in the energy performance mapping of the urban building stock. In this paper, we developed parametric building archetypes using an EPC database and conducted temperature clustering on TIR images acquired through drones and satellite datasets. We evaluated 1725 EPCs of existing building stock in Cambridge, UK to generate energy consumption profiles. Drone-based TIR images of individual buildings in two Cambridge University colleges were processed using a machine learning pipeline for thermal anomaly detection and investigated the influence of two specific factors that affect the reliability of TIR for energy management applications: ground sample distance (GSD) and angle of view (AOV). The EPC-level results suggest that the construction year of the buildings influences their energy consumption. For example, modern buildings were over 30% more energy-efficient than older ones. Parallelly, older buildings were found to show almost double the energy savings potential through retrofitting than newly constructed buildings. TIR imaging results showed that thermal anomalies can only be properly identified in images with a GSD of 1m/pixel or less. A GSD of 1-6m/pixel can detect hot areas of building surfaces. We found that a GSD > 6m/pixel cannot characterise individual buildings but does help identify urban heat island effects. Additional sensitivity analysis showed that building thermal anomaly detection is more sensitive to AOV than to GSD. Our study informs newer approaches to building energy diagnostics using thermography and supports decision-making for retrofitting at a large scale.
... However, the distribution of LST varies considerably in both time and space. Data that are related to each other in terms of time lags and between two nearby places will infuence one another, which can afect the results of inferential analyses [21]. ...
Land surface temperature (LST) is a critical indicator variable in climate science. In this study, the variation of LST on the island of New Guinea during 2000 to 2019 was investigated using a cubic spline model and a multivariate regression model. The data were obtained from the National Aeronautics and Space Administration moderate resolution imaging spectroradiometer database. This study focused on 90 subregions with 105-pixels of latitude 90 kilometer apart. These subregions were categorized into 10 super-regions. The results showed that the mean change in LST for all 90 subregions was +0.086°C per decade with a confidence interval of (0.028, 0.144)oC. There were five super-regions with a significant mean LST change. LST increased significantly in the central-north, central-south of the island (super-regions B1, C1, and C2 with 0.117°C, 0.162°C, and 0.185°C, respectively) and the southern part of Papua New Guinea (super-region E2 with 0.217°C), whereas it decreased in the middle part of the Indonesian territories (A2 with −0.122°C). The results also showed that LST variation occurs at the subregional level. Climate change mitigation methods are critical for reducing temperature rise and limiting any negative effects on the region.
... Defined by its high spatial resolution and temporal frequency, this approach allows for extensive analyses, yielding regular and long-term data crucial for grasping the behavior and effects of heatwaves. This technique overcomes the limitations posed by unavailable or poorly distributed ground station networks [38,39]. Previous studies [16,[40][41][42][43][44][45][46] have demonstrated the capability of multitemporal remote sensing data from several satellites to analyze, map, and monitor the spatial and temporal dynamics of heatwaves. ...
... This is due to the heat exchange between the land surface and the near-surface atmosphere, making the dynamics in air temperature and LST consistent [50,51]. Various satellites with Thermal Infrared (TIR) airborne sensors, including the Advanced Very-High-Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and LANDSAT, have been used for this purpose [39,[52][53][54]. Among these, data from MODIS have been pivotal for the retrieval of LST dynamics and trends, providing the longest consistent time series covering vast global regions [55]. ...
In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (Tair) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (p<0.05) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.
... The soft computing techniques have recently become popular for observing environmental/climate changes. So, [19] had been used a standard linear model based on Landsat images for modelling LST changes in Quebec, Canada, thus it discovered that LST changes could be detected effectively from data estimation of satellite images. Whereas, ...
Land Surface Temperature (LST) is an important factor in global climate change, vegetation growth, and
glacier. Its impact will be more in monsoon area because of monsoon failure and uncertainty and unpredictable in rainfall.
In this article we perform LST estimation using LST algorithm on Landsat 8 Operational Land Imager (OLI) Sensor and
Thermal Infrared Sensor (TIRS) dataset of Baghdad city for the period June 2021-2022. TIRS sensor exhibits two thermal
Band 10 and 11. LST algorithm require brightness temperature value of both band 10 or 11 as well as land surface emissivity
calculated from OLI bands (NIR and RED) for estimation of LST. However, the estimated emissivity values over few land
use/land cover of Landsat-8 OLI have been compared with the literature values. The results show that the satellite derived
emissivity values are in the acceptable range and the NDVI is effective in deriving surface emissivity. The derived surface
temperature values are found to be in good agreement with the field measured values, indicating that the methodology can
be adopted for the study over urban areas. As well as, these results shown that there is a thermal contrast in LST of June
2022 is higher than June 2021 by approximately 0.5 C°.
... The areas around the city's central core with built-up areas and urban facilities have higher LST due to the impervious surfaces that expose such areas to greater solar radiation. The findings align with recent studies which have opined that the modification of land use due to various socio-economic factors has influenced local climatic condition in urban areas (Dang et al., 2020;Kestens et al., 2011;Obiefuna et al., 2021;Olofin et al., 2022). Also, land use change has contributed to the observed UHI intensity over the study areas mainly through the processes of urban sprawl, degradation of cropland and healthy vegetation. ...
The rapid urbanisation and associated land use changes have profound impacts on the local climate and environmental conditions in urban areas. This study aims to analyse the dynamics of land surface temperature (LST) and its relationship with land use/land cover (LULC) patterns in Kano Metropolis, Kano State, Nigeria. The research utilizes remote sensing data and geospatial techniques from multiple sensors, such as Landsat MSS, ETM + and OLI/TIRS, spanning a period of 38years (1984 – 2022), to processed, analysed and investigate the spatiotemporal variations in LST and their drivers. Statistical analyses, such as correlation and regression models, are employed to quantify the associations between LST and LULC variables. Findings show that urban area increased from 7% in 1984 to 32% in 2022, while bare land decreased from 82% in 1984 to 49% in 2022. Vegetation also increased slightly from 11% in 1984 to 19% in 2022. The LST increased with a mean value of 16°C in 1984, 25°C in 2003, and 30.5°C in 2022. Results still revealed a negative correlation between vegetation health and land surface temperature, indicating that as vegetation health declines, land surface temperature increases due to the lack of cooling effects from transpiration while a positive correlation exist between the built-up index and land surface temperature, signifying that as urban areas expand, land surface temperature rises due to the urban heat island effect. The research emphasises the significance of implementing land use planning and management strategies to address the adverse effects of urban heat and improve the urban microclimate. The findings offer valuable guidance for policymakers, urban planners, and environmental practitioners, assisting them in making informed decisions for sustainable urban development and enhancing the residents' quality of life in Kano Metropolis.
... Consequently, these factors have had a significant impact on urban climates, exacerbating the heat island effect and contributing to the occurrence of extreme weather events [4,5]. These adverse effects, in turn, negatively affect the residents' health, air and water quality, as well as the buildings and infrastructure durability [6][7][8]. In response to the escalating urbanization rates, urban development strategies have gradually shifted from uncontrolled expansion to prioritizing high-quality and endogenous growth. ...
The escalation of anthropogenic heat emissions poses a significant threat to the urban thermal environment as cities continue to develop. However, the impact of urban spatial form on anthropogenic heat flux (AHF) in different urban functional zones (UFZ) has received limited attention. In this study, we employed the energy inventory method and remotely sensed technology to estimate AHF in Beijing’s central area and utilized the random forest algorithm for UFZ classification. Subsequently, linear fitting models were developed to analyze the relationship between AHF and urban spatial form indicators across diverse UFZ. The results show that the overall accuracy of the classification was determined to be 87.2%, with a Kappa coefficient of 0.8377, indicating a high level of agreement with the actual situation. The business/commercial zone exhibited the highest average AHF value of 33.13 W m−2 and the maximum AHF value of 338.07 W m−2 among the six land functional zones, indicating that business and commercial areas are the primary sources of anthropogenic heat emissions. The findings reveal substantial variations in the influence of urban spatial form on AHF across different UFZ. Consequently, distinct spatial form control requirements and tailored design strategies are essential for each UFZ. This research highlights the significance of considering urban spatial form in mitigating anthropogenic heat emissions and emphasizes the need for customized planning and renewal approaches in diverse UFZ.
... While it is unclear at this time how climate change may affect the spread of COVID-19, research suggests that rising global temperatures will affect the timing, distribution, and severity of future disease outbreaks. The climate shifts caused by this worldwide occurrence will be studied in this specific setting (Kestens et al. 2011;Nkwunonwo 2013). Maps of the urban thermal field variance index (UTFVI) reveal a positive UHI phenomena, with the highest UTFVI zones occurring over developed area and none over the adjacent rural territory. ...
Due to expanding populations and thriving economies, studies into the built environment’s thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3 °C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST’s effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30 °C and 29.91 °C, respectively, with a mean LST of 25.1 °C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31 °C and 25.35 °C, respectively, with a mean LST of 21.33 °C. There was a 3.8 °C drop in the LST of this grid. The minimum and maximum NDVI were also − 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.
... The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability [23]. ...
Earth observation data have assumed a key role in environmental monitoring, as well as in risk assessment. Rising temperatures and consequently heat waves due to ongoing climate change represent an important risk considering the population, as well as animals, exposed. This study was focused on the Aosta Valley Region in NW Italy. To assess population exposure to these patterns, the following datasets have been considered: (1) HDX Meta population dataset refined and updated in order to map population distribution and its features; (2) Landsat collection (missions 4 to 9) from 1984 to 2022 obtained and calibrated in Google Earth Engine to model LST trends. A pixel-based analysis was performed considering Aosta Valley settlements and relative population distribution according to the Meta population dataset. From Landsat data, LST trends were modelled. The LST gains computed were used to produce risk exposure maps considering the population distribution and structure (such as ages, gender, etc.). To check the consistency and quality of the HDX population dataset, MAE was computed considering the ISTAT population dataset at the municipality level. Exposure-risk maps were finally realized adopting two different approaches. The first one considers only LST gain maximum by performing an ISODATA unsupervised classification clustering in which the separability of each class obtained and was checked by computing the Jeffries–Matusita (J-M) distances. The second one was to map the rising temperature exposure by developing and performing a risk geo-analysis. In this last case the input parameters considered were defined after performing a multivariate regression in which LST maximum was correlated and tested considering (a) Fractional Vegetation Cover (FVC), (b) Quote, (c) Slope, (d) Aspect, (e) Potential Incoming Solar Radiation (mean sunlight duration in the meteorological summer season), and (f) LST gain mean. Results show a steeper increase in LST maximum trend, especially in the bottom valley municipalities, and especially in new built-up areas, where more than 60% of the Aosta Valley population and domestic animals live and where a high exposure has been detected and mapped with both approaches performed. Maps produced may help the local planners and the civil protection services to face global warming from a One Health perspective.
... In previous studies, meteorological data interpolation has commonly used ordinary kriging interpolation or thinplate spline interpolation (Xie et al. 2015a;Hu et al. 2017). Because temperature levels are closely related to urban surface morphology (Weng et al. 2004;Chander et al. 2009;Kestens et al. 2011), developing temperature interpolation models based on land-use regression methods has become a new research hotspot. Shi et al. (2019) used hourly temperature data from 40 meteorological stations in Hong Kong to develop a land-use regression model and generate a daytime and nighttime cumulative high-temperature hour distribution map with a resolution of 10 m to achieve a spatial understanding of extreme high-temperature weather conditions in Hong Kong. ...
Against the background of global climate change, the increasing heat health risk from the combined effect of changes in high temperature, exposure, vulnerability, and other factors has become a growing concern. Yet the low number of temperature observation stations is insufficient to represent the complex changes in urban heatwaves, and subdistrict-scale (town, township, neighborhood committee, and equivalent) heat health risk and adaptability assessments are still limited. In this study, we built daytime and nighttime high-temperature interpolation models supported by data from 225 meteorological stations in Beijing. The models performed well at interpolating the cumulative hours of high temperature and the interpolation quality at night was better than that during the day. We further established a methodological framework for heat health risk and adaptability assessments based on heat hazard, population exposure, social vulnerability, and adaptability at the subdistrict scale in Beijing. Our results show that the heat health risk hotspots were mainly located in the central urban area, with 81 hotspots during the day and 76 at night. The average value of the heat health risk index of urban areas was 5.60 times higher than that of suburban areas in the daytime, and 6.70 times higher than that of suburban areas in the night. Greater population density and higher intensity of heat hazards were the main reasons for the high risk in most heat health risk hotspots. Combined with a heat-adaptive-capacity evaluation for hotspot areas, this study suggests that 11 high-risk and low-adaptation subdistricts are priority areas for government action to reduce heat health risk in policy formulation and urban development.
... La réalisation de ce projet a nécessité la bonification d'une méthodologie employée précédemment par le CERFO (Boulfroy et al., 2013;Varin et al., 2016), basée initialement sur l'étude de Kestens et al. (2011). La puissance de cette approche, malgré sa complexité (Barrette et al., 2018), est sa robustesse face aux conditions d'acquisition des images. ...
... et se basait sur l'étude deKestens et al. (2011) en utilisant l'imagerie Landsat-7 ETM+ et SPOT. Le CERFO a plus récemment (2016) réalisé une cartographie des ICFU de la communauté métropolitaine de Québec avec une méthodologie simplifiée et plus performante (R 2 : 0,84), utilisant uniquement des images satellitaires Landsat-8 OLI/TIRS(Alhawiti et Mitsova, 2016;Varin et al., 2016). ...
