Monitoring the impact of land cover change on surface urban heat island through google earth engine. Proposal of a global methodology, first applications and problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities

Insights into heat islands at the regional scale using a data-driven approach

Urban heat island (UHI) phenomenon is crucial in the context of climate change. However, while substantial attention has been given to studying UHIs within cities, our understanding at the regional level still needs to be improved. This study delves into the intricate dynamics of the regional heat island (RHI) by examining its relationship with land use/land cover (LULC), vegetation, and elevation. The objective is to enhance our knowledge of RHI to inform effective mitigation strategies. The research employs a data-driven approach, leveraging satellite data and spatial modeling, examining surface and canopy-layer regional heat islands, and considering daytime and nighttime variations. To assess the impact of LULC, the study evaluates three main categories: anthropized (urbanized), agricultural, and wooded/semi-natural environments. Furthermore, it delves into the influence of vegetation on RHI and incorporates elevation data to understand its role in RHI intensity. The findings reveal meaningful variations in heat islands across different LULCs, providing essential insights. Although urbanized areas exhibit the highest RHI intensity, agricultural regions contribute notably to RHI due to land use changes and reduced vegetation cover. This emphasizes the significant impact of human activities. In contrast, wooded and semi-natural environments demonstrate potential for mitigating RHI, owing to their dense vegetation and shading effects. Elevation, while generally associated with reduced heat island, shows variations based on local conditions. Ultimately, this research underscores the complexity of the RHI phenomenon and the importance of considering factors such as different temperatures and their daily variation, landscape heterogeneity, and elevation. Additionally, the study emphasizes the significance of sustainable spatial planning and land management. Targeted efforts to increase vegetation in high daytime land surface temperature areas can reduce heat storage and mitigate RHI. Similarly, planning for agroforestry and green infrastructure in agricultural areas can significantly increase resilience to climate

Daytime Variation of Urban Heat Islands: The Case Study of Doha, Qatar

abstract: Recent evidence suggests that urban forms and materials can help to mediate temporal variation of microclimates and that landscape modifications can potentially reduce temperatures and increase accessibility to outdoor environments. To understand the relationship between urban form and temperature moderation, we examined the spatial and temporal variation of air temperature throughout one desert city—Doha, Qatar—by conducting vehicle traverses using highly resolved temperature and GPS data logs to determine spatial differences in summertime air temperatures. To help explain near-surface air temperatures using land cover variables, we employed three statistical approaches: Ordinary Least Squares (OLS), Regression Tree Analysis (RTA), and Random Forest (RF). We validated the predictions of the statistical models by computing the Root Mean Square Error (RMSE) and discovered that temporal variations in urban heat are mediated by different factors throughout the day. The average RMSE for OLS, RTA and RF is 1.25, 0.96, and 0.65 (in Celsius), respectively, suggesting that the RF is the best model for predicting near-surface air temperatures at this study site. We conclude by recommending the features of the landscape that have the greatest potential for reducing extreme heat in arid climates

Examining Urban Heat Island Effect and Its Public Health Implications with Remotely Sensed Data

The Urban heat island (UHI) as a byproduct of urbanization has long been studied utilizing remote sensing technologies. However, issues remain to be addressed. Land surface temperature (LST) as the indicator of surface UHI can be retrieved from remotely sensed data, but its accuracy is limited as existing studies neglect the neighboring effect. Further, while LST serves well as an indicator of surface thermal condition, it lacks the ability to reveal human heat stress, which is an environmental hazard that can seriously affect productivity, health or even survival of individuals. Although human heat stress has long been studied and can be quantified by many heat stress indices, it has never been explored across continuous spaces. Aiming to address these issues, the objectives of this research include: (1) taking into account the neighboring effect during LST retrieval using a moving window method; (2) revealing human heat stress with remotely sensed data; and (3) exploring the relationship between heat stress and land cover composition and configuration. My results indicate that the accuracy of LST estimation is improved when neighboring effect is considered. Discomfort index (DI) as an indicator of human heat stress can be retrieved from remotely sensed data, and its spatial distribution and relationship with land cover composition is largely affected by relative humidity. Spatial configuration of different land covers has an impact on DI, which may provide insights for policy makers and urban designers on mitigating hazardous environmental effect brought by urbanization

ASSESSING EQUIVALENT TEMPERATURE TRENDS IN MAJOR EASTERN US CITIES

Summer (JJA) temperature (T) and equivalent temperature (TE) for 18 of the largest cities in the eastern United States are investigated for two time periods: 1948-2014 and 1973-2014. Because temperature provides an incomplete description of lower tropospheric heat content, we supplement with TE, which also accounts for the energy associated with moisture. An auxiliary investigation using air mass data from the Spatial Synoptic Classification (SSC) augments the investigation of T and TE trends. The trend analysis revealed significant trends in Tmin at all stations over the 67-year time period and over most stations for the shorter (41-year) period. Minimum TE likewise increases nearly everywhere in the longer series, but at only around half of the stations in the shorter series. Stations with increasing TE in the shorter period are primarily coastal or located in the southern and upper Midwest, where there has also been a noticeable lack of warming. Our results also exhibit a decrease in the diurnal TE range that accompanies the documented decrease in diurnal temperature range over the same period. Trends in T and TE are evaluated in the context of changes in air mass frequency. A heat wave analysis was also conducted to identify changes in intensity and frequency using T and TE Overall, our findings suggest that TE provides a more comprehensive perspective on recent climate change than T alone. With heat wave frequency and intensity projected to increase, we recommend adoption of TE to account for changes in total surface heat content

How can the floor area types of a university campus mitigate the increase of urban air temperature?

The urban heat island (UHI) under the current climate change scenario could have a major impact on the lives of urban residents. The presence of green areas undoubtedly mitigates the UHI, and modifes some selected anthropized surfaces with particular characteristics (e.g., albedo). Here, we use a university campus as a good template of the urban context to analyze the mitigation efect of diferent surface types on the air temperature warming. This study provides some of the best practices for the future management of land surface types in urban areas. Through the development of a simple air temperature mitigation index (ATMI) that uses the temperature, water content (WC), and albedo of the investigated surface types, we fnd the green and anthropized surfaces according to their areal distribution and mitigation efects. The fndings address the importance of poorly managed green areas (few annual mowings) and anthropized materials that permit a good balance between water retention capacity and high albedo. In the case of impervious surfaces, priority should be given to light-colored materials with reduced pavement units (blocks or slabs) to reduce the UHI

On the assessment of surface urban heat island: size, urban form, and seasonality

To what extent cities can be made sustainable under the mega-trends of urbanization and climate change remains a matter of unresolved scientific debate. Our inability in answering this question lies partly in the deficient knowledge regarding pivotal humanenvironment interactions. Regarded as the most well documented anthropogenic climate modification, the urban heat island (UHI) effect – the warmth of urban areas relative to the rural hinterland – has raised great public health concerns globally. Worse still, heat waves are being observed and are projected to increase in both frequency and intensity, which further impairs the well-being of urban dwellers. Albeit with a substantial increase in the number of publications on UHI in the recent decades, the diverse urban-rural definitions applied in previous studies have remarkably hampered the general comparability of results achieved. In addition, few studies have attempted to synergize the land use data and thermal remote sensing to systematically assess UHI and its contributing factors. Given these research gaps, this work presents a general framework to systematically quantify the UHI effect based on an automated algorithm, whereby cities are defined as clusters of maximum spatial continuity on the basis of land use data, with their rural hinterland being defined analogously. By combining land use data with spatially explicit surface skin temperatures from satellites, the surface UHI intensity can be calculated in a consistent and robust manner. This facilitates monitoring, benchmarking, and categorizing UHI intensities for cities across scales. In light of this innovation, the relationship between city size and UHI intensity has been investigated, as well as the contributions of urban form indicators to the UHI intensity. This work delivers manifold contributions to the understanding of the UHI, which have complemented and advanced a number of previous studies. Firstly, a log-linear relationship between surface UHI intensity and city size has been confirmed among the 5,000 European cities. The relationship can be extended to a log-logistic one, when taking a wider range of small-sized cities into account. Secondly, this work reveals a complex interplay between UHI intensity and urban form. City size is found to have the strongest influence on the UHI intensity, followed by the fractality and the anisometry. However, their relative contributions to the surface UHI intensity depict a pronounced regional heterogeneity, indicating the importance of considering spatial patterns of UHI while implementing UHI adaptation measures. Lastly, this work presents a novel seasonality of the UHI intensity for individual clusters in the form of hysteresis-like curves, implying a phase shift between the time series of UHI intensity and background temperatures. Combining satellite observation and urban boundary layer simulation, the seasonal variations of UHI are assessed from both screen and skin levels. Taking London as an example, this work ascribes the discrepancies between the seasonality observed at different levels mainly to the peculiarities of surface skin temperatures associated with the incoming solar radiation. In addition, the efforts in classifying cities according to their UHI characteristics highlight the important role of regional climates in determining the UHI. This work serves as one of the first studies conducted to systematically and statistically scrutinize the UHI. The outcomes of this work are of particular relevance for the overall spatial planning and regulation at meso- and macro levels in order to harness the benefits of rapid urbanization, while proactively minimizing its ensuing thermal stress

Study of the urban heat island (UHI) using remote sensing data/techniques: a systematic review.

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus andWeb of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020. This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020.info:eu-repo/semantics/publishedVersio

Towards an operational model for estimating day and night instantaneous near-surface air temperature for urban heat island studies: outline and assessment

Near-surface air temperature (NSAT) is key for assessing urban heat islands, human health, and well-being. However, a widely recognized and cost- and time-effective replicable approach for estimating hourly NSAT is still urgent. In this study, we outline and validate an easy-to-replicate, yet effective, operational model, for automating the estimation of high-resolution day and night instantaneous NSAT. The model is tested on a heat wave event and for a large geographical area. The model combines remotely sensed land surface temperature and digital elevation model, with air temperature from local fixed weather station networks. Achieved NSAT has daily and hourly frequency consistent with MODIS revisiting time. A geographically weighted regression method is employed, with exponential weighting found to be highly accurate for our purpose. A robust assessment of different methods, at different time slots, both day- and night-time, and during a heatwave event, is provided based on a cross-validation protocol. Four-time periods are modelled and tested, for two consecutive days, i.e. 31st of July 2020 at 10:40 and 21:50, and 1st of August 2020 at 02:00 and 13:10 local time. High R2 was found for all time slots, ranging from 0.82 to 0.88, with a bias close to 0, RMSE ranging from 1.45 °C to 1.77 °C, and MAE from 1.15 °C to 1.36 °C. Normalized RMSE and MAE are roughly 0.05 to 0.08. Overall, if compared to other recognized regression models, higher effectiveness is allowed also in terms of spatial autocorrelation of residuals, as well as in terms of model sensitivity