Spatial and temporal distribution of urban heat islands

Santos, Alexandre Rosa dos; Oliveira, Felício Santos de; Silva, Aderbal Gomes da; Gleriani, José Marinaldo; Gonçalves, Wantuelfer; Moreira, Giselle Lemos; Silva, Felipe Gimenes; Branco, Elvis Ricardo Figueira; Moura, Marks Melo; Silva, Rosane Gomes da; Juvanhol, Ronie Silva; Souza, Kaíse Barbosa de; Ribeiro, Carlos Antônio Álvares Soares; Queiroz, Vagner Tebaldi de; Costa, Adílson Vidal; Lorenzon, Alexandre Simões; Domingues, Getúlio Fonseca; Marcatti, Gustavo Eduardo; Castro, Nero Lemos Martins de; Resende, Rafael Tassinari; Gonzáles, Duberlí Geomar Elera; Telles, Lucas Arthur de Almeida; Teixeira, Tiago; Santos, Gleissy Mary Amaral Dino Alves dos; Mota, Pedro Henrique Santos

doi:10.1016/j.scitotenv.2017.05.275

Cited by 95 publications

(48 citation statements)

References 39 publications

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“…Previous studies have mainly focused on studying differences in ground temperatures as a function of land-use land cover and its evolution at the city level [16,16,20,[33][34][35], temporal trends in urban SUHI in urban areas [18,36,37], the refreshing impact of parks on their surroundings [15,38,39], the evolution of SUHI as a function of day and night [9,34,40,41], the impact of vegetation on LST at the urban scale [12,36,37,42], comparison of surface temperatures and air temperature [8][9][10]20,43,44], the impact of surface temperatures on health [30,41] and transversely at surface temperatures at moderate resolutions (MODIS 1 km) [16,20,[34][35][36]42,45] but, to our knowledge, there are no similar studies such as ours that analyze the thermal monitoring of site redevelopment at such a detailed spatial and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

et al. 2019

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Climate change is a global challenge with multiple consequences. One of its impacts is the increase in heatwave frequency and intensity. The risk is higher for populations living in urban areas, where the highest temperatures are generally identified, due to the urban heat island effect. This phenomenon has recently been taken into account by local elected officials. As a result, developers have decided to use solutions in redevelopment projects to combat high temperatures in urban areas. Consequently, the objective is to study the land-surface temperature evolution of six main urban redevelopments in Lyon, France, from 2000 to 2017. Three of them (the Confluence, Kaplan, and Museum sites) were composed of industrial areas that have undergone major transformations and are now tertiary or residential areas. Two sites have been more lightly transformed, particularly by increasing vegetation to reduce heat stress and urban flooding (Dock and Garibaldi Street). Finally, the Groupama Stadium has been built into agricultural and wooded areas. Changes in vegetation cover (NDVI), water (MNDWI), and moisture (NDMI) content, built areas (NDBI) and bare soil (NDBaI) are also monitored. The results show that the Confluence and Kaplan sites were accompanied by a decrease in surface temperature and an increase in vegetation and moisture, whereas the Groupama Stadium displayed a rise in surface temperature and a decrease in vegetation. On the other hand, the Museum, Dock, and Garibaldi sites did not exhibit clear and uniform trends, although an increase in surface temperature was shown in some statistical tests. The disparity of the results shows the necessity to include a significant amount of vegetation during redevelopment operations in order to reduce heat stress.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

et al. 2019

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“…Therefore, such a solution would be less applicable in the practice of land use planners. The UHI effect is a complex phenomenon that is influenced by many factors (daily variation of UHI [51], used surface materials [38] or more complex sub-indicators [40] and so on). Due to the inter-domain character of indicator-based matrices used in supporting land use planning, there is a need to include one simple and easily understandable UHI factor.…”

Section: Discussionmentioning

confidence: 99%

“…The UHI interacts in different scales, from the human body to city size [39] and therefore its impact should be analyzed using a more complex approach. Due to the spatial and temporal distribution of UHI the range of temperature increase can vary from 2 • C to over 7 • C [40]. In order to better understand the real impact of UHI it is crucial to highlight how big an impact it has on thermal comfort [41].…”

Section: Introductionmentioning

confidence: 99%

The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland)

Kazak

2018

Sustainability

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Abstract:The increasing level of antropopression has a negative impact on environmental resources and has reached the level of our planetary boundaries. One limitation is land use change caused by urbanization. Global policies prove the need to undertake action in order to develop more sustainable human settlements, which would be adapted better to potential future climate change effects. Among such changes are the increase of average temperatures and extreme events like heat waves. Those changes are more severe in urban areas due to land use development, and result in the urban heat island effect (UHI), which has a negative impact on the thermal comfort of citizens. The paper presents a decision support system that can be used for the assessment of areas to the potential exposure to the UHI effect. The system integrates scenario analysis, land use modelling in cellular automata (Metronamica), and an indicator-based assessment in a geographic information system (ArcGIS). The applicability of the model is illustrated through developing scenarios for the future land use allocation of the Wrocław Larger Urban Zone (Poland). The results of the calculations show which scenario is the least vulnerable to UHI effects. Moreover, for each scenario, cores of urban areas were identified, in which certain urban design patterns accounting for adaptation to climate change could be implemented. The study provides a guideline for local authorities on where to focus actions in order to create more sustainable urban structures and to better adapt to climate change and environmental extremes.

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“…To this aim, we consider spatiotemporal data for the municipality of Vila Velha, located between latitudes 20°19 and 20°32 south and longitudes 40°16 and 40°28 west, on the coast of the Espírito Santo State of Brazil. This municipality covers a total area of 209, 965km 2 , with an estimated population of 479, 664 inhabitants [40]. Figure 1 shows the location of the studied area.…”

Section: Study Area and Eo Variablesmentioning

confidence: 99%

mosquitoes.pdf

Mudele¹,

Bayer²,

Zanandrez³

et al. 2019

Preprint

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Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most A PREPRINT -NOVEMBER 26, 2019 informative ones, a more parsimonious but equally effective and explainable model can be obtained. Moreover, the results can be empirically interpreted for use in vector control activities.Keywords Ae. aegypti · Machine learning · Random forest · Remote sensing IntroductionStudying urban ecosystems is a hot topic within various scientific clusters [1][2][3][4][5]. The compounding effects of human activities through urbanization, carbon emission and other biodiversity changes are modifying urban ecosystems, thus creating a need for continuous assessment of the environment to ensure health and quality of life suitability for dwellers [6]. With a repository covering over 40 years of data collected, Earth observation (EO) data from orbiting satellites present a gold mine for environmental change monitoring [7]. Due to recent advances resulting in higher resolution sensors (spectral, spatial and temporal), freely accessible data, and efficient processing algorithms, it is possible to extract static and dynamic phenomena happening on the Earth surface and use this information for various environmental characterization procedures [8][9][10][11].One majo...

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Spatial and temporal distribution of urban heat islands

Cited by 95 publications

References 39 publications

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

Evaluation of the Effect of Urban Redevelopment on Surface Urban Heat Islands

The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland)

mosquitoes.pdf

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