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

Renard, Florent; Alonso, Lucille; Fitts, Yasmin; Hadjiosif, Adeline; Comby, Jacques

doi:10.3390/rs11030299

Cited by 91 publications

(59 citation statements)

References 92 publications

(128 reference statements)

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“…In future studies, other factors, for example, high-resolution building height, albedo, and sky views related to urban LST could be investigated in the downscaling process. The results can potentially contribute to improving our understanding of heat exchange processes in urban environments and has wide contributions in meteorological, hydrological, and ecological research [52].…”

Section: Discussionmentioning

confidence: 95%

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

Zhang

Jiang

et al. 2020

Remote Sensing

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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.

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Section: Discussionmentioning

confidence: 95%

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

Zhang

Jiang

et al. 2020

Remote Sensing

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“…Semi-Empirical Methods (SEMs), Physically-Based Methods (PBMs), and multi-channel Temperature/Emissivity Separation (TES) methods are three distinctive categories for LSE retrieval from space [7]. PBMs and multi-channel TES methods are not operational for Landsat data to obtain LSE due to the limitations presented in many studies, such as the requirement of more than two TIR bands or nighttime images [7,46,[76][77][78][79]. SEMs contain the Classification Based Emissivity Method (CBEM) [74,80] and the NDVI Based Emissivity Method (NBEM) [81,82], which are suitable for LSE estimation from Landsat data.…”

Section: Land Surface Emissivity (Lse) Modelsmentioning

confidence: 99%

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

2020

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Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

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“…GIS-based methodology to evaluate green roofs and green urban areas 9 [4][5][6][7][8][9][10][11][12] Outdoor thermal comfort: UHI mitigation strategies and thermal comfort indexes 20 [2,4,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Energy savings and Indoor thermal comfort from Green roofs 5 [1,[31][32][33][34] 2.…”

Section: No Of Papers Sourcementioning

confidence: 99%

“…Various studies have been conducted to investigate UHI, using, for example, satellite images, surveys, building footprints and morphological parameters [13,23]. Renard et al [22] examined UHI by analysing satellite images, and the study showed that heavy renovations are necessary to achieve a decrease in surface temperature and that the results are related to the increase in green urban spaces. A similar approach [13] confirmed that, by increasing the green roofs and changing the albedo of roofs, surface temperatures reduce linearly.…”

Section: Outdoor Thermal Comfort: Uhi Mitigation Strategies and Thermmentioning

confidence: 99%

“…Images with limited cloud cover over the sites were selected, and in this case the percentages of cloud cover were 3.8% and 0.8%, respectively. According to [22], NDVI is a simple but precise indicator that can be used to identify the presence of vegetation, and it is calculated with Equation (1). As far as the Landsat 8 images are concerned, the spectral bands used to calculate NDVI are band 4 (red) and band 5 (near infrared).…”

Section: Outdoor Space Analysismentioning

confidence: 99%

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The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

Mutani

Todeschi

2020

Atmosphere

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There is growing attention to the use of greenery in urban areas, in various forms and functions, as an instrument to reduce the impact of human activities on the urban environment. The aim of this study has been to investigate the use of green roofs as a strategy to reduce the urban heat island effect and to improve the thermal comfort of indoor and outdoor environments. The effects of the built-up environment, the presence of vegetation and green roofs, and the urban morphology of the city of Turin (Italy) have been assessed considering the land surface temperature distribution. This analysis has considered all the information recorded by the local weather stations and satellite images, and compares it with the geometrical and typological characteristics of the city in order to find correlations that confirm that greenery and vegetation improve the livability of an urban context. The results demonstrate that the land-surface temperature, and therefore the air temperature, tend to decrease as the green areas increase. This trend depends on the type of urban context. Based on the results of a green-roofs investigation of Turin, the existing and potential green roofs are respectively almost 300 (257,380 m2) and 15,450 (6,787,929 m2). Based on potential assessment, a strategy of priority was established according to the characteristics of building, to the presence of empty spaces, and to the identification of critical areas, in which the thermal comfort conditions are poor with low vegetation. This approach can be useful to help stakeholders, urban planners, and policy makers to effectively mitigate the urban heat island (UHI), improve the livability of the city, reduce greenhouse gas (GHG) emissions and gain thermal comfort conditions, and to identify policies and incentives to promote green roofs.

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

Cited by 91 publications

References 92 publications

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

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

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

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