{"title":"Exploring the cooling potential of green roofs for mitigating diurnal heat island intensity by utilizing Lidar and Artificial Neural Network","authors":"Abdulla Al Kafy, Kelley A. Crews, Amy E. Thompson","doi":"10.1016/j.scs.2024.105893","DOIUrl":null,"url":null,"abstract":"<div><div>Urban areas frequently exhibit higher elevated temperatures than their rural counterparts due to the prevalence of structures over natural resources, a phenomenon known as daytime surface urban heat island (DSUHI). This study simulates the cooling effects of green roofs (GR) for mitigating DSUHI by utilizing 2D and 3D urban morphological parameters over downtown Austin, Texas, USA. We estimated spectral indices using Landsat 8, Sentinel-2A, and Lidar data to include built-up, vegetation, waterbodies, daytime land surface temperature (DLST), buildings (height volume and density), sky view factor (SVF), and solar radiation (SR). Finally, we integrated eleven different neural network algorithms for GR simulation, validation, and correlation between DLST and the above urban features- the strongest model generated an R<sup>2</sup> of 0.783 and an RMSE of 0.925°F. We found converting 4.2% of the total rooftop area to GR resulted in an average DLST decrease of 2.80°F. The most significant cooling effects occurred with buildings heights between 13 and 28 m, high SVFs, SR, and closer proximity to water bodies. Our findings amplify the strategic importance of GRs in urban morphology and planning, guiding green infrastructure development to mitigate and foster urban environment sustainability.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105893"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007170","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Urban areas frequently exhibit higher elevated temperatures than their rural counterparts due to the prevalence of structures over natural resources, a phenomenon known as daytime surface urban heat island (DSUHI). This study simulates the cooling effects of green roofs (GR) for mitigating DSUHI by utilizing 2D and 3D urban morphological parameters over downtown Austin, Texas, USA. We estimated spectral indices using Landsat 8, Sentinel-2A, and Lidar data to include built-up, vegetation, waterbodies, daytime land surface temperature (DLST), buildings (height volume and density), sky view factor (SVF), and solar radiation (SR). Finally, we integrated eleven different neural network algorithms for GR simulation, validation, and correlation between DLST and the above urban features- the strongest model generated an R2 of 0.783 and an RMSE of 0.925°F. We found converting 4.2% of the total rooftop area to GR resulted in an average DLST decrease of 2.80°F. The most significant cooling effects occurred with buildings heights between 13 and 28 m, high SVFs, SR, and closer proximity to water bodies. Our findings amplify the strategic importance of GRs in urban morphology and planning, guiding green infrastructure development to mitigate and foster urban environment sustainability.
期刊介绍：
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;
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