Surface temperature reduction in urban areas- Case of Kochi, India

The literature studies in tropical monsoon cities have analyzed the city as a whole, often resulting in generic recommendations without considering geographical subunits like municipal wards. This lack of localization limits the effectiveness of proposed solutions. Additionally, satellite images used in these studies were retrieved randomly from a season, potentially missing peak Land Surface Temperature (LST) conditions necessary for accurate analysis. Kerala, particularly during March to May, faces intense heatwaves due to natural factors such as the northward movement of the sun and surrounding oceanic humidity, leading to high temperatures and prolonged heatwaves. Cochin, a rapidly urbanizing city in Kerala, faces similar challenges with elevated surface temperatures impacting public health. This thesis aims to comprehensively investigate LST in a tropical monsoon city, Kochi, using Geographic Information Systems (GIS), remote sensing data, and statistical methods. The objectives include analyzing spatial variations in LST across urban wards and identifying LST hotspots during the warmest days of the year, as well as determining LST drivers in the city. The literature study focuses on geographical conditions of Kochi, where the tropical monsoon climate causes significant seasonal temperature variations. Literature reviews on research articles on different cities in tropical monsoon climatic zone emphasize the importance of retrieving both daytime and nighttime images for comprehensive temperature analysis. Some literature study articles consider demographic factors, high-density roads, and green spaces, proposing specific solutions for urban heat mitigation. The literature study infers to retrieve Landsat data from peak temperature days and validates remote sensing data with ground truth measurements. The thesis is organized into sections including introduction, literature review, methodology, analysis, findings, and conclusions. It uses statistical method to pinpoint the warmest day of the year in Kochi, and uses remote sensing for analyzing LST, NDBI, NDVI, and other parameters to identify LST drivers. The ward-wise analysis ensures precise, localized recommendations for planners and decision makers. Findings reveal that green cover negatively impacts LST, while built-up areas, roads, and industrial activities increase it. Ward-specific recommendations offer more compatible solutions for urban planning. Conclusions indicate that ward-wise LST analysis provides valuable insights for strategic planning, emphasizing the importance of increasing green spaces, modifying road materials, and introducing green roofs, to reduce LST and enhance thermal comfort in Kochi. These findings contribute to urban planning and public health strategies, particularly in the context of rising temperatures and climate change.