Assessing urban heat island dynamics and mitigation potential in a tropical city using high-resolution remote sensing and land-use change analyses

Project Abstract

Urban heat island (UHI) effects in tropical cities threaten public health, energy security, and urban livability, driven by rapid urbanization, impermeable surfaces, vegetation loss, and microclimatic variations. This study investigates UHI dynamics and identifies viable mitigation potentials in a fast-growing tropical city by integrating high-resolution remote sensing, land-use change analyses, and in-situ meteorological observations over a five-year period. We combine Landsat 8 and Sentinel-2 imagery to derive Landsat-derived Land Surface Temperature (LST) at 30 m to 100 m spatial scales, validated with ground-based infrared thermography data, enabling precise mapping of nocturnal and diurnal UHI intensity patterns. Urban morphological metrics including building density, sky-view factor, albedo, vegetation indices (NDVI), and evapotranspiration proxies (ET) are extracted from multi-temporal imagery to quantify drivers of thermal heterogeneity across residential, commercial, industrial, and green spaces. The methodology employs a spatiotemporal regression framework and machine learning approaches to disentangle the relative contributions of surface characteristics, anthropogenic heat flux, and climate variability to observed UHI trends. Land-use change detection is conducted using object-based image analysis and time-series classification to assess urban expansion, impervious surface growth, and green space fragmentation, with? cross-checks against municipal planning records. We assess mitigation potential through scenario modeling that combines increased tree canopy cover, enhanced albedo materials, reflective roofing, and expanded blue-green infrastructure, evaluating impacts on UHI intensity, cooling degree hours, and heat-related morbidity risk indices. The study also examines mesoscale meteorological patterns, including wind corridor effects and boundary layer dynamics, to understand how urban form modulates heat distribution across neighborhoods. Results reveal pronounced UHI hotspots in dense commercial corridors and peri-urban built-up zones, with nighttime temperatures up to 5–7°C higher than surrounding rural areas during dry season spells, while well-vegetated pockets exhibit significant buffering effects of 2–4°C. LST correlates strongly with building density (R2 > 0.65) and impervious surface fraction, while NDVI inversely associates with surface temperatures (R2 ~ 0.60). Land-use trajectories indicate a 12–18% increase in impervious surfaces over the study period, correlating with amplified UHI intensity and extended heat stress periods. Scenario analyses indicate that a 15–20% increase in tree canopy combined with cool roof strategies could reduce peak hourly UHI by 1.5–3°C in core urban zones and 0.8–1.6°C citywide, translating into substantial reductions in cooling energy consumption and heat vulnerability. Uncertainty assessments highlight sensor calibration, cloud contamination, and temporal mismatch as primary sources of error, mitigated through fusion of multi-sensor data and ground-truth campaigns. The research contributes to urban climate resilience planning by providing data-driven guidelines for targeted green infrastructure placement, building design standards, and land-use zoning that align with tropical urban dynamics, while offering a replicable framework for other rapidly urbanizing tropical cities. The findings underscore the critical role of integrative remote sensing and land-use analytics in diagnosing UHI patterns and informing cost-effective mitigation strategies tailored to local socio-ecological contexts.

Project Overview

What This Project Is About

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A plain-language overview of the topic and what the project investigates.

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The Problem It Addresses

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What problem or gap this project tackles and why it matters to the field or society.

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Objectives of the Project

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1. Identify how urban design and land use influence heat in a tropical city.
2. Measure surface temperatures using high-resolution images to map hot and cool areas.
3. Evaluate simple, practical strategies to reduce heat in streets and buildings.
4. Provide evidence to support local planning decisions and climate resilience.

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What You Will Do Step by Step

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1. Gather openly available high-resolution satellite images and land-use data for the city.
2. Process images to extract temperature cues and overlay them with land-use maps.
3. Analyze which areas are hottest and why (materials, shade, green cover, rooftops).
4. Test mitigation ideas in a basic, data-supported way (e.g., more trees, lighter surfaces).
5. Summarize findings in a simple report with clear visuals for stakeholders.

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Expected Outcome

A concise understanding of where heat is concentrated and practical, low-cost options to reduce it in the city.