Urban Heat Island Mapping Using MODIS LST Night Imagery in Google Earth Engine | Complete Tutorial

Описание: In this video, we demonstrate how to detect and classify Urban Heat Island (UHI) effects using MODIS Land Surface Temperature (LST) night imagery within Google Earth Engine. The process starts by loading administrative boundaries and defining a point of interest to extract the relevant region. We then focus on winter night-time land surface temperature data from MODIS for the period of January to March 2020 to analyze UHI patterns. The data is filtered, processed, and scaled to reflect accurate temperature values. We calculate the mean winter LST over the selected urban region, and then compute the UHI intensity by evaluating how much each pixel’s temperature deviates from the regional mean. This helps in identifying urban hotspots and their thermal severity levels.
A histogram is generated to visualize the distribution of UHI values, and we classify the UHI intensity into five categories: Weak, Moderate, Strong, Very Strong, and Extreme. To ensure the analysis is focused only on urban zones, we incorporate MODIS land cover data to mask the results and isolate built-up areas. Finally, the classified UHI severity map for urban areas is visualized and exported to Google Drive, providing a ready-to-use geospatial output for further mapping or research. This tutorial is ideal for GIS professionals, climate researchers, urban planners, and students interested in urban climatology, remote sensing, and Earth Engine scripting.
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Resources:
Code link: https://code.earthengine.google.com/f...
MODIS Terra Land Surface Temperature and Emissivity 8-Day Global 1km:
https://developers.google.com/earth-e...
FAO GAUL: Global Administrative Unit Layers 2015: https://developers.google.com/earth-e...
MODIS Land Cover Type Yearly Global 500m: https://developers.google.com/earth-e...
Whether you're studying climate impacts, mapping urbanization, or working on sustainability projects, this video gives you a practical walkthrough to analyze urban heat patterns at night using satellite data.
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