{"id":2728972,"date":"2025-07-13T16:01:57","date_gmt":"2025-07-13T23:01:57","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2728972"},"modified":"2026-01-06T14:24:23","modified_gmt":"2026-01-06T22:24:23","slug":"learn-to-map-urban-heat-with-landsat","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-living-atlas\/imagery\/learn-to-map-urban-heat-with-landsat","title":{"rendered":"Learn to map urban heat islands with Landsat imagery"},"author":270422,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,37141,22931],"tags":[780062,780072,768182,771442],"industry":[],"product":[36581,36551],"class_list":["post-2728972","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-decision-support","category-imagery","tag-analysisoptimized","tag-lawrasteranalysis","tag-urban-heat","tag-urban-heat-island","product-arcgis-living-atlas","product-arcgis-online"],"acf":{"authors":[{"ID":7071,"user_firstname":"Robert","user_lastname":"Waterman","nickname":"Robert Waterman","user_nicename":"robe9031","display_name":"Robert Waterman","user_email":"RWaterman@esri.com","user_url":"","user_registered":"2018-03-02 00:19:16","user_description":"Robert Waterman leads the ArcGIS Living Atlas Imagery Team at Esri. Robert takes great pride in the work the team does in democratizing imagery and remote sensing, improving accessibility and helping others better understand our planet through Earth Observation with ready-to-use content and capabilities.","user_avatar":""},{"ID":270422,"user_firstname":"James","user_lastname":"Sill","nickname":"James Sill","user_nicename":"jsill","display_name":"James Sill","user_email":"JSill@esri.com","user_url":"","user_registered":"2021-08-30 15:08:30","user_description":"James is Product Engineer for Imagery Analytics at Esri. James' work is focused on expanding Earth Observation data and analytics available in the Living Atlas. He has a passion for using modern remote sensing, machine learning, and cloud computing to help the scientific and conservation community to gain a better understanding of our planet.","user_avatar":""}],"short_description":"Enabling communities around the world with on-demand urban heat mapping.","flexible_content":[{"acf_fc_layout":"content","content":"

Enabling communities around the world with on-demand urban heat mapping.<\/h2>\n

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According to the United States Environmental Protection Agency<\/a> (EPA), heat islands are urbanized areas that experience higher temperatures than outlying areas. Structures such as buildings, roads, and other infrastructure absorb and re-emit the sun\u2019s heat more than natural landscapes such as forests and water bodies. Urban areas, where these structures are highly concentrated and greenery is limited, become \u201cislands\u201d of higher temperatures relative to outlying areas. These heat islands can have significant impacts on human health and energy consumption.<\/p>\n

Mapping heat islands aids in understanding impacts and creating mitigation strategies. However, with variable data sources and methodologies to consider, this can be a complex undertaking. This article is focused on intra-urban heat islands, mapping the variability of surface heat intensity within a defined urban area extent. Furthermore, our goal is to provide a simple, cost effective, and repeatable process for anyone, anywhere, to begin measuring and analyzing urban heat. This is reflected in the methodology and data source selected.<\/p>\n

Jointly managed by NASA and the USGS, Landsat is the longest running spaceborne earth imaging and observation program in history. Landsat Level-2 science products<\/a> provide global multispectral imagery from 1982 to present, including a derived Surface Temperature product.<\/p>\n

ArcGIS Living Atlas of the World enables access to the entire Landsat Level-2<\/a> archive as a single imagery layer optimized for analysis. This article provides a step-by-step tutorial for using Landsat imagery from Living Atlas, and analysis tools in ArcGIS Online, to perform analysis and map surface intra-urban heat islands (SIUHI) for urban areas around the world.<\/p>\n"},{"acf_fc_layout":"content","content":"

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Table of Contents<\/strong><\/h3>\n