{"id":2987525,"date":"2026-11-05T13:46:26","date_gmt":"2026-11-05T21:46:26","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2987525"},"modified":"2026-11-05T13:56:37","modified_gmt":"2026-11-05T21:56:37","slug":"explore-imagery-layers-with-charts","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts","title":{"rendered":"Explore imagery layers with interactive web charts"},"author":299162,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[22931],"tags":[24311,572002,30821,776582,33371],"industry":[],"product":[36571,36551],"class_list":["post-2987525","blog","type-blog","status-publish","format-standard","hentry","category-imagery","tag-analysis","tag-arcgis-living-atlas","tag-charts","tag-imagery-layer","tag-map-viewer","product-arcgis-enterprise","product-arcgis-online"],"acf":{"authors":[{"ID":299162,"user_firstname":"Ling","user_lastname":"Tang","nickname":"Ling Tang (Redlands)","user_nicename":"ling_tang","display_name":"Ling Tang","user_email":"Ling_Tang@esri.com","user_url":"","user_registered":"2022-01-10 21:15:59","user_description":"Dr. Ling Tang has been exploring Remote Sensing and GIS for more than 20 years. She is a Senior Product Engineer for imagery at Esri where she focuses on the development of best practices for visualizing and analyzing scientific big data, machine learning and image classification on ArcGIS platform. Before joining Esri, she was a former research professional at NASA GSFC worked on a couple of precipitation missions.","user_avatar":""}],"short_description":"Turn complex imagery data into an interactive visual conversation - directly using web imagery charts in Map Viewer. ","flexible_content":[{"acf_fc_layout":"content","content":"

Web imagery layers can contain an enormous amount of information. The challenge is often not displaying the pixels\u2014it is finding a clear way to explore their\u00a0spatial patterns, compare categories, and connect chart observations back to image pixels on the map. Imagery charts provide that bridge.\u00a0<\/span>\u00a0<\/span><\/p>\n

In this blog, it walks through two examples. The first uses the <\/span>World Terrestrial Ecosystems layer<\/span><\/a> to explore\u00a0ecosystem classes across the globe and within different\u00a0regions. The second uses the <\/span>Sentinel-2 Level-2A<\/span><\/a> image collection to investigate spectral patterns in the\u00a0Amazon coastal flood zone. Together, they show how web imagery\u00a0charts can support both thematic exploration and pixel-level identification.<\/span>\u00a0<\/span><\/p>\n

Explore global ecosystem patterns with a bar chart<\/span><\/span>\u00a0<\/span><\/h2>\n

The <\/span>World Terrestrial Ecosystems layer<\/span><\/a> in ArcGIS Living Atlas of the World<\/a> contains 431 classes. A map communicates where those classes occur, while\u00a0a chart adds another perspective: it helps reveal which classes are most prevalent and how the distribution changes from one region to another.<\/span>\u00a0<\/span><\/p>\n

In Map Viewer, open the Charts pane (right white toolbar) for the imagery layer and choose Bar chart<\/strong><\/a>. The available chart types also include Histogram<\/strong> <\/a>and Scatterplot<\/a>.<\/strong> They varies depending on the imagery layer properties.<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2987533,"id":2987533,"title":"Figure1","filename":"Figure1.png","filesize":1366122,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts\/figure1-14","alt":"","author":"299162","description":"","caption":"Create a chart from the imagery layer by choosing a chart type in the right Charts pane. ","name":"figure1-14","status":"inherit","uploaded_to":2987525,"date":"2026-11-05 19:01:18","modified":"2026-11-05 19:01:53","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1298,"height":723,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1.png","medium-width":464,"medium-height":258,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1.png","medium_large-width":768,"medium_large-height":428,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1.png","large-width":1298,"large-height":723,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1.png","1536x1536-width":1298,"1536x1536-height":723,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1.png","2048x2048-width":1298,"2048x2048-height":723,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1-826x460.png","card_image-width":826,"card_image-height":460,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure1.png","wide_image-width":1298,"wide_image-height":723}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Configure categories, fields and groups<\/h3>\n

For th<\/span>is<\/span> example, set field <\/span>ClassName<\/span><\/strong> as the category<\/span>, u<\/span>se <\/span>C<\/span>ount<\/span><\/strong> as the numeric field with a <\/span>Sum<\/span>\u00a0<\/span><\/strong>aggregation and<\/span> split the results by <\/span>Landcover<\/span><\/strong>. The resulting grouped bar chart summarizes the imagery by ecosystem class<\/span>es<\/span> while preserving the land-cover breakdown.<\/span><\/p>\n

What the chart reveals<\/h3>\n

At<\/span> the<\/span> global extent, the largest class is the Boreal Mist Forest on Mountains<\/strong>. Selecting a bar highlights the corresponding pixels and see where the class occurs.<\/span><\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2987539,"id":2987539,"title":"Figure2","filename":"Figure2.png","filesize":886686,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts\/figure2-15","alt":"","author":"299162","description":"","caption":"A grouped bar chart compares ecosystem classes and their associated land-cover types.","name":"figure2-15","status":"inherit","uploaded_to":2987525,"date":"2026-11-05 19:14:14","modified":"2026-11-05 19:14:31","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1300,"height":806,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2.png","medium-width":421,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2.png","medium_large-width":768,"medium_large-height":476,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2.png","large-width":1300,"large-height":806,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2.png","1536x1536-width":1300,"1536x1536-height":806,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2.png","2048x2048-width":1300,"2048x2048-height":806,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2-750x465.png","card_image-width":750,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure2.png","wide_image-width":1300,"wide_image-height":806}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Let map extent become an analytical filter<\/span><\/span>\u00a0<\/span><\/h3>\n

Global summaries are useful, but regional patterns can be very different. Turn on “Filter by extent” button in the chart to recalculate the chart for the current map view extent. As the map moves, the chart becomes a dynamic summary of the visible area.<\/span> T<\/strong>ropical Desert on Plains<\/strong> is the largest class in Africa, while Tropical Moist Forest on Plains<\/strong> is dominant in South America. This extent-aware interaction makes it easy to compare regions without creating a separate analysis for each class.<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2987544,"id":2987544,"title":"Figure3","filename":"Figure3.png","filesize":614128,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts\/figure3-9","alt":"","author":"299162","description":"","caption":"Filtering by map extent updates the chart so regional ecosystem patterns can be examined interactively. ","name":"figure3-9","status":"inherit","uploaded_to":2987525,"date":"2026-11-05 19:23:24","modified":"2026-11-05 19:23:44","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":885,"height":808,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3.png","medium-width":286,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3.png","medium_large-width":768,"medium_large-height":701,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3.png","large-width":885,"large-height":808,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3.png","1536x1536-width":885,"1536x1536-height":808,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3.png","2048x2048-width":885,"2048x2048-height":808,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3-509x465.png","card_image-width":509,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure3.png","wide_image-width":885,"wide_image-height":808}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Save the configured chart<\/span><\/span><\/h3>\n

Once the chart is configured, save the chart properties with the layer as <\/span>a new layer item<\/span>. The saved layer with the configured chart can become the source in the map, allowing the chart configuration to be reused in a later session or shared with others.<\/span><\/span>\u00a0<\/span><\/p>\n

Use scatterplot on a large Sentinel-2 image collection<\/span><\/span><\/h2>\n

Imagery charts also work with large image collections. In the second workflow, we add the <\/span>Sentinel-2 Level-2<\/span>A layer to Map Viewer and navigates to an Amazon coastal flood zone.<\/span><\/p>\n

Select image scenes with Image Collection Explorer<\/h3>\n

Open Image Collection Explorer<\/strong><\/a> to review the images available for the area of interest. The panel presents scene thumbnails and metadata, including image properties like acquisition dates and cloud-cover values. Here a small set of 4 Sentinel-2 scenes is selected and used to create a new layer focused on the study area.<\/span><\/span><\/p>\n

 <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2987556,"id":2987556,"title":"Figure4","filename":"Figure4.png","filesize":1272027,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts\/figure4-8","alt":"","author":"299162","description":"","caption":"Review scene metadata and select the images that support the analysis. ","name":"figure4-8","status":"inherit","uploaded_to":2987525,"date":"2026-11-05 19:37:59","modified":"2026-11-05 19:38:09","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1182,"height":820,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4.png","medium-width":376,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4.png","medium_large-width":768,"medium_large-height":533,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4.png","large-width":1182,"large-height":820,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4.png","1536x1536-width":1182,"1536x1536-height":820,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4.png","2048x2048-width":1182,"2048x2048-height":820,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4-670x465.png","card_image-width":670,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure4.png","wide_image-width":1182,"wide_image-height":820}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Apply a short-wave infrared processing template<\/h3>\n

With the imagery layer selected, open Processing templates<\/strong><\/a> pane and switch to Short-wave Infrared for Visualization<\/strong>. In this rendering, water bodies become easier to distinguish from surrounding land, preparing the layer for further spectral exploration.<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2987564,"id":2987564,"title":"Figure5","filename":"Figure5.png","filesize":1441971,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts\/figure5-8","alt":"","author":"299162","description":"","caption":"A short-wave infrared template emphasizes water bodies in the selected imagery.","name":"figure5-8","status":"inherit","uploaded_to":2987525,"date":"2026-11-05 19:47:29","modified":"2026-11-05 19:47:55","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1160,"height":816,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5.png","medium-width":371,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5.png","medium_large-width":768,"medium_large-height":540,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5.png","large-width":1160,"large-height":816,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5.png","1536x1536-width":1160,"1536x1536-height":816,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5.png","2048x2048-width":1160,"2048x2048-height":816,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5-661x465.png","card_image-width":661,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure5.png","wide_image-width":1160,"wide_image-height":816}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Identify<\/span> spectral clusters with a scatterplot<\/span><\/span><\/h3>\n

Create a scatterplot using Near InfraRed (Band 8)<\/strong> on the x-axis and Shortwave InfraRed (Band 12)<\/strong> on the y-axis. The chart plots pixel values from the imagery and exposes clusters that represent different spectral responses.<\/span> T<\/span>wo clusters are clearly visible. Selecting a cluster in the chart highlights its pixels on the map, providing a fast way to identify the group associated with water bodies. The chart configuration also allows the axis variables to be changed to other available bands, such as Red (Band 4), supporting other similar spectral explorations.<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2987570,"id":2987570,"title":"Figure6","filename":"Figure6.png","filesize":707432,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts\/figure6-5","alt":"","author":"299162","description":"","caption":"The scatterplot reveals spectral clusters and links selected points back to pixels on the map.","name":"figure6-5","status":"inherit","uploaded_to":2987525,"date":"2026-11-05 19:55:09","modified":"2026-11-05 19:55:20","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1301,"height":799,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6.png","medium-width":425,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6.png","medium_large-width":768,"medium_large-height":472,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6.png","large-width":1301,"large-height":799,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6.png","1536x1536-width":1301,"1536x1536-height":799,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6.png","2048x2048-width":1301,"2048x2048-height":799,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6-757x465.png","card_image-width":757,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Figure6.png","wide_image-width":1301,"wide_image-height":799}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

From visual summary to spatial insight<\/span><\/span><\/h2>\n

These workflows demonstrate two typical uses of imagery charts. A bar chart can summarize a classified imagery layer and compare categories. A scatterplot can expose spectral structure and connect clusters to real locations.<\/span>\u00a0<\/span>Whether you are studying global ecosystems or examining satellite scenes, imagery charts make complex raster information more approachable. They invite exploration, keep the map and chart synchronized, and help uncover valuable insights without leaving Map Viewer.<\/span><\/p>\n

Web imagery charts support both tiled imagery layers and dynamic imagery layers, as well as the WCS imagery layers. We welcome you to explore those chart types and share your feedback with us.<\/p>\n"}],"related_articles":"","show_article_image":true,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/card.png","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/11\/Wide.png"},"yoast_head":"\nExplore imagery layers with interactive web charts<\/title>\n<meta name=\"description\" content=\"Turn complex imagery data into an interactive visual conversation - directly using web imagery charts in Map Viewer.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/explore-imagery-layers-with-charts\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Explore imagery layers with interactive web charts\" \/>\n<meta property=\"og:description\" content=\"Turn complex imagery data into an interactive visual conversation - 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