{"id":1134752,"date":"2021-02-08T14:20:30","date_gmt":"2021-02-08T22:20:30","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1134752"},"modified":"2022-04-18T17:12:24","modified_gmt":"2022-04-19T00:12:24","slug":"change-detection-of-time-series-imagery","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/change-detection-of-time-series-imagery","title":{"rendered":"Change Detection of Time Series Imagery"},"author":10352,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,22931,22771],"tags":[42191,564122,30071],"industry":[],"product":[36561],"class_list":["post-1134752","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-imagery","category-natural-resources","tag-arcgis-image-analyst","tag-change-detection","tag-time-series","product-arcgis-pro"],"acf":{"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_card-1.jpg","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_banner-1.jpg","authors":[{"ID":10352,"user_firstname":"Hong","user_lastname":"Xu","nickname":"Hong Xu","user_nicename":"hxu","display_name":"Hong Xu","user_email":"hxu@esri.com","user_url":"","user_registered":"2019-12-20 16:44:39","user_description":"Hong Xu is a Principal Software Product Engineer on Esri\u2019s imagery team, where she has been contributing since 1999. Her work focuses on advancing analytical methods for Earth observation data, including image time series (image cubes), hyperspectral imagery, multidimensional raster analysis, and altimetry data. She is passionate about bridging scientific research and software development to help users better understand environmental change through geospatial analysis.","user_avatar":""},{"ID":8222,"user_firstname":"Julia","user_lastname":"Lenhardt","nickname":"Julia L","user_nicename":"jlenhardt","display_name":"Julia Lenhardt","user_email":"JLenhardt@esri.com","user_url":"","user_registered":"2018-08-03 17:12:51","user_description":"Julia is a product engineer on the Raster team. She has been with Esri since 2014 and has a background in remote sensing and GIS for environmental research. In addition to image analysis, she has a passion for music, running, good food and animal welfare.","user_avatar":""}],"short_description":"Analyze your time series imagery using tools and APIs that implement the CCDC and LandTrendr algorithms.","flexible_content":[{"acf_fc_layout":"content","content":"

There are a lot of ways<\/a> to analyze a time series of satellite imagery in ArcGIS, and in this blog we\u2019ll introduce two new algorithms that perform time series change detection<\/strong>: Continuous Change Detection and Classification (CCDC), and Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr). We know, it\u2019s a mouthful, but hopefully this blog will help break it down.<\/p>\n

Imagine a time series of imagery, where the history of a single pixel value over time can be graphed like the chart below. The CCDC and LandTrendr algorithms offer a way to analyze each pixel\u2019s history to identify the times at which the pixel value\u2019s behavior changed.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1134762,"id":1134762,"title":"timeseries_imagestack","filename":"timeseries_imagestack.jpg","filesize":20781,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/change-detection-of-time-series-imagery\/timeseries_imagestack","alt":"Pixel value history over time","author":"10352","description":"","caption":"","name":"timeseries_imagestack","status":"inherit","uploaded_to":1134752,"date":"2021-02-06 04:42:45","modified":"2021-02-08 22:18:25","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":925,"height":347,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack.jpg","medium-width":464,"medium-height":174,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack.jpg","medium_large-width":768,"medium_large-height":288,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack.jpg","large-width":925,"large-height":347,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack.jpg","1536x1536-width":925,"1536x1536-height":347,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack.jpg","2048x2048-width":925,"2048x2048-height":347,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack-826x310.jpg","card_image-width":826,"card_image-height":310,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/02\/timeseries_imagestack.jpg","wide_image-width":925,"wide_image-height":347}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

The CCDC and LandTrendr algorithms are relatively new in the image processing world, and highly relevant. They can help you answer the following questions using a stack of images collected over time:<\/p>\n