An Accessible NDVI Classification Tool for Urban and Suburban Vegetation Change Analysis

This paper presents a web-based research method for studying changes in vegetation in urban and suburban contexts between 2018 and 2024. The system uses the Normalized Difference Vegetation Index (NDVI) to analyze imagery for each time period and classify land surface types. After classification, correlation and regression analysis are applied to explain connections between urbanization and vegetation change over the time period. Two case studies are included to demonstrate the method’s validity: suburban Warrenton, Virginia and urban Portland, Oregon, which have contrasting physical characteristics and land use policies. By providing workflow documentation and using open source technology, this method democratizes land classification analysis for researchers in planning, conservation, and related fields. Potential improvements for this tool include: 1-higher resolution imagery; 2-conducting field research to verify results and build regional models; and to 3-improve NDVI application for broader land classification results. The research method is available for public use at: https://terrestrialresearch.com/machinelearning/landclass2.