We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. For this study, the vegetation conditions are characterized using a series of remote sensing vegetation indices developed using satellite imagery, including the Normalized Difference Vegetation Index (NDVI). The NDVI is a commonly used vegetation index that quantifies relative greenness of the vegetation based on the plant’s photosynthetic activity, measured as a ratio between the Near Infrared (NIR) and Red bands (Tucker, 1979). The NDVI equation follows: NDVI = (NIR band - Red band) / (NIR band + Red band). NDVI has a range of -1 to 1, though green vegetation theoretically ranges from 0 to 1. Dense green vegetation is represented with values closer to 1 while barren soil, rock, and less-dense surface vegetation has values closer to 0. Values below 0 often represent water due to its unique reflective characteristics. All raster products were developed using the Google Earth Engine (GEE) cloud computing software program for the Upper Gila River watershed. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. This Child Item consists of a multi-band raster stack of monthly NDVI images from January 2014 through July 2022 covering the area of the Bylas Fire. We included data from 2022, contrasting the full study which only includes data through 2021, to include additional data regarding our post-fire vegetation response analysis. Each band within the raster stack represents a month from 2014 through 2022 (i.e., band 1 is January 2014 and band 103 is July 2022).