5-year Relative Fractional Vegetation Cover at Abandoned Energy Development Sites on the Colorado Plateau

This data release contains a single vector shapefile and two text documents with code used to generate the data product. This vector shapefile contains the locations of 365 “plugged and abandoned” well sites from across the Colorado Plateau with their respective relative fractional vegetation cover (RFVC) values. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive (collectively termed “plugged and abandoned”). Understanding the rates, controls, and degree of recovery of these reclaimed well sites (well pads) to a state similar to pre-development conditions is critical for energy development and land management decision processes. We used the Soil-Adjusted Total Vegetation Index (SATVI) to measure post-abandonment vegetation cover relative to pre-drilling condition as a metric of recovery: relative fractional vegetation cover (RFVC). The Google Earth Engine cloud computing platform allows for the automated processing of hundreds of images for each of the hundreds of sites, permitting time series analyses that were not easily achieved with earlier image processing methods. The time-series package BFAST in R statistical software enables the efficient detection of breaks in temporal trends, helping to identify when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The code text documents include: 1) Google Earth Engine Script: Well Pad Means, Medians, and DART Percentile Time Series Collection 2) R Script: Generation of BFAST time series models and calculation of RFVC The Google Earth Engine and R code used for data processing, and the final shapefile were used for statistical analysis in the following paper: Waller, E.K., Villarreal, M.L., Poitras, T.B., Nauman, T.W., Duniway, M.C. 2018. Landsat time series analysis of fractional plant cover changes on abandoned energy development sites. International Journal of Applied Earth Observation and Geoinformation 10.1016/j.jag.2018.07.008