Across the southwestern United States, ponderosa pine forests that experience high-severity fires are increasingly recovering as grassland or shrubland ecosystems. The uncertainty of the outcomes and the disparity in eventual ecosystem structure and functioning hamper the development of appropriate land management strategies. To help provide information about future landscape composition, USGS scientists investigated whether phenology metrics derived from remotely sensed imagery could be used to differentiate among recovery pathways. The science team selected multiple sites with discernable post-fire vegetation communities in 10 fires that burned ponderosa pine forests in Arizona and New Mexico before the year 2000. At each site, the researchers used Google Earth Engine to calculate Normalized Difference Vegetation Index (NDVI) signals from 30-meter Landsat data acquired from 1984 to 2017. They calculated time series of annual vegetation metrics: amplitude, base value, peak value, and timing of peak NDVI. Results showed that succession pathways of grassland, evergreen shrubland, deciduous, and conifer-dominated vegetation exhibit distinct phenological characteristics as early as year 5 (amplitude) and as late as year 20 (timing of peak NDVI). The study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time series to identify and monitor ecological outcomes.
https://doi.org/10.3390/rs11232782
Google Earth high-resolution imagery showing the vegetation recovery progression after repeated burns of the same area by two fires in New Mexico: La Mesa (occurred before the left image in 1977; site shown here was subsequently planted with ponderosa pine) and Las Conchas (occurred in 2011; currently recovering with shrubland characteristics). The yellow circle delineates the approximate field of view of the Landsat satellite (30-meter pixel resolution).