Source or Sink? A comparison of Landfire- and FIA-based estimates of change in aboveground live tree carbon in California’s forests
Forests play a central role in addressing climate change, and accurate estimates of forest carbon are critical for the development of actions that reduce emissions from forests and thatmaximize sequestration by forests.Methodological challenges persist regarding howbest to estimate forest carbon stocks andflux at regulatory-relevant scales. UsingCalifornia, USA as a case study, we compare two approaches to stock-difference forest carbon estimation for aboveground live trees: one based on ground inventories and one on land cover classification of remotely-sensed data. Previous work using ground inventory data from the Forest Inventory andAnalysis Program (FIA) showed net aboveground carbon (AGC) sequestration by live trees inCalifornia forests, while estimates using land cover classification from the Landscape Fire andResourceManagement Planning Tools (Landfire) showed net reductions in live tree AGCover a similar time period.We examined the discrepancy by re-analyzing the FIA inventory data through the lens of a category-change analysis based on Landfire. This analysis showedmore than 50%of the live tree AGC in fewer than 4%of Landfire-equivalent categories and that the overwhelmingmajority (>80%) of forest area did not change height category betweenmeasurement periods. Despite the lack of categorical change, themajority of FIA plots increased in both 95th percentile tree height and in live tree AGC. Thesefindings suggest that an approach based on observing categorical changes risks undercounting AGC sequestration resulting fromgrowth and thus overstating the relative importance of AGC reductions that result from disturbances. This would bias AGCflux estimates downward, leading us to validate the conclusion that live trees inCalifornia were a net sink of aboveground carbon in the decade ending in 2016.Our findings suggest an inventory-based or hybrid approach is preferable tomethods that depend on categorical bins for estimating AGC in disturbance-prone forest ecosystems.