The U.S. National Vegetation Classification (NVC) Standard was adopted by the Federal Geographic Data Committee as a federal standard for reporting vegetation information across the country (FGDC 1997, revised 2008). The first web publication of the revised NVC was released in 2016 (usnvc.org). NatureServe and the U.S. Forest Service are collaborating to implement the NVC in the USFS Forest Inventory and Analysis (FIA) Program. The FIA program maintains information on the status and trends of the nation’s forests, and has used the SAF Cover Types to classify the nation’s forests. The NVC uses an ecological vegetation approach that integrates ecological factors with all vegetation layers into an 8-level hierarchy of types. The mid-level types are practical for classifying the nation’s forests, as FIA collects only tree species data on the standard “Phase 2” (P2) plots. The goal of our project is to classify P2 plots to the macrogroup level and eventually the group level, across the country.
In this first phase, for eastern U.S. forests, NatureServe scientists first completed a narrative-based key that used available FIA attributes and tree life-history data to assign P2 plot conditions to macrogroup types. The process of computerizing the key was iterative, where FIA analysts first developed a computer algorithm based on this key, then produced summary outputs of types and distributions, which NatureServe compared with published descriptions of the macrogroups, and submitted revisions. Once the computerized key was stable, we validated it using P2 FIA plots previously assigned to NVC groups and macrogroups by experts through the LandFire program. Assigning a macrogroup and, in the future, a NVC group, allows FIA to report vegetation data on eastern U.S. forests (and eventually all U.S. forests), using the NVC standard. This information will facilitate collaborations with other agencies using the NVC, including LandFire, National Park Service and the Bureau of Land Management. FIA data also improve the USNVC by providing valuable quantitative data on macrogroup tree composition and distribution.